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Accenture

Analytics - the extensive use of statistical and quantitative analysis, explanatory and predictive modelsand fact-based management to drive decisions and actions - is separating America''s major retailersfrom the rest of the pack.

For retailers, the competitive terrain is increasingly hostile. Geographic barriers to competition have eroded thanks to the Internet. Proprietary technologies are not proprietary for long, and breakthrough innovations in products or services are increasingly simple to imitate. The perfect storm of the financial markets’ unrelenting demand for results and consumers’ declining willingness to remain blindly loyal only serves to exacerbate retailers’ challenges.

So how can retailers fight back? The answer is: dynamic, robust insights into both their consumer base and supply chain that drive smarter decision making and more efficient execution. The key to this approach can be found in the sophisticated use of analytics – that is, the extensive use of data, statistical and quantitative analysis, explanatory and predictive models and fact-based management to drive decisions and actions.

As part of Accenture’s research into highperformance businesses, we’ve found that a growing number of forward-thinking retailers have already recognized the power of analytics and are building their customer strategies around data-driven insights. By using analytics to make better decisions and to extract maximum value from their business processes, retailers such as Tesco, Best Buy, JCPenney and Amazon are able to identify their most profitable customers, accelerate product innovation, optimize supply chains and pricing, and identify the true drivers of financial performance.

What truly distinguishes these leaders is the quality of the analytical insights into their business that then drives smarter strategy decisions and execution actions. Their competitive advantage is their analytical capability.

Analytics and High Performance

Accenture’s high-performance business research has found a powerful link between organizations with pronounced analytical orientations and market outperformance (see Figure 1). High performers are much more likely to value fact-based decision making and to have the skills and capabilities in place to effectively use analytics across their organization.

Retailers have used statistical data analysis for years but in most cases, analytics were simply a means to an end rather than a systematic approach to achieving a competitive advantage. As a result, while many companies can point to the realization of analytical benefits, most have obtained only a fraction of the potential benefits and little in the way of competitive differentiation. However, as retailers become more sophisticated in their use of analytics, they are shifting tactics. Instead of focusing on traditional metrics (such as sales per square foot, per department and so on) and routine customer segmentation, they are taking a much more integrated approach to merchandizing. A few leaders have recognized that underpinning their “customer-centric strategy” is a burgeoning analytical capability.

What separates retailers that merely use data and analysis from true analytical competitors – those that can nimbly outmaneuver rivals at every turn? Analytical competitors achieve large-scale results by:

• Focusing analytics on leveraging their distinctive capability – the sum of the integrated business processes and capabilities that allow a company to serve customers in differentiated ways and that create an organization’s formula for business success;
• Having a senior leadership committed to analytical competition and to building their organization’s analytical capability; and
• Strategically managing and applying analytics across the entire enterprise.

While the retail industry as a whole is making progress toward the creation of robust analytical capabilities in the service of strategic capabilities, relatively few retailers are truly analytical competitors. These retailers have discovered methods to apply analytics to their customer relationship management and supply chain management processes, deriving exemplary benefits from their efforts.

Analytics’ Growing Role in CRM and SCM

Analytics took a great leap forward when companies began using them to improve their external processes – those related to managing and responding to customer demand and supplier relationships. Once kept strictly segregated, the boundaries between customer relationship management (CRM) processes such as sales and marketing and supply chain management (SCM) processes such as procurement and logistics have been broken down by organizations seeking to align supply and demand more accurately. Many leading retailers are enhancing their CRM and SCM capabilities with predictive analytics, and they are enjoying market-leading growth and performance as a result.

At first glance, CRM and SCM would seem to have little in common. CRM seems less amenable to analytical intervention – at least, that might be the common perception. The traditional focus in sales has been on the personal skills of salespeople – their ability to form long-term relationships and to put skeptical potential customers at ease. And marketing has long been viewed as a creative function whose challenge has been to understand customer behavior and convert that insight into inducements that will increase sales. SCM seems like a natural fit for an analytical focus. Supply-chain-reliant firms have long relied on sophisticated mathematical models to forecast demand, manage inventory and optimize manufacturing processes.

Analytical competitors take the use of analytics much further than most companies. In many cases, they are pushing not only data but also the results of analyses to their customers. As companies integrate data on products, customers and prices, they find new opportunities that arise by aligning and integrating the activities of supply and demand. Instead of conducting post hoc analyses that allow them to correct future actions, they generate and analyze process data in near-real time and adjust their processes dynamically.

Customer Relationship Management Processes

Companies today face a critical need for robust CRM processes. For one thing, acquiring and retaining customers is getting more expensive, especially in an age where consumers are becoming savvier about their options every day. For another, consumers are harder to satisfy and more demanding.[1] To compete successfully in this environment, analytical competitors are pursuing a range of tactics that enable them to attract and retain customers more effectively, engage in “dynamic pricing” and translate customer interactions into sales.

Attracting and Retaining Customers
There are, of course, a variety of ways to attract and retain customers, and analytics can support most of them. One traditional means of attracting customers has been advertising. This industry has already been, and will continue to be, transformed by analytics.

One of the most impressive users of analytics to retain customers is Tesco. Tesco’s analytics-based success began in 1995 when it introduced its loyalty card, the “Clubcard.” The card functions as a mechanism for collecting information on customers, rewarding customers for shopping at Tesco and targeting coupon variations for maximum return.

