Early Consumer Insight Delivers Revenue Growth Opportunities for Retailers According to the December, 2010 Multi-Channel to Cross-Channel Retailing benchmark report, over half of leading retailers are increasing their focus on the collection and analysis of consumer insight at all channel interaction points including brick-and-mortar, online, and at the call center (51%). In fact, 25% of these same top retailers are using this data to identify and respond to optimal purchase behavior patterns across all customer segments. The collection and analysis of consumer data (from its raw form of analytical data to its polished form of predictive business intelligence) helps to increase consumer loyalty, improve the accuracy and effectiveness of cross-selling and up-selling, and sustain revenue growth sources. These benefits can be realized at all points in the cross-channel retail lifecycle. The purpose of this Analyst Insight is to assess the benefits of increased exposure to consumer analytics for increased sales organization-wide, both short term and long-term. Increased Consumer Insight a Top Retail Pressure One of the most fundamental challenges a retailer has is to grow revenue despite any economic climate, positive or negative. To accomplish this goal, 81% of retailers are relying on increased customer insight for new customer acquisition, and 75% are increasing efforts to derive additional value from existing customers (See the 2011 Aberdeen Business Review for more details). The challenge, however, is how to accomplish this task effectively. Aberdeen data shows that 62% of retailers are pressured to increase overall consumer insight quickly and accurately a key step toward growing and sustaining a customer base as mentioned above (Figure 1). Figure 1: Top Customer Insight-Related Pressures for Retailers Need to increase overall consumer insight Need to improve speed of access to relevant data Customers expect relevant information regardless of channel 36% 48% 62% May, 2011 Analyst Insight Aberdeen s Insights provide the analyst perspective of the research as drawn from an aggregated view of the research surveys, interviews, and data analysis. "We really needed to understand what made some customers make a booking, and what made other customers drop off the site [Our new analytics solution] allowed us to make quick decisions, based on what was and wasn t working on a merchandising level, allowing us to concentrate only on the products that yielded better conversions and margins. ~Manager of Search Marketing and Site Management, Anonymous Online Travel Retailer 0% 10% 20% 30% 40% 50% 60% 70% Percent of Respondents This document is the result of primary research performed by Aberdeen Group. Aberdeen Group's methodologies provide for objective fact-based research and represent the best analysis available at the time of publication. Unless otherwise noted, the entire contents of this publication are copyrighted by Aberdeen Group, Inc. and may not be reproduced, distributed, archived, or transmitted in any form or by any means without prior written consent by Aberdeen Group, Inc.
Page 2 Source: Aberdeen Group, May, 2011 When it comes to the need for data and insights in retail, consumer insights is the most critical data stream that shapes retail value chain planning and execution. The need for consumer data is enterprise-wide. However, the need for increased consumer insight is a cross-channel challenge for retailers. For example: From a digital retailing perspective, online and mobile commerce have given consumers increased amount of product information and eased access to competitive alternatives. Examples include smartphone-based barcode scanning capabilities as well as mobile search engine accessibility, allowing both new and existing customers to closely examine product price and details to make a more immediate and informed decision. Retailers are challenged to compete with this reality by offering a more personalized, direct digital retailing experience or miss an opportunity because a customer can easily find what they are looking for elsewhere. From an in-store perspective, the proliferation of retail categories in non-traditional retail formats (such as Wal-Mart s instore banking, optometry, and hair salon offerings) pressures these organizations to further scrutinize their customer base to match one established purchase pattern or trend analysis with that of a new or emerging one. From a call center perspective, the increased voice retail support provided to customers before, during, and after a sale yields important clues towards future purchasing patterns. A stated purchase or intent to purchase a new consumer electronics product, for example, may yield success in the cross-selling of extension cords, batteries, and other accessories. Retailers Focus on Cross-Channel Consumer Insight Utilization According to Aberdeen data, centralizing consumer data for coordinated cross channel offers, and using that centralized data to improve predictive customer management and operations, are two top strategic actions for retailers (both at 51%) [Figure 2]. In fact, when asked to be specific, retailers indicated three actions in particular that they are focusing this increased insight toward: 1) Improving customer service and support (69% are increasing focus on this step, compared to 31% who are not); 2) Devoting resources to understanding customer feedback (57% are increasing focus on this step, compared to 43% who are not); and 3) Focus on loyalty programs (52% are increasing focus on this step, compared to 48% who are not) "We have had our enterprisewide BI tool in place for eight or nine years. We use it for budgeting and planning within our finance group, and for category management analysis within our merchandising group. We are looking to replace the tool at this time due to customer demand. The users are unhappy with the tool; it did not have the full functionality that we desired." ~ Director IT, Major Travel Retailer, North America
