Using REAP (Retail Ecosystem Analytics Process) to Leverage Marketing Intelligence and Drive Retail Success
- Sep 23, 2010
The following examples illustrate the application of these principles. The samples chosen illustrate the interplay of various elements of REAP to drive strategy formation and execution in the store.
Retailer Assortment Rationalization
A project with a major discounter provides a case study for the use of shopper segmentation to support strategic assortment decisions to increase revenue and profit. Working with category management teams from leading brands, the chain began to define the specific shopping segments it wanted to nurture and develop and then analyze the role different categories played in reaching that segment and supporting total chain profitability. The teams quickly focused on families with children as the most profitable segment. Data showed that this group visited the channel most frequently and spent the most money. Deeper analyses showed that the particular retailer was doing the worst job among key competitors in reaching this segment.
HH with Children
HH without Children
Focusing on this broadly defined target, the teams then studied the contents of shopping baskets for different items and categories. The team identified that a basket with detergent in it averaged eight items; the biggest shopping basket contained an item of children's apparel; the second biggest basket had a toy in it; and, the most valuable shopping basket with a toy in it included a Barbie doll. By contrast a transaction with a case of motor oil was generally purchased by a man shopping alone, and the predominant share of transactions contained motor oil and nothing else.
Prior to the analysis, motor oil was featured in every weekly circular and held a prominent in-store display location. The marketing teams evaluated motor oil against the number of transactions and direct revenue generated from the ad. The thinking had been, even though the item was generally advertised as a loss, it generated tremendous traffic that yielded significant sales dollars. When this perspective was broadened to the total shopping basket, it was clear that oil delivered the wrong kind of traffic and that the category provided a negative impact on total store profitability. As a result, the line was de-emphasized and the advertising was invested in items and categories that would appeal to the newly identified target audience so that the featured items generated sales of other items of interest to the target.
An alternative approach that yielded poor results occurred at a big box general merchant. In an effort to improve profitability, groups, departments, and buyers were challenged to review their areas and make tough assortment decisions on items, categories, and departments based on their profitability. A number of intelligent decisions were made. However, because the shopper was not consistently placed at the center of the process at all levels, for a time a buyer decided to delist tennis balls that had a negative margin while continuing to carry tennis rackets that carried a high margin. The decision did not last long because you could obviously not be credible in the tennis market offering rackets without balls. This decision illustrates how easily bad decisions can be made when the perspective is not properly focused on serving the shopper.
Although the focus on defining a shopper segment and then positioning categories and advertising to meet her needs was a consistent success generator, it did not always translate to all channels in different markets around the world. At Mattel, our analysis showed that roughly 50 percent of total toy sales occurred in the period from January to September and 50 percent from October to December. This ratio held in virtually every developed market around the world, except France. In France, toy distribution is dominated by hypermarkets, and each hypermarket followed a similar strategy in building a large toy presentation for the holiday season and then shrinking it to a small area in January. Beyond that, the channel overwhelmingly stocked these small departments with remnants and did not update the assortment until the subsequent holiday season. As a result, the relative share between the two selling periods was 33 percent and 67 percent.
Armed with the data on the relative value of families with children, the value of different shopping baskets containing items from different categories, and the consistency of shopping patterns from developed markets around the world, we made a compelling case for the competitive opportunity open to the retailer who focused on this segment and leveraged all the categories of interest against the targeted shopper segment to build a preference for their chain. Unfortunately, the argument was not successfully received and a significant opportunity remained unfulfilled.
Brand Channel Segmentation
Just as retailers can apply segmentation analyses to target and develop groups of shoppers, brands can segment and target channels and chains to support their goals for reaching and servicing their customers.
Mattel faced a situation in which the growth and health of its business varied widely by segment. Its specialty business, primarily in doll stores, was strong; however, the mass business broke into an expanding business for its largest accounts, who were growing and capturing share, and a contracting business for the traditional outlets that were losing share. This share loss was across all categories of merchandise as strong regional players gave way to national behemoths. The eventual strategy was to maintain support for the shrinking accounts but recognize that it could not overcome the larger market forces to move into a growth position. Simultaneously, Mattel would work to become more intertwined with driving the total business for the largest accounts while, at the same time, focusing on opening new channels of distribution (see Figure 1.9).