Tesco uses the data it collects on purchases to group customers according to lifestyle. For example, a female shopper who makes weekly purchases, buys what’s on sale and uses coupons sent in the mail, is considered a value-conscious customer. Tesco says that it issues 7 million targeted variations of product coupons a year, driving the coupon redemption rate, customer loyalty and ultimately financial performance to market-leading heights. The results are impressive. While the direct marketing industry’s average response is only 2 percent, Tesco’s coupon redemption rate is 20 percent, and ranges as high as 50 percent.

Pricing Optimization
Pricing is another task that is particularly susceptible to analytical manipulation. Many retailers today are adopting analytical software as part of “scientific retailing.” Retailers usually begin by using pricing analytics to optimize markdowns – figuring out when and by how much to lower prices. Some then move on to pricing for all retail merchandise and to analysis of promotions, category mix and breadth and depth of assortments. Most retailers experience a 5 to 10 percent increase in gross margin as a result of using price optimization systems.

JCPenney was one of the earliest large retailers to adopt price optimization software and processes. A few years ago, the company began an analytically intensive program that integrated merchandising, pricing optimization and the supply chain. This approach helped the company add five points of gross margin, increase inventory turns by 10 percent and grow top-line and comparative store sales for four consecutive years (2001 through 2004). Operating profits also grew at double-digit rates.[2]

Converting Customer Interactions Into Sales
It is also possible to use analytics to improve the face-to-face encounters between customers and salespeople. Best Buy, for example, is acting on knowledge gained through customer interactions to improve those interactions (and, not incidentally, to boost sales). Over the last five years, the company has collected data on 60 million U.S. households. To maximize financial performance in each retail store, Best Buy used data-driven insights to develop profiles of eight customer segments.

To translate their insights into increased sales and market share, Best Buy needed to understand the best way to serve each segment. It began by establishing a few stores as laboratories. The company used analytics to determine, for example, the impact of pricing changes on customer perception and sales.

Incorporating the insights from data analysis and testing at the lab stores, Best Buy developed new store formats for each segment. A “Barry” store, for example, is targeted to young, male audiophiles and videophiles and contains a home theater store within a store.

While exact figures are not available, “customer-centricity” based on analytics has delivered significant business results to Best Buy – the new stores formatted around specific customer segments are generating sales at twice the rate of Best Buy’s traditional format.[3]

Supply Chain Management Processes

Contemporary supply chain processes blur the line between customer-oriented and supplieroriented processes. At several retailer market leaders, analytics penetrate deep into and across an organization, reaching all the way to suppliers.

Connecting Customers and Suppliers
We took an informal poll of consumers to find out which retailer is top of mind when it comes to being synonymous with both its consumers and its supply chain. No surprise – Wal-Mart was most frequently cited. Since their story has been covered so much, we offer a cursory look in its direction, followed by a more detailed review of Amazon.

Wal-Mart collects massive amounts of sales and inventory data into a single integrated technology platform. Its managers routinely analyze manifold aspects of its supply chain, and store managers use analytical tools to optimize product assortment; they examine not only detailed sales data but also qualitative factors such as the opportunity to tailor assortments to local community needs. This data is then made available to its suppliers. Wal-Mart buys products from more than 17,400 suppliers in 80 countries, and each one uses the company’s Retail Link system to track the movement of their products – in fact, the system’s use is mandatory. In aggregate, suppliers run 21 million queries on the data warehouse every year, covering such data as daily sales, shipments, purchase orders, invoices and returns.

Amazon’s business model, in contrast, requires the company to manage a constant flow of new products, suppliers, customers and promotions, as well as deliver orders directly to its customers by promised dates. To determine the optimal sourcing strategy (determining right mix of joint replenishment, coordinated replenishment and single sourcing) as well as manage all the logistics to get a product from manufacturer to customer, Amazon applies advanced optimization and supply chain management methodologies and techniques across their fulfillment, capacity expansion, inventory management, procurement and logistics functions.

Amazon sells over 35 different categories of goods, from books to groceries to industrial and scientific tools. The company has a variety of fulfillment centers for different goods. When Amazon launches a new goods category, it uses analytics to plan the supply chain for that good and to leverage the company’s existing systems and processes. To do so, it forecasts demand and capacity at the national level and fulfillment center level for each SKU. Its supply chain analysts try to optimize order quantities to satisfy constraints and minimize holding, shipping and stockout costs. In order to optimize its consumer goods supply chain, for example, it used an “integral min-cost flow problem with side constraints”; to round off fractional shipments, it used a “multiple knapsack problem using the greedy algorithm.”

Conclusion

While it’s still early in the game, these and other leading retailers are rapidly benefiting from their moves up the analytics experience curve. Those that are lower on the curve should prepare themselves to leverage this emergent source of competitive differentiation.

To learn more about the impact of analytics on retailing, see Competing on Analytics: The New Science of Winning by Thomas H. Davenport and Jeanne G. Harris, from Harvard Business School Press.

Endnotes

1. American Customer Satisfaction Index, University of Michigan, 2005 fourth quarter rankings, updated February 21, 2006 (http://www.theacsi.org/fourth_ quarter.htm).
2. Scott Friend, “Changing the Game: New Strategies for Merchandising Innovation” presentation at the National Retail Foundation conference, January 16, 2005, http:// www.nrf.com/Attachments.asp?id=6991 which cites Retail Info Systems News, October 2004.
3. Matthew Boyle, “Best Buy’s Giant Gamble,” Fortune, April 3, 2006.