Page 3 First, providing customer service and support is a crucial aspect to any retailers long term sales and loyalty strategy. Increased visibility into customer insights can help ease this process by highlighting previous successful (or unsuccessful) interactions, and related interventions that have resolve previous challenges. Customer service-related insights can also help retailers reach out to consumers based on previously indicated methods, easing the communication process necessary to resolve a particular situation. Enhanced customer insights can also help retailers better understand customer feedback. If a customer is unsatisfied with a particular product, service, or outreach, a retailer can use that consumer s demographic information for important clues to resolve that situation and prevent any future occurrences (i.e., sending coupons for diapers to consumers without children). Finally, loyalty programs offer retailers a way to encourage consumers with point-based redemption offers. These redemption offers can be tailored to match the particulars of consumer affinities across selling categories, such as apparel, electronics, and other specialty items. Given the proliferation of retail categories offered in non-traditional retail stores, it becomes particularly relevant to carefully offer reward products based on specified or analyzed previous buying trends, ensuring a more content customer. "We have defined our key customer groups (female shoppers) in great detail. We turned a new leaf by looking at our brand not from a merchandising, store or supply chain perspective but from a customer's vision of our brand. We now understand which customers to go back to with specific offers and can define our best customer." ~ Chief Operating Officer, Large Apparel and Fashion Retailer, Americas Figure 2: Top Customer Insight-Related Actions for Retailers Improve data/insights on the customer side 51% Centralize all customer data for coordinated offers 51% Identify areas of the business for obtaining rapid ROI 36% Improve customer engagement enterprise-wide 33% 0% 10% 20% 30% 40% 50% 60% Percent of Respondents Source: Aberdeen Group, May, 2011 Customer Purchasing Behavior Prediction and Analysis a Top Retail Operational Goal According to Aberdeen data, four-fifths (80%) of retailers surveyed are not using timely and accurate consumer insights to increase revenue and customer loyalty. However, more than half of these same organizations are seeking to change this situation by establishing better consumer insight Predictive Analytics Defined Aberdeen defines predictive analytics as data-based conclusions that takes into consideration historical and current information to make a forward-looking statement.
Page 4 collection and analysis procedures (51%). To that end, two key process capabilities have emerged as top strategies retailers are focusing on in the immediate future (Figure 3): Predict customer purchasing behavior (66% of retailers planning, 19% current) Real-time analysis based on segmentation, affinity, and preference (64% of retailers planning, 25% current) Predict Customer Purchasing Behavior Predicting customer purchasing behavior speaks to the very essence of increased cross-selling and up-selling for retailers, no matter the channel. If a retailer can understand what type of purchase a consumer is likely to make, they can not only tailor marketing efforts to ensure a timely purchase is made, but they can also offer similar companion products to increase order size at the same time. At the physical store, pre-purchase outreach is critical to this process as a retailer cannot provide a customized store experience for each and every customer. At the online and call center channel, however, customer purchasing behavior data can personalize the experience, making the purchase process direct, relevant and easy for consumers, and profitable for the retailer. Conclusions made from recent and past purchasing behavior are a key deliverable for effective consumer insights. Real-time Analysis Based on Segmentation, Affinity and Preference Sixty four percent (64%) of retailers surveyed are planning for real-time analysis based on segmentation, affinity and preference. These three characteristics form the basis for establishing solid consumer insight. Initial segmentation allows retailers to make conclusions as to likes and dislikes based on similar interests from others of a similar demographic. Similarly, affinities and preferences information helps to identify interests of different customer groups for different types of primary, complimentary, and promoted products. This information can help make product associations more relevant and direct. An affinity for a particular brand, for example, helps to identify likely short- and long-term product purchasing behavior intentions. When it comes to making the analysis of