Transforming Big Data into Big Marketing

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1 Transforming Big Data into Big Marketing issue 1 Start a dialogue with a few million customers by cutting through the noise to reach over-marketed customers inside this issue CRM and Big Data CRM Team Perspective: CRM and Big Data Which Retail Analytics Do You Need? A Marketing Success Story About FICO CRM and Big Data Why does big data matter? What do you do with it, how do you scale and how do you measure your return? The ability to draw deep customer insights from Big Data and bring them rapidly into operational decision making is transforming the discipline of marketing. Intelligent consumer devices, ubiquitous broadband networks and social media are all contributing to the explosion of Big Data. Standardized hardware components and service-based software architectures are enabling this data to be brought together and analyzed in massively distributed, parallel and virtual ways that bring down the cost of high-performance computing. As a result, Big Data techniques used thus far in very specialized settings are affordable and implementable for an increasing number of companies. As companies continue to collect more and more data on their customers, whether structured or unstructured, the looming question is what to do with it. How do you determine which data will help you make better decisions and which decisions are improved by leveraging the data you have collected? Start with your customer objectives: retention, organic growth, acquisition. Then lay out the actions you are currently taking to help attain each objective. Next, use predictive analytic modeling to help determine the data, from the enormous amount collected that will provide the most insight into whether or not a goal will be achieved. Once the data with the greatest impact has been isolated, you can determine how that insight can be applied to your decision making. Featuring research from

2 Source: FICO That raises another question: how do you scale this newfound insight in an efficient, effective, repeatable process? Customerlevel messaging and offer management, or one-to-one marketing, have largely gone untapped because of the heavy workload and investment needed to sustain a program. Tools and software need to connect with analytic modeling in a way that keeps expenses manageable while continually meeting or exceeding customer expectations. Finally, you need to know whether the investment in data collection, storage, modeling and decision automation has delivered a large enough return to justify it. You need to have the right mechanism in place to quantify the impact of big data on customer retention and acquisition, revenue growth and profit. Once you have the right systems and processes in place, big data can unlock future opportunities. But if you can t determine which data offers the most insight, then scale it and measure its impact, your investment may not deliver the return you expect. Learn more: Visit the FICO Labs Blog for the latest ideas and developments in the era of Big Data. Download Insights white papers, which regularly cover analytic innovations and best practices in marketing. If you have any questions about the best ways to put big data to work for your business, you can contact FICO at m 2 l Transforming Big Data into Big Marketing Transforming Big Data into Big Marketing is published by FICO. Editorial content supplied by FICO is independent of Gartner analysis. All Gartner research is used with Gartner s permission, and was originally published as part of Gartner s syndicated research service available to all entitled Gartner clients Gartner, Inc. and/or its affiliates. All rights reserved. The use of Gartner research in this publication does not indicate Gartner s endorsement of FICO s products and/or strategies. Reproduction or distribution of this publication in any form without Gartner s prior written permission is forbidden. The information contained herein has been obtained from sources believed to be reliable. Gartner disclaims all warranties as to the accuracy, completeness or adequacy of such information. The opinions expressed herein are subject to change without notice. Although Gartner research may include a discussion of related legal issues, Gartner does not provide legal advice or services and its research should not be construed or used as such. Gartner is a public company, and its shareholders may include firms and funds that have financial interests in entities covered in Gartner research. Gartner s Board of Directors may include senior managers of these firms or funds. Gartner research is produced independently by its research organization without input or influence from these firms, funds or their managers. For further information on the independence and integrity of Gartner research, see Guiding Principles on Independence and Objectivity on its website,

