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Bill Rouse
Wal-Mart Analytics,
ACNielsen Homescan & Spectra
Jon Busman
Marketing,
ACNielsen Homescan & Spectra
You’re pretty good at Sudoku. You know how the DaVinci code was solved. But do you know how to crack the retail C.O.D.E.? Turns out, you held the key to unlock sales potential at the store level all along: the consumer. As the only common denominator across brand, product, account and store activities, it is imperative that store-level strategies and tactics begin and end with the consumer.
Two common factors often hinder the ability to take advantage of store-by-store growth. First, consumer insights alone, without resulting actions, simply turn into overhead. Second, treating an entire retail chain as if it served a single shopper type often results in missed opportunities.
What’s needed, is to find an approach that enables scalable action based on store-level insights to position a company for success with consumers. Store-level growth opportunities exist, but they are often difficult to identify and procure with a reasonable return. It takes the right tools to integrate action and insight into a cohesive, repeatable framework
for success.
Getting started
The first step into the new world of store-level marketing begins by abandoning any preconceived notions about a consumer-centric approach. There is no silver bullet report with all the answers, no bedrock rule about how a category acts in response to consumers.
Realize that things are not always what they appear to be. Narrowly focusing solely on category-specific action is no better than looking down from a tall building and incorrectly noting that all people are the same. Consumer-centric planning is about analyzing specific groups of people and identifying how to impact them where they shopÑat ground zero, the store level. It is a different way of looking at business, one with an upside that pays off in sales and profits.
Winning at retail is enabled by applying a simple, systematic four-step process that we call “Cracking the Retail C.O.D.E.” The methodology employs a series of critical steps to optimize brand or product success in the marketplace. This consumer-centric approach links actions in the store—where they matter the most—back to the consumers most likely to purchase your brand.
Four steps to success
The acronym C.O.D.E. summarizes a methodology that begins with Consumer profiling, then moves to Opportunity gapping, Dynamic clustering and Executing for the consumer, the four steps to success in store-level marketing.
1. Consumer profiling—accurately captures the demographic profile of the brand’s consumer.
2. Opportunity gapping—quantifies store-level opportunities based on consumer demand potential and diagnoses the prospect.
3. Dynamic clustering—groups similar stores using multiple store attributes, including shopper demographics, the competitive set, and upside opportunity.
4. Executing for the consumer—takes findings from steps 1–3 and develops store-level tactical plans, giving the field force the right information to optimize in-store presence.
This strategic approach provides a deeper understanding
of the consumers around a given store, measures the gap between actual and potential demand, and offers an execution plan at the store and cluster level that fully addresses the potential demand. The result is improved operational execution, reduced out-of-stocks, fewer overstocks, enhanced promotion performance and better inventory management.
Step 1: Consumer profiling
The consumer should be the first and last consideration of any consumer-centric initiative and the C.O.D.E. approach is no different. Products do not buy themselves, just as shelves do not mysteriously empty by themselves. While price reductions, competition, brand equity, slotting and the like all influence purchasing, the common denominator is the consumer.
It is only appropriate then, that the C.O.D.E. approach starts with consumer profiling. There are various types
of consumer profiling information available, from panel purchase behavior to attitude and usage studies to focus groups. Traditionally, point-of-sale (POS) data has been
utilized to tell us “what happened” but not who drove it. Through consumer regression profiling, it becomes
possible to create a sales-weighted store profile by
estimating future consumer demand for products based
on historical sales data.
ACNielsen Homescan & Spectra recommends utilizing panel data to determine category and brand breaks, and Opportunity Finder solutions to “consumerize” product movement data, enabling retail-specific and item-specific store profiles. This approach yields granular analyses down to the SKU level. The analyses become retail-specific, based on the retailer’s own data, which adds power to the recommendations. Regardless of source, consumer profiles can be used individually or in combination to formulate step one
of Cracking the Retail C.O.D.E.
Step 2: Opportunity gapping
Are you leaving sales on the table? If so, how much? What needs to change to convert potential and lost sales into register rings? Opportunity gapping quantifies the opportunity cost to each store for missing the mark two waysÑeither with consumers or on the execution level. While fair share gapping is a common practice to determine if a brand or product is getting its expected share of the account or market pie, what if you could estimate how big the slice would be?