consumer data a real-time endeavor, retailers have some considerable work to do. For example, according to the February 2010 Retail E-Commerce Analytics benchmark report, 43% of leading retailers are tracking shopping cart abandonment rates at a daily level. The remaining are waiting even longer to monitor a metric that serves as a crucial determining factor for online sales. This is a surprising disappointment for e-commerce retailers, especially as the immediacy of e-commerce would seem to necessitate quick collection and analysis of data for a more personalized experience. "Detailed knowledge of how customers perceive our products, our services, our promotions, and the brands in all channels give us the most important facts to decide how to be closely personal with our customers." ~Director of Marketing, Large Specialty Retailers, North America "More focus on the customer gives her a more meaningful shopping experience with better customer service - so she will remember her experience and return. [We use] loyalty programs to drive repeat business." ~ Anonymous Brick and Mortar Craft Retailer
Page 5 Aberdeen s March, 2009 Cutting-Edge Customer Loyalty benchmark reveals that 37% of leading retailers are analyzing data from order value/basket sizes as a key promotional enabler for increased customer loyalty. This customer facing data helps retailers understand current purchasing trends, including how much is spent per transaction, and how many products are being included in that spend. Although this data does not provide a complete view of the customer (such as outreach preferences), it does help to model future trends, and adjust offerings to increase loyalty. Figure 3: Top Customer Insight-Related Capabilities for Retailers Predict customer purchasing behavior Real-time analysis of customer data based on segmentation Utilization of sales transaction data for predictive business operations plans Analyze customer data across all channels 19% 25% 27% 31% 66% 64% 63% 61% "It is critical to understand the impact of each touch-point, and its effectiveness, to enable marketing to deploy limited promotional dollars in the most effective manner." ~ CIO, Tier 1 Pharmaceutical Retailer, Europe Segment customers based on purchase behavior and affinity 29% 61% Integration of all sources of customer data in a centralized location 30% 58% Near real-time customer data capture at the point of service 34% 51% 0% 10% 20% 30% 40% 50% 60% 70% Percent of Respondents Plan to Use Currently Use Source: Aberdeen Group, May, 2011 Retailers Current on Data Collection, but Prediction Remains a Future Endeavor According to Aberdeen data, two technology enablers-based findings stand out among all others when it relates to increased customer visibility tools. The first is the top technology already in use: POS data capture application (57%). The second it the top planned implementation technology: predictive analytics (63%). Not surprisingly, this indicates that most retailers are already are using tools to gather customer data, but that few are actually using this data for predictive analytical insight into their customers and operations. Point-of-Service (POS) Data Capture Application Given the importance that the POS plays in the physical retail store, it is no wonder that retailers are turning to these devices for the collection of valuable customer insight. This insight not only helps to make the consumer experience more profitable from the perspective of immediate cross-selling
Page 6 and up-selling, it also helps to improve the accuracy of the transaction from a merchandising and marketing perspective, thus decreasing the necessary consumer interaction time, which increases long-term satisfaction rates. The POS is not the only customer interaction point of relevance for a retailer, but it is the system that a consumer must experience to complete a transaction in-store. Thus, it makes sense that retailers would look at POS as a key data collection point. Predictive Analytics It is not surprising to see predictive analytics tools as the number one planned enabler for retailers. Predictive analytics tools such as market basket analysis use existing data to make conclusions at both the micro and macro level about a customer s basket of transactions and future buying affinities. Predictive analytics can also detail a retailer s overall sales and operations-related strengths and weaknesses, accurate pairing of specific products for increased up-selling, and identification of specific consumers who are increasingly likely to purchase a product or service. These tools help retailers effectively craft their marketing, merchandising, and customer support strategies so they are not simply selling blindly, but rather personalizing the consumer experience for a more relevant, effective, and profitable experience at all consumer touch points. Predictive analytics solutions also serve as an important part of business intelligence (BI) solutions. BI solutions are designed to help retailers make conclusions about the state of their business using one or more sets of data. Predictive analytics can help form a good understanding of, for example, customer behavior, and then merge that data with other information for increased visibility. Supply chain data, for example, when merged with consumer analytics can help not only identify selling patterns, but accurately prepare for those sales patterns with increased inventory.