3 Research from Gartner RAS Core Research Note G , C. Fletcher, J. Radcliffe, E. Thompson, A. Sarner, K. Collins, J. Davies, P. Stakenas, G. Herschel, P. Sengar, J. Sussin, 4 April 2012 CRM Team Perspective: CRM and Big Data As big data affects the customer experience, anticipate challenges and opportunities in your CRM strategy. Four major technology trends will drive technology planning, investment and usage in 2012 and beyond; they are encompassed by the nexus of social, mobile, cloud and information. Analysis For our March 2012 Perspective research, we asked members of Gartner s CRM research team about the impact of big data on CRM initiatives: How will big data affect the customer experience? What types of CRM applications will be able to best leverage big data? Where are challenges likely to be encountered? Gartner has identified four major technology trends that will drive technology planning, investment and usage in 2012 and beyond: the nexus of social, mobile, cloud and information. Enterprise organizations are being challenged to adapt as these technologies, and the data that results from their adoption and deployment internally to the enterprise and externally with customers, expands exponentially. Big data is one of the key driving forces that is changing information management and analytics. It is a popular term generally used to reflect the exponential growth, wider availability and greater use of information (structured information and unstructured content) in the data-rich landscape of the emerging information economy. The big data phenomenon is an opportunity and a challenge. Organizations that fail to prepare for it appropriately may lose competitiveness or incur increased costs. Big data has particular implications for CRM and customerfacing applications. Big data initiatives can increase the breadth and depth of customer insight by providing a total view of customer actions, interests and behavior across multiple channels, enhancing the ability of the enterprise to provide customer-specific sales, marketing and support. Another driver of big data in CRM is the customers themselves, and their willingness to share large amounts of explicit preference data to help them participate in how they are being sold and marketed to. On the other hand, big data brings potential liabilities: the ability of organizations to intentionally or unintentionally misuse customer data; the technology challenges associated with collecting, storing, analyzing and using volumes of structured and unstructured data in a cost- and resourceeffective way; and the need to ensure that business units focus on deriving value and meaningful insight from big data. The effective use of big data is predicated on the ability of the enterprise to bring other technologies to bear, including the growing capability of processing platforms and in-memory computing, the increasing capabilities of analytic and business intelligence (BI) tools, and the ability of cloud computing to provide on-demand storage and bandwidth. E-Commerce: Gene Alvarez Combining internal customer and product behavior data collected via your organization s website is great for understanding what customers and products have done in the past. However, it s no longer enough for organizations to deliver a customer experience that drives customer satisfaction, loyalty and profitability. Leveraging new sources of data such as mobile customer information, social graphs, information from sensors and many other noncorporate sources of information can enable organizations to see trends, predict and shape demand, and deliver an engaging contextual customer experience. Organizations that do not include big data analyses as part of their sales strategies will miss opportunities to recognize demand early and to shape it. Marketing: Kim Collins Marketing organizations have access to more data than ever, including both structured and unstructured data. Social and contextual data can provide insight into customers wants, needs and personas that go well beyond what can be revealed with traditional demographic data. This vast amount of data often leaves marketing organizations paralyzed over how to appropriately analyze the information and attain action-oriented customer insights. Companies must also consider the privacy of customer information, which raises concerns over how the data is actually used to target and communicate with customers. Transforming Big Data into Big Marketing l 3

4 4 l Transforming Big Data into Big Marketing Much of the available information is likely to be irrelevant to the customer decisions your company is trying to make. Marketing organizations must first determine and prioritize appropriate marketing use cases for big data, then they can determine which of it is relevant to support their strategies and to take customer action. Establishing the process and strategy ensures that the right data is collected, relevant analyses are performed and appropriate customer actions are taken to achieve business or marketing results e.g., customer acquisition, retention, cross-sell/upsell; customer loyalty; and increased customer profitability. Voice of the Customer: Jim Davies Sources of voice of the customer (VoC) data are plentiful, ranging from survey results to social media dialogues to call recordings. These increasingly unstructured data sources provide valuable venues for analysis; however, analytics in such isolation inherently limit opportunities to understand customer expectations and experiences. Integrating the data from VoC sources provides additional insight and incremental value, but technical and organizational challenges prevent many organizations from embracing a holistic strategy for VoC data collection and analysis. Organizations should strive to obtain a single view of the VoC to capitalize on the business benefits associated with being able to act on the holistic customer voice. IT will need to determine the most appropriate data architecture and analytical models/ techniques to extract key customer insights at an individual customer level and an aggregate level. Those responsible for the customer voice (ideally a team led by a vice president of customer experience) need to conduct an internal audit to assess current capabilities, and prioritize future initiatives to collect VoC data based on the richness of the content. Don t be afraid to adopt a multiphase approach, adding further voice channels to the hub. Lead Management, E-Commerce and Price Optimization: Chris Fletcher Marketing and sales organizations are already feeling the effects of big data and struggling to understand how to derive maximum value for the customer, while respecting customer privacy. Multichannel lead management and sales processes can support customer interactions across , Web, e-commerce and social channels; however, most of the companies that have rolled out multichannel lead management are not leveraging the data and insight collected for use by other applications. Marketing automation has historically attracted less CRM investment than adjacent segments, such as sales force automation (SFA), customer service and support (CSS), and e-commerce. Advances in big data may provide the impetus for organizations to accelerate their adoption of marketing automation, marketing analytics, and multichannel campaigns. E-commerce interactions are driven by personalization and Web analytics insight, but that insight is limited to Web and e-commerce channels: Most CSS applications have limited access to insight gained from these Web interactions. On the other hand, price optimization is one of the few CRM applications that has been able to in reality, has almost been forced deal with large volumes of data from multiple sources. Most of the available price optimization applications provide the deep analytic insight necessary to support the complex pricing analytics needed to support real-time pricing recommendations for direct and indirect sales channels. Customer Analytics: Gareth Herschel Big data can have a profound impact on the customer relationship. A key step toward understanding its significance is drawing a distinction between big data and the potentially huge amounts of data that organizations already collect. Organizations can and do collect social commentary about their products and reputation, audio data from client calls and detailed transaction histories, including product characteristics. The power of big data is the ability to collect data in categories with which we are familiar at higher levels of granularity and timeliness than that to which we are accustomed. For example, instead of knowing where customers typically shop, you can know whether (or even where) in the store they are located at any time, and which other stores they may have visited before this one. Instead of knowing their sentiments, you can identify how their sentiments will be affected by the sentiments of those they are speaking with and how it changes based on their usage of the product. This unprecedented granularity has far-reaching implications. Managing the flood of data and analyzing it to discover meaningful patterns of behavior is not a trivial exercise. Creating a strategy that demonstrates the willingness and ability to manage the customer relationship to this level of granularity and delivers commensurate value to the customer for the loss of privacy inherent in this data capture will be the real challenge of big data.

