Direct Marketing magazine October 2014

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Special Report: Marketing automation

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Executive Roundtable: Quality not quantity

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Modernization in a Big Data world

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The Authority on Data-Driven Engagement & Operations

Should you catch the big data wave? ❱ 12

William “bill” Peterson runs marketing & strategy for CenturyLink Technology Solutions’ Big Data efforts.

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Vol. 27 | No. 10 | October 2014

The trouble with Big Data: Part 2

EDITOR Amy Bostock - amy@dmn.ca PRESIDENT Steve Lloyd - steve@dmn.ca DESIGN / PRODUCTION Jennifer O'Neill - jennifer@dmn.ca Advertising Sales Mark Henry - mark@dmn.ca Brent White - brent@dmn.ca CONTRIBUTING WRITERS Brian Jones Dina Al-Wer Geoff Linton Yvon Audette William Peterson Richard Boire Jonathan Schloo Information Builders Interactive Intelligence Marc Smith

LLOYDMEDIA INC. HEAD OFFICE / SUBSCRIPTIONS / PRODUCTION: 302-137 Main Street North

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SPECIAL REPORT Marketing automation: marketing’s best kept secret

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EXECUTIVE ROUNDTABLE Quality not quantity

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Modernization in a Big Data world

DM reader roundtable concludes “good data trumps big data”

Markham ON L3P 1Y2 Phone: 905.201.6600 Fax: 905.201.6601 Toll-free: 800.668.1838 home@dmn.ca www.dmn.ca EDITORIAL CONTACT: Direct Marketing is published monthly by Lloydmedia Inc. plus the annual DM Industry Source Book List of Lists .

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Big trends in Big Data

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Measuring engagement in a hyperconnected world: part 3 of 3

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COVER Should you catch the Big Data wave?

Why free data isn’t always good data

Targeting & Acquisition ❯❯

POSTMASTER: Please send all address changes and return all undeliverable copies to: Lloydmedia Inc. 302-137 Main Street North Markham ON L3P 1Y2 Canada

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Improving your single most important analytic October 2014

Supplement: Data in the Call Centre ❯❯28

Case study: Information Builders & Scotiabank Canadian bank standardizes on iWay to reduce development costs and boost revenue

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Contact centre enterprise analytics Advances in decision technologies enable a whole new class of contact centre analysis DMN.ca ❰

special Report

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Marketing automation:

marketing’s best kept secret Enrich your sales pipeline with analytics by integrating CRM and marketing

â?ą DMN.ca

October 2014

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special Report

By Jonathan Schloo

I

t’s an ageold problem: Sales and marketing don’t work well together.

Marketing thinks sales isn’t following up with the leads they’re driving. Sales thinks marketing isn’t driving enough quality leads. What’s the solution to fix the misalignment between marketing and sales? Integrating your CRM system to support your marketing efforts will provide analytics that are crucial for both marketing and the sales team, and this will drive bottom line results.

In the August issue of Direct Marketing, in my article about mobile CRM, I promised to share the secret to where the money and increased profits are truly driven from - it’s marketing automation.

If you don’t know much about marketing automation, here is a brief introduction. Marketing automation is a technology platform for marketers to create, nurture and pass leads to sales. Most marketing automation platforms (MAP) have functions for email marketing, social media, contact list management, forms, landing pages, website tracking and reporting.

Marketers use this technology to execute marketing campaigns in a scale that provide a customized experience to their audience.

There are a number of marketing automation platforms that provide all the features mentioned above, but the key is having a system that fully integrates with your CRM system to make use of all the data inside it. The benefits to integrating your CRM and marketing automation platform are primarily three-fold: 1. More leads (makes marketing happy): A marketing automation platform will help you drive more leads for sales. Period. A well integrated CRM/MAP pair will make the transition of the leads from marketing to sales seamless.

Natively integrated MAP’s will also arm a sales representative with a view of all the activity a lead has taken to get to sales. ie: engaging with email campaigns, filling out forms or simply going to your website. The sales rep can even see what website pages were visited and how often, before they speak to October 2014

the prospect. Your sales rep will be laser focused on the prospect’s key interest areas. The benefit of this is you spend your marketing dollars more effectively to produce more leads.

2. Better leads (makes sales reps happy): Marketing automation platforms offer lead scoring. This function allows marketers to work hand-in-hand with sales to define what is truly sales ready. Sales representatives are able to provide feedback on content, web pages or email campaigns that a good prospect usually engages with.

Marketing is then able to build a lead scoring profile to only pass leads that meet the defined criteria of prospect activity to sales while continuing to nurture leads that haven’t met that threshold. The benefit of this is your sales reps spend quality time with better prospects and that boosts your closing/win rates.

3. ROI on marketing spend (makes management happy): Marketers spend a lot of time filling the top of the sales funnel with leads, but what happens to the leads that aren’t ready to buy? A well integrated marketing automation platform allows a salesperson to indicate that marketing needs to nurture a lead more before it is ready to be sales qualified. The two-way communication between marketing and sales is key to ensuring you’re doing everything you can to produce a qualified lead. Marketing automation platforms also provide lead source reporting and ROI dashboards to understand how much revenue is being produced from marketing-generated leads and which marketing activities are the most fruitful for you. The benefit of this is management gets better value from their marketing spend.

Integrating your marketing automation platform with your CRM isn’t hard, but choosing a marketing automation provider that will be able to maximize the data in your CRM the right way isn’t easy. It is crucial to choose a provider that can accurately leverage your CRM data to execute customized marketing campaigns.

Here are a couple of questions that you should be asking when considering a

marketing automation solution that will provide the best integration: 1. Does your solution provide native integration? (Native integration ensures your data is surfaced inside the user interface of your CRM rather than a separate interface which will require new training and adoption for salespeople.)

2. What is your sync time between systems? (As close to real-time is ideal to ensure behavioral based nurture campaigns are timely and most importantly, sales can quickly act on leads.)

3. Can custom fields in the CRM be mapped in the marketing automation platform? (Your CRM likely has some custom fields in it.

Making sure that you can map that information to your marketing automation is crucial for being able to create customized campaigns.)

4. Is your solution an out-of-the-box solution or do I need middleware to integrate? (Middleware is a third party enabler that sits between the two systems. Middleware creates a variable for misalignment in syncing and can be quickly out of date when there are changes made to either system without updating the middleware) There is more to sales and marketing alignment than platform integration, but well integrated platforms make sales processes and marketing processes work in unison. Sales is able to work in a system they’re used to but they’re armed with a higher volume of leads, better quality leads and more information about each lead to make the follow up conversations more productive and thereby win more customers.

Jonathan Schloo is CEO of QualityIntegrity. com and is an expert on CRM and marketing automation, how to do CRM right, and how to get maximum value from your Microsoft CRM, SalesLogix, or Salesforce system. Leading a team of CRM experts for over 20 years to help you leverage technology to achieve your business goals. Phone 800-611-4343 ext.6406 jschloo@qualityintegrity.com *A special thanks to Salesfusion, a leading

marketing automation provider, for assisting with writing this piece.

DMN.ca ❰

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Executive Roundtable

Quality not quantity DM reader roundtable concludes “good data trumps Big Data”

By Amy Bostock

Introduction What’s sometimes lost in today’s romance with Big Data is that analytics isn’t much help without quality data. One notion that emerged in the recent public—and political— debate about making the long-form census voluntary was the widely held belief that we don’t need to worry about government statistics because we have so much data available from other sources—such as tweets, likes and online surveys. But if direct marketers want to develop accurate insights about consumers and deploy effective target marketing to the right people, they need accurate, authoritative data. Big Data can only be leveraged for quality analytics when these databases are weighted, benchmarked and analysed using a known, accurate, comprehensive universe—whether that is derived from a Census, other official statistical databases or reliable research from firms using best practices.

Our executive roundtable, sponsored by Environics Analytics, takes a closer look at the data used by marketers and discusses why authoritative data, including official government statistics, are so important. They examine how data can be used to run more innovative and targeted campaigns and how critical they are in the methodology, quality and measurement of effective direct marketing initiatives.

I have all this data…now what?!

A common theme with businesses today is that they have a lot of data.

Along with the many benefits of this increase in information come a number of challenges as well. ❱ DMN.ca

“Big Data is more data, but not necessarily good data,” says Vicky Sipidias, the senior manager in charge of customer experience at Hydro One Networks, “and that’s one of the challenges that we face - sifting through the data, making sense out of it and finding those nuggets of information that can be used to form actionable insights. It is important to remember that in the business world, smart decisions are based on facts and good information. With the introduction of Big Data, the fact finding process gets tricky – its takes more time, effort and resources.

In the end, she says, the payoff will be worth the effort.

“I think technology will continue to develop over time and new solutions will be developed. But, I think those that can effectively harness the new tools, tie it to good information derived from reliable sources (such as census data) and marry those pieces nicely together, are the ones that are going to win in the end. The key is to mine the data to form actionable insights and use the new information to feed the corporate decision-making model.”

But according to Paul Tyndall, a director in the Client Knowledge & Insights for RBC the technology and the tools are the easy part.

“You can build the best mousetrap in the world, you can build something that is going to provide some kind of insight that nobody has ever seen before and it’s going to be whiz-bang great, but without the ability to drive change within the organization, it won’t add value. And, this is something that really needs to be accommodated as you become more analytically-centric, more predictive and more prescriptive in what you’re

doing as an organization internally.”

And that push, he says, has to come from the top.

“It’s one thing to build a groundswell but without executive sponsorship, the shared agenda and the shared vision of what you’re looking for and what you’re looking to achieve, it becomes very problematic.

But, it is a very significant issue that, if you don’t have that buy-in, all your efforts are for naught.”

“I agree,” says James Thackray of Bell Canada, “and if you’ve got the leadership who believes in data analysis as a key to making business more efficient, that’s half the battle.

Then you can drive the different groups to embrace it and embrace the nuggets you get out of the data.”

Technology, adds Thackray, can be both a help and a hindrance when it comes to Big Data.

“Technology is changing so fast that you may invest in something today that you have to throw away in a year and a half. It’s early days, and there’s a lot of new technology coming from the internet-based companies that regular businesses can now adopt, but they’re really evolving quickly, so that is a bit of a struggle.”

There’s a huge benefit to be had with internal use of data to improve your interactions with customers. In the past, most companies had a good handle on information from billing data which could tell them what kind of use customers were making of your product. But things became less clear when it came to why they are using it? Or why they are having problems using it?

“And, that’s where we’re going to see the biggest benefit from getting in to these tools,” says Thackray, “to

understand the reason someone is not using your product or having problems with your product and getting that information really quickly.”

Managing customer expectations “Our clients, our consumers, are interacting with us in more ways than they used to and, depending on how we’re trying to interact with the client, these interactions can occur over a long period of time,” says Tyndall.

“There can be multiple touch points, it’s not just walk in, buy a can of peas and you’re out the door. It is a series of interactions and there are a series of channels that we are offering to our clients to interact with us.

“I think clients have blurred the lines between industries, and they expect that what’s possible in one industry would just naturally be possible in all industries. So, I think their expectations are higher in that, no matter how they’re choosing to interact with you about whatever thing they’re interacting with you about, they just expect that any touch point has full access to that information to carry on the discussion, or carry on the process, that was already in play.”

This, he says, puts pressure on the organization to ensure that they are sharing enough information to support those interactions, but not so much that they are overwhelming the different interaction touch points with too much information.

“So it’s a bit of a balancing act,” he says “between giving them enough information to interact intelligently with clients and consumers, but not so much that they don’t know where to even begin. I think we’re getting to that point where people have been erring on the side of giving too much, October 2014

Executive Roundtable because they want to provide access to everything that’s available, but then that puts too much pressure on the end point.”

According to Tim Warren, a Senior Account Executive with IHS Automotive (formerly Polk Canada), the upside is we have a lot of data but that’s also the downside in that it’s easy to get lost in the sea of information available today.

“Customers, often don’t know how to use the vast amount of data available to them and they get confused and overwhelmed. For data to be useful it must be understood and relevant. It’s our job to help navigate through the data and help our customers understand what is available and what they need, otherwise data will sit there, not be used and be of no value to them.

Another challenge, says Warren, is that organizations are cutting back and often don’t have resources able to work with information effectively.

So it becomes ever more important to include services and consulting when offering data.

Many marketers also assume that information in different industries exists in all markets and often look to the U.S. because of the close proximity.

“I am often asked, why we can’t do similar work or offer similar services in Canada as that of the U.S. says Warren. “Clients talk to their counterparts around the world and make assumptions that we have the same information in Canada as elsewhere which unfortunately is not always the case.”

Restrictions to access “I’ll come at it from a different perspective,” says Wayne Smith, Chief Statistician of Canada. “For me, Big Data, and that would include administrative data, like income tax records, represent an opportunity for Statistics Canada to be able to respond to questions that we otherwise wouldn’t be able to respond to, to provide data at low levels of geography, or for very small populations, that otherwise would not be possible. It also has the potential to reduce the cost of our programs, so that we can do more with the available resources, because it’s cheaper to re-task existing data than to go out and collect it with a survey. Additionally, it can relieve the burden on businesses and households, so they don’t have to answer questions if the data are already available from some other source.

