Analytics Innovation, Issue 9

T H E

L E A D I N G

V O I C E

O F

A N A L Y T I C S

ANALYTICS INNOVATION

JAN 2017 | #9

Predictive Analytics In Marketing Is Helping The Wrong People /23

+

Data Analytics In Gambling: A Double Headed Coin

Location Analytics Is On The Rise

In order to ensure that people keep gambling, casinos and bookmakers rely heavily on personalized marketing and understanding customer behavior.

We look at why it’s a good thing and a bad thing /6

Consumers expect marketing materials to be relevant to them in every sense, most of all location. With smartphones, it is now easier than ever to target consumers wherever they are, and companies are reaping the benefits /10

predictive analytics innovation summit March 30 & 31, 2017 | london

Speakers Include +44 203 318 4037 ikennedy@theiegroup.com theinnovationenterprise.com /2

ISSUE 9

EDITOR’S LETTER Welcome to the 9th Edition of the Analytics Innovation Magazine

Last year was bad for numerous reasons, with data practitioners among the most suffering, as trust in statistics hit its lowest point since the second half of the 17th century and the beginning of the enlightenment.

Polls around Trump and Brexit were proved dramatically wrong, while everyone seemed to be constantly condemning facts and expert opinion as outdated. It felt at times like the drive for empirical evidence that began with the age of enlightenment had reached its zenith and was on its way out.

While politicians may have e, however, business knows that facts mean profits and data means facts. This year should see data analytics on the rise again, with companies now reaching a level of maturity that means they are at a point where they are truly practicing advanced analytics - and seeing tremendous results as a consequence.

It used to be that only the largest companies, like IBM and Amazon, had the data and expertise required to use

it, but the technology is now available at a price where it is more affordable to companies of all sizes, and insights can be garnered by business staff without the same degree of expert knowledge.

The logical consequence of companies’ increased maturity in predictive analytics is automation of many processes in data science and analytics. Many are even now looking to adopt AI, having seen its potential as a game changing technology and believing that if they do not get in early they will lose ground on their rivals. There is, and always has been, a tendency in business to chase the next big thing in the quest not to fall behind. While this is understandable, many companies still lack the data and analytics foundation necessary to adopt it successfully, even if they think they are ready. In a recent survey of almost 1,000 digital professionals, 42% of businesses who responded said they don’t have a framework for structuring their measurement

requirements. Many of these very companies will be looking at AI and thinking they can implement that, but that simply isn’t possible if they do not have the data in place to train machine learning algorithms. These companies need to act fast, to implement data and analytics best practises as soon as possible, or they risk missing out even more than they have now.

James Ovenden managing editor

/3

Sports Analytics Innovation Summit March 8 & 9, 2017 | Melbourne

Speakers Include +61283104943 dwatts@theiegroup.com theinnovationenterprise.com /4

contents 6 | DATA ANALYTICS IN GAMBLING: A DOUBLE HEADED COIN

In order to ensure that people keep gambling, casinos and bookmakers rely heavily on personalized marketing and understanding customer behavior. We look at why it’s a good thing and a bad thing 10 | LOCATION ANALYTICS IS ON THE RISE

Consumers expect marketing materials to be relevant to them in every sense, most of all location. With smartphones, it is now easier than ever to target consumers wherever they are, and companies are reaping the benefits 12 | MACHINE LEARNING IS MAKING UNSTRUCTURED DATA ACCESSIBLE

Accessing value from the wealth of insights held within unstructured data has proven a massive challenge for organizations of all sized, but machine learning algorithms could be about to make it far easier 15 | DATA ANALYTICS TOP TRENDS IN 2017

We look at the most important technologies and challenges set to impact data practitioners’ lives this year

19 | WHY BATCH ANALYTICS IS NOT GOING ANYWHERE

Although stream-processing analytics is increasing in importance and many companies reducing their investment in batch-only processing, Alex Lane argues that it still has its place 23 | PREDICTIVE ANALYTICS IN MARKETING IS HELPING THE WRONG PEOPLE

The nuclear debate shows no Marketing departments are finally starting to truly get to drips with analytics, but could predictive analytics actually be causing some to target the wrong customers? We ask whether basic mistakes are being made 22 | CUSTOMER JOURNEY IS NOTHING WITHOUT DATA

Customer journey is critical to the success of an organization’s overall marketing strategy, and organizations must have a deep level of understanding of their customers’ behaviors. Deep analytics can help WRITE FOR US

Do you want to contribute to our next issue?

