1 Industry Point of View: The Future of Retail Analytics: Pervasive Customer Insights Retail is a data-intensive industry, and has always been so. Retailers serve millions of customers via multiple formats and channels, move hundreds of thousands of items, stock thousands of SKUs in hundreds of stores, in pursuit of that one goal a longterm and profitable relationship with the customer.
2 EKN 2 The purpose of business analytics is quite focused to improve business decisions and enable actions throughout the enterprise with the power of timely and contextual insight. The strategic use of business analytics is more complex, and dependent entirely on the questions being asked. Operational efficiency was management s mantra during the time when moving products efficiently and profitably through channels was a viable growth strategy. During this time questions revolved around improving forecasting techniques, moving product more efficiently through the supply chain, or rationalizing inventory. Gradually, large retailers began emulating each other in terms of maturity of business processes and investments in information systems. Then, in a flash, through the dynamic macroeconomic and consumer behavior changes of the last 5 years, operational efficiency went from being a route to differentiation and profitability to being table-stakes. In the new normal of retailing where the consumer is value-conscious, always on, mobile-enabled, socially active and channel-agnostic, retailers are finding it difficult to differentiate based on traditional factors such as price, promotions, location and assortment alone. The question is no longer how can you maximize the profitability of a consumer transaction; it is how can you maximize lifetime customer value. Ultimately, retailers will either pay lipservice to the idea of being customercentric, or institute the hard changes across organizational structure, business processes, skills and information systems that will make them truly so. Not only have the goals of business analytics changed dramatically, the volume, variety and velocity of data that a retailer needs to process through its business information systems have increased exponentially. The skills, organizational culture, information systems and tools that retailers require to be customer-centric are vastly different from those they have developed, honed, and near perfected through years of refinement. The battlefield has changed so dramatically and so quickly that most retailers just do not have the culture, processes and tools optimized for this new world order. Retailers are accustomed to considering analytics as a tool. A lever they pull to receive a defined informational outcome. Analytics isn t a shared responsibility; it is the domain of a select few with acrobatic statistical skills. And, too often, retailers analytics practices concern themselves more with generating intelligence than influencing action.
3 3 The Future of Retail Analytics: Pervasive Customer Insights Customer Insights: The Center of the Omni-Channel Retailer s Universe The future of retail analytics calls for customer insights to be an enterprise wide strategic capability embedded deep within the organization s structure and culture, enabled by integrated business processes and information systems. A state where contextual customer insight forms a key input to the retailers decision-making processes across all business functions. Knowing the customer better than competition and having the ability to orchestrate business decisions at the speed of insight is the new retail competitive battlefield. In EKN s view, the most critical capabilities retailers need in order to be truly-customer centric, are all driven by business analytics. Omni-Channel Integration Retailers can not orchestrate a seamless customer experience across channels without relevant customer insights powering their interactions with customers. All the touch points at which retailers and customers interact are also retailers best bets as far as capturing rich customer data is concerned. Personalized Engagement Brick and mortar retailers have two things that online retailers don t - personal access to customers and a richer history and variety of customer data. How they combine these two - timely delivery of customer insights at the point of action - will be basis of the personalized shopping experience everyone talks about. Continued Operational Excellence The application of customer insights is not limited to customer-facing use cases. In fact, given that retailers are already more mature at leveraging analytics across their operational functions, the integration of customer insights is an incremental opportunity not to be missed.