Consumer profiling is the first step in executing the C.O.D.E. approach, but without action it just becomes “nice to know.” In order to quantify opportunity, matching the consumer profile to the known shoppers of an account is critical. The degree of sophistication can vary from profiling your consumer and identifying a common trait within
a retailer (i.e., matching high income consumers to a high income account like Whole Foods), to scoring an account
at the store level based on the most volume-predictive
consumers, also known as the Spectra Demand Index.
In essence, while Step One determines your consumer’s
fingerprint, Step Two matches it to an account’s fingerprint—or even better, an account’s store-level fingerprints. Now you have a basic roadmap for tomorrow’s volume.
Windshield vs. rear-view mirror
The C.O.D.E. methodology puts marketers in the driver’s seat, steering brands and products by looking through the windshield instead of the rear-view mirror. This approach focuses action on stores that show growth opportunity, and suggests maintenance level support for those with lower growth potential.
Execution opportunity gaps materialize based on the yin-yang interaction of two opposing forces: demand drivers and demand inhibitors. Demand drivers include activities such as promotions, cross merchandising and correct shelving. Demand inhibitors include competition, out-of-stocks (OOS), distribution voids, and incorrect shelving or space allocation.
Case in point
In a typical promotion scenario, 16% of stores won’t have available space, 22% won’t display the promotion signage, 33% will put up the displays too late and 42% won’t have the skilled labor to execute the promotion. Opportunity gapping identifies problem stores—those that should have sold more based on the consumer fit and performance of similar stores.
Promotions can then be adjusted to focus resources on areas with the highest potential upside. In essence, opportunity gapping acts like a forward-looking diagnosis that determines if the financial upside return from a promotion is worth the effort of store checks, additional labor, or the promotion itself.
“Retailers and manufacturers are becoming more precise in their targeting of consumer segments and wish to optimize store
conditions at the local level.”
–Paris Gogos, Director
ACNielsen Retail Execution Services
Step 3: Dynamic clustering
Clustering, or localization, as the Harvard Business Review calls it, is the cornerstone of scalability. More than a capability, clustering has become an operating necessity in today’s fragmented marketplace.
Retailers and manufacturers are increasingly moving to micro views of their business to identify opportunities
associated with the demand drivers and demand inhibitors unique to each store. This is most clearly seen in the growing number of retailers adopting local or neighborhood
marketing initiatives. Most recently, Wal-Mart announced they were dropping their one-size-fits-all approach to stores. Similarly, manufacturers are becoming more precise in targeting of consumer segments, and wish to optimize store conditions at the local level.
Clustering attributes
While there is a virtually limitless set of criteria upon which clusters can be based, most fall into four major groupings:
1. Consumer Attributes. These may be as simple as
grouping stores based on a common trait like ethnicity, affluence of the shopper, or a volume-predictive measure of consumer fit.
2. Organizational Attributes. These may include DSD sales routes, warehouse locations, regions or districts, or
specific shelving sets.
3. Store Attributes. These may range from physical store attributes like size and presence of specific departments to proximities to high traffic intersections or landmarks like beaches or universities.
4. Performance Attributes. These can range from basic sales rates to promotion response to consumer-driven category opportunity gaps.
Dynamic clustering brings criteria together into a cohesive framework that leverages critical differences within the store segments. Some companies may be able to execute effectively using just two clusters; others may require 200; but clustering is a necessary prerequisite for integrating action to “crack the retail C.O.D.E.”
Step 4: Executing for the consumer
The final step in the C.O.D.E. approach takes us full circle, back to the consumer and how best to shape and direct activities to each store or cluster’s shoppers. Merchandising strategies based on item roles, promotional and sampling programs, space and facing allocations—all tailored to defined dynamic clusters—can now be managed by following the four-step C.O.D.E.
The C.O.D.E. approach makes high definition marketing possible, allowing marketers and retailers to zero in at the most granular level possible—the store. Working from a shared viewpoint, with shared definitions for target clusters or stores, manufacturers and retailers can collaborate on promotional and assortment strategies with optimal appeal to the right set of consumers, and operational strategies that take cost out of the system by reducing inefficiencies such as out-of-stocks and distribution voids.