Contact: jovenden@theiegroup.com for details ADVERTISING

For advertising opportunities contact: pyiakoumi@theiegroup.com for more information

| assistant editor george hill creative director oliver godwin-brown | contributors anne-claire herve , alex collis, alex lane, managing editor james ovenden david barton, olivia timson

/5

Data Analytics In Gambling: A Double Headed Coin Data is now collected by the casino from loyalty cards and cameras placed artfully around the pit, watching every aspect of player behavior which is then used in everything from targeting offers to positioning games machines

/6

Alex Collis, Analytics Pundit In 2014, British bookmaker, Ladbrokes, released its ‘Ladbrokes Life’ campaign, which portrayed five average ‘blokes’ bouncing joyfully around their average lives. They revolved primarily around playing football, drinking, and gambling, only one of which is good for you.

This kind of mildly irritating advert is par for the course in the $500 billion gambling industry, which court young adult males with appearances from ‘legends’ such as Chris Kamara (former pro soccer player) and Shane Warne (former pro cricket player). As young adult males are the most likely demographic to regularly gamble, their motivation for targeting this group is understandable, if troubling. It’s estimated that 3-5% of people who gamble develop an addiction to the activity, which can lead to an array of problems, not only for gamblers and their families, but society as a whole, with the social costs thought to exceed $4 billion.

Data is now collected by the casino from loyalty cards and cameras placed artfully around the pit, watching every aspect of player behavior which is then used in everything from targeting offers to positioning games machines

In order to ensure that people keep gambling, casinos and bookmakers, both online and in their bricksand-mortar operations, rely heavily on personalized marketing and understanding customer behavior.

Andy May, brand research and retail marketing director at Ladbrokes, said of the Ladbrokes Life campaign: ‘It’s time to make a real statement and say to customers that Ladbrokes understand you, knows what you like and how you bet.’ As in other sectors, the gambling industry relies heavily on big data analytics to understand its clientele. Casinos have long looked at big data to understand potential big customers. A 2001 article in Time magazine claimed that, in the 1990s, many would buy records from credit-card companies and mailing lists from direct-mail marketers that contained the names of people who demonstrated, as one reported titled the ‘Compulsive Gamblers Special’

put it, ‘unquenchable appetites for all forms of gambling.’ While casinos no longer need to resort to such brazenly unethical methods, it could be argued that those they do employ are not far removed. Data is now collected by the casino from loyalty cards and cameras placed artfully around the pit, watching every aspect of player behavior which is then used in everything from targeting offers to positioning games machines, with pit bosses employing similar tactics to supermarkets in putting them where they can maximize earnings.

One of the most effective casinos when it comes to big data is Caesars Entertainment, with data from the Total Rewards loyalty program it introduced 18 years ago estimated to be worth in excess of $1 billion. This dataset contains information on more than 45 million customers, with a team of 200 employed to analyze it. The scheme sees customers given rewards including meals, room upgrades, and tickets to shows, advancing through rewards tiers as they spend more. In return, Caesars gets a wealth of data around how the customer behaves while at their resorts.

The implications for analytics to be used in online gaming are even greater, with digital touchpoints across sites making it easy to monitor customer behavior for every second they are logged on, and target them for offers in real time accordingly.

Equally, while this data can be used to pinpoint problem gamblers to exploit them, it can also be used to help them, as a number of bookmakers are doing, but more need to in the future.

One major project has seen five of the largest UK bookmakers - Betfred, Coral, Ladbrokes, Paddy Power, and William Hill - make their industry data available to the Responsible Gambling Trust. More than 10 billion gaming machine events were

/7

In order to ensure that people keep gambling, casinos and bookmakers, both online and in their bricks-and-mortar operations, rely heavily on personalized marketing and understanding customer behavior

analysed over a ten month period to identify behaviors in problem gamblers, with machine learning algorithms employed to measure the predictability of each individual player’s interactions. An adaptive behavioural analytics approach was used to build statistical profiles of ‘normal’ patterns of behaviour for each anonymous customer, pinpointing anomalies that indicate precisely when a gambler’s behavior starts to change. An automated system uses this to understand, in real-time, if the change indicates a player being at risk of harmful play. When the algorithm identifies someone at risk, the operator could potentially send personalised individual interventions to reduce the effects of gambling-related harm.

The research identified 19 potential markers of harm. These included the frequency of gambling and how individuals behaved while using the machines. The resulting model was found to be 66% better at detecting players at risk of harm, compared to the industry standard. Other similar projects by companies such as BetBuddy, which was able to forecast problem gamblers to 87% accuracy.

