Measuring and Predicting Marketing Performance by SapientNitro

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Measuring and Predicting Marketing Performance By: Azlan Raj, Marketing Strategy & Analysis, SapientNitro

In the current economic climate, which includes a saturated and competitive online market, the ability to convert customers within a new age of consumer behaviour is becoming increasingly difficult (and expensive). There are three ways in which you can increase your profit margin: reduce costs, increase revenue, or both. To increase your online revenue, you can optimise on-site or you can throw money at your marketing efforts. However, instead of increasing your marketing spend in a time where businesses are tightening their budgets, you can also look at optimising your existing budget. Understanding how consumers engage with your brand touchpoints is the first step to maximising your marketing efforts. The ability to measure and then predict your marketing effectiveness opens up a new set of opportunities that will not only optimise your business performance but help to cultivate innovation within your marketing activity. INTRODUCTION TO MEASURING AND PREDICTING MARKETING PERFORMANCE Marketing has evolved over the last 50 years from billboard advertising right through to present day digital engagement. Whilst walking on the streets of London, I have seen digital billboards, digital signage, and even digital bins. If you walk into a store, you may see hybrid tills and computers with store assistants using iPads instead of running upstairs to check stock. We now live in an entirely new digital age.

Source: Mat Siltala, Dream Systems Media

ÂŠ Sapient Corporation, 2012

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This evolution has happened as part of a constantly changing society that has quickly adapted to the new technologies it has been presented with. It shows the adaptability of today’s simplicity-driven consumer. Children as young as 12 months are now able to use these technologies, and it’s forcing us to quickly evolve. Marketing has progressed accordingly, adapting to this new age of technology in order to meet consumer needs. However, analytics is often overlooked through this growth, and whilst more advanced techniques in measuring performance (through digital and even traditional channels) have come to fruition, the ability to effectively measure a multi-channel environment is still in its infancy in most businesses despite being high on the corporate agenda. Over 50% of C-Suite executives see data and analytics as a top 10 corporate priority, with almost 10% placing it right at the top of their agenda. This shows just how much higher a priority analytics should be in profile and efficiency than it currently is.

DIGITAL BUSINESS PRIORITIES ON CORPORATE AGENDA (% OF C-SUITE EXECUTIVES)

BIG DATA AND ANALYTICS

DIGITAL MARKETING AND SOCIAL TOOLS

FLEXIBLE DELIVERY PLATFORMS

MAY 2012

Top corporate priority

Not a top corporate or BU priority

Top priority for 1-2 business units

Top 10 corporate priority

Top 3 corporate priority

Not on the agenda

Source: McKinsey & Company, Marketingcharts.com

In addition, there is much talk about “Big Data” and the ability to measure every item of data, from every system, in every channel. However, few companies take the opportunity to effectively measure the data that is available now. The ability to track marketing (in most cases) can be completed without the entire headache of getting legacy systems to talk to each other, yet very few businesses are doing this in a way that will drive business change. The large majority of businesses don’t effectively attribute their channels to measure return on investment (ROI). A recent study from IBM has shown that 78% of CEOs believe that marketers aren’t empowering their teams to focus more on ROI and are losing sight of their main objectives. This is something that can be addressed by correctly investing and shifting the culture of a business from the top down in order to become a performance-driven (data-driven) organisation. Businesses need to consider their more tactical options as well as their wider strategic initiatives. It’s a fine balance, but some of the tactical work can feed into the wider data needs of the business.

POINT OF view HOW DO YOU EFFECTIVELY MEASURE AND PREDICT MARKETING PERFORMANCE? There is a lot of focus on two buzz phrases at the moment: “channel attribution” and “media mix modelling.” Search for either of these phrases in Google, and you’ll receive a list of blog posts about what they are and how effective they can be. These articles aren’t wrong. There are some very lucrative business benefits to using both. However, there is a level of understanding and maturity that goes alongside using either approach. In order to start using them to efficiently calculate ROI, you must first understand the differences and how they can be applied within your business. Channel Attribution Channel attribution allows businesses to look at the historic performance of marketing activity at a journey level. Single-touch journeys account for approximately 50% of conversions online. This means that the remaining 50% contain more than one touchpoint. Allocating performance solely to one channel can be a misrepresentation of the true effectiveness of your marketing activity. Key Benefits of Channel Attribution • Allows you to acquire cost savings by allocating more accurate spend across a multi-channel journey (e.g., payments to affiliates) • Allows you to understand where marketing channels have underperformed at channel level, campaign level, product level, and media level Media Mix Modelling Media mix modelling helps to predict the effectiveness of marketing. Using the historic data from channel attribution, media mix modelling leverages on the power of predictive analysis to forecast which channels will perform better based on certain criteria. Key Benefits of Media Mix Modelling • Allows you to use channel attribution data to predict future performance. It can predict the likelihood that a channel such as natural search will be more or less likely to convert when preceded by display or social • Allows you to look at permutations and combinations to calculate which channels work most effectively together • Allows you to stay ahead of your competitors by optimising your spend based on journeys that work for your customers • Allows you to reduce future spend whilst potentially increasing conversion (and ultimately ROI)

