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Next Generation Marketing Measurement

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Building next generation marketing measurement

Building next generation marketing measurement

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  • 1. Building a next generation marketing measurement system: the framework M er kl e Tho u g hT l eader s hip se r i e s A Database Marketing Agency
  • 2. Building a next generation marketing measurement system: the framework executive summary: marketers struggle more than ever to develop accurate, reliable, and consistent metrics around the performance of marketing spend. relying on incorrect metrics creates a snowball effect that compounds measurement errors along the entire marketing decision process. decisions on how much to spend on marketing in total, how much to spend by media, and how to coordinate a multi-media program are all directly influenced by the metrics used. for these reasons, the importance of evaluating your current measurement framework and building a solid foundation can’t be understated. n Marketers already employ processes to link a purchase with a marketing activity to create metrics on marketing performance. These marketing measurement systems fall into one of two camps, each with its own shortcomings: • Top-down attribution approaches that provide such a high level view that they fail to be actionable, or • Bottom-up attribution approaches that present a collection of very detailed information but fail to integrate into a single view. Better solutions do exist and can be achieved through sophisticated analytical and technological techniques. Merkle introduces a next generation measurement system based on Probabilistic Contribution (PC) scores (see definition on page 5). rather than attempting to link a purchase to a marketing activity, a contribution score is created, by integrating top-down and bottom-up measurement systems, for each factor that potentially influenced the purchase. The key advantage is integration - bridging multiple media/channels and points of time for a single purchase to provide a comprehensive view of the consumption pattern. This white paper, the second in a series of four on marketing optimization, addresses the critical elements needed to build a next generation marketing measurement framework. A third paper will discuss the data, infrastructure, and software tool requirements. and a fourth and final white paper will outline how to effectively integrate this system throughout the marketing decision making process. Building a next generation marketing measurement system: The framework | © 2008 Merkle 2
  • 3. Building a next generation marketing measurement system: the framework five factors fuel marketing’s roi dilemma What makes answering such fundamental questions around the ROI for marketing spend so difficult? Some of the main reasons for this include: 1. Consumers face daily ad bombardment. studies consistently show that consumers are exposed to a mind-numbing one to two thousand advertising impressions per day. The sheer volume of advertising makes drawing a line from messaging to consumer activity all the more difficult. 2. fragmentation of media. The nearly monolithic media channels of previous decades have splintered in the age of the internet, on-demand television, and satellite radio. For instance, the number of television channels increased over a hundred times in the last thirty years. The growing consumer trend of multitasking while accessing multiple media – especially prevalent in young consumers – throws a wrench into marketers’ ability to attribute action to marketing message. 3. failure to consider the complexity of consumers’ decision process. a purchase is not directly tied to the most recent advertising exposure, despite the fact that most organizations attribute sales to marketing spend in this manner today. purchase decisions are exceedingly complex and consumers are likely to be influenced by multiple advertisements over time and different types of media. 4. Consumers are in control. Consumers have more choice than ever over when and how they consume media, making it that much more difficult for marketers to even know if a message was received. 5. organizational challenges. Most marketing groups organize by media type creating a silo effect (e.g. brand marketing, direct marketing, and interactive/online marketing). These silos each develop their own goals, metrics, infrastructures, and partners to maximize the impact within the media they can control. Building a next generation marketing measurement system: The framework | © 2008 Merkle 3
  • 4. Building a next generation marketing measurement system: the framework Critical elements of marketing measurement Marketing organizations require the following key elements to effectively build and implement next generation marketing measurement: 1. Analytical acumen to custom-build a solution to link all marketing activities to consistent performance metrics (i.e. measurement framework) 2. Data availability: Access to a complete, accurate, and frequently updated marketing data universe 3. Infrastructure (Media management database) 4. Integration into business decision making (belief/buy-in and integration through software tools) and integration across all lines of business. Terms Used In This Paper Standardized language will be used in this paper to provide relevance across different industries. A few notable cases are highlighted below. Standardized terms are defined as follows: outcome of interest – This paper will use “purchase” to represent a generic term that could be applied to any performance measurement a company may use. Throughout the discussion, purchase can easily be replaced with response, new customer, revenue, profit, LTV, or any other metric that may be used to calculate desired outcomes. While it is true that some details related to techniques and implementation may change depending on the metric used, the discussion throughout