“We have rather exceptional powers to bring data together and link them, Jan Kestle is the President of Environics Analytics. making the data even more “I like to say that we help people solve useful. The problem is business problems with mathematics and that there’s a problem of statistics. We’ve built a comprehensive set access. We are looking, for of data and processes about the Canadian example, at smart meter market, from both a consumer and a data to measure energy citizen’s point of view, and we help our consumption. We’re also customers along the strategic roadmap of very interested in the understanding who their customers are. potential of mobile phone That involves identifying good data sources that they have, combining those data with data, credit card data, our data, and providing them actionable and debit card data to insight into who their customers are.” achieve better estimates of international travel in Canada - there are all kinds of possibilities. One of the issues is obtaining access, particularly from businesses. In principle, under the Statistics Act, we already have this authority, but it’s often not that simple, as you can easily imagine, particularly in the privacy environment we’re working in. Our biggest concern is making sure that people understand what the data is capable of, of what it represents, and that it isn’t re-tasked inappropriately to attempt to answer questions it isn’t meant to answer. Generally speaking though, we see big data as an opportunity.”

October 2014

// 7 Bob Humphreys runs all of the demand It comes down to generation and digital work for IBM in Canada. quality “It’s my team’s scope to apply data, to We are in this era apply insight in a way that we’re looking to where all the talk is truly transform how we go to market, really about Big Data and leveraging and moving much more to a dataanalytics. The New driven approach, more so than ever before.”

York Times has said the data scientist is the sexiest job of the 21st Century.

“It’s on everybody’s mind,” says Jan Kestle, President of Environics Analytics, “and I think getting support from the C-suite helps focus the requirement for data-driven decisions, evidence-driven decision making. But it also means that everybody thinks they’re an expert with their own opinions about which kinds of data are useful for which kinds of projects.”

The result, she says, is an increased focus on the importance of ensuring that quality is primary.

“I’ve been involved, over my career, in a lot of consumer marketing studies which compare the impact of interested in making data-driven a good modelling technique or good decisions, but I think that there are software to the effect of good versus a lot of people who don’t necessarily bad data,” she says. “It’s the quality of have enough of a background to data that makes a difference around ask the types of questions around business decision making. When we what data is being used to support think about Big Data, there is huge that,” says Tyndall. “So, I feel that, in opportunity, and it’s good to hear some cases, you can be led to make Statistics Canada talk about how the wrong decision because you’re administrative data, and data that’s presented with information that’s collected through the new Big Data based on the wrong data, or applied the methods, can be helpful. But, it’s wrong technique. I think that Big Data also important that these data be creates even more of an opportunity applicable and of the highest quality to make bad decisions that you think for a specified purpose. are based on data. But, if it’s not the “To turn to the elephant in the right data in the first place, if you’re room, when there’s all this data basing it on sentiment analysis of available and everyone has the Tweets versus something a little bit opportunity to do great things with more robust and stable, you’re going to it, it’s also really important to have potentially make the wrong decision good quality, official statistics that for your business, even though you’ve help you understand what the biases got charts and graphs and statistics are in those data, so you can develop that say something was better than the appropriate weights and bring the something else. And, so I feel that this best practices when using them. Along era where everyone is a statistician, with census data and administrative everyone has access to Big Data, just data that the government can produce leaves more opportunities for people comes some kind of definition of truth. to make poor decisions, which they Now, it may not be 100 percent, but feel are accurate because they’re based it’s probably a lot better to have good, on some type of data.” official statistics about key business In her role as senior manager in and consumer indicators than to just charge of customer experience at scrape the web or look at Facebook. But Hydro One Networks, Vicky Sipidias in the new environment that Statistics and her team is always conscious of Canada faces around the census and basing their decisions on facts. other kinds of government constraint, “That sample file is so important, by how can we ensure that the Big Data this I mean getting the right list and are being benchmarked, weighted and the right representative sample. With used appropriately in the context for unstructured data, there’s a higher which businesses want to use them? I risk of sample bias and sample error. think that’s the biggest challenge.”

Interpreting unstructured data it is a “I find most organizations are very DMN.ca ❰

Executive Roundtable Vicky Sipidias is the senior manager in charge of customer experience at Hydro One Networks. Her role is to develop a new core competency about understanding customers.

“As you can image, we are and always have been an asset-centric organization. So, to become a customer-centric organization will take time and it will involve a shift in organizational culture. This is important to Hydro One, because our industry is changing and with each new day, we face new competitive challenges. Now more than ever, we are reminded of just how important it is to satisfy customers and build relationships.”

difficult thing. It may quantify things quite easily but it doesn’t qualify things. So, asking the right questions, and understanding the underlying reasons “why?” is where insight comes from. And these insights serve as the fuel for good business decisions.”

Statistics in the age of digital marketing So how do you convince marketers, especially the young marketers that grew up in this digital world, about the importance of official government statistics and other old-school data?

“It’s more about them learning to be critical thinkers,” says Smith.

“There’s no telling, when you look at a table of numbers, whether it’s good or bad. You have to ask the right questions. Where did it come from?

How was this information developed?

What was the source? What was the target population? And, then be able to deduce whether this answers your question, or is it really basically irrelevant, because it isn’t the target population you were interested in, or perhaps it was a self-selected sample, and therefore, probably biased. And, they need to be critical about what they’re reading in social media.

There’s too much of a tendency to take many things at face value. It’s ❱ DMN.ca

an echo chamber which reinforces views that may be completely wrong.”

Bob Humphreys runs all of the demand generation and digital work for IBM in Canada and feels that the lack of proper skills is a big issue as marketing evolves as a discipline.

“If you take a look at the skills that are going to be needed now, of the critical thinking, the ability to begin to build some kind of justification, validity, critical thinking into the approach that’s going to be taken around structured and unstructured, marrying those things together, I think it’s going to be a huge issue,” he says. “For most organizations today, that level of skill, that level of perspective, isn’t there.

“Most analytics today is usually descriptive, maybe some degree of predictive. But, where we see this going is ultimately going to be prescriptive.

So, we start getting into the whole cognitive computing side of things and really beginning to use it to determine probabilistic outcomes as opposed to deterministic outcomes, and help invent, help define, what’s the art of the possible? That’s where we think it’s going to go, and it’s going to take time.

And, you think now about what that impact has on skills, what it takes now on how companies react to this fluidity of incoming insight all of the time, it’s going to be a very big shift to how marketers market.”

Thackray agrees. “I think the lowhanging fruit is that next-best-action event analysis, and you don’t need the whole population. You can find there are customers that need the action right now, and if you can crack that nut then you really bring new efficiencies to your company that you haven’t seen before. It’s early stages, but these young people out of university are really good and they bring other creativity too, in how they approach problems. So, yeah, they don’t have the full experience of how do I make sure this is representative. But, their different way of thinking, from traditional marketers, are bringing new life into older companies, and new ways of thinking of solving problems.”

“We don’t want to be Luddites,” counters Kestle, “those of us who are the hard-core statisticians or

// 8 universities teaching people critical modellers. I think it’s important to thinking.” acknowledge what you’re saying, but it’s really a balancing act. I think Pumping up DM campaigns with there were a lot of digital analytics official statistics coming out, and listening analytics, “We rely on official statistics and that were being done in the early days census data and blend that along that weren’t as good as what’s evolved. with our proprietary information So, I think it’s important that we ask and information from partners,” the right questions, that we demand says Sipidias. “We work with best the critical thinking, but not throw in class partners and rely on them the baby out with the bath water the for demographic information, other way either, and really use these socioeconomic information and more. data. But, I think that, as much as I This is information that our databases agree with you, Bob, we need to get and our CRM systems don’t have, into prescriptive. I would say that, or they’re not kept up to date. We in Canada right now, with the vast may have the right fields in our CRM majority of medium to large-sized system, but we don’t always have the companies that I’m working with, that right information. It can be outdated, they haven’t even done a good job in missing or simply wrong. So, we rely on the Gartner continuum where you official statistics because it is complete, go descriptive, diagnostic, predictive it’s the most trusted and reliable and prescriptive. They still haven’t source. We look for the closest thing to even done a good job of the descriptive ‘N equals all’. With official statistics we and diagnostic analysis of what understand how it’s collected, and that their customers are and what their people are open to offering the truth customers want. So, I think we need through the confidential collection to be sure that we’re building best process. This is radically different from practices all along that continuum.” the process by which information So, does the onus fall on industry is collected through the internet or veterans like our panellists to through other Big Data sources. By make sure that this new generation blending primary data with secondary of marketers understands the data from reliable sources, we are able importance of not only the new to better understand how to reach the channels for getting their data, but hearts and minds of our customers in also traditional channels, like the ways that we’ve never been able to do government statistics? so before.”

“The onus falls on those of us who are working with data, but it also Wayne Smith is the Chief Statistician of Canada. falls on the As such, he’s the Deputy Minister responsible for universities, Statistics Canada and all of its operations. it falls on the “My interest in Big Data is primarily as a source marketing of data for Statistics Canada. We already use it, and associations,” we’ll probably be using more of it. However, when we says Kestle. see big data masquerading as official statistics, we “The Canadian get quite concerned about the way people are trying Marketing to use it.”

Association does do a lot in terms of offering courses. But businesses aren’t investing as much as they should in ensuring that their staff is trained properly. I’ve been encouraged by what’s happening at the university level. I’ve been contacted by Queen’s, McMaster and Rotman in the last two months, asking if we’d come in and help develop their coursework. Would we come in and provide data that they can use? With everything that’s happening out there involving data and analytics, the business schools are starting to try and do more. But the responsibility of acquiring good data and making sure it’s used in an appropriate way requires more than October 2014

Executive Roundtable

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James Thackray of Bell Canada is around some kind of focused on business intelligence in the outcome, some kind of traditional marketing analytics areas behaviour. including the campaign management side.

That definition, says “There is a lot of focus on big data in Humphreys, is becoming our company. We’re swimming in data outdated. and we’re getting our hands around how “The campaigns to leverage big data with the right tools now are evolving to with some of our traditional IT partners.

And this data is for more than just engagement-based, that traditional marketing, it’s really helping looks at a much stronger on internal operations and customer inbound environment, a experience issues.” much stronger inbound set of dialogues that need to take place to have the kind of outcome that you want to have, in addition to the proactive touches that go in here. Now, you’re looking at models that look at the propensity to respond in the way that you hope. You look at models that are not just looking at the next investments that you want to make and the likelihood of being successful on them, but now tying all these things together of individual data, the interactions that they’ve had, the unstructured elements. You look at, now, the other transaction pieces of the internal data that we’ve got to The changing definition work with, and then underlying that of ‘campaign’ is still the foundational stats, that Campaigns, in the traditional sense, cent or 10 per cent growth year over we have from Stats Canada and other are a proactive touch, or series of year, everyone may see it as a great trusted sources, to help determine touches, with hopeful expectations success” says Tyndall. “But, if you look what do we want to do? at the baseline data, and see, well, that What’s the action Paul Tyndall of RBC is a director in the area actually grew by 20 per cent in that we want to take Client Knowledge & Insights group. population, then you’ve actually fallen place within that “Essentially, my role is to help the business behind the market, and you would given area, within leverage our internal and external assets as have a very different interpretation that given market, they relate to data. Our internal customer databases, external sources of data, strategic about where the opportunity lies. If within that given marketing research data, profitability models, you’re only looking at the numerator segment, within predictive models, anything like that that can help and not the denominator, you can that individual that understand how our clients are behaving, how be greatly misrepresenting how we want to deal prospects convert into clients, how they acquire, successful you’re being.” with? So, I don’t use and grow with our products and services, and think that the term where there are gaps and opportunities.”

Why marketing loves (or should campaign is as love) official statistics large or as robust as For Tim Warren, good, official what it needs to be in many cases.” statistics help to create current“It’s a very basic part of the simple, year estimates, or other data sets old stuff that you do around assessing like cluster systems that are very market share, determining where important marketing tools and is you are doing well and not doing well surprised with how many people don’t with certain types of customers?” realize the role Census data plays in says Kestle. “You can see what your their development. customer base is doing with certain “I love working with data and the services and products in different stories it can tell. Unfortunately areas, and you need that other base the story isn’t as good or complete to say, well, what am I missing? It’s a when data is missing. The challenge very basic part of the whole analysis, of is helping people understand the understanding where you’re doing well importance of something like and not well so you can determine why completing the Census and the and how to change your approach.” impact of not doing so. When the “I’d say without a broader set of average person is asked if they know baseline information that you miss how Census data is used they often opportunities, because if you look at go blank. They are unaware the your own internal data and you see, information is used to help shape in a certain area, that we’ve had 5 per

demographics and segmentation for us to integrate into a kind of ecosystem for marketers and analysts to use. They enable the use of a lot of other data, so survey data that comes from the Statistics Canada, like the Survey of Household Spending or the Survey of Consumer Finances, or survey data from IPSOS in the Canadian Financial Monitor, enable the development of financial tools and other uses by consumers. We can only create behavioural data, media preferences Tim Warren is a Senior Account Executive and psychographics— with IHS Automotive (formerly Polk Canada), based, in part, on which supplies critical information and services survey data from the throughout the automotive industry.

Environics Research, “Aside from data, we provide consulting Print Measurement and analytics into the industry. We work Bureau, Numeris closely across an organization to help our (formerly BBM), customers make good decisions. Whether it’s understanding the market, finding the next NADbank, Asking best location, future trends or targeting, we Canadians and other have information and services that can help.” sources—if we have good, official statistics and sound methodologies.”

“It’s a foundation for how we estimate the size of the our market, how we begin to predict where the market is likely to go in the next, say 12-month period, even 6-month period,” says Humphreys. “We use that in addition to many other different sources that help us to formulate the model and to predict what our market opportunity looks like. That then becomes the foundation for how we assess share, how we assess our

performance vis-à-vis the market.

On top of that, it’s also a foundational element in our predictive models that we use within the marketing activity that we have. So, if we’re trying to lay an investment down and determine whether or not that investment is a good bet or a bad bet, it’s an input into building those kinds of models. It’s an input to help us make sure that the model is robust enough that it captures the geographic elements of where is our market, where is the spend, where is the opportunity?”

