EKN: The Future of Retail Analytics - Pervasive Customer...

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 chan-nels, move hundreds of thousands of items, stock thousands of SKUs in hundreds of stores, in pursuit of that one goal – a long-term and profitable relationship with the customer.

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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 im-proving 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 in-vestments 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.

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 refine-ment. 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 informa-tional 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.

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.

The Future of Retail Analytics: Pervasive Customer Insights3

Customer Insights: The Center of the Omni-Channel Retailer’s UniverseThe 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.

In EKN’s view, the most critical capabilities retailers need in order to be truly-customer centric, are all driven by business analytics.

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.

Omni-Channel Integration

Retailers can not orchestrate a seamless customer experience across channels with-out 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 oppor-tunity not to be missed.

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Merchandising

DASHBOARD

USE CASE RETAILER EXAMPLES

Swiss retailer Globus uses Big Data in-memory computing and advanced analytics to gain valuable insight into its merchan-dise performance. It has the capability to process vast amounts of product data in real-time, analyzing sales patterns and pro-motions 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 seg-ments and develop sales promotions.

Marketing

DASHBOARD


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 inter-ests 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 num-ber - that ties demographics, shopping behavior and preferences into a track-able entity. It has a Guest Marketing Analytics de-partment, 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.

Customer Insights Across the Enterprise: Use Cases and Retailer Case Studies

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 lever-aging analytics strategically.

High. Ranked 2nd in terms of planned technology spending in analytics in 2013.


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 / pre-dictive / investigative analytics.

Rated as one of the top 3 func-tional areas in terms of leverag-ing analytics strategically.

High. Ranked 3rd in terms of planned technology spending in analytics in 2013.


Omni-Channel

DASHBOARD


UK-based retailer Burberry integrated all its channels - from stores, online, mobile to social networking sites. It uses innova-tive 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 previ-ously 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


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 his-torical 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.


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 lever-aging analytics strategically.

Highest planned technology spending in analytics across any retail business function in 2013.


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 / pre-dictive / 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 invest-ment in analytics in 2013.

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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

Structure and Culture Insight Delivery

Integrate loyalty datainto customer insights

43%

Currently use a MarketingSpend Optimization

solution

15%

Have no plan tointegrate public data

58%

Plan to implement apredictive analytics tool

in the next 2 years

58%

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

Have we clearly defined the strategic decisions weneed to take in order to succeed? Have we

complicated them to the relevant people inthe organization?

How well integrated are our sources of customerdata? Do we have the tools to analyze

structured, unstructured and semi-structured data?

Does our organizational stucture enableanalytics being embedded across business functions?

Do we have the right skills aligned appropriatelywith the expected outcomes?

Are we able to deliver contextual insights to therelevant person at the right time? Are our

employees empowered to make decisions?

Don’t have CustomerInsights Officer

81%

Want to move to ashared-service structure

for analytics

58%

Employ Data Analytics

84%

Don’t have C-levelsponsor for Big Data

initiatives

72%

Challenges that prevent retailers from leveraginganalytics more strategically

#1 Delivery of Insights to the right resource at the right time

Plan to implement DigitalDashboard for manage-ment in the next 2 years

58%

Currently use a MobileBusiness Intelligence

solution

3%


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 im-prove 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; syndi-cated consumer behavior data – Nielsen, IRI). With a solid foundation they can then focus on ex-panding 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 dif-ficult exercise. For analytics to drive customer-centric action, retailers need to re-orient their or-ganizational 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, distribu-tion, 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 re-sources sit within each department or are centralized in any one department (for e.g. in IT or Mar-keting). 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 dash-boards and cloud based analytics self-service.

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• 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 is-sues. 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 ana-lytics 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.


Gaurav PantResearch DirectorEKN

Russ Hill Jr.Senior Director Global Retail, CP, Wholesale Distribution Industry Marketing

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.

Gaurav: Even though retail is a data-rich industry, adop-tion 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, custom-ers are engaging on multiple information networks - online, social and on their phones. This is creating a rapidly growing pool of diverse, emotional and valu-able data. While customers have always had very specific needs and requirements, today they contin-ually 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 re-tailers 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 per-sonalization (reorienting marketing and promotions to be focused on an individual customer) and more personality (ensuring customer insights lead to ac-tion consistent with the brand promise).

Overall we are seeing retailers move from a tradi-tional operational reporting mindset to a more rap-id-response mindset where they want to use data to drive actions that can impact the business. No retail-er 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. Retail-ers’ analytics capabilities are in an infancy stage, pri-marily focused on reporting after-the-fact findings.

Every retailer is chasing the single-view-of-the-cus-tomer 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 func-tional areas within the enterprise.

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Augmenting this integrated, enterprise-wide trans-actional 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 prod-ucts or how social sentiment data can be a lead in-dicator of product performance. In a rapid-response model retailers can integrate these insights into pro-motions, marketing, merchandising and fulfillment and take meaningful actions that can help collapse the retail trading cycle.

Gaurav: What do you think are the biggest inhibitors preventing retailers from leveraging analytics effective-ly?

Russ: From an operational standpoint a few things stand out. The first is managing data and the at-tributes. With the new wave of unstructured data, retailers are almost paralyzed with data, and strug-gle on how to organize it and align it to the strategic direction of the company. Fundamental data man-agement and governance issues still need to be ad-dressed. 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 cus-tomer. The third is the gap in retailers’ business pro-cesses that stops them from responding quickly and precisely to an insight.

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 re-tailers 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 prior-itize issues that impact the enterprise versus serving the needs of a department or individual.

About EKNOur research agenda is developed using inputs from the end user community and the end user community exten-sively reviews the research before it is published. This ensures that we inject a healthy dose of pragmatism into the research and recommendations. This includes input of what research topics to pursue, incorporating heavy practi-tioner input – via interviews etc., and ensuring that the bend of research takeaways are oriented towards a real-world, practical application of insights with community sign-off. For more information, visit www.eknresearch.com. Email us at [email protected]

About SAPHeadquartered in Walldorf, Germany, SAP is the market leader in enterprise applications software in terms of soft-ware and software-related services revenue. Founded in 1972, SAP has a rich history of innovation and growth as an industry leader. SAP applications and services enable more than 197,000 customers worldwide in more than 120 countries to operate profitably, adapt continuously, and grow sustainably. With annual revenue (IFRS) of €14.2 bil-lion, SAP has more than 55,500 employees located in more than 130 countries worldwide. SAP is listed on several exchanges, including the Frankfurt stock exchange and NYSE, under the symbol “SAP.”

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