this paper will be broad enough to encompass any of these metrics. media/Channel – A distinction will be made between media and channel, with media representing communication flowing from the company to the consumer, and channel representing communication from the consumer to the company. Additionally, media will be discussed in three classes; direct, mass, and interactive. top-down and Bottom-up – The top-down and bottom-up approaches are designed to represent two common ways marketing organizations measure the performance of their media. The top-down approach starts with all media spend and then breaks the media spend into media, region, and timeframe. This is the broadest perspective commonly used in media mix analyses, but rarely takes very detailed information into account (individual level data, creative, quality of advertising, etc.). in contrast, the bottom-up approach starts with the very detailed level of information. This approach is common in direct and interactive media analyses (consumer segments, predictive models, decile analyses, etc). Building a next generation marketing measurement system: The framework | © 2008 Merkle 4
  • 5. Building a next generation marketing measurement system: the framework attribution – The term “attribution” represents any process designed to link a marketing activity with an outcome of interest (i.e. purchase). Typically, an attribution process attempts to link the purchase with a single marketing activity or campaign. More sophisticated, and less common, attribution processes apply probabilities across marketing activities to capture cross-media influence more accurately. This paper outlines a new approach to marketing measurement by: 1. Setting the stage through a discussion of typical attribution processes and the current challenges of marketing measurement systems. 2. introducing analytical framework for the next generation marketing measurement system. This paper describes a new metric called Probabilistic Contribution score (PC score) used as the key metric to link marketing activities with the outcomes of interest. 3. providing guidelines on the analytical approach required to calculate pC scores across all marketing activities. Probabilistic Contribution Score: The Probabilistic Contribution Score (PC Score) is a value assigned to any activity or event (marketing or otherwise) that influences the consumer toward a desired action (i.e. purchase a product). PC Score derives its name from: • Probabilistic – The sum of all the values for any given purchase equals • Contribution – Each value represents the relative contribution or influence specific activities or events have on consumers’ decision making process • Score – The values are designed to be informative within a given purchase or when rolled up to summarized reports Current Challenges facing marketing measurement systems If asked, many organizations claim to have robust and reliable processes to attribute sales to media spend. Forrester Research conducted a survey in 2008 that concluded that 69% of marketers believe they are at least somewhat effective at measuring marketing ROI. It has been Merkle’s experience, however, that after digging beneath the surface, most marketing measurement systems fall far short of robust or reliable, especially when trying to forecast marketing impact into the future. The 2008 ANA/ MMA Marketing Accountability Survey showed that only 10% of marketers felt they could forecast the effect of a 10% cut in budget, and just 14% said that senior management in their companies had confidence in their firms’ marketing forecasts. CFO’s are even more skeptical of the numbers. Ninety percent of CFO’s don’t use marketing ROI metrics to help set marketing budgets. “They don’t believe the numbers,” said Jeffrey Marshall, the retired editor in chief of Financial Executive magazine. Building a next generation marketing measurement system: The framework | © 2008 Merkle 5
  • 6. Building a next generation marketing measurement system: the framework Another recent Forrester study highlights some of the top barriers marketing organizations currently face to improving marketing ROI (see figure 1). Many of the challenges Forrester identifies are directly linked to the four challenges addressed by this framework (analytic sophistication, data access and accuracy, technology, common metrics and business integration). Figure 1 marketers face hurdles in staffing, data, technology, and common metrics Source: Forrester Research, “Database Marketers Evolve Their ROI Measurement” Building a next generation marketing measurement system: The framework | © 2008 Merkle 6
  • 7. Building a next generation marketing measurement system: the framework two Common approaches to marketing measurement Marketers already employ processes to link a purchase to a marketing activity to create metrics on performance. These marketing measurement systems fall into two camps: • Top-down attribution process - providing such a high level view that they fail to be actionable, or • Bottom-up attribution process - presenting a collection of very detailed systems that fail to integrate into a single view. Overview of typical top-down attribution processes The top-down approach starts with the entire marketing budget and then looks at how the marketing budget is split between major categories (media, regions, products, etc). The advantage of this approach is that it provides a framework to compare the performance of all marketing spend with common performance metrics to one another. This property is crucial to the building of a robust marketing measurement system, and is the reason why the approach outlined later in this paper uses the top-down approach as the starting point. This type of analysis is often termed media mix analysis. Figure 2 top-down and Bottom-up approaches Building a next generation marketing measurement system: The framework | © 2008 Merkle 7