Figure 1.9 Channel Segmentation
The result was growth in its total business by reaching more customers where they were shopping. The doll store business remained stable while the decline of the traditional accounts was managed.
Shopper Psychographic Segmentation
In addition to the broad shopper segmentation discussed in the discount store example, we can apply a more detailed shopper segmentation that uses a psychographic profile to identify and target shopper groups. In this type of segmentation, we broaden our view to consider a vast array of possibilities before settling on the key elements that drive performance and drive our strategy.
We begin by identifying a variety of potential traits. The possibilities could number in the hundreds as long as they are identifiable, discrete, measurable, and actionable. Potential distinctions could include attitudes, behaviors, demographics, media habits, and so on. These traits are then tested to isolate the key predictors that drive performance and profitability at retail. Shoppers are then broken into separate clusters with a quantification of their key differences and preferences. This cluster analysis is then utilized to develop retail strategy with an ideal positioning carried through all the key marketing elements inside and outside the store, using the design process previously outlined. The strategy is then implemented with measurement and further refinement (see Figure 1.10).
Figure 1.10 Psychographic Segmentation Process
A hypothetical example from the grocery channel illustrates the process. As a first step, we divide the shoppers who visit our chain into customer segments defined not by demographics but by their relationship to the store. In this analysis we have advocates, who are dedicated to fresh food and shop the chain religiously. They visit at a rate higher than the norm and buy almost all their products from us. Devotees are dedicated to the store and buy most of their needs from us, but they do not shop as frequently as our advocates, and they do not buy as a high a percentage of their total food needs from us. Convenience shoppers purchase select items from us when we fit with their schedule. Deal seekers pore over the weekly circulars and shop our stores when they see items on promotion that fit with their planned purchases for the week. Drop-bys visit the store only occasionally. Figure 1.11 breaks out the percentage of shoppers by category.
Figure 1.11 Shopper Segmentation
Having identified the relevant shopper segments, we then measure the share of sales and total profit associated with each group. The highly desirable clusters that included advocates, devotees, and convenience shoppers indexed at more than twice the profitability of deal seeker shoppers and drop-bys. The net is that, although the desirable segments represent 59 percent of the market, they deliver 76 percent of total revenue and 86 percent of the profit (see Figure 1.12).
Figure 1.12 Segmentation Profit Impact
From there we define the key retailer attributes that influence the choice of retail outlet for the targeted shopper segments and break the attributes into categories based on their level of importance in the decision-making process, as shown in Figure 1.13. In our example, important retail attributes include freshness, everyday value, quality, check-out speed, and cleanliness. Moderately important features included staff friendliness, assortment, and store location. For the key shoppers, private labels, promotional prices, and sampling were not important. Understanding the factors that mattered most to the shoppers we want to attract and retain, we then measure competitive performance to define areas of excellence and opportunities. With a full understanding of who is important to us, what is important to them, and how they see us in the competitive context, we can build a strategy that resonates with the targeted groups.
Figure 1.13 Store Feature Scoring
Earlier in our analysis we laid out the essential retail marketing dynamic in which performance is a function of the audience we attract, their acceptance of our message, and our persuasiveness in converting the shopper's openness into action.
Performance = (Audience x Acceptance) x Conversion
The acceptance of our marketing and product offerings is a function of the receptivity of the shopper, which is in turn driven by the relevancy of the stimuli we offer and the relaxation of the shopper.
Acceptance Receptivity = Relevance + Relaxation
By segmenting the universe of potential shoppers into the groups that will drive our profitability and then understanding what is important to these groups and how we stack up against those key drivers, we identify a clear path to a winning strategy that can deliver consistently improved results (see Figure 1.14).
Figure 1.14 Retail Marketing Dynamic