“We’re in a different position than everyone else,” says Kestle. “We’re a data provider and we use census data to create current-year demographics and segmentation systems. But the other use of those official statistics is building that foundation from the October 2014

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Executive Roundtable

future development, social policies, planning and much more. We need to improve awareness of the importance of participating in the Census and the implications of not doing so.

For a company like Hydro One, it’s the wealth of information that comes through products like Environics Analytics’ Prizm codes that blends the StatsCan information with all of the other communication information and all the wealth of databases that you put together, that allow them to understand the social values of their customers.

“The problem that we have is that everyone and everything seems to focus on urban centres, and in our case, we are a utility made up of 300 or more rural, suburban and small communities. This is where the information becomes “grey”. Finding reliable, trusted information, and getting as much as we can from good, reliable sources, is key to us.”

According to Tyndall, people need to have full awareness about where the data came from, how it was gathered, how large the samples were, biases, so that they can use all of it, but use it in the proper context.

“I’ve been in discussions where you use external data like census data and it will say the best responders are people with an average income of $75,000, for example. People will jump to the conclusion that, well, our best customers have an income of $75,000.

But no, they live in an area where the ❱ DMN.ca

average income is $75,000. That’s not their income, that’s the average in the area where they live. And, so just use it knowing what it means, and don’t extrapolate or jump to conclusions beyond how it was gathered or what it was meant for. Take it with a grain of salt, because once you take it to that level and help people understand, it’s can still be great data that fills in some gaps. But, it is not the same as your own data, where you know the balance on every account, for every client on a daily basis.”

“I think your point illustrates the fact that you couldn’t use the very reliable 12,000 responses from the Canadian Financial Monitor to look at a branch trade area if you didn’t have the official census data to use as a modelling tool, to say people typically behave according to their type,” says Kestle. “And that’s true. If I know the type at the neighbourhood level, then I can take those 12,000 responses and use some statistically-reliable method to project it to the ground. It’s not the same as taking the 12,000 and trying to spread them across DAs. You have to have robust statistical techniques to do that. The official statistics really help us do that, in a way that you can get a better projection for small areas than you can from a sample survey.”

But, our panellists agree, you still need to understand that data that has been modelled or linked in that manner is not the same as data that you have gathered at an individual level.

“In Canada, we’re really preoccupied with explaining how the data are modelled,” observes Kestle. “What surprises me is that, if you go to buy a piece of data in the U.S. and ask, ‘Was this modelled data or actual, individual data?’ the person on the other end of the phone doesn’t know the answer. They don’t even understand the concept. So you can buy all this household data that’s perceived to be reality when, in fact, most data are modelled from best practices.”

“That really screams that the ability to tell the story of what the insight is suggesting to you really needs to be crisp, which incorporates all that nuance around the interpretation of the finding, or findings,” says Humphreys, “and, in my patch, all I can say is they wouldn’t give a flying fazoo around the nuances of here are the different data sources, here are the different sensitivity surrounding all the various different data sources.

They want to know what’s the implication, what’s the suggested action, primarily, and leave it upon the team that put it together, and trust the team that put it together, such that it’s going to be good enough to actually act on. But, now that puts pressure on the criticality of thinking, the skills, the ability to interpret, the ability to bring the organization along, and that has a huge cultural impact on the kind of people that we need to have to help make that kind of thing come to life.”

Drowning in data With all of this data available, how, as marketers, do you decide what you’re going to use for what purpose within your organization?

“That’s a really tough one,” admits Thackray, “and, if you’ve got the time, it becomes an easy problem. If you have the time to wade through all the data and find the right attributes that are prescriptive of the outcome you’re looking for, it’s pretty straightforward.

The problem is, you don’t always have the time. You’ve got a deadline for a particular campaign or whatever, you don’t have the time to really get the right attributes in the right form to get to 100 per cent best you possibly could do. That might not be a bad thing. In a lot of cases, for us, you don’t need the 100 per cent best answer. You need a better answer than if you weren’t using the data. So, it’s a balancing act.

When you have the time, you can do a really good job. If it’s more important, you spend the time on that particular model to make it as perfect as you can.

If it’s an action which is only really going to affect 10,000 customers, well, okay, so what? Let’s go a little easier on the analysis and get to a solution quicker.”

“We start with the end in mind.

By this I mean, we start with the question that we’re asking of the data, i.e. “what is it that we want to know?” says Sipidias, “and, then we Continued on page 26 October 2014

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Cover Story

Should you catch the big data wave?

Why free data isn’t always good data

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October 2014

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Cover Story

By Bill Peterson

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Courtesy CenturyLink Technology Solutions

see a flood coming…and it’s big.

Big data, that is. Industry sources report nearly 90 percent of all data in the world was created during the past few years alone. It’s estimated

by IBM that more than 2.5 billion gigabytes (GB) was generated in 2012 –every day!

Over the past year there have been several stories about government agencies in Canada collecting publicly available, free data. Earlier this year it was reported by the federal privacy watchdog that our federal government has been collecting Canadians social media data, for example. While there are obviously concerns around privacy that need to be addressed, another pressing concern

October 2014

is whether these agencies are using the data to make important policy decisions.

Why is this a problem? Free data, even lots of it, doesn’t necessarily mean that it is the right data, and that the conclusions drawn from it, whether it is with respect to unemployment numbers or flu trends, can often be way off the mark. From a marketing perspective, looking to free data to help guide strategic decisions could be a very costly mistake.

To understand big data, it’s important to look at the size of the digital universe. In its recent report, analyst firm IDC notes all the data that consumers and businesses create and copy annually will hit 44 trillion gigabytes by 2020. In fact, this number is doubling in size every two years. This universe consists largely of unstructured data built by the power of the Internet - including email, Google searches, tweeting and social media.

According to a recent article in Forbes, data has become one of the most critical company resources: “Once you start tackling big data, you’ll learn what you don’t know, and you’ll be inspired to take steps to resolve any problems. Best of all, you can use the insights you gather at each step along the way to start improving customer engagement strategies.”

Recently, the New York Times investigated some of the drawbacks associated with big data. The piece notes that – while beneficial – big data is not the panacea to all that ails society: “although big data is very good at detecting correlations, especially subtle correlations that an analysis of smaller data sets might miss, it never tells us which correlations are meaningful. A big data analysis might reveal, for instance, that from 2006 to 2011 the United States murder rate was well correlated with the market share of Internet Explorer: Both went down sharply.”

Still, big data is rapidly being adopted by the marketing sector as a means to find new micromarkets and discover new customer insights. Given the abundance of data from which to choose, tapping into this insight seems like a natural fit.

Some companies are certainly benefitting from big data. According to a recent McKinsey report, “those that use big data and analytics effectively show productivity rates

and profitability that are 5 – 6 percent higher than those of their peers.”

Their analysis of more than 250 engagements over five years “revealed that companies that put data at the centre of the marketing and sales decisions improve their marketing return on investment (MROI) by 15 – 20 percent. That adds up to $150 – $200 billion of additional value based on global annual marketing spend of an estimated $1 trillion.”

While big data brings big opportunity, it also brings a lot of risk. The McKinsey report also found that “many marketing executives find themselves faced with overwhelming amounts of data and organizational complexity, rapidly changing customer behaviors, and increased competitive pressures.” This coupled with the potential inaccuracy of that data exposes marketing to many potential pitfalls.

UK research firm Dynamic Markets recently conducted a study that revealed bad data is also costing companies. In fact, three quarters of UK organisations are currently losing potential revenue due to poor contact data, and 94 per cent believe they have poor data quality in their organisations.

So, is all big data “Bad Data”?

Not necessarily. To ensure quality, accurate data, what’s necessary is a dedicated plan and methodology – backed by targeted solutions and managed services. But why managed services? The information universe is exploding. Due to IT budget cuts and other constraints, most in-house IT departments don’t have the resources to do the job right. Lack of infrastructure space – and the inability to purchase more equipment – often leads to inefficient storage and management of critical information.

What I have seen, from a personnel side, is that many IT directors lack the bandwidth to manage and ensure data integrity. This leads to information overload and poor decision-making.

That’s where managed services come in.

A good managed service provider will offer the technology foundation capable of optimizing storage, streamlining integration and driving quick retrieval and analysis of structured, semi-structured and unstructured data. This leads to cleaner, better managed data. A

dedicated managed service provider can also provide elastic bandwidth to expand connectivity and handle increased usage – while enabling the environment to scale down as necessary.

Almost as crucial is a provider that ensures critical information is always available and reliable. The goal is a “Tier 1,” IP global network with multiple layers of redundancy to guarantee extremely small windows of downtime. In layman’s terms: an Internet backup plan. This must be accompanied by a powerful security ecosystem that not only protects sensitive data, but also ensures strict compliance with industry and government mandates. Lacking a powerful global network and full connectivity at the core, a big data project is doomed from the start.

The ultimate goal is to find a partner, like CenturyLink Technology Solutions, that is able to offer these critical elements, while customizing services to match each unique business or marketing environment.

A strong partner will also deliver tailored managed services – ranging from business case development and big data environment planning to training and implementation services.

Big data is changing the way marketing conducts business.

Encouraged by vast amounts of information spread across the digital universe, many departments are undertaking significant investments to better harness the power of both structured and unstructured data.

But to meet these ambitious goals, adopters must stand clear of the data free for all – ensuring they leverage only the most relevant data sets to drive quality decisions. Be sure to align with a partner capable of developing a big data offering that reliably and safely stores, manages and analyzes every piece of critical information.

Otherwise, you run the risk of literally drowning in a flood of data.

William “Bill” Peterson runs marketing and strategy for CenturyLink Technology Solutions’ Big Data efforts. Prior to CenturyLink, Bill ran Product and Solutions Marketing for NetApp’s Big Analytics and Hadoop Solutions. In addition to his marketing role at NetApp, Bill was the Marketing Co-Chair for the Analytics and Big Data Committee, SNIA.

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Targeting & Acquisition

Social media & Big Data Using social marketing to attract high net-worth clients

By Yvon Audette

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espite the rising popularity of social media in personal and business communications, Canadian banks, while active in some areas of social media, are not leveraging it to the same degree or effect as many consumer and retail companies. There’s a particular gap when it comes to generating sales opportunities with high net-worth clients. This can be partially explained

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by the regulatory climate. Banks hesitate to mount social media campaigns in the wealth and asset management space because of potential regulatory ramifications.

However, while there are almost certainly restrictions to take into account, high net-worth clients represent a significant market that is not currently being tapped. It’s time to start looking beyond the barriers to see

how banks can access these investors through social media and what they should bring to the conversation.

This is where big data enters the picture. We know that banks, like most businesses running on a digital infrastructure, accumulate and house masses of data. There is already a general recognition that businesses need to find better ways to leverage this information to drive business. For

banks, big data may provide an “in” with high net-worth investors if they start asking themselves the right questions: ❯❯ What can we learn from our data to help us formulate social approaches? ❯❯ What specifically should we talk about? ❯❯ What products should we promote online?

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Targeting & Acquisition

Social trends in the wealth management industry ❯❯

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Financial advisors’ (FA) personal homepages are often used to stimulate social networking.

FAs at major wealth management firms use LinkedIn for prospecting and finding professional groups that fit the FA’s target market (e.g., doctors).

FA Twitter pages are also becoming more common, though compliance remains a challenge.

Many retail brokerages and asset management firms now have corporate Facebook and Twitter profiles.

Most major self-directed brokerages offer their own communities to clients—no sign of similar efforts among wealth management firms.

Are there opportunities to leverage technologies like crowd sourcing and crowd funding?

How do we incorporate data consistently into our strategic approach?

How can we use technology to make the most efficient use of data?

The fact is, banks currently use social media more as a branding tool than a targeting tool and have not embraced the idea of combining data with social media to target high networth investors. Canadian financial institutions that do so, however, may be opening a market rich with opportunity.

Not only do a larger proportion of social media users manage their investments, they are significantly more likely to conduct finance research. Over nine in ten high net-worth investors using social media conduct financially-related research compared to only 70% of non-social media users.

Situation and opportunity A recent study on the growing influence of social media on high networth investors identified multiple channels that financial institutions could use to develop key relationships, October 2014

target specific promotions and products, and get clients to explore what further products are available.

For this cohort, high service levels and improving on the customer commitment are extremely important.

People leading very busy business lives are attracted to services that save time and yield better quality results. LinkedIn, for example, likely due to its focus on business-related information and relationships, is very popular with the high net-worth community—significantly more popular for the ultra affluent than other channels—suggesting it as a logical focal point for targeting campaigns.

Banks, of course, tend to focus on high net-worth investors, but that doesn’t mean social media can’t also be used to target and attract other communities, for example lower net-worth consumers and commercial enterprises (which could include owner/operators or even board members). It’s really about finding non-traditional channels to connect to potential customers in a straightforward, efficient way.

Growing influence of social media Once the connection is established and the message out, however, how do you get prospects to “come in” the door? Urging them to “drop in to the bank and talk to an advisor” is no longer the most effective approach.

You have their attention—how do you close the loop and get them to begin engaging profitably in your digital space? Retaining financial

clients following an inheritance scenario provides an interesting example. Just two percent of children keep their inheritances with their parents’ financial advisor. Rather than worry about losing clients, however, financial advisors should view this as an opportunity. Consider using the social media platforms these younger generations are comfortable working and communicating in to build fresh relationships and

media has expanded at an extremely rapid pace, analytical software and technologies specifically relevant to the financial services industry are struggling to keep up. At the same time, banks still face a titanic information management task with respect to internal information silos and data management.6 So whether you collect external data from social media, or try to cut your internal data to apply it to social media, it’s a

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Social media is proving to be an invaluable tool, with five million high net-worth investors in the U.S. and Canada actively using social media to help them with their financial decisions.