5 Customer Information Management and Master Data Management: John Radcliffe Organizations are increasingly exploiting internally generated big data (e.g., data from log files, sensors and smart meters), as well as externally generated big data (e.g., data from social networks, such as Twitter, Facebook and LinkedIn). They are also exploiting structured data and unstructured data sources, including text, images, video and audio. Many of these data sources and data types are related to customers. Big data is not just about the volume of information, nor is it purely about new technologies, such as Hadoop or MapReduce. The information management challenges arising from big data, what we call extreme information management, can be classified according to three categories and 12 dimensions. These categories include quantification (e.g., volume, velocity, variety and complexity), access and quality assurance. Organizations will leverage new technologies such as Hadoop and MapReduce; however, they will need to do it in combination with established technologies and investments. Big data can be analyzed in aggregate, such as in the analysis of customer sentiment by customer segment. However, going further, if you can identify individual customers, products and suppliers referenced in big data sources (such as social networks), and, if you can capitalize on that knowledge, then it could be a powerful extension to CRM capabilities. Identifying the big data associated with specific customers will depend on identity resolution technologies and techniques, but you will also need to link that insight to the single view of the customer maintained by the organization in its master data management (MDM) system and to maintain that link. In this way, the extended knowledge from big data can be leveraged in the context of established business applications and BI systems. Big Data and CRM: Adam Sarner At its core, CRM is a business strategy designed to meet and anticipate the wants and needs of customers in a profitable way. However, to truly understand the customer, information must connect with each part of a business process, such as the sales, marketing and customer service functions. Web 2.0, social, mobile and every other enormously hyped technology area points to the demand (at least from a business sense) for increasingly expanding information, interactions, and connections between companies and customers. Customers are participating in how they are being marketed and sold to, because they are a big component in determining what they want. An explosion of information is now available, explicitly and implicitly, for collecting customer demographics, psychographics, customer behavior, their clicks, their aspirations, their sentiment, their networks, their location, their reputation, online and offline transactions, stage of a buying process, life cycle stage, etc. All this information will be mandatory to meet lofty goals, such as a 360-degree view of the customer. Big data will not be an exercise in merely collecting massive amounts of this data; rather, it will be about making the right information accessible and action-oriented for both the company and the customer for core CRM. Web Customer Experience and Configure, Price, Quote: Praveen Sengar Although big data has remained a hot theme across sales and marketing organizations, few are making a lot of progress on it. Organizations are looking to improve their Web customer experience by getting more consistent and personalized to drive conversion, repeat purchasing and overall loyalty to the platform. However, challenges such as the difficulty of capturing customer data, integrating data across departments or organization silos, and effectively leveraging external sources of data, while respecting customer privacy requirements, are great hurdles to cross to achieve corporate objectives. Successful organizations have been focused on capturing incremental data with each interaction and keeping the data capture process simple. At the same time, companies are defining which data elements are critical and are looking at their objectives as to what can be inferred and what is essential. A configure, price, quote (CPQ) system can support improved sales effectiveness through customer insights from lead to CPQ to contract process. Organizations are not yet closely integrating VoC from direct, indirect or inferred channels into their selling process. By embracing big data, sales organizations can increase their operational effectiveness with a better understanding of what to sell and how and where to sell it. Sales Performance Management: Patrick Stakenas For the sales organization, big data is used to build trends, patterns and statistics from large datasets captured from CRM, ERP and other sources. However, big data analytics are too often used to develop absolute predictions. The information irrefutably carries tremendous weight and power in decision making regarding sales; however, common sense and human interaction must always prevail. In analyzing the value of big data related to selling and performance, it is not necessarily the process of gathering large amounts of data and pushing out macro trends, but rather how the data is used to build action- Transforming Big Data into Big Marketing l 5