WHY USE PREDICTIVE ANALYTICS? Media mix modelling combines the data obtained from channel attribution with the insight of predictive analysis to help define how marketing budgets can be spent in the future. Predictive modelling should only be addressed once you reach a certain level of confidence with your attribution modelling. However, there are many questions that your channel attribution model should leave you with, which predictive analysis could help answer:

“Predictive modeling is the area of data analytics concerned with forecasting probabilities and trends” Predictive Modeling Resources, 2012

POINT OF view • Why is this channel underperforming, and will it work better in any other scenario? • Why does it (or doesn’t it) perform better? • Is there a tactical system that can be used to maximise marketing efforts? • How likely is my campaign to succeed based on this past experience, and what impact will it have? Predictive analytics will allow you to understand the likelihood that a channel will further perform within a particular scenario, and the deeper impact that may not be initially noticed by merely looking at the attribution alone. But why do you need predictive analysis if you already use advanced attribution modelling? Predictive analytics delivers information outside of channel attribution. As mentioned previously, media mix modelling aims to deliver future insights rather than reflect on the historic data. The difference here is that media mix modelling (predictive analytics) uses the channel attribution data as a springboard to deliver further insight. This is why it is imperative to have a robust attribution model in place prior to introducing predictive modelling. Once in place, predictive analytics will allow you to address some of the key questions above. For example, if Channel A is performing well and we increase or re-allocate budget: a) Will it improve its performance in the short term? b) Will it perform better under different journeys? (e.g., before or after Channel B) c) What is the likelihood that it will improve performance longer term? d) What effect could this change have on other channels? (e.g., Channel B) In order to be at a level to deliver the answers to the above questions, you need to evolve your marketing analytics so that you can confidently understand your marketing performance. THE MARKETING ANALYTICS MATURITY MODEL Assessing your maturity within marketing analytics is the first step in understanding how to develop your marketing efforts beyond basic tracking. SapientNitro’s Marketing Analytics Maturity Model outlines the five key stages in developing your marketing analytics maturity in order to ensure that you maximise your spend.

Source: SapientNitro,2012

POINT OF view 1. Reporters Reporters look at historical data and produce flat content reports to which they can monitor what is happening at the most basic level. Reporting is about reviewing through hindsight. The reporting culture is focused on “sense checking” that the data matches the expectations of a particular campaign. 2. Measurers Measurers will take into account success criteria and ensure that valuable key performance indicators (KPIs) are in place, whilst aiming to maintain data integrity. Measurers will also aim to evolve analytical data into something that can drive actionable business change. Measurers start to look at multiple data sources and more dynamic reporting. 3. Optimisers Optimisers look at the present and make changes accordingly. Optimising companies tend to be more mature with regards to their infrastructure and culture. As a result, they will be more strategically evolved with their approach to marketing analytics despite the reactive nature of optimising. Optimisers will also start to look at individual channel performances in silos to help deliver agile improvement. 4. Analysers Analysers ask the question “why?” in order to deliver deeper insights that can influence the strategic direction of the business. Analysts in more analytically mature companies are valued because of a culture that lives and breathes data–driven change. Analysing companies aren’t solely reactive but they will take a holistic view of optimisation and targeting, whilst also beginning to deliver a multi-channel approach to analysis. 5. Futurists Based on the art movement, this culture of analytics maturity oozes innovation by using historic data to forecast and predict the performance of marketing. By effectively strategising and operationally implementing changes in the business, the futurist culture will be innovative by aiming to deliver the ultimate performance in a multi-channel environment in order to ensure that a wider picture is always accounted for. WHAT ATTRIBUTION MODELS ARE THERE? A number of different attribution models exist; all of which are named depending on the author. Each model has its pros and cons, but the appropriate application of these are dependent on your business. Exploring a range of models can help you to understand where marketing channels are delivering (or under-delivering) and give you a clearer picture of the end customer. Last Touch Allocation Last Touch Allocation is the most common method of attribution and is used in the majority of businesses today. Last Touch measures the final channel in a customer journey and allocates all purchases and conversions against this final channel. Therefore, all success metrics are given to the channel that “closes” the sale.