  • 8. Building a next generation marketing measurement system: the framework Certain industries, like consumer product goods and pharmaceutical companies, have a long history of using media mix modeling to help determine marketing spend. These industries tend to have in-direct relationship to their consumers and involve purchasing of less expensive products conducive to a short purchase consideration time (see figure 3). These industries developed media mix modeling capabilities early on partly because it was one of the few ways to understand marketing performance when there is no direct to consumer relationship. These industries also benefit from standardized data sources (like pos data) which have allowed standardized media mix modeling methodologies to develop. as relationships move more towards direct-to-consumer and the purchase consideration increases, the data and methodology required for media mix modeling becomes more customized for each company. This customization requires more investment to develop, and therefore it is typically the companies with significant marketing spend that can justify the modeling work. Figure 3 Choosing the measurement approach PurChase Consideration time most established top-down measurement systems non-direCt direCt to Consumer *Note – the company placements above are for illustration purposes only. In some cases a company can be put in multiple areas due to different product offerings. For example, a Dell desktop computer would be in the top right (as shown) whereas a replacement ink cartridge for Dell printers would be in the lower right. Building a next generation marketing measurement system: The framework | © 2008 Merkle 8
  • 9. Building a next generation marketing measurement system: the framework However, there are some significant weaknesses to the top-down approach, including: 1. it takes too long. it is not uncommon for this type of analysis to take over six months to complete. The deliverable is often a presentation of how media performed for the analysis time period along with recommended changes. By the time the recommendations are presented, the marketing landscape may have already changed, making the analysis outdated before it is even presented. Another way to view this pitfall is that media mix work is often managed on a project basis, as opposed to an ongoing system. 2. it fails to take into account the specifics within each media. There are a number of options and choices when spending dollars within a media. If the budget for that media is being cut back due to historical poor performance, then perhaps the allocation of spend within the media should be changed, not necessarily the size of the budget. For example, there are many decisions and analyse within a direct mail program that are not adequately summarized by simply when just total dollars spent. 3. lack of integration with media execution. Media mix analyses often conclude with recommendations on how to shift marketing dollars between available media. It is rare for these analyses to address the complexities associated with the execution of each media. of particular importance are inventory and cost constraints. For example, a recommendation to increase spend in spot TV may require introducing new day parts or networks which typically changes the price to purchase the media. as cost per impression increases, the relative effectiveness of that media is likely going to decrease. These weaknesses can be summarized by the project vs. process nature of the analyses. These analyses are often conducted ad-hoc without becoming part of the marketing measurements systems. By not being fully implemented into the marketing culture, these types of results rank as merely interesting – but not relevant enough to change marketing strategy and spend. Additionally, because the top-down measurement approach fails to take specifics into account there is a need for the more detailed bottom- up approaches. Overview of bottom-up typical attribution processes The bottom-up approach starts with detailed information within a media and then establishes a system to measure performance of segments or campaigns within the media. The nature of the data and the type of metrics calculated vary depending on the media discussed (which is part of the problem). Some metrics are efficiency based (cost per impression) while others are performance based (cost per purchase, ROI). It is more common to see performance based metrics available where it is easier to connect media spend directly to individual level purchases. Building a next generation marketing measurement system: The framework | © 2008 Merkle 9
  • 10. Building a next generation marketing measurement system: the framework Direct and indirect attribution are two commonly used practices as the foundation for bottom-up type metrics. A brief overview along with some examples and pitfalls are highlighted below. direct attribution is often classified as an attribution process that uses some kind of unique identifier to link a purchase to a specific marketing activity. examples include asking customers to provide a specific code from the back of their catalog , a unique 1-800 number that is only used for a particular run of a television advertisement, or tracking a click on a banner ad by directing to a specific landing page. While each of the examples above has their own merit, they ultimately fail in producing a reliable attribution process. They fail because the direct attribution is only recording the last marketing exposure before the purchase, but not necessarily what is driving the purchase intent in the first place. A common example is unique 1-800’s on TV ads. A TV ad may drive desire to purchase, but people don’t use the unique 1-800 number on the ad, but rather search for the product on Google, go to a landing page, and then use that unique 1-800 number. The TV ad is given less credit than it deserves, and search engine marketing gets more credit than it deserves. indirect attribution, or business rule attribution, uses some assumptions to link a responder to a marketing effort. For example, a unique 1-800 number is not used but