6 6 l Transforming Big Data into Big Marketing oriented insights and usable sales intelligence that can be related at the client level to influence the selling process and buying decisions. Managing big data (typically customer data), is about understanding buyer behaviors and translating that information to usable sound bites for sales, whether it s face-to-face, over the phone, or the message in marketing and advertising. Relying too much on big data analytics risks losing the personal approach to selling and having the selling process become merely marketing messages, trending and hypotheses. Peer-to-Peer Customer Communities: Jenny Sussin Peer-to-peer customer communities are being used by companies to provide insight into customers throughout their life cycle and to provide opportunities for upselling, cross-selling and customer support. These communities supply real-time customer information and product/service feedback that makes their contribution to CRM (social CRM) unique, compared with similar methods of gathering information across the company. Big data and the difficulties associated with the sorting of big data make it a challenge for this information to become optimally impactful across the organization as it gets stuck in the weeds of organizations databases. The real opportunity for big data in the customer community space is the capability to combine real-time data and to mine similar data that has already been gathered across the enterprise to provide holistic insights into how customers function and perceive the business. This provides more opportunities for upselling, cross-selling and customer support. Customer Experience Management: Ed Thompson In 2010, Eric Schmidt of Google was quoted in Techcrunch as saying that we now create as much information every two days as we did from the dawn of civilization to Two years later, we are creating even more information, so we can assume that, every two days, it s everything up to 2005 or Big data is the coming together of structured and unstructured data in unprecedented amounts, and it requires new technologies to make sense and derive value from it. Although some organizations are focusing on the underlying technology in Apache Hadoop, such as MapReduce and HDFS or associated technologies (e.g., Pig, Hive, Hbase, Sqoop, Flume, Zookeeper, Oozie, Ambari, Whirr and Mahout), others are looking at what benefits it can ultimately bring to the customer. The customer experience can be improved in several ways. Most companies focus first on better listening, monitoring and feedback analysis. Others look at open data initiatives that demonstrate trust to the customer by sharing what they know with the customer for their mutual benefit. Others look at the new forms of analysis as a means to gather information on the root causes of the process failures that result in customer dissatisfaction. However, the biggest benefits to the customer experience during the next five years will come from personalization. Personalization tends to result in complexity and cost for the supplier think private banking or tailored suits. However, the ability to scale up data volumes, incorporate unstructured data and develop new forms of analytics enables organizations to scale personalization to the masses. Action-Oriented Advice for CRM and Big Data Any initiative involving CRM and big data is likely to affect multiple organizations, processes and technologies. Use the following guidelines to assess the potential for big data in your CRM processes: Align CRM and big data opportunities with company vision and priorities. Ensure that planned investments in the aggregation and analysis of large volumes of customer data and insight support these key initiatives. Build stakeholder support in the specific functional areas, such as e-commerce or social CRM, that are best-positioned to capitalize on big data. CRM and big data projects are often, by definition, large and potentially daunting. Prioritize CRM and big data projects by contrasting the estimated resources required technical, human and financial with the potential to achieve or exceed corporate goals. Consider moving smaller, more manageable big data opportunities higher up on the priority list to show tangible, albeit smaller, results, and to build expertise and skills in the organization. Smaller, stepped wins will help win senior management support for larger, longer-term projects. Create a cross-functional team that bridges the business teams and the IT organizations to identify technical and resource requirements. Identify gaps in required skills and technology infrastructure, and build preliminary business justification models early in the project that will rationalize required investments by demonstrating the potential benefits. m

7 Source: FICO Which Retail Analytics Do You Need? To increase ROI, match the right analytic techniques to your business objectives Retailers who know their customers analytically are making smarter strategic decisions about online/ brick-and-mortar store design, merchandising and other investments. Analytics also enable them to implement these organizational-level strategies in individual-level offers. They aim for the sweet spot where customer behavior unites with what the retailer and its suppliers need to accomplish. Choosing the right analytics for the job is becoming a job in itself, however. Numerous vendors have crowded into the marketplace offering a jumble of similar-sounding solutions. Beneath the surface, there are actually significant differences in the analytic techniques being employed, the types of insights they provide and the business benefits they deliver. One-size definitely does not fit all requirements. To obtain a substantial return from your analytic investment, choose the right technique for what you re trying to accomplish. This white paper: Find out how retailers are using analytics to replace generalized mailings with targeted mailings that achieve up to nine times the ROI The most competitive retailers today are increasing response rates and revenues by using predictive models and other analytics to make relevant, personalized, precisely timed offers to customers. Analytics provide a concrete means of realizing the long-standing exhortation to Know your customer. They enable retailers to treat customers differently, even individually, based on insights into their desires, preferences and future behavior. Helps you match analytics solutions to your business needs. Explains what each analytic technique can tell you about customers and the kinds of actions you can take informed by such insights. Discusses specific benefits, such as increasing ROI with better incentive targeting, improving supply chain throughput and cash flow by accelerating customer purchasing patterns, and building loyalty program membership, usage and retention. Shares FICO case studies illustrating the value retailers are seeing from analytics. Transforming Big Data into Big Marketing l 7