The ultra affluent (more than $5 million in investable assets) are passionate about investment research.

Regardless of whether or not they use an advisor, social media users tend be more active with their investing.

Two-thirds of high net-worth investors visit LinkedIn monthly, which is consistent with Facebook and over twice the visits to Twitter and Google+.

LinkedIn is the most trusted social platform for financial services companies to engage with high-net-worth investors when they are in a professional mindset.

Social media is about relationships and finance companies need to engage with their customers.

retain them as clients—or to attract those newly available clients fleeing what they perceive as old-school communications methods.

The big data play—targeting in social media Certainly the financial industry is excited over the rich source of customer data that social media offers.

By carefully harvesting, analyzing, and leveraging social media data, banks may be able to gain valuable insight into customer investment patterns, market trends, and value propositions, to name just a few. Yet, while big data will be key to effectively targeting high net-worth and other communities, banks are finding it difficult to determine exactly what data is most valuable. While social

challenging task in a fast-changing landscape.

Nonetheless, firms across the financial services industry have leveraged data analysis and social media to varying degrees of success by having a customized strategy, specific goals driving multi-platform initiatives, and a strong understanding of the strengths of each platform.

Corporate Insight’s Social Media Leaders Report identifies American Express’s social strategy as “embodying the greatest number of best practices as a whole” and “using more social media channels more effectively than any other firm.”

The company launched its first online community in 2006. A year later, it launched OPEN Forum, its most successful proprietary DMN.ca ❰

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Targeting & Acquisition

kind of granular information is highly competitive financial services becoming a competitive necessity. markets. Social media reaches a wide It does remain important that breadth of individuals overall and high any initiatives in this vein take net-worth investors in particular, but the current, stringent regulatory it can also improve brand perceptions. environment into account.

Research suggests that 28 percent of Banks work hard to enhance all high net-worth investors would ❯❯ Try to leverage your broader social/marketing approach to their customer commitment, perceive a financial company as target specific communities and clients. but balancing stricter regulatory “innovative,” “a leader in the industry,” ❯❯ Look at ways to offer products, services, and communications protections for consumers always or “on the cutting edge” if they offered in a more digitally relevant fashion, such as chat-based access enters into the picture. Once social media tools delivered through you understand the regulatory, the appropriate social platforms to social bankers versus traditional email contact. legislative and privacy boundaries, (that is, business-oriented platforms ❯❯ Consider incorporating less traditional services into your own you can begin to use your data rather than sites focused on personal marketing structure; crowd sourcing, for example, has been to more effectively optimize interaction where aggressive financial successfully used by some start-ups in Silicon Valley. customer relations and augment marketing may seem intrusive). It your existing marketing structure seems increasingly clear that the with social media. insurance. advantages of social media targeting community, which focuses on small On the flip side, LinkedIn is a can be ignored only at banks’ own risk. business owners.

Social media will continue to drive more appropriate forum for sharing That means acting now to not only Financial advisor Vanguard uses opportunity business-related information. develop data-based strategies, but to Twitter to the fullest by engaging With over 90 percent of high netWhen data can be cut to reveal this implement the technologies required consumers, providing real-time worth investors participating in social kind of client-specific information, the to put those strategies into action. customer service, sharing valuable media in some form, an integrated marketing and targeting value is clear. content, and even hosting livesocial media marketing strategy is Consider how much you can learn just tweeting events.

Yvon Audette is Partner, Advisory Services, necessary—not only to get results, but by understanding who people talk to Insurance company Esurance has National Service Line Leader, IT Advisory at simply to remain relevant in today’s and connect with. By analyzing this a strong grasp of Facebook marketing KPMG. data, you can develop campaigns, having conducted a insights around a broad variety of contests and campaigns to range of individuals promote its products, services, and that may be good private philanthropic efforts. banking clients. Then There are some key steps involved in consider who those initiating a social targeting strategy. individuals connect to If your target is high net-worth and any information investors, data analysis should focus One barrier to banks developing a strong social strategy is that available about those on identifying them, both from while they have plenty of data on hand, they do not always have the connections. Those your own data and by collecting technology to leverage that data in ways that drive business. The first social network may help and analyzing social media data. step in transforming your retail approach is to think “digital first.” you define community Next, it’s important to collect data How can you optimize and enhance your services and operations to behaviours and identify on what specific users are doing on leverage your wealth of legacy information? You need the technology usage patterns, leading your own different platforms so to improved channel you can accurately tailor your client in place to analyze, cut, and apply that information in ways that drive optimization and communications. For example, most the targeting process. This is a major paradigm shift, as “digital increasingly targeted people use Facebook for personal first” thinking can lead to some very sophisticated technological promotional capabilities. updates, which might suggest sharing requirements, but having the infrastructure in place to leverage big Proactively using information about personal products, data has been a successful approach for some financial institutions. analytics to extract this such as household mortgages or

Initial steps to improve social media targeting

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Why us? We make software solutions that empower all your decision-makers…because everyone makes decisions.

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iWay Software

Get Social

Omni

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Kicker Targeting & Acquisition

Improving your single most important analytic Why customer segmentation is important to your business strategy

By Brian Jones

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egmentation in its simplest form is about putting customers into “like” groups.

It’s truly one of the most powerful analytics. At the core of its strength is its ability to support businesses in the laser focussed targeting and managing of their customers.

With the amounts of information increasing, segmentations are about to get smarter. Big Data is creating unique opportunities. And advantage will go to companies that are able to aggregate vast amounts of information that build predictive segmentations.

But it’s critical to get your segmentation right because in a customer centric world, customer segmentation has become an integral part of business strategy.

What’s right for your brand?

Think of airlines and their frequent flyer programs. With a program in place, an airline can easily identify their “best customers” and understand their needs. This understanding enables them to deliver a different experience vs. those delivered to their “next best customer”. The strategy is clear in this example. It’s to protect and reward their best customers, in order to retain them. This is a simple segmentation to address an important strategy, and supporting a strategy is exactly what a segmentation should do.

Most businesses can identify their best customers or their most profitable ones. So segmentations don’t have to be complex to work, they just have to be right for your business. What’s right for your business is dependent on your businesses strategy. Building a segmentation that supports your strategy is key. By “support” I mean it provides direction on achieving your goals and objectives, as well as helps to measure your progress. Therefore, when developing your segmentation think how you will use it and the decisions it will help you make.

Getting started Segmentation requires customer data, and often starts with sources of data simple to access. If you’re looking for a less complex segmentation, and October 2014

your strategy is “growing existing customer spend”, then look to your customer lists. If those lists identify information such as who the customer is, the amount spent, and on what and when they spent it, then you are clearly on your way with one of the most basic, but nonetheless effective segmentations. It’s called an RFM segmentation. RFM stands for Recency (when they last bought), Frequency (how often do they buy), and Monetary Value (what they spent). These pieces of data can reveal considerable insights that help manage customers in a more relevant way. In their simplest form they are used to know how often to mail and when to mail, after the “who” to mail has been determined.

Executing against an RFM will help increase number of customer transactions and their size.

While an RFM can be effective and simple, it is merely one of dozens of ways to segment. Others range from basic demographic segmentations through to more complex segmentations that cluster behaviours, attitudes, opportunity, benefits, needs, life-stage, and channel shopped. These are just a few.

Segmentations can also be overlaid with other segmentations enabling you to narrow targeting which adds more relevancy to your marketing. Think of the value of overlaying a Heavy/ Medium/Light buyer segmentation with a buyer demographic. “Weighting” the demographics through purchasing information helps further define what your customers look like, and provides greater insights on where to invest your marketing dollars and what message to deliver.

Improving segmentation performance through loyalty programs Customer loyalty programs provide rich customer data bases, which can be leveraged to create very effective segmentations. Loyalty cards have a unique identifier which is the loyalty card number. Whenever you buy a product and use the card, the purchase is linked to you. Businesses store this data in their data bases, and leverage

the power of analytics to introduce insights into their organization.

Some retailers have become great users of loyalty program data. For example, drug stores can use their customer loyalty data bases to segment customers by life-stage.

Life-stage is an ideal segmentation for a drug store as it enables them to target their customers with offers that are relevant to their stage in life.

Here’s how it works…what we buy tells the retailers a lot about ourselves.

The first purchase of razors and shaving cream or feminine care, or the purchase of baby food or incontinence products - all speaks to our life-stage and our needs. The ability to manage the customer based on this knowledge helps drive customer value. For example, baby food buyers are of considerable value to a drug store. The range of products they require is wide and deep, and their frequency of shop and transaction size is high. Therefore, a drug store will want to engage this customer segment and earn their loyalty. With the customer insights they can begin to market products and services that specifically address their needs, and do so at the right time.

This will help drive the customers’ experience, increase loyalty, drive transaction size, and grow the customers’ life time value.

Taking it to the next level When attitudinal data is overlaid with behavioural data, more refined segmentations can be created. The combination of the two can begin to deliver a greater one-to-one marketing experience which drives campaign performance. Attitudinal data, like behavioural data, is also inherent in a shopping basket. For example, those who purchase a disproportionate amount of health food products can be put into a “healthy” segment. When combined with those purchasing baby food, you start to get a clearer picture of your customer. Data could confirm that the customer is an active female in her early 30’s whose health conscious attitude will likely be reflected in what she feeds her infant.

This type of information becomes a

marketers dream. Everything from what to market to them and how often to market to them becomes clearer.

Knowing the customers’ life-stage ensures retailers are relevant to their customers which drives engagement and loyalty while ensuring their marketing dollars are spent in the most efficient way. Highly relevant marketing has proven time and time again to increase the performance of marketing campaigns. As we all work our way through our teens, 20’s, 30’s and so on, the products and categories we buy provide all types of information for retailers that ensure the experience that we have as customers is relevant to us.

Summary In a customer centric organization, the customer segmentation becomes core to the enterprise strategy.

Therefore how you segment your customers becomes a very strategic decision. The key is access to great amounts of customer data. The more the information can be linked back to a customer, the better. Having access to both attitudes and behaviours for example helps you to define segments while providing guidance on messaging content, offers, and frequency. Consider the value of data provided through customer loyalty programs, specifically those programs that link purchases back to an identifiable individual.

Lastly, explore other sources of data, perhaps those available through your vendors, or through deep search engine technologies. Big data creates great opportunities in this area. The more data you have the greater opportunity to build highly competitive smart segmentations that support competitive differentiation while delivering your goals and objectives.

Brian Jones is the Principal of Active Data (www.active-data.ca) as well is an Associate of Alpha Insights (www.alphainsights.ca). Brian can be reached at brianjones@active-data. ca. View Brian’s profile athttp://ca.linkedin. com/in/brianjones99/, or reach him directly at brianjones@active-data.ca

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Engagement & Analytics

The trouble with Part 2

By Richard Boire

B

efore I continue, let me preface this article with my underlying bias where I consider the definition and roles of data science and data mining to be identical. In the last article, the discussion focused on the notion that the traditional four step data mining approach and methodology in building solutions to business problems and issues has not changed in the new Big Data world. As a reminder, this four step data mining approach is as follows: 1. Identifying the business problem 2. Creating the analytical file 3. Deploying the necessary technology and tools to build the solution 4. Implementation and deployment of solution Within the Big Data world, this above approach commences with the problem we are trying to solve.

The building of predictive model represents just one outcome of data mining. Data mining and analytics can be deployed in a multitude of scenarios to solve business problems beyond the building of predictive models. But let’s first discuss the building of predictive models. As discussed in the last article, it is quite common to develop excellent predictive model solutions in the so-called “traditional database environment”. Excellent results, as determined by the performance lift differential between the top decile and bottom decile or observing the KS statistics, are unlikely to be significantly improved by delving into the social media sphere of Big Data.

Why? In all models that perform very well(8 to 1 or better between the top decile and bottom decile), key variables that are most important as model predictors relate to behaviour or what the customer has actually done with the company. ”Behavior” type variables represent the richest data in building predictive tools.

Looking at what the customer has

❱ DMN.ca

“transacted” with you or what they have done is very powerful in terms of what they will do in the future. Given the “behavior” hypothesis statement above, we often see that the most powerful predictors in a given attrition model are changing purchase behavior prior to defection. Other variables related to what, where and how they purchase tend to be identified as key trends in defection. It is this rich “transaction/behavior” information that makes practitioners question the “value” of crossing over into the Big Data digital divide of social media. Beyond the all the privacy and governance issues surrounding this data, the practitioner will ask “How much incremental performance in the model will we obtain from social media commentary along with accompanying metadata.” Some subjectivity by the practitioner will determine whether the model is good or marginal. Obviously, marginal models would tend to delve into the Big Data ether in order to increase model performance presuming privacy and data governance are non-issues.

Online models On-line models represent pseudobehaviour type predictive models in the sense that we are using solely the customer’s web/navigation behavior to predict that person’s next online behavior .The most relevant on-line models are those that predict an outcome which represents conversion or purchase behavior or even likely to purchase. Log files are used to construct variables based on the web site last visited by the customer, time of visit, duration of visit as well as variables related to his page viewing behavior while on the current company’s web site. Despite the fact that we are not necessarily looking at transaction purchase behavior, we are looking at prior web behavior to predict a future web-behaviour.

In this sense, on-line models can be pretty powerful in predicting clickstream on-line behaviour or what pages an on-line customer is most likely to look at next. However, conversion models or the ability to predict likelihood to purchase can be trickier as much of the purchase behavior may be influenced by other channels as well as transaction behavior prior to the conversion activity. Unless the company is Amazon where conversion models can be pretty powerful as the buying options are confined to the Amazon website, most companies have alternate avenues for a customer to make a purchase thereby adding more noise to online models.