Page 7 Figure 4: Top Customer Insight-Related Enablers for Retailers Predictive Analytics Cross-channel enterprise relational database 16% 26% 55% 63% Centralized data warehouse Business Intelligence application for consumer insights 32% 43% 40% 51% POS data capture application 26% 57% 0% 10% 20% 30% 40% 50% 60% 70% Percent of Respondents Plan to Use Currently Use Source: Aberdeen Group, May, 2011
Page 8 Aberdeen Insights Online Commerce and the Complicated Role of Site Analytics According to Aberdeen data, retailers of all types (online, brick and mortar, call center) are looking for increased customer visibility, no matter what channel(s) they happen to sell on (51%). This visibility may come from historical purchase patterns, response to marketing campaigns, channel preferences and other related metrics. In the sphere of online commerce, however, several other important metrics may an important part of isolating and understanding consumer trends for future sales opportunities. These sources of data include: Behavioral analytics. Behavioral analytics capitalize on the navigational flow and other actions a consumer takes on a web site to monitor and improve online experience, and dynamically adjust content as necessary. This provides retailers with the benefit of achieving a forward-looking customer segmentation strategy that can adjust merchandising/content strategies based on customer behavior while shopping. According to Aberdeen data, Best-in-Class retailers are almost twice as likely as Industry Average respondents to utilize purchase behavior analytics to shape promotions, personalization, and site content. Web analytics. Web analytics provide retailers with information such as page views, click-through, time-on-site, and bounce rates. These metrics help sharpen Search Engine Optimization (SEO) and Search Engine Marketing (SEM) efforts, web site fitness, and overall promotional efforts. According to survey respondents, 23% of Best-in-Class retailers have assigned dedicated personnel to managing web analytics (compared to 20% of Industry Average, and 15% of Laggard respondents). At first glance, these sources of data would seem to provide a more complete customer profile. This is supported by the fact that Best-in- Class retailers are more likely to use both of these metrics for increased margins and customer satisfaction rates (See February 2010 Retail E- Commerce Analytics benchmark report). Upon further consideration, however, it should be remembered that the maturity curve of predictive analytics is still at its low point (See Enablers section). Both Web and Behavioral analytics provide a very narrow view of customer activity, and confine their consumer analysis to online activities only. Retailers would be wise to take a phased, nuanced approach to the complexities of online behavioral and Web behavior, so as to not overcomplicate their overall customer insight strategy.
Page 9 Conclusion: Retailers Place Enhanced Focus on Customer Analytics for Sales Enablement Few retailers would argue that a difficult economic recovery requires new and creative ways of reaching customers to offer products and services. Most of these creative ways depend on a closer, more intimate understanding of consumer activity at all touch points to personalize the shopping interaction. This is for the benefit of the retailer in the form of increased cross-sells, up-sells and consumer loyalty. It is also for the benefit of the customer in the form of a more direct, informed, and relevant experience to decrease time need for product searches and overall interaction steps. In order to realize these benefits, however, retailers must rely on consumer insights to help guide this personalized selling experience goal into fruition. This can start with data collection processes at, for example, the POS, continue into a predictive analytical model, and end with increased business intelligence for a dynamic, macro and micro view of customer and business operations. In a challenging economy, such insight can be a competitive differentiator for more satisfied and profitable existing and new customer base. Recommendations for Action Aberdeen data suggests retailers take the following steps toward achieving optimal consumer insight and predictive analytics strategies: Increase organization-wide pervasiveness of online analytics practices to achieve greater customer visibility. According to the February 2010 Retail E-Commerce Analytics benchmark report, a clear correlation exists between Best-in-Class results and increased use of analytics processes and technologies. Despite this, adoption percentages remain low in capabilities such as the identification of analytics to outline performance gaps, analytics training requirements, and tracking of conversion rates. While these lower figures can be attributed to the relative infancy of online analytics (with the exception of marketing), they must be given more attention company-wide. Given that performance, process, and organizational improvements are multi-faceted and multidepartmental, retailers would be wise to extend accessibility of online metrics organization-wide. Interestingly enough, the fact that more than two-thirds of leading retailers (83%) are embracing performance dashboards either currently or within the next 12 months (which translate analytical activity into actionable information for those not involved in the nuances of day-to-day e- commerce management), may mean that this suggestion may have already resonated for top performers.