8 What Value Does Analytics Bring to Retail? For retailers, one of the greatest values of analytics is to provide decision points for determining how to treat customers differently. Analytics provide a reliable means, based on statistically valid data analysis instead of hunches or observational judgments, of deciding what actions to take with your customers. Will it be profitable to offer free delivery? Are we offering a discount to customers who would buy this product anyway? Consider what happens when consumers visit an online or brickand-mortar store for the first time. Initially, the retailer knows nothing about these potential customers and thus treats them all the same. At some point, the consumer may click on a product or category, or make a purchase. This consumer behavior is likely to trigger a business rule that initiates an action by the retailer. Coupons might be printed for complementary products. An might be sent offering a discount on a product in an abandoned online shopping cart. The More You Know, the More Relevant Actions You Can Take Here s an overview of the analytic techniques that are most valuable in retail and the kinds of targeted actions they re enabling retailers to take. The benefits of analytics can begin with the first visit, but they really come into play as retailers develop customer relationships for longerterm revenues and profits. For this reason, while our discussion starts with a common technique used with first-time customers, we ll focus on more powerful analytics that deliver stronger value. Collaborative Filtering Inferring Behavior Based On Similarity Collaborative filtering enables retailers to take a degree of targeted action even for first-time customers. This type of analytics is often behind the product recommendations offered on e-commerce sites and the printed coupons generated at instore checkout. 8 l Transforming Big Data into Big Marketing In such instances, consumers are differentiating themselves by their behavior. The retailer, however, is still treating them the same because everyone who exhibits the same behavior receives the same offers. Inevitably the offers made will be more relevant to some recipients than to others, and responses will vary accordingly. Because the retailer doesn t know anything more about these consumers beyond that they clicked on or purchased a product, there s no reliable basis on which to make a more specific decision on a more relevant offer. Now let s look at what different types of analytics can tell retailers about their customers, giving them more decision points to consider when determining which actions to take. The form of collaborative filtering most often used in retail is sometimes referred to as an affinity model or lookalike model. It infers how an individual will behave based on how other individuals who look similar (share one or more characteristics) have behaved in the past: People who buy/view product X often buy product Y. Collaborative filtering doesn t have to be triggered by a current transaction. It can be used to target subsequent outgoing campaigns and other kinds of promotions. Still, this analytic technique is fundamentally transaction-oriented. The algorithms used are best suited to modeling data about items purchased or viewed. They re not effective for modeling purchase and view data with the wide range of other information retailers have in their databases (e.g., attitudinal data, seasonal purchase patterns, natural product adjacencies, basket builders) or can access from external sources (e.g., demographics, public records, third-party marketing information). For known customers, therefore, other types of analytics provide far more powerful and accurate insights.

9 Clustering Algorithms Segmenting Known Customers by Their Past Behavior Regression Models Predicting Propensity, Response, Revenue, Attrition and Other Individual Customer Behaviors Regression models enable retailers to predict how individual customers are likely to behave. With such specific insights, retailers can differentiate between customers to a much greater degree, further increasing the granularity of segmentation and the relevancy of offers. In fact, retailers can go as far as to essentially create segments of one. Clustering algorithms enable retailers to differentiate between customers in broad ways such as Customers who like leadingedge technology and Customers who are value conscious. One of the benefits of painting customers with this kind of broad brush is that it can help direct and justify large-scale expenditures on store design, new merchandising schemes and promotional programs. Using analytics in this way (often called behavioral segmentation ) enables retailers to make far more accurate decisions than can be achieved through traditional methods of database querying on customer attributes such as recency, frequency and monetary value of past purchases. Analytics are more accurate partly because they can handle greater data complexity. While query-based segmentation generally involves no more than three to six customer attributes, analytic-based segmentation can encompass dozens or even hundreds of attributes, greatly expanding the range of possible segmentation schemes. Regression analysis delivers this level of specificity and accuracy because while it can encompass a vast range of internal and external data, its power comes from pinpointing the specific customer attributes most predictive of a future behavior. Relationships between numerous attributes and other variables are examined to see how a change in the value of one variable affects the value of another, dependent variable. Attributes that prove to be highly predictive of a behavioral outcome are incorporated into a predictive model. For example, a regression model can be built to predict a customer s propensity to make a purchase in a product category or to discontinue using a service. With many more ways to group customers, and the ability to try lots of alternative groupings quickly, retailers can make better strategic and resource allocation decisions. One large national retailer, for example, has used analytics-driven segmentation to better understand and serve those customers who account for the bulk of the company s revenues. Using their characteristics to define population segments, and using these segments to guide decisions from store layouts to how staff interacts with customers, this retailer increased same-store sales in the first quarter of implementation alone by 8.4% resulting in a 15% increase in total revenue. Because these models can predict for each individual customer the likelihood of such an outcome, they open up the possibility of unique treatment. Moreover, by using multiple regression models, retailers can gain a much clearer picture of the customer. Knowing that Jane is not only likely to buy a 48 TV, but that she tends to like the Sony brand, but doesn t tend to go for cutting-edge products, enables the retailer to greatly increase the relevancy of individualized offers and interactions. Transforming Big Data into Big Marketing l 9