4 EKN 4 Customer Insights Across the Enterprise: Use Cases and Retailer Case Studies Merchandising DASHBOARD Data Growth Analytics Maturity Strategy Alignment Technology Spending High. One of the top 3 business functions where retailers are experiencing the most data growth and correspondingly see the biggest opportunity for doing their first project. Relatively mature. One of top 3 business functions in terms of analytics maturity. 3 in 4 retailers use basic / predictive / investigative analytics. Rated highest in terms of leveraging analytics strategically. High. Ranked 2nd in terms of planned technology spending in analytics in USE CASE RETAILER EXAMPLES Swiss retailer Globus uses Big Data in-memory computing and advanced analytics to gain valuable insight into its merchandise performance. It has the capability to process vast amounts of product data in real-time, analyzing sales patterns and promotions for thousands of products across time frames, shops and regions within minutes. The retailer has also provided access to these insights to its managers, helping them respond quicker to market conditions. American retailer Guess uses advanced analytics to provide a real-time view of best-selling products and available inventory to its executives. Its analytics solutions run on large customer data sets to analyze merchandise sales, create customer segments and develop sales promotions. Marketing DASHBOARD Data Growth Analytics Maturity Strategy Alignment Technology Spending High. One of top 3 business functions where retailers are experiencing the most data growth and correspondingly see the biggest opportunity for doing their first project. Relatively mature. One of top 3 functions in terms of analytics maturity with more than 60% of the retailers using basic / predictive / investigative analytics. Rated as one of the top 3 functional areas in terms of leveraging analytics strategically. High. Ranked 3rd in terms of planned technology spending in analytics in USE CASE RETAILER EXAMPLES Wal-Mart s Global.com unit leverages big, fast data and social analytics to quickly identify evolving customer tastes. Its Social Sense project identifies the popularity of items through social media, helping buyers identify underserved demands and new product interests. Its Shoppycat tool recommends suitable products to Facebook users based on the hobbies and interests of their friends. It uses its Social Genome technology, among others, to help customers find presents for their friends. Target s predictive analytics program can deduce whether an individual shopper possesses characteristics that make them particularly good targets for a specific marketing effort. It assigns each shopper a unique code known as the Guest ID number - that ties demographics, shopping behavior and preferences into a track-able entity. It has a Guest Marketing Analytics department, responsible for delivering Target the competitive advantage of knowing its customers better than any other retailer. An Active Data Warehouse effectively manages complex user queries on large data volumes in a mixed workload environment across the entire enterprise.
5 5 The Future of Retail Analytics: Pervasive Customer Insights Omni-Channel DASHBOARD Data Growth Analytics Maturity Strategy Alignment Technology Spending High. One of top 3 business functions where retailers are experiencing the most data growth and correspondingly see the biggest opportunity for doing their first project. Relatively immature. More than 40% of the retailers either only do basic reporting or do not perform any analytics. Rated lowest in terms of leveraging analytics strategically. Highest planned technology spending in analytics across any retail business function in USE CASE RETAILER EXAMPLES UK-based retailer Burberry integrated all its channels - from stores, online, mobile to social networking sites. It uses innovative technology and data analytics to help analyze data collected from all the sources to identify individual customers and profile them in real-time. The speed of analytics has increased by a factor of 14,000 - a request now takes one second where it previously took five hours. All store employees at any location can identify a customer, at almost the moment they walk in, know about their past purchases and are able to make personalized recommendations. South Korea-based retailer NS Shopping has integrated mobile and social channels into its retail environment and uses Big Data analytics to obtain a centralized, real-time view of customer and product data from across all channels. This information is used by the company s e-commerce and marketing teams to provide shoppers with personalized product recommendations. Supply Chain DASHBOARD Data Growth Analytics Maturity Strategy Alignment Technology Spending Low. Not likely to experience much data growth. Relatively mature. One of top 3 functions in terms of analytics maturity with more than 60% of the retailers using basic / predictive / investigative analytics. Rated among the top 3 retail business functions that leverage analytics most strategically. Low. Given the high level of analytics maturity, retailers do not plan make significant investment in analytics in USE CASE RETAILER EXAMPLES US-based online retailer Amazon.com built a new supply chain process and system on a non-stationary stochastic model. The approach supports fulfillment, sourcing, capacity, and inventory decisions. Amazon developed new algorithms for joint and coordinated replenishment. It also implemented a new national forecasting approach at the SKU level, based on historical demand, event history and plans, forecasts for each fulfillment center, inventory planning, procurement cycles, and purchase orders. UK-based retailer Tesco uses sophisticated modeling tools to simulate performance of its distribution depots based on historical sales data to optimize stock. It has an in-house analytics team that performs regression testing to understand correlation between factors such as weather data and special offers among others, and sales patterns.