As the gambling industry grows and evolves, casinos and bookmakers have a responsibility to identify the minority of gamblers who can’t control themselves and help them, not target them for advertising and encourage them to keep gambling. Data science is vital for finding problems. Nine independent studies have shown that problem gamblers generate anywhere from 30-60% of total gambling revenues, and you’d be hard pressed to find a CEO willing to spurn that proportion of income. However, casinos need to realize the long term benefits of having a business that encourages people to treat gambling as a fun activity, not as a way of life.

/8

Sports analytics innovation summit April 5 & 6, 2017

london

Speakers Include

ikennedy@theiegroup.com

+44 203 318 4037

/9 theinnovationenterprise.com

Location Analytics Is On The Rise

Anne Claire, Analytics Expert

Consumers expect marketing materials to be relevant to them in every sense. They don’t just want things about what they’re interested in, they want things they are actually in a position to use. Four out of five want ads pertinent to where they are, according to Google, while research has found that geotagged Instagram posts get 79% more engagement that those without.

Our location is now easier to determine than ever thanks to mobile.

/ 10

The majority of us share our location multiple times a day through apps such as Google Maps and Facebook, and as our cities become smarter, with more and more sensors being introduced collecting data on the movements of the population, it is only going to get easier to know where people are and are likely to be.

Utilizing this information has seen the popularity of location analytics rise dramatically. Location analytics is the process or the ability to gain insights

Digitally connected devices are capable of creating a connected society, and in a result, we may have cities and countries, where almost everything is digital

from the location or geographic component of business data. Forrester estimates that the adoption of location analytics will increase to more than two-thirds of data and analytics decision-makers by the end of 2017, up from less than 50% currently. It can benefit business in many ways, but it is marketers who stand to gain the most, using the information to connect with audiences on a new level. They can, for example, send geo-targeted push notifications to mobiles, which research has found to be 6-8 times more effective than other notifications.

Indeed, in a Posterscope survey of 100 marketers, respondents said location data could improve ROI by an average of 60%.

There are, however, issues with introducing location analytics, particularly around privacy and security concerns. There is clearly something creepy about strangers knowing where you are, leaving people understandably reticent to tell them. Important to allaying people’s concerns is tying requests for people to share their location with immediate benefits to them. For example, providing coupons when people check into your store and special offers.

MacDonalds has taken an especially interesting approach, joining forces with popular game Angry Birds.

Players can add levels to games without paying extra fees simply by logging into the WiFi at certain MacDonalds’s restaurants - who have paid for the privilege as it entices people in.

It can also be used in store to help understand people’s purchasing

behavior. One customer will generate more than 10,000 unique data in a single visit from various sensors placed throughout a store, indicating where they will go, at what point they make the decision to pick up an item, essentially re-creating the online shopping experience for the brickand-mortar environment. US fashion retailer Nordstrom, for example, has spent millions introducing technologies like sensors and Wi-Fi signals into its stores that enable them to track such information. Location analytics firm RetailNext is also tracking over 500 million shoppers per year by collecting data from more than 65,000 sensors installed in thousands of retail stores to provide shops with actionable insights they can translate into better product placement and marketing solutions.

benefits, noting: ‘Thanks to reports based on mobile location data, they (businesses) can undertake more targeted promotion, align their staff and resources more closely with demand, establish a new business on the best site and manage traffic flows more effectively.’ However, such data is expensive, starting at €700 per report, which somewhat limits the good it can do as it prices it out of the hands of academics and NGOs. As it becomes more popular among businesses, however, this could see it decrease in price, which will benefit everyone.

Location data can even be used to understand wider social issues.

Tracking the spread of disease has already helped to quash the spread Zika and Ebola, while Chinese internet search giant Baidu has used billions of location records from its 600 million users to better understand the Chinese economy by tracking the number of people around offices and shops to measure employment and consumption activity.

The value of location data is rising rapidly and mobile operators are beginning to realize this by selling the data on. Belgian telco Proximus, for example, recently started to sell anonymous location data about users connected to its mobile network, which it says will benefit marketers and event organisers. Proximus CEO Dominique Leroy listed some of the

/ 11

Machine Learning Is Making Unstructured Data Accessible

James Ovenden Managing Editor

In a 2013 report by IBM, the amount of data created everyday was estimated to be roughly 2,500,000TB. It very likely greatly exceeds this now, as wearables, AI, and connected devices have increasingly embedded themselves into society, gathering a veritable tidal wave of additional information for organisations to interrogate.

This data comes in three forms: unstructured, semi-structured, and structured. Since the dawn of IT, structured data has been the main resource of analysts. Even today, this is the case. In a 2015 IDG Enterprise study on big data and analytics, 83%

/ 12

of IT professionals said structured data initiatives were a high priority at their organizations, while just 43% said unstructured data initiatives were a top priority. Yet, it is estimated that 90% of all data is either semi-structured or unstructured. For organizations, this is a tremendous number of potential insights to be leaving off the table.