10 Here are a couple examples of how leading retailers are applying such insights: 10 l Transforming Big Data into Big Marketing One FICO client, who implemented propensity models for all of its product categories, can dynamically generate individually tailored s for customers. Every customer is scored for propensity to buy in each product category. Based on where the customer scores the highest, the retailer s automated decision system determines which of several overall mailing themes is most relevant, then adds features or offers in six product categories. No more than 20 customers in a million receive the same set of recommendations. This kind of tailored promotion has enabled the retailer to achieve open rates of up to 50% and up to 9 times the ROI compared to generalized mailings. Another retailer is using predictive analytics to build membership and activity in its feebased premium loyalty program. The program delivers 12 individually selected relevant offers per month to each member; these are loaded onto the customer s loyalty club card and can also be accessed from the retailer s website or from kiosks in stores. Each offer package includes rewards aimed at increasing buying frequency and customer retention. It also includes an offer encouraging the customer to try a product in a relevant category where they have not purchased before. In the first six months alone, this retailer increased program membership by over 43%. Time-to-Event Models Predicting When a Specific Customer Behavior Is Likely to Occur Knowing that a customer is likely to behave in a certain way is useful, but knowing when they are likely to do it is even more powerful. Retailers are working anywhere from a few days to a few weeks out in their decisions about what to promote to whom. The propensity of a customer to buy a specific product, however, varies over time, as shown in Figure 1. A time-to-event model predicts a window of opportunity when the customer is most likely to act. By timing campaigns and other promotions to these windows, retailers can improve relevancy for their customers, driving higher response rates. Similarly, the likelihood that a customer will attrite from buyers clubs and premium services varies over time. Some customers fail to renew, and others just stop using the service. Retailers want to prevent this from happening, but if they act too soon to offer retention incentives, they may be spending unnecessarily. Knowing when attrition is most likely to occur enables the retailer to time actions for the highest likelihood of success at the lowest cost. Here s how some FICO clients are using time-to-event models: A large retailer is using this type of analytics to increase the ROI from promotional mailings. When a popular new DVD or videogame comes on the market, for example, the retailer sends offers only to those likely to buy the product within the offer redemption period. Response rates are twoto-three-times higher than when the same offer is sent to everyone. And because the retailer is not wasting customer time with irrelevant offers, future promotions are likely to be received with due attention. A home improvement retailer used time-to-event models in an campaign directing recipients to one of several web landing pages featuring a specific do-it-yourself project, such as painting or replacing flooring. The customers targeted for the s were those identified by the models as likely to purchase in that product category within a short period of time.

11 A large retailer is improving its ability to predict when customers are about to make a big purchase by incorporating customer clickstream data into time-to-event models. Many consumers do extensive online research before making an online purchase or walking into a store to inspect the item. By analyzing clickstream data from its site, along with customer purchasing histories and historical behavior patterns, the retailer can pinpoint the right moment to make an offer. Early results are impressive and too competitive to be revealed. Uplift Models Predicting How Much a Retailer Action Is Likely to Change a Customer s Behavior If a propensity model predicts a customer is likely to buy a given product, why should the retailer go to the expense of sending a promotional offer? Uplift models help retailers determine if an investment is likely to be worth the result. Often used in conjunction with time-to-event models, they predict the amount of change likely to occur in a customer behavior as a direct result of a particular retailer action. Uplift models can save retailers millions by enabling them to avoid offering discounts to customers who will purchase without them. For example, such a model might predict whether or not sending a 20%-off offer is likely to increase a particular customer s propensity to buy a pair of designer jeans within the next two weeks. The retailer can then send the coupon only to customers whose behavior it s likely to change. Will 20% off be much more effective than 10% off? Than free shipping? Is offering 12 months of interest-free credit necessary, or will 6 months be nearly as enticing? Uplift models provide the analytical insights retailers need to make precise decisions about where to put marketing spend for higher ROI. Uplift models are based on cutting-edge analytic techniques that can predict individual customer sensitivities to price incentives, redemption terms and even promotional package design. For instance, one FICO retail client that helps to make markets for new products by spending heavily on promotion, is using uplift models to increase its return on this investment. The analytics provide insights that are enabling the retailer to accelerate the purchasing behavior of so-called laggers customers who historically haven t been among the first to purchase. By targeting these customers with offers that are likely to change their historical behavior, the retailer is increasing the concentration of sales in the first two months of the product lifecycle its critical period before competitors can draft off of their momentum. Given shrinking product lifecycles, pushing sales forward in this way is becoming ever more critical to this retailer s success. Maximizing and Measuring the Value of Analytics in Operations As retailers add better analytics, they increase the number of decision points for differentiating between customers and making more targeted decisions. But just as having lots of data can be overwhelming and of little value in and of itself, so it is with the analytic predictions drawn from this data. Their business value depends on the retailer s ability to operationalize them. On the other hand, when uplift modeling indicates a customer s behavior is likely to be affected by a promotion, it can also help retailers determine which promotion will have the most impact. The challenge is to bring all of the relevant analytic insights together into day-to-day decisions. To do that, retailers need a powerful business rules management system (BRMS) and optimization engine. Best-in-class systems incorporating these technologies can take the analytic output of thousands of models and deploy them in decisions across millions of customers. A BRMS is a fundamental capability since customer behavioral predictions are usually linked with actions through business rules. For example: If a customer has a high propensity for purchasing new kitchen cabinets in the next 90 days, and is 20% more likely to act within the next 30 days if they receive Coupon A, then include them in this ing. Transforming Big Data into Big Marketing l 11