6 EKN The Readiness Gulf. EKN s research reveals gaps in retailers analytics capabilities and maturity that are impediments in moving towards this future state. Decisions Data and Tools Have we clearly deﬁned the strategic decisions we need to take in order to succeed? Have we complicated them to the relevant people in the organization? Most important goal from analytics initatives in 2013 #1 Customer Insights #2 Operational Excellence Challenges that prevent retailers from leveraging analytics more strategically #2 Lack of clearly articulated analytics strategy Structure and Culture Don t have Customer Insights Oﬃcer 84% Employ Data Analytics 43% Integrate loyalty data into customer insights 58% Plan to implement a predictive analytics tool in the next 2 years 58% Have no plan to integrate public data 15% Currently use a Marketing Spend Optimization solution Insight Delivery Does our organizational stucture enable analytics being embedded across business functions? Do we have the right skills aligned appropriately with the expected outcomes? 81% How well integrated are our sources of customer data? Do we have the tools to analyze structured, unstructured and semi-structured data? 58% Want to move to a shared-service structure for analytics 72% Don t have C-level sponsor for Big Data initiatives Are we able to deliver contextual insights to the relevant person at the right time? Are our employees empowered to make decisions? Challenges that prevent retailers from leveraging analytics more strategically #1 Delivery of Insights to the right resource at the right time 58% Plan to implement Digital Dashboard for management in the next 2 years 3% Currently use a Mobile Business Intelligence solution 6
7 7 The Future of Retail Analytics: Pervasive Customer Insights From a business analytics perspective, a few key takeaways emerge from this data: Retailers acknowledge the importance of customer insight. However, their ability to excel at infusing customer insights into key decision making processes is limited by their low maturity of analytics across business functions, a lack of a clearly articulated analytics strategy and the inability to deliver insights in a contextual and timely manner. EKN recommends retailers define key decisions and problem statements at each business function, process and task level. Retailers should then identify the insights that can help them improve those decisions, the data that is needed to power those insights, and the business process and information systems change required to enable the data analysis. Retailers have always had to contend with large amounts of data, and with significant reduction in data storage costs the volume aspect of data is not one that daunts them. The two challenges retailers are faced with is how well they integrate the various sources of data into their customer analytics strategy and how well equipped are they to make sense from a greater variety of data (structured, unstructured, semi-structured). EKN recommends retailers focus their data integration efforts most aggressively on the core sources of customer data (transaction data POS, Omni-channel; loyalty and CRM data; syndicated consumer behavior data Nielsen, IRI). With a solid foundation they can then focus on expanding the data sources (social media, mobile, enterprise systems, machine logs) and their ability to process types of data, based on the specific needs of the decisions they most want to focus on. The one opportunity most retailers are missing out on is integrating freely available public data (e.g. Census data, weather data, data from web-scraping) meaningfully into their customer insights strategy. This is data that is typically rich in history, is good for trend analysis and the fact that it is readily available can help strengthen decision points. Decades of product-centric practices have made the cultural transition to customer centricity a difficult exercise. For analytics to drive customer-centric action, retailers need to re-orient their organizational structure to be truly focused on the customer as well. Whereas it is entirely possible to optimize individual business functions as part of a product-centric strategy (e.g. sourcing, distribution, marketing, merchandising), being customer centric requires all business functions to work in concert, and in that sense, for all to be integrated and optimized towards achieving stated customer engagement goals. EKN recommends retailers move away from an analytics organization structure model where resources sit within each department or are centralized in any one department (for e.g. in IT or Marketing). The true power of business analytics comes from the insights provided by tools and the intuition provided by the years of experience of retailers executives and employees. In EKN s view retailers should clearly identify the business users of insight and invest in tools that make it easier for them to consume and act on insight. These include visualization tools, digital dashboards and cloud based analytics self-service.
8 EKN 8 In EKN s recent Future of Analytics survey, retailers overwhelmingly rated their inability to deliver insights to the right person at the right time as their #1 analytics challenge. This is also a common takeaway from EKN s conversations with retail leaders. In the words of a senior retail leader at a large specialty retailer, We ve got the tools, or we ve got investments lined up in areas where we don t. We ve got a fair shot at getting the data integration piece working right. It ll take some doing but the charter is clear. Our biggest challenge is delivering the insight and empowering the recipient to take meaningful action. The unspoken truth is that when it comes to analytics software, it isn t the cost or the ROI that gives us pause. It is whether we believe we are ready. EKN sees this issue as symptomatic of the structure and business process integration related issues. However, retailers can take some short-term steps while the more medium and long-term transformations play out. EKN recommends extending current analytics initiatives via mobile and tablet-based delivery to employees. Retailers should also explore Software-as-a-Service based analytics solutions that allow them to expand analytics initiatives via a more efficient effort and cost model. Ultimately, retailers will either pay lip-service to the idea of being customer-centric, or institute the hard changes across organizational structure, business processes, skills and information systems that will make them truly so. Business analytics, with customer insights as its primary focus, embedded deep and wide across the enterprise will form the central nervous system of this retailer of the not-so-distant future.