Structured data is anything that fits in a relational database that exists within a certain set of values or contained a specific set of characteristics. Semistructured data has no data model but some kind of structure, i.e. emails, zipped files, HR records and XML data. Unstructured data, meanwhile,

Both structured and unstructured data are necessary to use analytics to its potential, to build a full picture of a company’s health and to pinpoint areas for growth is everything that does not fit into relational databases. This includes videos, powerpoint presentations, company records, social media, RSS, documents, and text.

indispensable when it comes to its unstructured counterpart because of the differences in scale. A human being simply cannot compute that amount of data.

Both structured and unstructured data are necessary to use analytics to its potential, to build a full picture of a company’s health and to pinpoint areas for growth. Essentially, structured data analytics describes and explains what’s happening, while unstructured data analytics explains why it’s happening. Knowing what’s happening may enable you to form an idea of what’s going on and take action, but without understanding why you are running too high a risk that it’s wrong.

We spoke to Dave Copps, CEO of Brainspace, makers of unstructured data analytics and eDiscovery software that uses machine learning.

Dave noted that, ‘Before, all we really did with unstructured data was search, get a load of documents together and hack at it with keywords. Technologies like Tableau and Quickview were always good for looking at structured data, but those that tried to use unstructured data were really just taking it out and putting it into structured data platforms. Once you pull words out of a document, you destroy their context.

So, say you’re analyzing resume´s. If you take the Java out of a software developers CV, you don’t know if that’s only in there because the person has said ‘I suck at Java.’ What we’re doing is, rather than just analysing words, we’re looking at the whitespace between the words - the context1.’

There are several reasons that companies have hitherto largely not analzyed their unstructured data in any meaningful way, central among which is simply the absence of necessary tools to do it. Advances in machinelearning have, however, meant that many now are, allowing organisations to analyze their mountains of unstructured content in ways they could not before.

Machine learning is valuable for the analysis of structured data, but

Machine learning is valuable for the analysis of structured data, but indispensable when it comes to its unstructured counterpart because of the differences in scale

There are a number of areas where machine learning-driven unstructured data analytics software can be applied - eDiscovery, internal discovery, and defence intelligence, among the major ones. Copps uses the example of the recent VW scandal, noting that they could have saved billions of dollars in fines if they had been able to analyze the communications earlier to identify the culprits. Marketing would be another area where there is big potential, with machine learning helping to make available the mass of public opinion from social interactions, not just whether they’ve mentioned a company, but how they’ve mentioned it, providing a far more rounded view

of the customer.

Take, for example, Donald Trump.

MogIA is an AI company that which analyzes data from Google, Twitter, and Facebook. They have predicted that Donald Trump will win on election day simply because he has had more public engagement — a number gathered by looking at Facebook Live and Twitter. This is, however, just the number of results. It says nothing of the context, which is often likely to be negative given the reaction to many of his irrational statements.

Essentially, however, it is a search problem. How search works is fundamentally flawed when trying to analzye unstructured data through a tool designed for structured data. When you have half a million documents indexed for search, users are just left to blindly throw words in there, guessing at the contents. New tools that use machine learning and better data visualizations to show the results mean that before searching, you can see what’s in there and search accordingly - often finding things you didn’t know would be in there.

/ 13

Premium online and on-site courses delivered by industry experts academy.theinnovationenterprise.com

/ 14

With 2016 drawing to a close, we can look back on what can only be described as a mixed year for data analytics. The polling data in the run-up to Brexit and Trump’s election, for instance, have seen many dismiss the power of data, with Mike Murphy, a Republican strategist who predicted a Clinton win, saying after the election, ‘My crystal ball has been shattered into atoms. Tonight, data died.’

Data Analytics Top Trends In 2017 Dave Barton, Head of Analytics

The popularity of embedded analytics has grown exponentially over the past several years, and we expect this curve to go up over the course of the next year

However, reports of data’s demise are premature. Data analytics has not spent the last several years being exulted by organizations of every hue for no reason, and a recent IDC report still had the big data and business analytics market growing at a rate of over 11% in 2016 and at a compound annual growth rate of 11.7% through to 2020.

In 2017, there will be new challenges for analytics practitioners to deal with.

There will also be new technologies and new ways of working to help overcome them. We’ve outlined our predictions for what 2017 will bring analytics.

Unstructured Data Will Dominate The Analytics Landscape Unstructured data is any data that does not fit into relational databases.

It is estimated that 90% of all data is either semi-structured or unstructured.