we know that the consumer received a catalog two weeks prior to the purchase. In this case we use indirect attribution to assume the catalog drove the purchase. This process has some advantages and disadvantages over direct attribution. Indirect attribution allows a marketer to attribute a purchase when direct attribution is not possible. However, indirect attribution rules come down to little more than an educated guess. How long should the time window be? What if we know the consumer received a mail piece one week ago? One month ago? Six months ago? This decision will drastically impact the perceived performance of the direct mail campaigns. even worse, the pattern feeds on itself (up to a point). if it looks like direct mail performs well then a company will do more direct mail, which leads to an even higher likelihood that a consumer who purchases your product received a mail piece within the time window. This assumption inflates direct mail performance metrics even further. eventually the direct mail performance metrics will drop off as volume increases, leading to a perceived equilibrium relative to other marketing options. However this equilibrium is typically far from the true equilibrium. a common pitfall for organizations is the sense of sophistication associated with very complex direct and indirect attribution rules. The attribution rules continually become more refined and complicated, leading the marketer to believe they have a world-class marketing attribution system and therefore are doing very well at linking purchases to marketing activities. Building a next generation marketing measurement system: The framework | © 2008 Merkle 10
  • 11. Building a next generation marketing measurement system: the framework Most of the problems associated with direct and indirect attribution systems can be placed into two general categories: cross-media/channel tracking and effects over time. 1. Cross-media/channel tracking – Most attribution systems attempt to link a purchase to a single marketing communication, whereas in reality consumers do not just use one source of information to make a purchasing decision. 2. effects over time – multiple advertising exposures may contribute to the ultimate purchase even within a single media, but most attribution systems will just attribute the purchase to a single instance. Current marketing measurement systems ultimately leave CMOs with two problems: • an inability to see the whole picture. Today’s systems force CMOs to look at either broad brush metrics that fail to consider specifics of individual media or at detailed metrics within a media that fail to translate into metrics that can be compared across media. • a disconnect between budget planning and execution. Typically, the budget planning process has two steps: first, each media or channel is allocated a certain budget to spend and second, each media or channel attempts to gain the best performance it can from the allocated budget. This process effectively creates a major planning constraint where the budget by media is considered fixed. Each media may be doing the best it can given the budget, but the process is sub-optimal when considering all marketing spend together. The optimal process would enable a fluid budget allocation by media depending on performance, opportunity, and cross-media interactions. Of course the budget allocated to each media can be changed over time but the process to do so is typically arduous, requiring an effort to prove the performance benefit of a media with respect to other media, which becomes futile since there are no common metrics to compare. Building a next generation marketing measurement system: The framework | © 2008 Merkle 11
  • 12. Building a next generation marketing measurement system: the framework introducing a next generation measurement system The next generation measurement system forgoes the classical approach of attempting to link a purchase to a marketing activity. Instead, a contribution score is created for each factor that potentially influenced that purchase. The key advantage of the probabilistic contribution score (PC Score) is that it bridges multiple media/channels and points of time for a single purchase. While the scores have little bearing on measuring media for a single purchase, the reporting makes perfect sense when summarized. Determining Probabilistic Contributions The goal of the next-generation system is to assign a PC Score for every marketing communication that a consumer may have been exposed to. The sum of the PC Scores for any given consumer purchase will equal 100%. Depending on the scope of the measurement system, PC Scores can be computed for the following categories and non-marketing activities (the list below is not exclusive): • Mass media • Direct media • Pricing • Promotion (i.e. sales or other special offers) • Creative, messaging, versioning • Brand awareness or baseline effect • Natural and economic environmental factors • Competitive actions and spend • Legislative or regulatory changes • Service levels and customer satisfaction ratings The scope of categories a particular organization will want to tackle depends on the purpose, amount of available data, sophistication of analytical skill set, and organizational readiness. Media mix modeling would be an accurate description if this approach is limited to mass and direct media. Marketing mix modeling is a more suitable descriptor if pricing, promotion, and environmental factors are considered. In our experience, just using the model above to incorporate mass and direct media alone will produce a system more advanced than the majority of measurement systems today. Merkle would recommend adding a number of other factors deemed to have the most impact. These components usually include a combination of pricing, promotion, brand/baseline effect, and competitive spend. assuming a system is in place to assign the pC score for every purchase, then extracting meaningful information from the system is a matter of rolling the data up to the appropriate level. For example, if we just want to know what media provided the best ROI, we would roll up the PC Scores associated with every purchase over a specific period of time. The result will show total number of units sold for each media used. once the total cost for each media is factored in we can get the roi for each media. A similar roll up logic can be applied to geographies, customer segments, products, etc. Building a next generation marketing measurement system: The framework | © 2008 Merkle 12