But as we observed in the offline predictive models rich with transaction-behavioural variables, can online models be enhanced by looking at Big Data (i.e. social media information) information. Once again, if the practitioner is obtaining the typical expected performance lift from a predictive model using behavioural data, appending Big Data to online log file data is unlikely to increase performance lift. In most cases, appending Big Data (i.e. social media data), governance issues aside,

4-step data mining approach 1. Identifying the business problem 2. Creating the analytical file 3. Deploying the necessary technology and tools to build the solution 4. Implementation and deployment of solution will yield minimal lift in model performance.

Behavioural advertising and Big Data So where is the big bang in the use of data mining and analytics regarding Big Data. One of the biggest benefits of BIG data is the notion of behavioural advertising analytics. For the first time, we can measure the impact of “eyeballs” on an ad. In the past, this kind of impact was always limited in the sense that we could never measure “eyeballs” on billboard or magazine/ newspaper advertising and had to rely on sample research studies for radio

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Engagement & Analytics

and TV advertising. With behavioural advertising, we can determine the number of clicks on a particular ad on a given website. Advertisers can then determine the cost per impressions (CPM). With this information, advertisers can be selective in determining where to place their ads given the cost per impression (CPM) on that given website. The analytics can even be more granular by looking at the cost per impression (CPM) for a specific page. As a matter of fact, testing strategies can be easily executed to determine the impact of different offers/communication options.

Companies such as Google and Yahoo have become experts in this domain area given their revenue model of paid advertising. In most of these cases, behavioural advertising is about the use of analytics for measurement and relying on what has happened in the past regarding CPM and then making decisions going forward regarding the placement of ads. However with the advent of Big Data technologies, it is now possible to employ more advanced analytics by developing tools which predict CPM for a given website. A good example of this is an ad for a new product. Huge amounts of data exist regarding CPM and the clickstream characteristics of a given website could be used to estimate or predict the CPM of the ad for that new product on that particular website.

Location Analytics Location Analytics has become another area of exploitation in the Big Data era. As our society has now become hinged to their mobile devices, the GPS technology now embedded in these devices can be used to determine the location of customers.

Using this information, a given store

can tie in the product preferences of this customer and market them accordingly. For example, I may be going up a particular aisle in a store which has hockey tape. An SMS message would then alert me to the fact that hockey tape is in this aisle.

Furthermore, I may be driving and a certain restaurant of which I am a preferred customer is nearby. An SMS message in my car would then alert me to the proximity of the restaurant.

Now you might say, where is the data mining or analytics? In the case of the retailer, it is determining my product preferences while for the restaurant, it is determining whether or not I am a high value or loyal customer.

In both cases, the data mining and analytics information needs to be integrated with the GPS technology in order to produce the appropriate SMS communication.

Telematics Another area of great growth and awareness within Big Data is telematics. Here, technology can now provide software in a car that monitors the behavior of the driver. The implications of this are enormous for automobile insurers. As I mentioned before, one of the key pieces of information in building predictive analytics solutions is behavior-based information. With telematics, we now have access to behavior as the software devices capture the behaviour of the driver. Data pertaining to the speed, braking, etc. provides very rich driverbehaviour information. Analytics practitioners within insurance salivate at the thought of being able to potentially integrate this information into existing tools that are used for pricing of policies within the property and casualty area. Besides risk, car

manufacturers can better understand the style of drivers for specific cars. This information can then be obviously used to modify the design of existing cars or in the development of new cars which balance the needs of satisfying the consumer while at the same time building a safer car. Suffice to say, this technology is just beginning to take hold in this country but the volume of data will be explosive if we think of what can be captured during the driver’s daily behavior.

Other data sources in the Big Data world As Big Data explodes, sources of data that were once considered outside the realm of analytics from a practitioner’s perspective are now available as different inputs that might yield unique insights in solving a problem.

A good example of this is the weather data. For instance, can we attribute different types of weather to the sale of certain items. What about stock market data? Does the trend in the TSX or DOW impact certain sales items more than other sales items. In the past, much of consumer behavior was determined thru market research.

Market research will certainly continue to be a valuable science in understanding the psychology behind consumer purchase behavior but it will not be required to understand what customers actually purchase.

With Big Data, we now know what consumers did and can tie it back to a myriad of different information sources. The data is there but the challenge is integrating this data into an analytical file in order to tell a story and to ultimately identify decisions and action steps that are better for the end stakeholder.

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The “explorer” mentality in the data miner Data, big or small, is no longer the limiting factor in solving business problems. For Data geeks, it is similar to a child being in a candy factory. The challenge for geeks, unlike children who will simply attempt to consume all that is available until they become ill, is to derive sense from all the is information. This is not to imply that they will have the necessary answers or solutions upfront but that they have the required information that might be considered in solving a particular business problem. Data Miners are at heart explorers. In some cases, this may necessitate identifying what the actual business problem is. But the real commonality amongst the data mining community is a thirst for knowledge that will ultimately resolve the business problem. This requires an “explorer” mentality in being able to extract and create the right information and to then develop a variety of different solutions. In this kind of environment, as Tom Davenport indicates, “the data scientist is essentially hacking away at the data“ but with a view to building the best solution. This inquisitiveness and academic curiosity amongst data miners is not new. In fact it is this inquisitiveness and curiosity that will expand the discipline in new techniques and approaches thru the practice of using data (big and small) to solve business problems.

Richard Boire is a partner with analytics company Boire Filler Group, Richard Boire has been helping companies use their information to help in making better decisions for over 30 years. His role is more on the analytics/ technical side with a view on this to improve overall business results

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Engagement & Analytics

Modernization in a Big Data world

By Marc Smith

A

lthough Big Data remains, for some, an overhyped term, the reality is that the explosion of data is unlike anything we’ve seen before – and it is here to stay.

Companies big and small are looking to sustain value and competitive edge by leveraging all of their data assets – regardless of how big, how structured, or how fast moving that data. In todays’ competitive markets, new upstarts have the luxury of building their infrastructure, governance and organizational processes from scratch to best monetize their data asset. For example, organizations like Expedia are able to increase conversion rates through offers that leverage real-time pricing information for hoteliers and historical customer data to predict ❱ DMN.ca

what offers would be most relevant to consumers.

However, large organizations who have evolved their IT infrastructures over several decades don’t have it so easy. Much of their IT budget is spent on operating their legacy systems, and it is difficult for IT to keep up with the pace of change that Big Data demands. Analytics is at the forefront of this pace. These organizations need to modernize several key aspects of their business systems, processes, and culture; in essence re-architect their enterprises to compete and thrive in a data-rich world.

Adapting to modern needs or habits Everyone is talking about factbased decision making, but are their organizations set up to do it in the most efficient way? Let’s face it, most organizations have old systems and

infrastructure of different vintages, some that haven’t been upgraded or replaced in decades. In response to this, upstart and leading edge competitors will leverage their data to out maneuver, smarter and faster, while laggards struggle with their systems and fumble through the new digital world of social media, mobile, and machine-to-machine communication in addition to their impact on delivering services and interacting with customers.

This also begs the question, is everyone getting the right information at the right time? Usually, at an enterprise scale, the answer is no.

Some systems in organizations may be better than others, and individual lines of business within a large organization may have good intelligence within their domain.

Rarely will you find large organizations that have interconnected all of their

data at an enterprise level. There have been huge strides in this area through capabilities in data warehousing, but those systems are showing stress in handling the growing volumes of data and an appetite of the business for fact-based decisions. Most organizations rely on decision support systems to make decisions against silos of data that are incomplete and out of date. Often these decisions are made in the interest of an individual business unit or department, but are not what is best for the enterprise.

Trustworthy and complete data has to be shared across the enterprise and externally to partners, otherwise no one has the right, or complete picture.

Leveraging data and analytics allows companies to experiment against their populations in ways that were never before possible.

This provides the opportunity to use an experimental approach to October 2014

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Engagement & Analytics determine outcomes of different tactics and offerings BEFORE they are rolled out fully. For example, I may choose to buy impressions on a website based on a small section of my population leveraging a new method of segmentation that generates a higher conversion rate click through an offer to a purchase. It is this ability to testand-learn that gives organizations the agility to find the best paths, and start to double down on those strategies.

Speed of execution is paramount and can make the difference between thriving in your market and being left in the dust, outsmarted by your competition.

Installing modern equipment There are very powerful reference architectures on how to leverage massively parallel processing (MPP) to handle big data analytics. These MPP infrastructures were once reserved for universities, military science and research using build-for-purpose and very distinct software systems. Today these same reference architectures with very fast networks and massively parallel processing servers are readily available, affordable, scalable and agile.

Enter: the era of the modern data center. Organizations are deploying server clusters as networked appliances, leveraging commoditybased hardware and commercial and open-source software that is readily available to take advantage of these server farms. These modern data center architectures are critical for IT to deliver services that are fundamental to their business strategies. IT organizations leverage virtualization to provision this infrastructure out as cloud-based services, or services that can be rapidly turned on for business consumption using ways to provide data, analytic platforms, and full application systems on-demand.

Utility of these modern data centers are being provisioned through cloud-based services, either internally or externally to the organization.

Internal cloud services are done when organizations have the means to create the data centers and provision applications fast that can integrate with their legacy systems. New entrants who do not have their legacy systems with different vintages can by-pass this step completely and leverage cloud services that are provided from external organizations that maintain the modern data centers. This eliminates the need October 2014

for organizations to make capital investments in the data center yet still compete using on-demand cloud services, dramatically lowering their barriers to entry.

Adopting modern ideas or methods There’s a cultural change that needs to happen in organizations to take advantage of Big Data and advanced analytics. Changing a culture is not easy, and it takes focus and leadership.

Todays’ leaders need to have a bias toward analytics, where strategic risktaking is based on empirical evidence that is driven from analysis of all the available data. These leaders need to transform their organization from a world where our ability to harness the data is scarce to a world where it is abundant. Investments in developing a competency in analytics requires the right skillset to get the data organized, the right infrastructure to use the data efficiently, and new processes on how that data is managed and governed.

New roles are emerging at senior levels of the organization, like Chief Data Officers (CDO) and Chief Analytic Officers (CAO). These are senior level roles who often participate in the board of governors in their organizations. These roles are pressing forward to develop data governance practices to set organization-wide policies on how data is treated and exploited. Analytic centers of excellences coordinate activities in leveraging analytics in operational decisions and supports the use of proven practices more broadly across these organizations.

But where does this leave the Chief Information Officer? This role emerged in automation of systems and their ongoing operations and support.

Big Data and Analytic applications puts tremendous pressure on how IT manages and governs their processes today. For example, release cycles of classic IT systems takes far too long to get new systems deployed and are too rigid to take advantage of this experimentation near impossible.

Systems need to be able to react quickly to changing inputs and new models that perform better need to be deployed fast. We can’t treat analytic systems the way that we treat classic IT system development, and new governance policies and processes for this Big Data phenomenon must emerge.

CIOs must transform their infrastructures to take advantage of these new trends in affordable ways.

When it comes to data they must adopt systems where data storage costs drop dramatically. Never mind all that effort spent on deciding what to keep and what to discard – now we can keep it all! We need to change the mindset of decision makers to leverage their data and analytics to run their business. Those who don’t will get left behind.

Presenting information visually Data visualization is the presentation of data in a pictorial or graphical format. Though the term may be new, the concept is not. For centuries, people have depended on visual representations, such as charts, graphs and maps, to understand information more easily and quickly. As more and more data is collected and analyzed, decision makers at all levels will increasingly look to data visualization software to find relevance among millions of variables, communicate concepts and hypotheses to others, and even predict the future.

Organizations will need to think of their data as a great but unedited story which needs the help of visual analytics to bring it to life.

In summary As the Big Data phenomenon rages on, few are considering that it’s more than just data and software, addressing notion of culture and adaptation.

For large established organizations, a cultural change needs to take place for organizations to take advantage of Big Data and advanced analytics. Like any transformation there is room for innovation and for leaders to emerge to guide these organizations through the transition and ensure that they emerge healthy and able to thrive in the digital world of Big Data.

Marc Smith is an Enterprise Architect in the SAS Americas enterprise architecture practice. He works with teams across US and Canada to solve customers’ complex business problems and to drive sales through expertise in high performance analytics.

Marc has been architecting, implementing, and selling Business Analytics applications and solutions for over 20 years in financial services, government, telecommunication, retail, health and life sciences, energy, mining and metallurgy industries.

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Big trends in Big Data At this year’s Data Marketing conference being held in Toronto on December 10-11, some of the country’s top minds in data analytics will be sharing insights into some of this year’s big trends in Big Data. Here are just a few.

Courtesy Data Marketing

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Engagement & Analytics

By Dina Al-Wer

1. Predictive social listening There are millions of tweets, Facebook postings, blog posts, comments, etc., taking place every day social media.

Some of that discussion is relevant to brand marketers and market researchers.

“It has become almost a cliché, but marketers don’t control what their brands are about anymore, people do,” says Larry Friedman, Chief Research Officer of TNS Global. “Through social listening, brands can understand how the brand conversation is evolving, and what this might mean for their businesses.”

Friedman has experience in a wide variety of research areas, including brand equity research, tracking, digital communications, social media, customer experience research, strategic/segmentation studies, and new product development.

In his role as Chief Research Officer his focus is on consulting with internal and external clients on study design of “complicated” engagements, and helping determine what the results mean from a business perspective; and keeping TNS on the cutting edge of developments in market research.