This includes videos, powerpoint presentations, company records, social media, RSS, documents, and text - all of which are vital to understand for businesses. While structured data analytics describes what’s happening, analysis of unstructured data gives you the why.

/ 15

In a 2015 IDG Enterprise study on big data and analytics, 83% of IT professionals who responded said they have made structured data initiatives a high priority for their organizations, yet just 43% said the same of unstructured data initiatives insights quicker as time is not wasted requesting reports from external agents, and it allows findings to be distributed to those who need it across the organization.

The popularity of embedded analytics has grown exponentially over the past several years, and we expect this curve to go up over the course of the next year. Logi Analytics’ study found that business users are now adopting embedded analytics at twice the speed they are traditional BI tools, while Gartner’s 2016 Embedded Analytics Report also recently found that 87% of application providers claimed embedded analytics is important to their users, up from 82% in 2015.

The Data Scientist’s Role Evolves

However, much of this wealth of valuable insights is currently going untouched. In a 2015 IDG Enterprise study on big data and analytics, 83% of IT professionals who responded said they have made structured data initiatives a high priority for their organizations, yet just 43% said the same of unstructured data initiatives.

The reason for this is simple. The tools needed to analyze such a large scale of data have not existed.

However, machine learning and data visualization tools are now making it possible. In 2017, with these tools improving exponentially in quality and decreasing in cost, we expect to see far more companies putting unstructured data at the top of their agenda.

/ 16

Embedded Analytics Set To Take Off Embedded analytics is the fastest growing area of Business Intelligence (BI). In a study from self-service analytics firm Logi Analytics, more than 66% of IT teams said they are now using embedded analytics in their organizations, while almost 30% said they were considering it.

Embedded analytics consist of any consumer-facing BI and analytics tools that have been integrated into software applications, operating as a component of the native application itself rather than a separate platform.

Embedded analytics allows end users to utilize higher quality data because the standards of governance are improved. They can also pinpoint

According to a Forrester survey, businesses will invest 300% more in artificial intelligence (AI) in 2017 than they did in 2016. This has significant ramifications for analytics, with machine learning able to analyze data at a scale humans simply couldn’t.

As Forrester notes, it will ’drive faster business decisions in marketing, e-commerce, product management, and other areas of the business by helping close the gap from insights to action.’ In their 2015 survey, just 51% of data and analytics decisionmakers said they could easily obtain data and analyze it without the help of technologist, yet they anticipate this rising to 66% in 2017.

But does this mean the end of the data scientist in 2017? Another recent poll from KDnuggets asked when most expert-level Predictive Analytics/ Data Science tasks currently done by human Data Scientists will be

automated. A not insignificant 51% of respondents said that they expect this to happen within the next decade, while just a quarter said they expect the process to take over 50 years or never. Joel Shapiro, Executive Director of the Program on Data Analytics at Kellogg’s School of Management at Northwestern University, notes: ‘In the right cases, data can be automatically generated and analyzed. But analytics is fundamentally about using analysis to do something differently. I am very skeptical of off-the-shelf analytics products that claim all you have to do is load in your data and it will spit out actionable insight.’ In the short term, data scientists are unlikely to be replaced, however, as more of the traditional reporting and queries are carried out by AI, we expect to see many data scientists see their role become more creative over the next year.

Behavioral Analytics Advances The amount companies spend on digital ads is expected to grow to as much as $77.37 billion in the US alone next year, and understanding the audience is vital to ensuring this is money well spent.

Psychologists have attempted to understand different personality types and behaviors using checkboxes for decades, and digital marketers now have a significant amount of data about their customers available that could enable them to do the same

The ability to predict someone’s personality presents a clear opportunity for targeting advertising, enabling marketers to segment audiences according to personality type rather than by age or gender, which is crass and highly unreliable.

The benefits of personalization are well documented, with a 2015 Harris Poll study finding that 95% of respondents would be more likely to respond to personalized outreach. The Aberdeen Group also found that agencies best at personalization achieved up to a 36% higher conversion average and a 21% stronger lead acceptance rate.

about their customers available that could enable them to do the same. In a media release earlier this year from Universiti Teknologi Malaysia, research scholar Dr Ikusan R. Adeyemi said, ’Our research suggests a person’s personality traits can be deduced by their general internet usage,’ and it could do so using machine learning algorithms by analyzing just half an hour of web browsing. With marketers now more au fait with analytics and tools that can judge people’s personality increasingly refined, next year should see a real drive in the practice.

Prescriptive, Not Predictive, Analytics Rules The Day Predictive analytics have dominated the data landscape this year, but they can only take you so far. In 2017, more companies are going to start looking at prescriptive analytics, with Gartner predicting the market to grow to $1.1 billion by 2019 - 22% CAGR from 2014.