12 Related rules like these make up a decision strategy, that will generally be tested on a small population segment, and the results analyzed. Retailers can make this process faster and more efficient by using technology solutions that allow models to be deployed directly (i.e., without any kind of recoding) into the BRMS powering operational decisions. Retailers can further compress test-and-learn cycles accelerating performance gains by using experimental design. Also called multivariate testing, this is a methodology with which large numbers of decision strategies are tested simultaneously on smaller population subsets. Because this approach enables testers to infer what the results would have been on untested populations, it yields more learning from fewer tests. Another way to speed up operational improvements is through decision modeling, depicted in Figure 5. Every decision, even one based on a single If, then business rule, could be described as a model, since it is a representation of how a decision is being made. But when large numbers of analytic predictions are used to differentiate and treat customers individually, the number of rules can explode. The decision process can become difficult to manage and even to fully comprehend. A decision model simplifies such complex decisions by mathematically mapping the relationships between all the factors and outputting an actionable result, such as a recommended customer treatment. Moreover, explicit modeling of customer reactions to a range of retailer actions (often called action-effect modeling ) clarifies complex decisions and exposes key performance drivers. Consider our previous example of a campaign aimed at accelerating purchases by customers with a high propensity to buy kitchen cabinets. An action-effect decision model could determine on a customer-by-customer basis what the net impact would likely be on revenue, costs and profit. Such a decision model would almost certainly be used with an optimization engine to identify the best treatment for each customer given the real-world constraints (mailing volumes, store locations, program spend limits, etc.) of the retailer and its suppliers. For example, the home improvement retailer, whose experience with time-to-event models was described above, optimized its decision strategy to maximize incremental margin per customer transaction given specified volume constraints. 12 l Transforming Big Data into Big Marketing

13 In this way, retailers can execute portfolio-level business strategies with precision at the level of individual customers. They can find the sweet spot where what the customer wants and what the business and its partners want intersect. It s the realization of the very definition of successful marketing Find a need and fill it. Success, of course, must be measurable, and thus retailers need systematic testing practices and rigorous measurement. They must able to determine how much of a result is due to analytics as opposed to other decision elements and operational factors. For example, a retailer might come to the erroneous conclusion that a discount coupon program produced inadequate ROI because of failing to properly control for selection biases in the coupon targeting. Causal modeling/matching techniques can be used to eliminate or mitigate such biases. These techniques can also empirically tease apart operational outcomes, so that the impact of the analytics on the coupon campaign can be isolated and accurately measured. Choose the right analytics technique for your business goals Analytics Type Works By Best Used For Benefits Collaborative Filtering Inferring customer behavior based on similarities (e.g., People who bought A also bought B ) Treating first-time customers Enables limited differentiation when no historical behavioral data on a customer is available Clustering Algorithms Grouping customers based on similar historical behavior patterns Creating broad customer segments for promotional programs and strategic planning Lifts response rates (generally from the traditional 1-2% to 5-6%) over traditional query-based segmentation Performs data-driven customer differentiation at a large enough scale to guide/justify business investments in store design, merchandising, etc. Regression Models Identifying customer attributes predictive of future behaviors Predicting individual customer behavior for 1-to-1 marketing and other personalized treatment Is generally up to twice as effective as collaborative filtering for targeting known customers Increases response rates (often doubling performance) and boosts conversion rates over clustering algorithms Time-to-Event Models Predicting when a specific customer behavior is likely to occur Timing offers for when a customer is most likely to buy; accelerating customer behavior Lifts performance by as much as 50% over regression models (2% lift in response rates not uncommon) Uplift Models Decision Models Predicting how much a particular action by a retailer is likely to change a forecasted customer behavior Mapping the mathematical relationships between numerous predictive model outputs and other decision elements, including a range of possible retailer actions and customer reactions Determining whether or not a particular action will be worth the expense Managing and improving complex decisions; capturing key results drivers, including constraints, for use with an optimization engine Lifts performance by as much as 50% over other analytic techniques Saves millions by enabling retailers to avoid offering incentives for products customers would buy anyway Pinpoints the best offer, given all retailer/supplier objectives and constraints, for each customer Transforming Big Data into Big Marketing l 13