According to Friedman, data has a huge role to play in social listening.

“We’re starting to understand how you can use information from listening in a quantitative sense to build predictive models of “business success”, whether you are talking about success from an attitudinal perspective like brand equity, or things like brand share. To the extent you can give a manager a heads-up that there may be a worrying business result on the horizon, the “data” you glean then ❱ DMN.ca

from social listening can then become critical to helping managers run their businesses. We have high hopes that further developments along these lines will turn much of tracking research, for instance, into a windscreen view of the market, from a rear-view mirror view of the market. We have a very exciting future ahead of us.”

Friedman has 3 rules when it comes to social listening success: ❯❯ Data cleaning is essential to detecting the “signal in the noise” – you need to make sure you’re listening to the right geography; the content has to be around the right topic (you don’t want to confuse mentions of “apple” with “Apple”); coupon “spam” has been eliminated. ❯❯

Coding has to be accurate.

While there has been great progress in the field of text analytics, many listening systems still employ pretty inaccurate automated coding systems.

❯❯

Be careful of “magic metrics”.

Many individual listening metrics have not been connected to business outcomes. You may need to use models that combine a wide variety of metrics in order to find the actionability you are looking for.

2. Personalization As a Senior Manager, Customer Segmentation at TD Bank Group, Nick Necsulescu is a customer strategy and analytics leader, with more than 11 years of business performance measurement experience. He has a wealth of experience in both digital analytics, as well as traditional

database marketing. He believes that personalization is the key to enhancing customer experience and that data is the key to personalization.

“It comes down to the customer experience,” he says. “Personalization creates a modified, adaptable experience, in which data and analytics are used to improve the communication with the customer.

By knowing the customer better, the experience is enhanced. As opposed to customization, personalization is driven by the organization not the customer. Personalization is a success when the customer is more satisfied with their experience, as a result.”

In today’s omni-channel environment online personalization, he says, is critical to a business.

“Digital (online or mobile), are critical channels for almost every organization, and it will continue to become a more significant channel.

Therefore, organizations need to take advantage of this opportunity and “positively surprise” the customer through personalization.

Personalization is also much more difficult to deploy and implement digitally, as customer behavior can be altered potentially in real-time, but still not as quickly as when a customer speaks with a rep through a call center or in person. Therefore, getting it right in the digital space is where organizations will win in the future.”

Going forward, Big Data, he explains, will play a critical role in merging and combining customer information and touch points, for the purpose of enhancing the customer intelligence and profiles.

“When the customer profile

has a wealth of information, an organization’s decision making will be greatly improved. The less data available, the more guesses will have to be made from an analytics and marketing perspective with regards to driving a customer experience and the allocation of funds.”

But marketers are facing a number of struggles when it comes to segmentation. First and foremost, says Necsulescu is how to deal with unstructured data.

“Properly merging the traditional structured data sets with the unstructured is a complex challenge.

Gathering so much digital information from multiple social media platforms and making sense of it, is another challenge. Therefore the work requires more creativity. Which organization does this best, will succeed.”

He offers three tips on how to deal with data and segmentation: 1. Don’t ignore any data source initially 2. Don’t expect perfection when merging data 3. Include customer interaction data points within your segmentation model 3. Innovation As the AVP of Research & Development at AVIVA CANADA. Hashmat Rohian leads a crossfunctional team charged with bringing data-driven insights and solutions to the masses and making fast, fact-based decision making a reality across Aviva.

As an academic and practitioner, his work concentrates on taking an issue-to-outcome execution approach putting the focus on innovation in the October 2014

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Engagement & Analytics

October 2014

4. Turning silos of Big Data into smart marketing “I consider data an essential part of my daily work that directly impacts all the outputs I deliver,” says Dr. Chao He, the Senior Manager of Strategy, Insights, and Research at HSBC Bank Canada. “In today’s organizations, it is vital for marketing (as well as other business units) to make data/ fact-driven decisions whenever possible. For me, dealing with data means gathering, analyzing, and synthesizing data from various resources and eventually generate a cohesive story that will help business stake holders not only understand the key market, consumers, and customer insights, but also grasp the key implications and guide their decision making processes.”

The data He works with comes from three categories: 1. Customer data, which includes customers’ transactional and usage data gathered through their interactions with the business, as well as customer feedback gathered through research and complaints.

2. Business insights as well as competitive intelligence, which includes internal business performance data, and information on industry trends and competitive activities 3. Market and consumer insights, which includes macroeconomics, market and consumer trends, as well as social buzz (through social listening).

He has three goals when it comes to data which he summarizes as the three ‘I’s’. ❯❯ Integrate: “The whole is greater than the sum of the parts” (Kurt Koffka). Data from different sources often present different sides of one coin. It is easy to understand that different data sources may complement each other in painting the full picture. But it is more important to understand that when data conflicts data, a lot of times, it may generate even greater learning that can identify the underlying reasons of the conflict, and define actions to mitigate the gap. ❯❯

Insights: Data without purpose is just random numbers. The key purpose of any data mining activity is to extract and distill the story: What is the key message from the data? What can be done based on those learning? To generate Insights, the first priority is to

understand what data implies, (although beneficial, it is not always needed to know why it is so). ❯❯

Influence: The work is only half done when the report is written.

It needs to be leveraged to guide business initiatives in order to demonstrate the benefits of data.

To influence key decision makers, it is vital to clarify and communicate effectively. Clearly articulated core messages and straightforward recommendations will make it easier for stakeholders to turn the data into actions.

But there are challenges. For instance, dealing with unstructured data and making sense of it.

“Today more and more data are coming as unstructured (e.g. social buzz), unlike well-structured data, it takes more advanced techniques and a fine balance between art and science to accurately interpret its meaning.”

Combining data and ‘blink’ for most effective decision making is another challenge faced by marketers.

“It is not uncommon that some business partners are still used to make decisions based on past experiences or even ‘gut feeling’. In fact, ‘gut feeling’ from a seasoned business person sometimes takes far

less time and is almost as accurate as a data driven process. Those are the so-called “blink” decisions, as defined

by Malcolm Gladwell in his book Blink.

However, blink-decisions are not always reliable, hence the challenge is to identify when and how to use it. It will be ideal to leverage blink decisions to point quick decisions that save time, and then back up with sound data, but reality is not always that simple.”

Here are his top 3 tips for marketers working with silos of data: 1. Understand the business. Only if you understand the business objective, challenge, and root question; your deliverables will truly add value. So learn as much as possible about the business.

2. Always know your (data’s) limit.

Understand what it can do and it can’t do. Do not over-generate.

3. Seek alignment with other data sources. Try cross checking with others whenever possible and see if there is synergy or additional insights can be identified.

The Data Marketing Conference & Exhibition will take place on December 10-11, at the Eaton Chelsea Hotel, Downtown Toronto. For more information, please see the insert in this month’s issue of DMN or visit our website www.datamarketing.ca

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digital space and tying data, execution, learning, analytics technology directly to making better and faster decisions for improved business outcomes.

“I lead the Data Science R&D. Basically, my mandate is to help the organization make fact-based decisions; better and faster decisions, with the right visualization tools, technologies, and statistical techniques that allow us to make the right decision at the right time and embedding analytics into our business processes.”

Data, he stresses, is the key to making good decisions. Good data, with good statistical techniques expedite the fact based decision making process. It can answer questions for us, such as; what is our next best action, what channels, customers or processes do we need to optimize, where do allocate our capital, if the payback is enough to justify the initial investment of time, resource, and people, etc.

“When engaging with the business, there is a bit of education on the ‘Why’ rather than the ‘What’. It’s all about the ROI at the end of the day. Hence, you always have to link data analytics, with ROI and value added.

He has 3 goals when it comes to data.

“Our number one goal, which is our vision, is to make fact-based, datadriven decisions. This can be achieved by deploying predictive analytics, and sophisticated learning algorithms.

We are implementing new ways of organizing, analysing and storing data.

“My second goal would be to apply this data within our digital and offline journeys, hence improving the customer’s journey in a digital arena as well as offline. And my third goal would be, automation; what part of the business can be automated? Can fraud be automated? Can we shave off some costs and time by automation in our claim processes?”

He believes that data plays a role in driving innovation within an organization. In fact, that’s his job.

“Optimal pricing might touch customers, but portfolio optimization might not. Hence, data can guide us through sustaining and disruptive questions like quarterly and annual planning; where should we invest?

Which line of business? How do we deliver personalized services and target to segments of one? Data can help us not only to optimize our business, but our customers too, it helps us with marketing solutions as well as with fraud claims.”

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Engagement & Analytics

Measuring engagement in a hyper-connected world: Part 3 of 3 Practical tips and methodologies

A recap of the previous two articles in the series Engagement is the ultimate goal for marketers and is part art and part science. This series on engagement aims to build a framework for marketers. Part 1 of the article series defined engagement as a holistic measure of customer interest that is based on cumulative interactions over time. The six correlates of customer engagement are: value, trust, efficiency, relevancy, consistency, and control.

Part 2 outlined the three building blocks and foundations of engagement (comprehension, emotional connection, behaviour). Marketers first need to ensure that customers understand what their brand is and then an emotional bond can be nurtured. The behaviour outcome is about actions: whether it is clicking on content (response), calling a call centre (customer service interaction), or shopping behaviour (recency, frequency, monetary). Part 3 of the series outlines how companies can measure engagement.

❱ DMN.ca

By Geoff Linton

M

arketing shouldn’t be this difficult! Where has the science and discipline gone? When I look around at industry conferences I see lots of busy looking marketers who are stressed because they have taken too much on to their plates. Most of the conference rhetoric is about the plethora of digital tools they are using. It’s all fluff with little discussion on marketing fundamentals: target audience, positioning, and results.

If your company is more customer centric than product focused then “customer engagement” should be a part of your core strategy. Getting a better understanding of your customers gives your company a big advantage.

The major marketing challenge is that today’s customers are always on the go and they are more empowered (smarter, better informed, and better connected).

Customers want information and service on their terms (24/7 and use a mix of online and offline channels). Building a two way communications model can be complex.

The second challenge is determining what customer information marketers should collect and focus on. Engagement is multifaceted but collecting too much information can overwhelm a marketing team. Start simple and focus on the core KPIs and behaviour gaps.

This article outlines how various organizations are measuring engagement.

The reality is that many organizations are only scratching the surface (and reporting on just a few engagement metrics). But marketing leaders are more disciplined and detail focused. They know “what gets measured gets done.” There are 8 layers in the “engagement onion” that marketers can peel back. Some parts of the onion are sweet and some parts might make you cry!

The engagement model & measurement methodology Customer centric marketing has many layers.

It’s like the bulb of a large onion and you can’t see the core. You need to make a cut so you can see the layers inside. Each layer tightly covers the next. Each layer is a different aspect and dealt with by a separate manager.

Let’s start on the outside and talk about each layer as we peel the onion back.

Layer 1: Brand The brand encapsulates everything in marketing. Brand brings together the features and attributes into a key value proposition.

There is plenty of noise and competition in the market and customers need to cognitively understand what you are about. If they don’t understand…you have a roadblock.

The uniqueness and strength of market positioning helps brands stand out. Top of mind awareness of a brand helps when customers are “in-market” for your product.

Agencies are stewards of the brand and October 2014

// 25

Engagement & Analytics marketers use research studies to track key brand awareness and competitive gaps. The problem with some research is that it is self-reported info and purchase intent measures may not actually translate to trial/adoption/loyalty.

Brands should have a few key metrics that align closely with their marketing plan. Quantify these and measure any changes. Brand metrics are a rough indicator and marketers need to drill down.

Layer 2: Key performance indicators (KPIs) The C-Suite looks at high level metrics and with a more holistic point of view.

Growth in revenue and customer spend indicates that customer engagement is strong. The C-suite also typically focuses on correlates of profitability like customer tenure or satisfaction. Many marketers measure their Net Promoter Score (NPS) because it measures the satisfaction of the base (and it is a metric that all internal stakeholders can understand).

But data driven companies like banks take it a step further and create their own proprietary “loyalty index” (this is often a more accurate predictor of loyalty). The most sophisticated customer centric marketers quantify the average Life-Time-Value (LTV) of their base.

Everybody on the marketing team should be aligned to KPIs but the CMO is ultimately accountable. There is a reason that CMOs typically have a short tenure. Tip: Link all the metrics on the inside of the onion to the topline KPIs. Layer 3: Loyalty metrics Loyalty is a fuzzy concept for many marketers and it is composed of a number of factors. Frequency and patronage is important but average spend is also a key metric. Customers who are most engaged visit often, shop more frequently, spend more money, and use multiple channels to interact.

They spend a greater share of wallet with you. The biggest value is through word-of-mouth and inspiring loyal customers to refer and share.

Marketing executives (VPs and Directors) should track their loyalty metrics and look for changes in behaviour. It’s very important to track loyalty across segments and make sure that core deciles are protected. Don’t overlook the top 20%; maintaining strong engagement for your best customers is critical.

October 2014

Layer 4: Customer service Customer service is the front line and where the moment of truth happens.

Whether marketers are efficiently onboarding new customers, fixing issues, or cross-selling/upselling, a lot happens through a customer care centre. The number of complaints, saves, and overall customer disposition are data points that can be used to measure engagement. Most marketers in middle management do a decent job of the general tracking and they flag major variances but the customer service data is not often integrated or actionable.

Layer 5: Other interactions Engagement is an emotional commitment and the rise of digital gives marketers an opportunity to leverage some new metrics. Leading marketers sometimes use unique data points to infer positive sentiment. P&G tracks “shareability” on their dashboard. One of our CPG clients (who had a crosssection of household health products) tracked the amount of User Generated Content created each quarter. Ratings and reviews are also important and if you can connect reviews with customers it can provide valuable nuggets. The jury is still out on the value of a fan or social follower but social data points can be factored into an engagement algorithm.