Prescriptive analytics uses the insights revealed by predictive analytics and provides a call to action based on what it finds. It analyzes current data sets for patterns and evaluates the outcomes of the multiple scenarios that could be enacted based on decisions that could be made based on the data, providing decision makers with hypotheticals as to the impact of each option. Just 10% of organizations currently use some form of prescriptive analytics, according to Gartner, but this will grow to 35% by 2020, and with the increasing buzz we have seen already this year, it seems likely companies will look to implement prescriptive analytics in their droves next year.

Psychologists have attempted to understand different personality types and behaviors using checkboxes for decades, and digital marketers now have a significant amount of data

/ 17

Predictive Analytics Innovation Summit February 22 & 23, 2017 | San Diego

Speakers Include + 1 415 237 1472 acollis@theiegroup.com theinnovationenterprise.com / 18

Why Batch Analytics Is Not Going Anywhere Alex Lane, Deputy Head of Analytics

The idea in business that taking the wrong action is better than no action is something that seems to have been universally accepted, although it is easily countered by the equally true saying ‘fools rush in’.

But if you run your life on aphorisms, you’re inevitably going to run into contradictions every now and then.

The idea that speed is the new currency has seen organizations increasingly look at ways they can gain insights from the vast stream

of data being collected in real time.

Subsequently, they are moving away from the batch processing traditionally favored in big data and towards streaming analytics. But is it always the best option?

In a recent OpsClarity Inc. survey of 4,000 big data professionals, 92% of respondents said they plan to leverage stream-processing applications this year, while 79% will reduce or eliminate investments in batch-only processing.

Batch analytics is high latency

/ 19

Systems based on streaming analytics require more resources at the beginning, although they do become more cost-effective as time goes on and there are many open source stream processing tools available

analytics whereby a large volume of data is processed all at once, yet there is a delay between collection and storage of data sets, the processing for analysis, and reporting. Streaming analytics, on the other hand, is low latency analytics, defined by Forrester as ‘software that can filter, aggregate, enrich, and analyze a high throughput of data from multiple disparate live data sources and in any data format to identify simple and complex patterns to visualize business in real-time, detect urgent situations, and automate immediate actions.’ Speaking to us

/ 20

recently, Slim Baltagi, Director of Big Data Engineering at Capital One, used the comparison that batch analytics was like drinking bottled water while streaming was like drinking straight from the hose.

With batch processing, events are collected together based on the number of records or a certain period of time and stored somewhere to be processed as a finite data set. This can lead to unnecessary latencies between data generation and analysis and action on the data, which means it can lose its value. This could actually

result in additional costs and reduce a company’s competitive edge. There is also an implicit presumption in batch analytics that the data is complete, which may not be the case.

In streaming analytics, the system remembers the query and every time the data changes, the answer adapts accordingly. This allows for high volumes of data processing in very little time. Most data is available as a series of events, for example, click streams, mobile apps data, and so forth, that is continuously produced by a variety of applications and systems

in the enterprise. Streaming analytics does not rely on typical enterprise sources, instead it uses social media data, sensor data, and the like. By connecting to external data sources, applications can integrate certain data from both internal and external sources into the same central hub.

The benefits of streaming analytics are many. For one, the speed at which data is processed, analyzed, and fed back into local systems greatly accelerates decision-making. The data has the potential to identify costs before they spiral, errors before they swell into a problem, and risks before they become existential threats. With zero data waiting time, the data is also more accurate, as nothing gets lost, overseen or outdated, as the velocity and volume of data is not an issue. Essentially, it offers everything that batch processing does, only a lot faster, so that action can be taken in time to exploit the information whether this be visibility into what customer behaviors, potential new products, or fraud detection.

There are, however, disadvantages with streaming analytics. Systems based on streaming analytics require more resources at the beginning, although they do become more costeffective as time goes on and there are many open source stream processing tools available, including Apache Storm, Spark Streaming, and Apache Flink. Since streaming analytics occurs immediately, companies also have only a small window to act on the analytics data before the data loses its value, which is not something all companies are capable of doing. There is also an issue around a lack of experts in streaming analytics, which is still a fairly nascent technology. Forrester analysts Mike Gualtieri and Rowan Curran in a Q3 2014 Forrester report on Big Data and Streaming Analytics noted that, ‘The streaming application programming model is unfamiliar to most application developers.’ The dearth of Data Scientists is a

much publicized problem, and since streaming analytics is still a recent technology adoption is slow by most developers due to their lack of expertise.

There are now more mobile devices than people in the US, and as the IoT grows there is going to be a huge growth in the number of sensors.