POINT OF view First Touch Allocation First Touch Allocation measures the first channel in a customer journey and allocates all purchases and conversions against this channel. Therefore, all success metrics are given to the channel that initially engages the consumer. In this example, all revenue would be attributed to the display channel:

Last Touch Weighted Allocation Last Touch Weighted Allocation measures all the channels in a customer journey and allocates all purchases and conversions against touchpoints depending on the position within that journey. Therefore, the closer the channel is to the Last Touch, the higher the allocation. Higher positional weighting means that all success metrics are hierarchically distributed to each channel accordingly. In this example, all revenue would be attributed to channels depending on their position:

First Touch Weighted Allocation First Touch Weighted Allocation measures all the channels in a customer journey and allocates all purchases and conversions against touchpoints depending on the position within that journey. Therefore, the closer the channel is to the First Touch, the higher the allocation. Higher positional weighting means that all success metrics are hierarchically distributed to each channel accordingly. In this example, all revenue would be attributed to channels depending on their position

Linear Allocation Linear Allocation measures all the channels in a customer journey and allocates all purchases and conversions equally against each touchpoint. Therefore, all success metrics are equally distributed to each channel. In this example, all revenue would be attributed to each channel equally:

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Last Touch Split Allocation Last Touch Split Allocation measures all the channels in a customer journey and allocates all purchases and conversions based on location. Therefore, 50% is attributed to the Last Touch and the remaining 50% is equally distributed to the remaining channels. In this example, all revenue would be attributed as below:

First Touch Split Allocation First Touch Split Allocation measures all the channels in a customer journey and allocates all purchases and conversions based on location. Therefore, 50% is attributed to the First Touch and the remaining 50% is equally distributed to the remaining channels. In this example, all revenue would be attributed as below:

Bespoke Allocation Different suppliers approach the Bespoke Allocation model in different ways, so ensure that you are considering some of the common mistakes when choosing the right company to build your attribution model. The trick to a Bespoke model is making sure that the turnaround is efficient enough to influence change. Your model should adapt around your sales cycles to enable a quick delivery on actionable insights. A Bespoke model will attribute to a channel based on a number of different factors but can be represented in the example below:

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Considerations Each of the above models has pros and cons, so the right model will depend on your business requirements. Working with a trusted partner will help you define this but, to get you started, here are a couple of items that you should consider when looking at attribution models: • Does this model fairly distribute credit to the appropriate channels in a journey? Am I looking at closing the conversion or starting it? This may vary per department within the business and is something that you should consider based on the channel usage. • Will this model give me the speed and flexibility that I need to be effective? This is dependent on your sales and change cycles, but you need something that will work within your timeframes. If your marketing plan needs to be updated weekly but you can only turn around a bespoke model within two weeks, then your data is obsolete. Adapt according to your needs. • Will this model allow me to predict future outcomes? If you’re using a one-touch attribution methodology, then the benefit of understanding how channels work together becomes insignificant. However, understanding permutations and combinations of channels will develop your ability to spend your budget wisely. • Will this help me reduce my costs or optimise spend and performance? This is probably the most important question you will need to ask. Understanding whether the ROI of implementing a model will outweigh the time and cost spent in creating the model is key in the success of initiating an attribution or predicting an analytics approach. COMMON MISTAKES IN CHANNEL ATTRIBUTION There are a set of common mistakes companies make when implementing channel attribution, which can hinder the timeliness and effectiveness of the value that can be delivered. The following is a list of issues we’ve experienced with clients: Data Sources This isn’t in regards to getting legacy systems to speak to each other, but more around the fact that multiple data sources are required to calculate attribution—and they often use different systems that collect data in different ways and use different metrics. Many models aren’t accounting for this difference, which can lead to data integrity issues. For example, if you are using clicks against visits, are you taking into account that a visit can contain multiple clicks? How are those clicks being calculated? Or, if you are measuring offline activity against purchases, are you taking into account that offline can drive other channels, not just vanity URLs? What impact does that have on the journey? Tag management solutions pull this data together, but you need to check how your solution amalgamates this data. To get the true impact of attribution, amalgamation alone isn’t enough. Understanding your data sources and how to accurately collect your data is often overlooked, but is a key differentiator for a higher level of accuracy.