The C.O.D.E. holds the secret to finding opportunity gaps, making the most of consumer profile information, and improving logistical execution to squeeze more bottom-line profit out of even more top-line sales.
See below for a case study that illustrates the C.O.D.E. process in action.
Cracking the C.O.D.E.
A Case Study
Step 1: Consumer profiling
Joe’s Cookies utilized a hybrid of ACNielsen Homescan
Panel data and POS-based profiles to identify its preferred consumer. Since Joe’s Cookies had low penetration in the marketplace, the company used panel data to profile the total category, and then used Spectra Opportunity Finder Solutions to profile individual SKUs.
The result of this analysis: Joe’s Cookies gained an understanding of how its brand consumers differed from the overall cookie category and competitors. The typical Joe’s Cookies buyer skewed to African-American and Hispanic ethnic make-up, earned $50,000Ð$100,000 per year and lived in a household with children. See chart 1.
Step 2: Opportunity gapping
Joe’s Cookies then ranked retailer stores based on the “fit” between the store consumer profile and the Joe’s Cookies consumer profile, quantifying the consumer opportunity gap. The analysis determined that store opportunity varied greatly once the consumer was inserted into the equation.
For example, Joe’s Cookies found Store A and Store B identical in every transactional way. Joe’s Cookies had two facings in each store, and the store shelf set and total sizes were virtually identical. However, sales results for Joe’s Cookies were anything but identical. Store A sold approximately $90 per week of cookies, while Store B sold closer to $230 per week.
A consumer trade area analysis for each store uncovered very different shopper bases.
Store A was located in an urban setting with many households without kids in its consumer trade area. Store B, on the other hand, was in a rural setting with many households with kids in its consumer trade area. As a result, Store A was not the underperforming store it initially appeared to be, but in fact, had captured most, if not all, of its opportunity. Store B, initially thought to be over-performing in its trade area, was actually under-performing and should have sold an incremental $130 more per week. See chart 2.
Instead of allotting resources against a store that appeared to be an under-performer, Joe’s Cookies targeted the real under-performing store. Joe’s Cookies followed the C.O.D.E. method and assessed the different demand drivers and demand inhibitors affecting the store in order to chart a path for Store B growth. This exercise was repeated for other chains to diagnose the amount of unconverted opportunity by account and develop tactical plan for realizing untapped potential.
Step 3: Dynamic clustering
The next step for Joe’s Cookies was to make similar recommendations to its retailer that could be executed in a scalable manner. Joe’s Cookies clustered similar stores based on
consumer, store, and performance attributes including
consumer fit, opportunity gaps, competitive interaction
and existing store sales.
In doing so, Joe’s Cookies focused its efforts against several types of consumers and enabled action that was scaled yet appeared customized on the shelf. Dynamic Clustering also identified which clusters were not only a strong fit, but quantified the upside opportunity. This approach allowed Joe’s Cookies to take action where it was needed and to minimize where it was meeting demand. Demonstrating the power of Dynamic Clustering in action, when applied from the category down to the SKU level, Joe’s Cookies executed against the opportunity for its brand and the category. See chart 3.
Step 4: Executing for the consumer
Turning to tactical considerations, Joe’s first cluster (Blue Collar Suburban) represented $2.7 million in sales, spread across 20 stores. It appears to be under-performing with respect to many product types and within certain cookie sub-segments.
Joe’s Cookies implemented a dynamic clustering framework and pursued tactics that included:
- identifying cluster potential to assist with assortment
decisions;
- determining the competitive forces affecting each dynamic cluster to assist with tactics
- assigning key merchandising roles to SKU-level items by dynamic clusters to determine the turf and image enhancers;
- allocating shelf size based on dynamic clusters
- performing SKU-level rationalization based on consumer demand and potential sales by cluster to put the right product in the right store.
The Retail C.O.D.E framework has helped many manufacturers and retailers unlock hidden sales opportunity previously masked by results aggregated at the account, region or trade area level. C.O.D.E. proponents integrate a variety of consumer and retail information to de-code available opportunities and implement comprehensive action plans designed to improve product and category performance. Are you ready to crack the Retail C.O.D.E.? Your sales depend on it. Your consumers demand it.
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