Streaming analytics is only going to become more important for ensuring that this data has value. Organizations will need to adapt their existing data management and analytics processes for the IoT age, but they need to be careful. They need to select the best data processing system for the job at hand rather than going for what’s trendy, as whether batch or stream is required depends on the types and sources of data and processing time needed to get the job done. Not every job requires low latency analytics. They also need the infrastructure in place, or risk causing more harm than good.

While streaming analytics is undeniably growing, and with good reason, batch analytics is not going away any time soon.

The idea that speed is the new currency has seen organizations increasingly look at ways they can gain insights from the vast stream of data being collected in real time

/ 21

Gaming Analytics & Social Media & Web Analytics Innovation Summits April 26 & 27, 2017 | San Francisco

Speakers Include +1 415 610 5595 ikennedy@theiegroup.com theinnovationenterprise.com / 22

Predictive Analytics In Marketing Is Helping The Wrong People David Barton Head of Analytics

The rise of predictive analytics has brought many benefits to marketers.

The tremendous amount of both real-time and historical data available about customer behaviour has afforded them the unprecedented ability to anticipate customer needs as opposed to merely respond to them. It has also enabled them to build ideal customer groups and target promotional materials.

Marketers can easily identify the characteristics of their ideal/most likely customers, discover what they are like and what is likely to drive them to purchase, and entice them to open accounts and begin what will be a hopefully long lasting relationship with promotions appropriately tailored to their desires.

/ 23

While predictive analysis is undoubtedly one of the most exciting tools in marketing, its widespread adoption has led to a strange situation whereby noncustomers are valued more than actual customers, in which the onus for many marketers is to bring in new customers rather than retain the old ones This has, for many, led to improved brand experience and increased sales.

However, while predictive analysis is undoubtedly one of the most exciting tools in marketing, its widespread adoption has led to a strange situation whereby non-customers are valued more than actual customers, in which the onus for many marketers is to bring in new customers rather than retain the old ones. In a recent interview with Forbes, Shreesha Ramdas, CEO of Customer Success Automation startup Strikedeck, explained that this is happening for four reasons: ‘1) the number of solutions on the demand gen side outweigh those on the loyalty side (there are a lot of companies emerging that support demand gen, yet fewer that support loyalty), 2) it’s often easier to measure and quantify impact on the lead gen side, 3) this is the status quo and worked just in the pre-SaaS/ subscription world, and 4) it is much easier to measure and celebrate new revenue from new customers than revenue saved by reducing churn.’

Focusing on new customers at the expense of olds ones is a short termist strategy that’s foolish for a number of reasons. As people begin to get aggrieved that they are not getting the same offers as new customers, they will likely look elsewhere. Indeed, somewhat ironically, they will likely look to rivals who are doing similar starter offers. The realization that their loyalty is not being rewarded is also going to irritate some of your best sources of / 24

Focusing on new customers at the expense of olds ones is a short termist strategy that’s foolish for a number of reasons

promotion - your referrers. In the age of social media, where people rely more than ever on recommendations - 81% of consumers are likely to look at recommendations and posts from family and friends when considering a purchase according to Forbes research - these people are vital.

Another reason customer retention is so important is simply that it is so much cheaper than acquiring new ones, and more profitable. Predictive analytics simply needs to be focused in the right way. You have far more data from your loyal customers by virtue of understanding their purchase history. Using this data, you can anticipate when they are likely to purchase and incorporate a deeper level of personalization into your emails and social media - particularly necessary in an age of adblocking, in which consumers expect ads to be wholly relevant to them. It also means your traditional loyalty programs can reward the best customers rather than just any customers, looking at recency, frequency, and spend metrics to identify the most genuinely loyal customers as opposed to just offering them to everyone. Analytics can even be used to personalize these rewards - instead of receiving generic offers, each customer is given their own unique experience with individualized incentives.

Despite the rewards, many marketers are still failing to consider their current customers. Sailthru’s research also revealed that only 18% of companies focus on customer retention and conduct strategic customer marketing.

This is something companies are beginning to appreciate, though. A recent eMarketer report found that the majority of US marketers plan to allocate more of their budgets to customer loyalty in 2017, and about 13% anticipate raising spending significantly.

All of this is not to say that marketers should completely forgo any attempt to look for new customers and appeal to them. It is, however, to say that the best way of doing this is by appealing to your current customers. This will not only ensure loyalty, it will mean that a large part of the job of attracting new customers is done for you through word of mouth. It will give you an audience more receptive to your social media campaigns and more likely to share posts, an audience less likely to complain and to be more tolerant of your mistakes, and - most importantly - an audience that is going to consistently spend money with you.