  • 13. Building a next generation marketing measurement system: the framework Probabilistic Contribution Requires an Integrated Approach The process to assign PC Scores requires a combined top-down and bottom-up approach. The process is a two-stage process, with the top-down approach laying the rough baseline and the bottom-up approach refining the weights where possible. The process of integrating the top-down and bottom-up approaches is best understood through the following simple example. For simplicity, assume the following events take place: 1. The marketer uses only two media to sell their product, TV and direct mail. 2. This company places three spot TV advertisements in a particular region at weeks 1, 3, and 8. 3. Additionally, the company executes a direct mail campaign at week 6. 4. A purchase is made at week 9. 5. The company uses unique 1-800 numbers to track the performance of every marketing activity. 6. The consumer who made the purchase did not use one of the unique 1-800 numbers available but was mailed a dM piece 2 weeks prior to purchase. The top-down and bottom-up approaches ultimately produce very different answers to the question of what marketing activity drove that purchase. Bottom-up approach scenario The bottom-up approach would likely use business rules around the 1-800 numbers. For example, the following rules could be applied: 1. If the consumer uses one of the unique 1-800 numbers to make the purchase, then attribute that purchase to the marketing activity using that 1-800 number. 2. If a number other than one of the unique 1-800 numbers is used, then check to see if the customer was mailed a Direct Mail piece within the last three months. If they were mailed then attribute the response to the mail piece via indirect attribution process. 3. If neither 1 or 2 apply, then allocate the purchase to a brand awareness bucket. Since the consumer did not use a unique 1-800 number but was mailed within the three month time period leading up to the purchase, we could conclude that the purchase was due to the direct mail piece. Building a next generation marketing measurement system: The framework | © 2008 Merkle 13
  • 14. Building a next generation marketing measurement system: the framework top-down approach scenario Another way to approach this process is to use the top-down or media mix modeling approach. In this approach, we estimate the effective media exposure through an ad-stock transformation (see figure 4). The concept is that a marketing activity can produce purchases over an extended period of time. For example, a TV advertisement could cause a consumer to call and purchase immediately, or could lead to the consumer purchasing weeks or months later. Using ad-stock transformations we create the effective media exposures for each media at any given time. Figure 4 shows this process, where the red line represents the effective TV exposure over time and the blue line represents the effective direct mail exposure over time. Since the purchase occurred at week 9 we can multiply the effective media exposure by their coefficients estimated through the media mix models to get probabilities that the purchase was due to each possible marketing activity. In this case we may conclude that the purchase was 60% likely due to the TV exposures, 10% due to direct mail exposure, and 30% due to neither (i.e. brand awareness). Figure 4 top-down or media mix modeling approach PurChase made effeCtiVe exPosure week The two approaches listed above produce nearly opposite answers. The top-down concludes that TV had the biggest influence in the purchase, whereas the bottom-up concludes that the purchase was due to the direct mail piece. The top-down approach more effectively integrates the influence of multiple media over multiple time points, but fails to take into account some detail-level information (i.e. if that Building a next generation marketing measurement system: The framework | © 2008 Merkle 14
  • 15. Building a next generation marketing measurement system: the framework consumer received a mail piece or not). On the other hand, the bottom-up approach takes the detail- level information into account but uses rigid business rules to conclude an all or nothing answer. an integrated approach to measurement Merkle suggests an approach that utilizes the best of both methodologies (see figure 5). The top-down approach is used as the baseline since it has the desirable properties of a consistent performance metric across media and naturally takes into account cross-media impact and impacts over time. But the result of the top-down approach is modified based on the known, detail-level information available. In the given example, we would start with the top-down solution but then adjust the probabilities by the detail-level information known. For example, we know two important pieces of information about the consumer: first, we know that the consumer did receive a mail piece two weeks prior to purchase, and, we know that the consumer was scored as a decile two name using a likelihood to purchase predictive model. Given this information, the integrated outcome is 50% likelihood due to DM, 20% due to TV, and 30% due to general brand awareness. Building a next generation marketing measurement system: The framework | © 2008 Merkle 15