Digital marketing managers are the ones who need to own this category and make it meaningful.

Layer 6: Segments Most brands have a well-defined primary and secondary target audience. But marketing execs are often surprised to find that their current customers may be quite different from their ideal audience.

Companies should regularly profile their customers by demographics and psychographics. Histograms can identify skews and gaps.

One of the great things about database marketing is that marketers can track activity and satisfaction rates by deciles. With recency/ frequency/monetary data, marketers can measure message or shopping fatigue. Segment management should be the responsibility of the director and in large companies you may have managers assigned to this role.

Layer 7: Campaign Campaign execution is typically the spot where marketers spend the most time. Benchmarking against the industry is a way to measure

Fast facts

• Brand advocates spend 2x more than average customers (Source: Branderati, DMnews article redefining the HVC) • HVCs are now being profiled on not only monetary but 3 other factors: influence, engagement with the brand, and advocacy on behalf of the brand.

relative campaign performance. But the benchmarks need to be based on specific data and your sector (otherwise they aren’t credible in the C-suite). A more precise measure of engagement is to track momentum (positive trend of increasing click stream metrics). If customers really like you they will open, click, go deeper on your website, and convert at a higher rate.

to start with the building blocks of engagement and then align them with your measurement strategy. You can go as deep or as shallow as you want.

Some marketers will be happy just to peel back 3 layers. Customer centric and database driven leaders probably want to get down to the middle of the onion (layer 8). Remember that stakeholders at different levels look at engagement through different glasses and make sure that you are aligning your engagement approach to the C-suite!

Layer 8: Individual The most sophisticated marketers are using automated algorithms to score individuals. 360 degree reports provide a detailed history of a customer’s engagement in your campaigns (and from this behavioural patterns can be produced). Points can be assigned for specific actions and a total engagement score can be calculated for each individual. Once an engagement algorithm has been created then individual scores can be easily tracked or flagged.

Geoff Linton is a co-founder of Inbox Marketer Corp. and a direct marketing expert with more than 25 years of applied experience in both client and agency roles. His experience spans many industrial sectors, including financial services, telecommunications, consumer packaged goods, technology, manufacturing, and entrepreneurial businesses. Geoff has guided clients in digital messaging strategy, and analytics for Inbox Marketer over the last 12 years. Previously Geoff was Associate Director for the Air Miles program where he spearheaded major launches and was actively involved in targeted marketing initiatives and customer/campaign measurement. Geoff holds both a P.Eng and MBA from Queen’s University in Canada. @Geofflinton

Conclusion Marketers have been mystified on how to measure engagement. But with today’s technology and breadth of data available, it’s actually easier than ever to quantify. The key is for marketers

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Executive Roundtable Continued from page 10

look to see, based on that question, what are the best reliable sources of information and data points that we to feed our analysis. Then, we gather our information and build the models, that will allow the information to speak for itself and help answer the underlying question(s).”

“We don’t always start with the data,” says Warren. “By ensuring a good understanding of the objective or question to be answered allows one to focus attention to data that’s relevant.

Because information is everywhere, looking for data without a clear understanding of your objective can lead down the wrong path.”

“Technology,” says Kestle, “is a very important factor in this decision.

While I agree that you have to understand the business problem, technology is so powerful now that you can do a lot of trial and error and let the data speak. Although I think it’s really important what you’re saying, you need to start from the data. There are also a lot of methodologies that allow the data to just crunch and show you a result, and you can test it because of the computing power that we have.

It makes a huge difference in how we make decisions.”

“I think, that the practical reality for many organizations is that you get more demands and requests for information and insights than you can deliver on,” says Tyndall, “and so you need to prioritize them based on things like how big is the potential impact of this? You’d like to do everything for everybody, but the reality is most companies don’t have all the time and resources to do that, so we have to prioritize. But, I do agree that some information is better than no information. So, if you can just do a very little bit, and just say the average socio-demographic profile of these people versus these people is this, that will get you a lot further along than not knowing even the basics.”

“Just one other important criterion for determining what data and analysis to do, and that is what are you actually going to be able to implement?” adds Kestle. “It’s very important in that whole process to ask, upfront, ‘What are you going to do with this information? What’s your plan for the year? How can you put it into action?’”

“That’s huge, and it becomes the nice-to-know versus the need-toknow,” says Humphries.” ❱ DMN.ca

The data analytics buy-in Despite all that data can do for a company’s marketing strategy, there continues to be resistance from the C-suite to buy into a data and analytics strategy. So what’s behind this resistance?

“I think it’s ideology,” says Humphreys. “There’s an ideology limitation, where they may not have the appreciation of what the data can do.”

“To me,” says Kestle, “I see a lot of it as organizations not having enough infrastructure and people resources to do things differently. In organizations, the change management that a new data governance or analytics strategy could require would take a lot of effort to create a more integrated approach.

And I don’t feel very optimistic about that happening.”

“I think once you see the first few successes, even if they’re cobbled together, of how using this data can really improve operational efficiencies in a company, then the C-suite sort of goes aha, I see how this affects my bottom line, and I think we should be investing more here,” says Tyndall.

“I’m seeing, more and more, that our clients are becoming more agile,” says Warren. “We’ve said it is difficult to implement change, but I’m pleasantly surprised at how quickly they can react. And, I’m also very impressed with the leadership I’m seeing.”

The future of data use “At the analytics conferences, we’re starting to see the simplification theme,” says Kestle. “It was a kind of storytelling for a long time and now it’s a lot more about simplification and how the results get presented.”

“And, it’s not about data,” says Sipidias. “It’s about the high-level insights, and the trust in the process of analysis. The trust that comes from the hard work of gathering reliable information from great sources. That goes back to that important mix of official stats and everything else that we decide to put together.”

“I also think that the vendors have to be transparent, the buyer has to be knowledgeable, and they have to challenge and test the results,” says Kestle. “I think that the process requires a transparency, an open discussion, and having people collectively share knowledge about what the business challenges are that they’re using the data to solve, and what the methodologies are that are helping them. That’s how the industry can develop the best practices.”

But is there is an expiration date on good data? For instance, how long can we keep using the 2011 census data, while still feeling confident in that data?

“Well, there’s no simple answer to that,” says Smith, “for example, the unemployment rates are probably already out of date even before we publish them. On the other hand, a lot of the demographic data for areas that are stable tend to remain very stable. A lot of the characteristics of the population in Nova Scotia are probably very stable, and probably survive, largely intact, up to the time of the next census. On the other hand, in the suburbs of Calgary or around Fort McMurray, I don’t think you would want to really count on the information there being particularly long-lived. In fact, part of the challenge with Fort McMurray is due to the way in which we conduct the census, which actually counts people at their usual place of residence, and not where they’re currently residing. This is a problem for the local municipality; because that’s the population they have to manage. So, it really depends—is this a very stable area or is this a very volatile area? Has there been a lot of immigration, a lot of construction? If there has been, then you need to start being cautious a couple of years out about how well the data are representing that population in that area. On the other hand, for very static areas, like most of the Maritimes, and most of Quebec, I think the demographics hold up pretty well, and the labour market data probably holds up decently well until we get around to the next census.”

The role of social media According to Kestle, when it comes to blending social media data with more traditional sources, it really comes down to critical thinking, and understanding what the problem is you’re trying to solve.

“It’s about understanding the strengths and weaknesses in the different data sources and using them appropriately,” she says. “I don’t think there’s any blanket solution, it’s just a lot about having the expertise and ethic around ensuring that the data are used for the purposes for which they were collected and designed, and then using data from a lot of different sources.”

“I think one concept is you can get the same information from multiple sources, but you need to assess the validity, the reliability, of getting it from those different sources, and

make your own decision around what would be dubbed the gold version of that information,” adds Tyndall.

The next Census So what can Statistics Canada, and other data providers, do going into 2016, to make sure that the data businesses need is still available? What are some of the options going to be?

“The next Census of Population will be held in 2016 and will be in the same format as the 2011 census,” says Smith. “So there will be a mandatory piece and there will be a voluntary piece. The content of the census has not been determined yet, neither has the placement of the specific questions on the mandatory versus voluntary parts. One thing that we’re looking at very seriously is linking income tax to the census piece so we’ll have income for everybody on a comprehensive basis. The questions are still before the government and there will likely be a decision made towards the end of the year on what the specific content will be, and which questions go where.

“The decision by Statistics Canada to access the income data from the tax records for the 100% census is hugely important, because it means that we will have an unbiased income number,” says Kestle.

In 2016, Smith says they are aiming towards getting really good data for the larger urban areas, particularly anything above 5,000, to try and ensure that the data in those areas are very robust.

“We’re also looking at ways to try and get the response rates up on the voluntary piece. But, at the end of the day, this is a random sample of 2.5 million households, 6-plus million people, and the data are therefore robust. They are not as good as if they were collected on a mandatory basis, but they are still very, very robust.”

There are also rumours of a major new survey conducted by Statistics Canada looking at job vacancies and labour markets across the country. It will also be looking at wage data and trying to penetrate down to smaller areas (i.e. down to the economic region).

“To be able to penetrate down to that level is to deal with the fact that there is no other way to get at local labour market job vacancies in a reliable way that gives data that’s sufficiently robust to make decisions about things like temporary foreign workers,” says Smith. “That’s the plan, but not yet confirmed. It needs approval from the governing council, ultimately.”

October 2014

Data in the Call Centre Supplement

• Case study: Information Builders & Scotiabank • Contact centre enterprise analytics

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Data in the call Centre supplement

Case study:

Information Builders & Scotiabank Canadian bank standardizes on iWay to reduce development costs and boost revenue

W

ith more than 12.5 million customers in 50 countries around the world, Scotiabank offers a wide array of products including personal, commercial, corporate, and investment banking services. With CAN$527 billion in assets and 70,000 employees, the organization is one of North America’s leading financial institutions and Canada’s most international bank.

As part of its ongoing process improvement, Scotiabank retail banking executives needed to automate a complex sales reporting process for their branch operations.

The bank’s Information Technology and Solutions (IT&S) organization was tasked to deliver on this business need. Initially the IT&S organization planned to develop the capability internally. It considered using technology from the big stack vendors as well as specialized point solutions.

The department selected integration technology from iWay Software because of its flexibility, its modular design that permits the addition of new data sources, and because it is scalable enough to handle large infrastructure requirements now and in the future.

Scotiabank has a number of core banking systems, which operate on a variety of computing environments and have different access methods and delivery channels. To streamline integration among various service delivery channels, such as the call center and online customer service, as well as across different product and

The organization:

Scotiabank, one of North America’s premier financial institutions and Canada’s most international bank.

❱ DMN.ca

service lines, Scotiabank standardized on Information Builders’ iWay Software integration technology.

Developers in Scotiabank’s IT&S department use iWay Service Manager to create reusable interfaces among the core banking systems and iWay DataMigrator to automate bulk data movement among various information systems and banking channels. “iWay is decreasing our overall number of point-to-point interfaces, which is a big time-saver for our developers,” says Martine Lamoureux, vice president of development for Core Banking Technology at Scotiabank.

“Whenever we have a new project, we try to reuse existing iWay interfaces.

Over time we are creating more and more standardized interfaces based on our iWay integration gateway. Creating data extracts gets easier and easier.”

Getting started Scotiabank IT&S provides global technology solutions to support the bank’s core businesses: Canadian banking, international banking, global wealth management, and Scotia capital. Its technology-based solutions enable Scotiabank to achieve sustained profitable growth and a competitive advantage. Canadian Banking’s use of iWay technology began when developers were creating a sales reporting system called Sales Builder to encourage cross-selling and to track complex sales-results rules. Sales Builder supplies crucial information to employees in the retail, small business, and wealth

The challenge:

An outdated sales information infrastructure was creating delays in the delivery and validation of key information needed to support critical growth strategies.

The strategy:

management banking lines of business, helping to ensure that sales officers are well informed, properly motivated, and appropriately rewarded for their efforts.

With the help of iWay Professional Services, Scotiabank automated a tedious data-entry and reporting process, transposing information from many different databases and multiple banking systems to consolidate the results into a centralized database. The database then feeds Scotiabank’s Sales Builder application.

This is just one example of the type of integration project that Scotiabank has embarked on with iWay Software.

The banking giant also uses iWay to streamline corporate acquisitions, extend the life of its legacy systems and facilitate international growth and expansion.

“It doesn’t really matter what types of business applications we build or purchase so long as we have a robust integration platform to connect them together,” says Mike Bekic, director of Branch and CRM Technologies, Scotiabank. “That is our objective with deploying iWay.” iWay Service Manager correlates information from IMS, VSAM, Oracle, and DB2 databases and transposes it into a common relational format, placing the data into distinct event tables in a DB2 database. iWay DataMigrator then accesses the database tables to populate a Sales Hub data mart, which resides on a mid-tier WebSphere/Java™/DB2 environment. The Sales Hub permits

Create a comprehensive sales reporting environment with automatic data feeds from a variety of financial product lines; use iWay to capture data from multiple retail banking systems to deliver weekly sales reports for the bank’s 1,024 branches.

sales and marketing professionals to generate reports about customers, sales, forecasts, and other essential customer relationship management (CRM) activities.

“We didn’t have to write any Java code to do this,” says Bekic. “It was all handled using standard iWay transformation flows and visual tooling.”

Automating manual processes Scotiabank also used iWay Service Manager to develop data interfaces to the Sales Builder system, which services about 90 percent of the company’s sales officers in the Canadian operation as the basis for assessing its performance in selling credit cards, credit lines, mortgages, and investments.