This is backed up by a huge body of research, all of which shows greatly increased ROI to those who prioritize their loyal customers. According to Forbes Insights/Sailthru, companies that increased their spend on retention in the past one to three years saw an almost 200% greater likelihood of growing market share in the past year compared with those spending more on acquisition. Gartner, meanwhile, found that companies that focus their marketing on existing customers saw a 20% increase in revenues, and according to Marketing Metrics, the probability of selling to an existing customer is 60-70%. whereas the probability of selling to a new prospect is only 5% to 20%. / 25

Customer Journey Is Nothing Without Data

/ 26

Organizations must have a deep level of understanding of their customers’ behaviors and how they differ between each channel - where they drop off, which route they take to purchase point, and so forth

Olivia Timson, Analytics Expert With the breadth of options now available to consumers, customer journey is more important than ever. Indeed, in a recent survey by Salesforce, 88% of top marketing teams said they believe customer journey to be critical to the success of their overall marketing strategy.

The range of channels customers use to interact with organizations is now hugely varied, and keeping track of them all is a job in itself. More important, however, is ensuring that customers engage with them, and that each step drives them towards both purchase and an enduring relationship.

To do this, organizations must have a deep level of understanding of their customers’ behaviors and how they differ between each channel - where they drop off, which route they take to purchase point, and so forth. They must also employ a high degree of personalization to ensure they remain

engaged. In a recent study conducted

by Econsultancy in association with IBM, 76% of respondents said they expect companies to understand their needs, 80% that brands do not recognize them as individuals, and just 35% that communications they receive from their favorite brands is relevant to them.

Companies have, for a number of years, been looking at data for this, examining historical data for patterns that they can use to segment their audience and target them with materials accordingly. By analyzing and monitoring cross-channel paths using free software as simple and easy to understand as Google Analytics, companies can identify vital insights about groups of people who exhibit similar patterns of behavior and recognize opportunities that may once have gone undetected.

Predictive analytics takes this one step further. Using predictive analytics helps to determine customers’ next move, the probability of churn, and interest in certain product or offer. It essentially enables the personalization of customer journeys in response to events as they occur in real-time, and actions can be put in place to react to them, whether this be emailing them with a certain promotion or ensuring that they are pushed to the front of the queue in a live chat.

One company who has employed predictive analytics successfully throughout the customer journey is online auctioneer eBay. For example, if someone abandons their shopping cart before purchase, an email can be sent gently reminding the customer that it’s still in there. They also use data to identify interesting events that indicate what customers are likely to care about and personalize the search results and deals that appear to them across both the site itself and social media. This does not even require for a product to have been in their search history. If their browsing histories are similar to other people who have bought a certain product, eBay will be given to understand that the user will also likely be interested in it too and promote to them accordingly.

They can also gain understand what they want from that product. If the algorithm was to determine that you’re the kind of person to be interested in, say shoes, it will also determine the style you’ll likely be looking for, the brand, and how much you’re willing to spend based on metrics like household income and buying history.

There are, however, pitfalls, and marketers must ensure they do not go too far. As you move from channel to channel and you’re presented with the same advert for shoes, it can feel like you’re being stalked by a pair of Nikes. Speaking to us recently, Kuntal Goradia, Customer Experience & Digital Analytics at PayPal, noted that: ‘Customer analytics industry is empowered with rich information about not only their browsing journey

/ 27

but also their habits, including what places they visit, where they like to eat, where they shop, how much walking/running they do per day, how many hours they sleep per day, who we hang out with….. so many of us use Yelp, Open table, Uber, Lyft, Amazon, Fitbit, Apple Watch, and Facebook, and we can’t imagine our lives without a smart phone. Most of these companies use data to create and improve the products that are now an integral part of our lives. But with the power comes responsibility and unfortunately some companies exploit it. Our governing laws are not keeping up with the speed of innovation. As a consumer, we must take precautionary steps on what we share online and as business leaders, we need to keep pushing the regulations that protect the consumers.’

used to service the customer, deep analytics is a good thing. However, great care must be taken to ensure the insights are secured and contacts with the customer do not result in embarrassment or worse.’

Both are correct, but these are not considerations that should put anyone off optimizing customer journey with data, it is just something to be aware of. Without data, customer journey will be a mess.

Douglas Daly, Senior Manager of Data Science at Capital One, agreed, noting that, ’As long as the data is

In a recent study conducted by Econsultancy in association with IBM, 76% of respondents said they expect companies to understand their needs, 80% that brands do not recognize them as individuals, and just 35% that communications they receive from their favorite brands is relevant to them

/ 28

Want to contribute?

channels.theinnovationenterprise.com/authors contact ghill@theiegroup.com / 29