  • 16. Building a next generation marketing measurement system: the framework Figure 5 an integrated approach utilizes the Best of Both methodologies aPProaCh logiC result Top-Down Approach PurChase made Likely responded to TV (60%) effeCtiVe exPosure May have responded to DM (10%) • Media mix modeling Neither TV nor DM (30%, i.e. • Aggregated data Brand) week Quality Integrated Approach Media exposure detail above AND Likely responded to DM (50%) Customer received mail piece at time point 6 May have responded to TV (20%) • Top-Down enhanced Customer was decile 2 name Neither TV nor DM (30%, i.e. Brand) with Bottom-up …. …. integrated Quality Bottom-up Approach If used unique 1-800 number then direct Responded to DM (100%) attribution (TV or DM) • Business rules Else indirect attribution (known exposure • Atomic data to DM) Else …. • Did receive mail piece within response window • Did not use unique 1-800 number • So attributed to DM through indirect attribution (match-back) This integrated approach retains the key advantage of both the top-down (consistent metric) and bottom-up (use of detailed information) approaches. Building a next generation marketing measurement system: The framework | © 2008 Merkle 16
  • 17. Building a next generation marketing measurement system: the framework Guidelines for creating PC Scores The specifics to create the pC scores vary widely depending on the situation, so a description of a detailed process to create the scores will not be attempted here. Some general guidelines are described below, listed by category. top-down influence (media mix modeling) • Media mix models should be fit at the smallest geographic level possible. DMA-level model is ideal. • Caution should be used with respect to small sample size for models at a small geographic level. Bayesian Shrinkage or other adjustment factors should be considered. • It is important to keep the media mix models based on as recent data as possible. Consider having a semi-automated model building process if many models are used (geographic models, product models, channel models, etc). Bottom-up influence (Qualitative data) • The adjustments to the PC Scores based on qualitative data may be analytically driven or decided based on industry or company experts. Remember that creation of PC Scores is part art and part science • The impact of qualitative data varies depending on the media. Knowing that somebody opened an e-mail and clicked through to your website is very concrete and so would have more impact than what commercial ran in a dMa (since we don’t even know if that individual saw the commercial). • Because the qualitative data can change quickly, to have an easy method to integrate new types of qualitative data into the models (new creative, for example). testing and Validation • Because there is no ‘right’ answer, assessing accuracy can be a challenging process. • When possible, use controlled tests to validate influence of each media. For example, a direct mail campaign with a random holdout sample will provide the ‘true’ influence of that direct mail piece since all other factors are controlled for through the randomization. • Creditability is established over time. Evidence can be gathered multiple ways, the best of which being when an in market test design produces the results forecast by the media mix models. Building a next generation marketing measurement system: The framework | © 2008 Merkle 17
  • 18. Building a next generation marketing measurement system: the framework summary and Conclusions Marketers have an unprecedented opportunity to increase the ROI on marketing programs by implementing a next generation measurement system. By utilizing the best elements of both the high-level, top-down approach and the detailed, bottom-up approach, marketers achieve a much more accurate view of their customers purchasing behavior. The top-down approach is used as the baseline since it has the desirable properties of a consistent performance metric across media and naturally takes into account cross-media impact and impacts over time. But the result of the top-down approach is modified based on the known, detail-level information gleaned form the bottom-up approach. The powerful combination of these two measurement systems and probabilistic contribution scores provides a much clearer and reliable marketing metrics. our next paper will focus on the data, infrastructure, and software toolset required to operationalize the integrated measurement system. The fourth and final paper will focus on integrating the measurement framework into the business decision making and marketing execution process to ensure value is driven from the solution. How Merkle Can Help Merkle works with several clients to develop the infrastructure and analytics to enable the quantification of brand engagement across their prospect and customer base and make information-based decisions on their brand equity. Merkle specializes in information-based marketing strategies and is one of the nation’s leading database marketing firms. With a proven track record in developing winning strategies based on information insight for large consumer-focused organizations, Merkle works with many of the nation’s leading businesses, including Procter & Gamble, Dell, Capital One, GEICO, and DIRECTV. Building a next generation marketing measurement system: The framework | © 2008 Merkle
  • 19. Building a next generation marketing measurement system: the framework about the author scott nuernberger is senior director, Quantitative solutions. scott has over eight years of experience in developing and implementing analytical solutions to marketing programs for many different companies, including GEICO, AEGON, Nationwide Insurance, MBNA, Fidelity, and Dell. Prior to joining Merkle, scott worked for american express as a statistician and modeler and taught graduate students statistical methods and experimental design at Cornell university. scott has dual Bs degrees in Brain and Cognitive sciences and statistics from The university of rochester, a Ms degree in statistics from Cornell University, and an MBA from Johns Hopkins University. Building a next generation marketing measurement system: The framework | © 2008 Merkle 18