9 9 The Future of Retail Analytics: Pervasive Customer Insights In Conversation EKN Research Director Gaurav Pant sat down with SAP s Senior Director Global Retail Russ Hill Jr. to talk about the need for retailers to build stronger customer analytics capabilities, and what SAP is observing from its vantage point of assisting retailers with some of their key analytics initiatives. Russ Hill Jr. Senior Director Global Retail, CP, Wholesale Distribution Industry Marketing Gaurav Pant Research Director EKN Gaurav: Even though retail is a data-rich industry, adoption of analytics has been slow. Are you seeing a shift in how retailers view analytics? Russ: Retailers have the advantage of years upon years of transactional data, but the analytics that has been done has been largely operational reporting. With the consumerization of technology, customers are engaging on multiple information networks - online, social and on their phones. This is creating a rapidly growing pool of diverse, emotional and valuable data. While customers have always had very specific needs and requirements, today they continually display these needs and preferences via their digital body language - the trail of data they leave behind in everything they do. With greater power in the hands of consumers comes greater expectation from the shopping experience their favorite retailers offer. The value-piece for retailers is tapping into this stream of customer data to elevate customer engagement across 3 distinct dimensions of the oft-used P-word more personal (combining insights with human intuition), more personalization (reorienting marketing and promotions to be focused on an individual customer) and more personality (ensuring customer insights lead to action consistent with the brand promise). Overall we are seeing retailers move from a traditional operational reporting mindset to a more rapid-response mindset where they want to use data to drive actions that can impact the business. No retailer wants to be data-rich but insight-poor. Gaurav: There is a lot of industry-speak on retailers needing to integrate customer insights across their business, but little detail on how they should do it. Can you share your perspective on how retailers should go about leveraging and integrating customer insights into their enterprise? Russ: Customer insights can mean different things to different people. From after-the-fact findings such as who shops with you and what they buy, to more predictive insights such as how they may behave when offered a specific coupon or promotion. Retailers analytics capabilities are in an infancy stage, primarily focused on reporting after-the-fact findings. Every retailer is chasing the single-view-of-the-customer silver-bullet, but getting there isn t only about technology integration. It s about breaking the data silos of the organization and enabling horizontal integration of customer data access across all functional areas within the enterprise.
10 EKN 10 Augmenting this integrated, enterprise-wide transactional data-pool with other data sources, such as social data and weather data, can help retailers develop a more precise manner by which to serve customers. For instance, predict how certain types of weather can drive demand for certain types of products or how social sentiment data can be a lead indicator of product performance. In a rapid-response model retailers can integrate these insights into promotions, marketing, merchandising and fulfillment and take meaningful actions that can help collapse the retail trading cycle. The fourth, and the most important inhibitor, is the lack of a holistic analytics strategy. The analytics strategy can t be built in a silo, and it can t just be a business intelligence strategy. The most effective retailers make it a point to involve all lines of business when creating the analytics strategy and direction. This helps everyone see the bigger picture and prioritize issues that impact the enterprise versus serving the needs of a department or individual. Gaurav: What do you think are the biggest inhibitors preventing retailers from leveraging analytics effectively? Russ: From an operational standpoint a few things stand out. The first is managing data and the attributes. With the new wave of unstructured data, retailers are almost paralyzed with data, and struggle on how to organize it and align it to the strategic direction of the company. Fundamental data management and governance issues still need to be addressed. The second is the lack of a unique customer ID that can help identify customers. This goes back to the point about creating a single view of the customer. The third is the gap in retailers business processes that stops them from responding quickly and precisely to an insight.
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