Scotiabank wanted to replace its manual sales reporting process, which was dependent on officer input, to an automated one that captures sales opportunities and then reports the data back to sales officers in the form of coaching and sales results reports. Rather than relying on a manual system, management made the decision to obtain the information directly by gathering it from 21 system inputs and feeding it into the Sales Hub. In the manual, operator-driven reporting system, the interpretation of complex business rules resulted in inconsistency of reported results across the 1,024 branches.

By embedding the business rules within the automated system, better

Information Builders solution: iWay Service Manager, adapters, DataMigrator, WebFOCUS, and Professional Services.

The results:

Scotiabank’s developers spend less time on integration projects with less code to maintain; the business can open new channels more quickly; sales staff gain faster access to results. Banking officers free up more than 70,000 hours for more customer interaction time.

October 2014

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Data in the call Centre supplement consistency and improved reporting integrity have been achieved.

The CRM system gives Scotiabank’s Customer Knowledge and Insights department the intelligence it needs to reach out to customers and qualify leads in the contact management system, aligning each customer’s profile and product mix with the skill set of the correct sales officers.

“With the kind of capacity we have created here we can realistically expect to generate an additional $250 million in annual sales volume across all of the products that we deliver to customers,” says Mike Henry, senior vice president of Customer Experience and Distribution Strategy at Scotiabank.

“That translates into millions of dollars in bottom-line lift for us.”

Henry estimates that Scotiabank spent about $6 million to develop these IT capabilities for an expected return of $2.5 million per year on a go-forward basis. Just as important, Scotiabank has received a positive response from the sales staff. “Employees are very pleased that we have made them more productive by automating these CRM processes,” he notes.

Creating a flexible rules engine Information Builders Professional Services used the iWay Business Rules Engine and iWay Complex Event Processing capabilities to govern some of the CRM data management processes. The project team, consisting of subject matter experts, iWay Professional Services, and the Scotiabank’s Integration Competency Center personnel, developed a series of business rules to determine what depicted a sale and how that sale was funded.

For example, if a sales officer helps a customer invest $10,000 into Certificates of Deposit, and then a month later persuades the customer to roll $5,000 of that investment into a mutual fund, there is a different compensation structure for each type of deposit. iWay updates the appropriate banking systems and applies a consistent set of business rules to govern how the sales officer will be compensated for each type of sales activity. If a sale involves a transfer of funds from one location to another, the system can reconcile the results from new and previous sales.

This is important since the bank bases sales performance on net new money coming into the bank.

“This was not just a technology project. It was about creating business October 2014

value by making our employees more productive and more satisfied, so they can focus on what matters most: our customers,” explains Henry. “We devised more than 500 business rules to automate a very complex and boring activity for our personal bankers and financial advisors. This successful project freed up more than 72,000 hours per year that these staff members can now spend advising customers. It gives them more time to provide the excellent service that helps our customers to get ahead financially, and that’s what Scotiabank is all about.”

Reusing iWay interfaces Bekic credits iWay Professional Services for helping Scotiabank’s in-house staff to understand the iWay integration platform as part of these ambitious IT endeavors. For example, they designed the rules engine to be callable from an XML process flow, which permits rules to be dynamically added or changed without modifying the associated business applications.

“The key to this architecture is to abstract the business rules away from the product systems,” says Bekic. “We don’t need to change 15 or 20 systems to update the rules. We just change the business logic in the mid tier using the web-based interface.”

Scotiabank has created a Center of Expertise (CoE) to promote best practices for using the iWay integration environment, along with a robust enterprise service bus as a universal integration layer. The service bus includes a fully redundant enterprise topology with automatic failover to ensure business continuity. iWay receives financial, product, and sales data via web services interfaces, batch interfaces, and interactive data-input procedures, transforms it into a common format and loads it into a data warehouse. iWay Managed File Transfer (MFT), powered by iWay Service Manager, automates multi-step integration scenarios with complete auditing, notification, and security.

All iWay integration processes are reusable, so Scotiabank can leverage these ETL and MFT assets for other business scenarios involving these core banking systems. For example, the bank also used iWay to streamline the development of a new liquidity system, which has 14 interfaces to legacy applications. Using the iWay integration platform and adapters simplifies integration tasks for developers since they do not need to

manually transform the data.

“The iWay toolset has allowed us to be more productive by creating web services rather than writing a host transaction or transformation,” Bekic explains.

Lamoureux estimates that creating new interfaces with iWay takes about half the time of what they formerly had to do in Java. Scotiabank applied these economies of scale to a Liquidity Analysis engine application, used by their treasury department, as well as to a new adjudication module. She foresees further standardization using the iWay platform, since it includes a flexible integration engine and hundreds of adapters to interface with various databases and applications.

Whether it’s extracting, transforming, and loading data; handling sophisticated file transfers; monitoring business activity; or managing complex business rules, Scotiabank is leveraging iWay’s ability to perform many types of integration scenarios.

Measuring success one system at a time Scotiabank has several gauges for measuring the success of each iWay project, including development time,

the ability to open new business channels more quickly, and a growing repository of integration assets, which means they don’t have to develop each new interface from scratch.

Ultimately, Scotiabank’s senior management wants to have a common integration platform not only for its operations in Canada, but potentially forother core business groups outside of Canada.

Scotiabank plans to use iWay business activity monitoring technology to automatically update the Sales Builder reports as key events occur, and iWay MFT technology to further automate the movement of large volumes of data. Bekic and his team are also exploring iWay Data Quality Center to improve the overall accuracy of customer information and to simplify regulatory issues.

“Our partnership with Information Builders is very important to us,” Bekic concludes. “It’s a constant learning environment, and we see our progress as a win-win for both companies.”

For more information visit http://www.informationbuilders.com

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Data in the call Centre supplement

Contact center enterprise analytics Advances in decision technologies enable a whole new class of contact center analytics

I

t’s surprising to think the role of analytics and computer-aided decision-making in contact centers remains narrow in scope.

Yet it is. Analytic tools like those for business intelligence, speech analytics, performance management, and workforce management serve only a few focused purposes, primarily data capture and data presentation.

Put in greater perspective, while such systems help analysts solve specific low level tactical and business problems, none have kept the promise of the truly intelligent decision support computer. These tools and systems do not serve to improve strategic decision-making across the enterprise.

The strategic decision-making cycle In contact centers, how and why do executives make strategic decisions? What is the standard process? Like most operations, the contact center is 90% reactive and 10% proactive. Although executivedriven improvement initiatives are common, the majority of decisions are born out of necessity — they are reactive decisions to changes in the center’s operating environment.

Because of this, the decisionmaking process is typically: 1. Monitor the operation. Analysts determine whether operating conditions such as handle times or contact volumes have changed or are outside acceptable parameters.

2. If there is a change, determine the range of likely scenarios.

Given the uncertainties of ❱ DMN.ca

a changing operational or business environment, it’s up to the forecasting team to stick their neck out and say “here’s where we believe we will come in.” Conversely, the better forecasters will stick their neck out less and say “it could be X, Y, or Z.” Determining the set of possibilities will lead to the best business decisions.

3. Develop new plans for all scenarios. Each scenario must be vetted and the resulting business plan (the decision) is determined.

4. Decide and implement. Simple.

5. Repeat.

The contact center decision support ideal: a crystal ball Ideally, an intelligent decision support tool for contact centers would mimic the steps of the decision-making cycle, and would help executives answer specific and important business questions at every stage. This system would serve as the executive’s “crystal ball,” helping them understand the operational and financial risks and trade-offs of their business alternatives.

“Handle times are creeping up, what should we do?” “Should we open or close centers?” “What is the effect of combining email agents with phone agents?” “Service is slipping, should we outsource technical support?” For contact centers, this crystal ball would need to be: ❯❯ Comprehensive in scope ❯❯ Enterprise-wide

❯❯ ❯❯ ❯❯ ❯❯ ❯❯

Long-term/strategic Quick and provably accurate Flexible/robust Able to analyze business risk Actionable

Enter enterprise analytics Enterprise Analytics is the application of specialized decision technologies to contact center performance monitoring, forecasting, scenario development, plan development and evaluation, business risk analysis, and ultimately, strategic decisionmaking.

Distinct mathematical modeling technologies, coupled with improvements in computer processing speed, now enable more sophisticated contact center analytics. By working in conjunction, technologies for data warehousing, forecasting, discreteevent simulation modeling, and mathematical optimization (integer programming) derive “super model” power to improve decision-making significantly for contact centers.

Further, when used together with a strong business process improvement orientation, these technologies enable the development of an Enterprise Analytics business process. There are four such technologies/processes: 1. Automated forecasting and its appropriate role In many organizations, the role of forecasting is both simple and narrow to determine expected contact volumes and handle times accurately. It is narrow because contact volumes are neither the only nor the most important business October 2014

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Data in the call Centre supplement driver to forecast. It is simplistic because the true value of a forecasting team is not a single forecast, but is a part of a contact center monitoring system and a larger planning process.

Leading analytic organizations recognize this, and view forecasting differently: ❯❯ They view forecasts as the baseline and variance to forecast as a warning indicator to understand.

They worry less about forecasting “error,” and instead assume that any variance to forecast is either natural variability of the business or a change in the environment that needs to be explored. ❯❯

❯❯

They automate forecasting so they can apply forecasting expertise to every important contact center metric. It is essential to make the forecasting process as easy as possible, to allow for the use of sophisticated forecasting methodologies.

The best forecast doesn’t necessarily mean the lowest “error.” The final downstream product of the forecast is a set of decisions, and the forecast that produces the best decision is the better forecast. When viewing competing forecasting methodologies against hold-out data, the best forecasters take the next logical step and ask, “which methodology poses the most operational risk to the organization?”

Among several mathematical technologies for forecasting, the most important item to consider is that the data stream being forecasted matches well with the mathematical methodology chosen. Contact center executives can improve their forecasting process by simply reminding forecasters that the purpose of the forecast is to make decisions “reliably.”

2. Automated variance analysis Variance analysis is normally used in the context of budget analysis.

That is, variance to budget is an item explored but only if line item costs are too high. This is clearly short-sighted.

In contact center operations, variance to plan should be analyzed regularly to certainly include costs, but also to include all major assumptions associated with the strategic operating plan. This includes (by center and staff group), wage rates, handle times, October 2014

volumes, vacation plan, employee attrition, and so on.

Variance to forecast for each of these items should be investigated for: ❯❯ A mistake: Was there a math error when developing the forecast? ❯❯

Root cause: What is the reason, internally (the operation) or externally (the market environment, for the variance?

❯❯

Permanence: Is this variance expected to be part of a long-term trend or is it a single event?

❯❯

Manageability: Can managers bring the item out of variance back into plan?

By researching variance, an operation has the best chance of developing the appropriate tactical or strategic plan: the business response.

3. Developing response plans Enterprise Analytics require two key planning capabilities. The first is the ability to simulate the operational performance of contact center environments quickly and accurately.

The second is to automatically and optimally develop best response business plans given the appropriate business constraints. The two mathematical problems to solve, in more detail.

First, a quick and accurate model of the whole, interdependent operation is required. A contact center operation must have the ability to answer specific what-if questions accurately, like: “If handle times increase over the next 7 months, what will my service levels be if I do not hire?”

In the past, analysts relied on the notoriously inaccurate Erlang equation for answering strategic questions.

Now that contact centers are multiskilled and multichannel (email, chat, and so on), the Erlang approximation is even more misleading. The better modeling technology is discreteevent simulation, which allows for the accurate analysis of any contact center system, whether single-skilled, multicenter, multichannel, or multi-skilled. It specifically answers questions like: “If I were to do X, what operational or financial performance could I expect?”

Second, it’s imperative that the Enterprise Analytic function have the ability to develop best responses to business environment changes or specific what-if scenarios quickly,

via an optimal capacity planning model. Integer programming is a mathematical modeling technology that addresses problems of this kind by answering questions precisely, such as: “What is the fewest number of agents I can hire, in which groups, in which centers, and at what time of year to meet increased call volumes over the next 18 months? Or, would overtime cover these peaks?”

With discrete-event simulation and integer programming working together, contact centers can perform what-if analysis (simulation) quickly and accurately, and can determine their best business response (integer programming) quickly and optimally.

4. Enterprise performance-risk outcome matrix (EProm) The final step in the Strategic Analytic process may be the most important.

Because mathematical technologies automate much of the strategic planning process, the time required to forecast, build what-if scenarios, and determine the best business response to every scenario is surprisingly short. More importantly, it is of a significantly higher quality (that is, more accurate and comprehensive)

than manual or spreadsheet planning processes.

These technologies allow a different take on the planning process — they allow us to monitor and optimally plan for business uncertainty.

EProm is a methodology that enables intelligent decision-making in an uncertain environment. Simply, in the face of uncertain scenarios, each permutation of business response to possible operational scenarios can be planned out. This can be as simple an exercise as developing response plans for each of the possible scenarios. But this is the twist: analysts must also determine what will happen if their assumptions are wrong. They need to know what the operational risk is of missing a forecast and choosing the wrong course of action.

This article is adapted from the white

paper “Contact Center Enterprise Analytics: Advances in Decision Technologies Enable a Whole New Class of Contact Center Analytics,”

by Ric Kosiba, Ph.D. Kosiba is vice president of the Interaction Decisions™ strategic planning solutions group at Interactive Intelligence, Inc. Download the full white paper at www.inin.com/cm

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// 32

Resource Directory

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// 33

Resource Directory FUNDRAISING

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// 34

Resource Directory LIST SERVICES

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