POINT OF view Tag Management Solutions Although tag management solutions pull data together, they won’t necessarily push out the models required to give you the answers you need. Channel attribution requires an investment to be effective but make sure you fully understand this amount of investment before making your decision; find out exactly what you’re paying for prior to signing on the dotted line. Again, finding the right provider will help you make the right decision on tag management solutions to deliver the right tool for your needs. Data Trust Is all of your marketing activity tracked? Are you taking data trust into account? If not, how can you trust the performance of your attribution model? For example, if you don’t correctly tag your paid search (PPC), it will appear as a natural search in most analytics tools. Your ability to accurately tag your marketing activity will directly impact your results. Creating the right governance processes and levels of trust within your business is often overlooked, but can be a key influencer when looking at channel attribution. Future Marketing Spend Next steps are often misunderstood within channel attribution. Many people think that attribution is as simple as saying, “Optimise your marketing spend by moving your budget accordingly.” In most cases, it’s not as easy as turning marketing on and off. For example, if you find that third party websites are delivering a high conversion rate, you can’t simply increase the number of websites that link to you. This involves research, communication with these third parties, and—potentially—an additional budget. After this, you will need to measure what impact these new sites have on conversion in comparison to any budget that has been reallocated. In addition, there is the added complication that elasticity is rarely taken into account for the longer-term impact. Here’s the scenario: You look at your attribution model(s) and realise that you need to increase your paid search budget. Once this happens, you realise that your conversion drops and it’s not as effective as you initially predicted. Few companies take into account that the increase in traffic may increase irrelevant traffic or, more importantly, can reduce ROI due to the fact that the marketing may hit the point of saturation and that each channel could potentially peak, reducing productivity. Any predictive models have to take into account the elasticity of any marketing predictors. Also, what is the halo effect on other channels? Is there an impact that actually decreases the productivity on a wider level? Even though there is a certain level of confidence when making changes, this is a test and should be monitored accordingly. Total Attribution Performance Attribution should be like good analysis and optimisation—ongoing and recurring. Iterative improvements to attribution help to continually optimise your marketing activity. Don’t attempt to calculate total attribution all at once. The upfront investment, in time, will not pay short-term dividends. Graduate your modelling to evolve with your business. By the time you calculate attribution with a bespoke model right down to the product or activity level, your sales cycle will be likely to render your data invalid. The product can become out of date, the campaign may finish, or the impact to change could reduce over time (reduction in competitive advantage). Think quick wins versus longer-term goals when looking at marketing optimisation.

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ÂŠ Sapient Corporation, 2012

POINT OF view WHAT ATTRIBUTION TOOLS ARE AVAILABLE? Although not the primary purpose of this paper, it’s difficult not to mention the tools that can deliver attribution data. There are a number of tools that have their associated strengths and weaknesses. Forrester has released their findings and point of view on some of the available attribution tools: Risky Bets

Contenders

Strong Performers

Leaders

Strong

Visual IQ Adobe GroupM

Current ering

ClearSaleing

Converto

Market presence Full vendor participation

Weak Weak

Strategy

Strong

Source: Forrester Wave: Cross-Channel Attribution Vendors, Forrester Research, Inc.

Things that you should also consider when researching tools are tag management solutions and data collation processes. Always ask yourself, “What is the easiest and most effective way to collect data within my organisation? Can the tools I already have get me started?” CONCLUSION There are a few key takeaways that you should always think about when looking at attribution. • Where am I now? • Where do I want to go? • How do I get there? • What do I need to get there? You won’t necessarily need a fully bespoke solution. It is dependent on your specific business requirements, however, what you need is something that you can react to and is clearly actionable. Continuously evolve your measurement as there isn’t one right answer, and it changes per business. Remember, you are looking at full customer experiences and you should be analysing their journeys, not isolated channels or behaviours. Don’t think bigger with attribution and modelling—think smarter.

About the Author

Azlan is an award-winning digital consultant who specialises in digital marketing communications and performance. Working in a range of industries, Azlan has worked with a number of blue chip organisations to help optimise their marketing efforts through effective campaign planning, strategy, and most importantly, accurate measurement.