Retailers and wholesalers have made staggering investments in technologies to enhance decision-support systems that create practical information from mountains of data. Now the time has come, many operators say, to analyze the return on investment.
How do retailers and wholesalers know when they have rounded the bend into profit, or even when a system's cost has been counterbalanced by its benefits? Even among those closest to the decision-support systems, payback estimates range from six months to three years. But in the fast-paced supermarket environment, operators want to see quick benefits.
"The payoff investing in decision-support is there, especially with space and category management," said one operator in the Western region of the country. "With space management alone we gained 2% in gross."
To ensure a return on investment, operators must exercise discipline at the outset of the project, said industry observers. To be measurable, programs must be targeted for the most important payback opportunities. These might range from sales or turn improvements from price, promotion or frequent-shopper programs, to space management. The strategic nature of new decision-support systems and investments, however, was evident in the reluctance of retailers and wholesalers to speak openly about the subject.
"You have to know where you want to go to get to where you want to be," said one retailer.
"It is important to have information well structured and laid out," said another. "It is important to know your business and utilize your data. If a plan doesn't get communicated and information is simply gathered, and not warehoused and utilized, you will never get a payback."
To be useful, information must be viewed within the context of the retail business, operators said. While the continual analysis of information creates knowledge, knowledge not applied is useless, they said. Putting that knowledge to work can provide a sustainable competitive edge, and this applied knowledge is the key to the ROI lock.
"We have had and continue to have an information explosion," said another industry observer. "Information must be organized for structure. We have to be able to understand the noise and break it into specific mission-critical decisions that are needed to be made today."
Retailers and wholesalers are looking to decision-support systems to reduce the time it takes to analyze information, giving them more time to act on their decisions. For example, when exploring the right assortments, segments and product categories, operators seek speed as they respond to list and de-list recommendations; private label; operations applications; collaborative planning, forecasting and replenishment; and scan-based trading.
"Retailers have been dissatisfied in the timelines and usefulness of data," said one source. "Market pressures and consolidation are forcing operators to get a handle on their own performance and not rely on industry information," he said.
Frequent-shopper programs are often held up as prime initiatives that provide fast paybacks of decision-support investments.
For example, one Midwest operator seized immediate benefits when decision-support systems were applied to new store-opening promotions. Direct-mail pieces were being sent to addresses where the only criteria were ZIP codes. Current shoppers along with a competitor's shoppers received the same mailing.
Using frequent-shopper-card information, the chain shaved the competitor's shoppers off the list and saved close to $125,000. This closely offset the $200,000 investment in the system, said an industry insider. "When operators make uninformed marketing executions, they waste money. When the data is usable in one-to-one communications, operators can make good decisions."
A Texas chain also uses decision-support systems with frequent-shopper information for planning new stores. The chain examines geographical areas along with shopper addresses to determine where to seek new store locations. While the new store may overlap a marketing area of an existing unit, the goal is to build incremental revenue without cannibalizing the revenue of existing stores.
When a Houston-based chain used its decision-support tools to look more deeply into market-basket analysis, it discovered that when a particular branded soft drink was in a customer's market basket, other specific items were consistently identified within the same basket. As a result, the chain employed a display-optimization program, merchandising the soft drink at full value along with two of the other identified items. According to industry insiders, the chain saw a 6% increase in profits as a result of the initiative.
Another retailer used a decision-support program to conduct a price analysis of bananas. After evaluating pricing bananas at 29 cents; 39 cents or 49 cents per pound, the retailer discovered that while more product could be moved at the lower price level, the total market basket was average and the operator's profitability was lower. Pricing at 39 cents resulted in the movement of much more product plus better profitability.
A specialty operator with stores across the country used frequent-shopper information to target its best customers with the goal of building long-term relationships without resorting to price promotions. As a thank you to these top shoppers, store managers offered to conduct tours, giving them behind-the-scenes information about product mix and store layout. According to an industry observer, the chain realized market-basket increases following these events within this group of select shoppers.
"The return on investment on loyalty programs is rooted in finding ways to improve business with the best customers," said an executive familiar with this kind of program. "It used to be that frequent-shopper programs were put into use to find lost customers, not lost profit. You have to shift resources to the best customers, not the cherry pickers."
While these specific examples serve as beacons of payback potential, industry experts agree that results vary from operator to operator and chain to chain.
"Major change takes time," the executive said. "Some operators are further into the evolutionary change than others. Some have changed their organizations to take advantage of it. Technology is what you make of it. Retailers have a lot of assets to manage. Now they have to learn to manage their customers," he said.
"It is simply not good enough to let vendors drive the business," said an industry source. "There is only so much you can do with products. Retailers are now delving into customer management to examine who buys what and when, what else did they buy, and looking at how to satisfy the customer's needs. That requires a lot of data and structure," he said.
"Even if you look at the ROI of other operators of like chains and examine their programs, you document broad-based answers," said another industry contact. "Every time you will get different answers. The main desire should be to improve business with a limited amount of bandwidth. To take on a project, you have to prioritize resources and dollars. It makes perfect sense to identify the low-hanging fruit opportunities and target them. All technology will provide a return; priority is the issue."
Despite the payback potential, retailers and wholesalers still need to be dedicated to practical project goals. Much like category management practices, decision-support can be profitable when programs are moved from theory to practice.
"Measuring return is not simply a 20% to 30% ROI anymore. Today the measurement is staying in business or not," said a source close to the industry.