14 FICO Retail Solutions FICO Customer Dialogue Manager FICO Customer Dialogue Manager is a multichannel marketing platform that enables marketers to design, execute and manage precisely timed and targeted campaigns that engage customers across all channels based on their known interactions and preferences. By integrating and harnessing enormous volumes of customer data from every channel, it provides a basis for gaining sharp insights into customer interactions and behaviors. Marketers can apply those insights to generate more individualized, interactive dialogue with customers in a coordinated fashion across channels, including social media. Through dialogue, you learn more about what the customer wants, which leads to increased sales and marketing ROI. FICO Analytic Offer Manager FICO Analytic Offer Manager combines proven predictive analytics and optimization capabilities to deliver highly personalized, relevant offers to customers right when they are most likely to act on them. Going beyond simply making recommendations, the solution enables precisely targeted offers based on observed propensities for specified time frames. It combines these customer-level predictions with retailer business objectives and constraints to pinpoint the optimal decisions for achieving an overall goal, such as maximizing redemption, revenue or margin. FICO Analytic Offer Manager can integrate with FICO Customer Dialogue Manager to enable marketers with millions of customers and potentially thousands of offers to deliver relevant and timely offers to individual customers across all channels digital, social, mobile or traditional. Conclusion Retailers of all sizes are bringing analytics into their operations. The key to making choices and investments that deliver on your expectations is to understand what various types of analytics do and how they fit (or don t fit) what you re trying to accomplish. It s also important to think about analytics as an incremental process rather than a packaged solution. No one approach serves all purposes. Wherever you are in the spectrum of analytic benefits, there s a next step you can take for the next level of benefit. Find out more about what type of analytics you need most: Watch the video Achieve Growth Through Smarter Retail Decisions Download Insights paper #32 Top Retailers Compete With True 1-to-1 Marketing m FICO Marketing Accelerator Service 14 l Transforming Big Data into Big Marketing FICO Marketing Accelerator Service delivers a clear path for improving customer-centric marketing success, helping marketers get from where they are today to where they need to be tomorrow. FICO marketing specialist assess current strengths and capabilities, identify opportunities for improvement and enhancement, and lay out steps to move to a customer-centric marketing model. The service emphasizes the use of data in customer identification, decision making and advanced analytic solutions. FICO Marketing Accelerator Service is flexible and customizable, addressing the technology, analytic or strategic capabilities needed to achieve goals and solve specific business challenges.

15 Source: FICO A Marketing Success Story Client The Coca-Cola Company Challenge Use interactive technology to reward loyal consumers of Coca-Cola brand soft drinks and connect them to the company in new ways. Solution Pin based loyalty program run on the FICO Precision Marketing Manager Platform Results To date, 11 million+ registered members have entered 600 million+ PIN Codes; due to the program s success, Coke was recently named to the CIO 100 list. Read how Coca-Cola was able to boost sales and site traffic while breaking ground in customer loyalty. m Transforming Big Data into Big Marketing l 15

16 About FICO FICO (NYSE:FICO) delivers superior predictive analytics solutions that drive smarter decisions. The company s groundbreaking use of mathematics to predict consumer behavior has transformed entire industries and revolutionized the way risk is managed and products are marketed. FICO s innovative solutions include the FICO Score the standard measure of consumer credit risk in the United States along with industryleading solutions for managing credit accounts, identifying and minimizing the impact of fraud, and customizing consumer offers with pinpoint accuracy. Most of the world s top banks, as well as leading insurers, retailers, pharmaceutical companies and government agencies rely on FICO solutions to accelerate growth, control risk, boost profits and meet regulatory and competitive demands. Building a world class experience is an evolution, not a revolution, and FICO analytic tools and applications deliver ROI at every stage of maturity. Learn more at FICO: Make every decision count. Big Data analytics will revolutionize how products are developed and distributed to how organizations communicate with customers. Advances in technology have created challenges, but also opportunities to increase sales and profit. FICO is helping the world s largest marketing organizations succeed by finding actionable customer insights within massive amounts of data and making high-volume decisions more accurate, predictable and profitable at every turn. 16 l Transforming Big Data into Big Marketing

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