Smart moves

With business intelligence and predictive analytics at their fingertips, grocers are better prepared to serve shoppers.

The lingering recession has conditioned consumers to shop at supermarkets that deliver the most value. Industry observers say the only way to be successful and deliver value during each store visit is to understand shopper preferences and anticipate their demand—a practice that requires the use of sophisticated analytics.

“In times like these, retailers are looking for every opportunity to increase sales,” says Alexei Agratchev, co-founder and CEO of RetailNext, a San Jose, Calif.-based provider of in-store data collection and analysis technology. “But with price-sensitive shoppers everywhere we turn, retailers are adopting sophisticated analytics in a way they never have before.”

By using analytics to uncover customer-specific details, retailers are primed to deliver merchandise and services to shoppers on a localized level. However, the key to maintaining sales is to consistently tap this data to ensure they understand the products, and continuously deliver to most relevant categories and merchandise that are important to shoppers.

The good news is retailers have a long history of using business intelligence (BI) to improve their decision-making strategies. At the heart of business intelligence is a robust, reliable data warehouse, say observers.

Eden Prairie, Minn.-based Supervalu consolidates data into a single enterprise data warehouse from Teradata, based in Dayton, Ohio. A long-time partner of Teradata, Supervalu’s data warehouse provides business users with the information needed to quickly respond to market changes. Supervalu is so bullish on using transactional and customer-specific data, that it recently upgraded the capacity of its data warehouse, which features report templates as well as backup and recovery services.

While the capacity of data warehouses has exploded in recent years, grocers have no problem filling these repositories. In fact, they find themselves collecting more data than ever before—and volume will only increase.

“The amount of data that a retailer collects doubles every 18 months,” said Anthony Paoni, professor, Kellogg School of Management at Evanston, Ill.-based Northwestern University, at the recent SAP Retail Forum in Chicago. “Momentum continues to grow as more consumer data flows to retailer databases.”

Grocers have long had a wealth of data, “but limited time and limited technology budgets have made it difficult to turn data into information that can be acted upon,” says Diana McHenry, global sales manager for SAS, a Cary, N.C.-based software provider.

As grocers integrate robust reporting tools and artificial intelligence software to their data warehouses, they gain the tools needed to leverage this information and improve business decisions, say observers.

BI is slowly becoming a mission critical application for retailers across the board, especially as they add consumer-centric strategies to compete in a saturated marketplace. Among their top analysis priorities: to learn more about their customers’ shopping behaviors.

In fact, 74% of retailers planned to increase their consumer insights and data gathering initiatives last year, according to “Retail Horizons: Benchmarks for 2010, Forecasts for 2011,” a report from the NRF Foundation, the research and education arm of the National Retail Federation, based in Washington. This was a jump from 65% a year earlier.

When embarking on complex consumer-centric retailing strategies however, grocers are finding their existing BI tools are falling short, say observers.“Consumer-centricity requires grocers to interact with shoppers from their perspective,” says Bill Franks, Teradata’s chief analytics officer, global alliance programs. “They need to take into account how they market campaigns, what medium they use, how they merchandise stores, and how and where they interact with shoppers.”

However, companies that focus solely on historic data to answer these questions are finding it difficult to manage and merge this information with new data. It also is an inhibitor to retailers as they try staying abreast of current internal trends when making future decisions.

“When using traditional BI, data is often based on a two-day lag,” says Randy Evins, industry principal for food and drug, SAP Retail, based in Newtown Square, Pa. “Within this configuration, there is often information that cannot be used or that doesn’t correspond with efforts.”

Part of the reason retailers continue to struggle with historical data sources is because the technology was introduced in an era when organizational roles and data sources were not integrated, explains Paul Donovan, solutions manager, retail industry business unit, SAP Retail. “Grocers want to understand consumers better, but not many have integrated customer loyalty information with sales data,” he says.

As a result, historical data is often to blame for inaccurate, incomplete and even invalid views of shoppers’ current preferences.  “To become a more analytics-focused retailer, companies must bring in [current] information and use it to make projections,” Donovan says. Besides requiring deeper analytics, many companies realize they need speedier access to information. “The only way for analytics to work is to get information on a near real-time basis, says Donovan.

Predictive analytics

Near-real-time data also gives retailers the opportunity to move away from static reports, “and toward actual insights and recommendations,” says Patrick O’Reilly, president, Applied Predictive Technologies (APT), a technology provider and consulting firm based in Arlington, Va. “Clearly, retailers feel challenged as the volume of data increases, but this is where predictive analytics can help retailers make the most of that data as they go forward.”

Like existing BI tools, predictive analytics enable grocers to understand shopper preferences, market more targeted campaigns, and make better assortment decisions.
However, historic data does not support forward-thinking decisions.

“Just like a car, you need a rear view mirror to see what’s behind you, but more importantly, you need a good windshield,” Paoni said at the SAP Retail Forum. “Retailers need look ahead at how key performance indicators will shape future business decisions.”
More specifically, predictive analytics allows grocers to create more granular customer segments.

As a result, grocers can create precision-marketing programs, and perform market basket analysis that can optimize the in-store product mix, and even improve the way a bricks and mortar store is organized.

“Predictive analytics lets grocers peer into the future and find ways to better engage customers and meet their needs with a localized merchandise assortment, relevant promotions and personalized communications,” SAS’ McHenry says.

When applying predictive analytics to consumer-centric programs, grocers can understand the best areas to invest marketing and merchandising budgets to improve sales lift, as well as which specific stores will deliver the strongest return on investment.

“It is not just about growing sales, it is about using predictive data to best serve shoppers that are most loyal,” says APT’s O’Reilly. “As grocers adopt consumer-centric programs, they are eager to learn about potential sales growth and how to leverage the most relevant customer relationships to reach their goals.”

Using APT’s Test & Learn for Sites software, Lakeland, Fla.-based Publix expects to accurately and efficiently measure the impact of new capital allocation, merchandising, marketing, pricing and operations initiatives.

Predictive analytics also allows the chain to intelligently target the rollout of these initiatives across its more than 1,000 supermarkets spanning five states.

“At Publix, we are passionately focused on customer value,” says Publix spokeswoman Maria Brous. “The Test and Learn approach has helped us better understand which investments create the most value for our customers. Our goal is to efficiently roll out high-value initiatives that will appeal to them.”

Observers expect predictive analytics to improve pricing, replenishment and in-store assortments, as well as eliminate out-of-stocks.

Besides delivering a higher level of customer service, “predictive analytics can also increase gross margins between 1% and 3%, which can equate to millions of dollars a year,” SAP’s Donovan explains. 

A view from all angles

When pursuing business goals, such as growth and profitability, industry observers say supermarket chains must have a 360-degree view of consumer demand, product movement and replenishment. The only way to support this process is to extend analytical capabilities to supplier partners.

“When pursuing business goals, all trading partners must have full participation and work from the same vantage point,” says Peter Charness, president of Chicago-based Manthan Americas, a provider of retail analytics software.

The only way that this collaboration can be effective however, is to provide the same information that category managers and other internal associates use. “Category managers view daily results, but suppliers don’t often have that benefit,” he says. “The most successful retailers will be those that extend the same metrics to their trading partners so everyone stays on the same page.”

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