Page 10 Make customer analytics an enterprise-wide affair for more accurate and relevant decision-making. According to the November, 2010 Cross Channel Retail benchmark report, the most popular part of a retailer s organization to receive cross-channel insights is the marketing department (75%). Given the critical role customer analytics play in promotions and other marketing activities, this makes sense. However, other departments would be wise to take advantage of these analytics data sources as well. Customer service departments, for example, can benefit from this data as well to personalize their messaging. Similarly, store operations managers can also use customer analytics to personalize certain parts of the in-store experience, such as at the POS and at other interactive kiosks. Maintain the relationship between business intelligence and customer analytics to increase operational visibility. According to Aberdeen s April 2010 Pervasive Business Intelligence benchmark, just 44% of Best-in-Class retailers are using BI solutions to predict customer purchasing behavior. To maximize the ROI of an implementation, retailers should be able to trace the need for increased customer purchase behavior data accessibility to a retailer's number one reason for existence: selling a product and increasing revenue. From a customer data management standpoint, this may seem obvious. Motorcycle Superstore, for example, is a retailer that used an advanced analytics solution to increase marketing effectiveness and overall sales online. From a more indirect business data perspective, however, increased purchase behavior data is also traceable. Case in point: drug store retailer Walgreens, which recently implemented a BI system to help HR executives manage store-level talent, all in the name of selling more effectively and increasing customer retention. For more information on this or other research topics, please visit www.aberdeen.com.
Page 11 The Customer Connected Store: 2011 Store Automation Best Practices; February 2011 Multi-Channel to Cross-Channel; December, 2010 Related Research State of Customer-Centric Retail; May 2010 Pervasive Business Intelligence; April 2010 Re-Stocking The Marketer's Digital Toolbox, June 2010 Retail E-Commerce Analytics; February 2010 Author: Greg Belkin, Research Analyst, Retail and Banking Practice (greg.belkin@aberdeen.com) Sahir Anand, VP and Principle Analyst, Retail and Banking Practice (sahir.anand@aberdeen.com) Max Gladstone, Research Associate, Retail and Banking Practice (max.gladstone@aberdeen.com) For more than two decades, Aberdeen's research has been helping corporations worldwide become Best-in-Class. Having benchmarked the performance of more than 644,000 companies, Aberdeen is uniquely positioned to provide organizations with the facts that matter the facts that enable companies to get ahead and drive results. That's why our research is relied on by more than 2.5 million readers in over 40 countries, 90% of the Fortune 1,000, and 93% of the Technology 500. As a Harte-Hanks Company, Aberdeen s research provides insight and analysis to the Harte-Hanks community of local, regional, national and international marketing executives. Combined, we help our customers leverage the power of insight to deliver innovative multichannel marketing programs that drive business-changing results. For additional information, visit Aberdeen http://www.aberdeen.com or call (617) 854-5200, or to learn more about Harte-Hanks, call (800) 456-9748 or go to http://www.harte-hanks.com. This document is the result of primary research performed by Aberdeen Group. Aberdeen Group's methodologies provide for objective fact-based research and represent the best analysis available at the time of publication. Unless otherwise noted, the entire contents of this publication are copyrighted by Aberdeen Group, Inc. and may not be reproduced, distributed, archived, or transmitted in any form or by any means without prior written consent by Aberdeen Group, Inc. (2011a)