Store Execution: Insight where it is needed most

Understanding store execution – where promotion plans meets consumer behavior – remains a major challenge in the consumer goods industry. Yet understanding the nuances of store execution is essential to first understanding, then improving, the return on ever-growing trade promotion spending. Leading companies are finding ways to shed light on this dark corner of the consumer goods world. And they are identifying significant opportunities to improve performance.
 

Today’s in-store performance tracking tools are limited at best. Syndicated data providers visit a sample of stores on a periodic basis and record their findings concerning in-store merchandising and promotional pricing. These findings are then extrapolated to model overall market conditions…which may or may not reflect reality, both in terms of ‘average’ conditions over a period of time nor the specific in-store conditions on Best Food Day, unless the audit happens on that specific day. Alternatively, third- party services scour the weekly circulars and record what advertisements are provided to which consumers in key markets.
 

In our experience, both of these measures fall short of manufacturers’ needs on two dimensions. First, while the solutions provide a market average of retailer performance, they do not deliver store-level insights to localize store-specific actions – the ‘where’ question. Second, it is a major challenge to integrate these insights with other data sources to understand the root cause of performance variability to improve future event design – the ‘why’ question.

In their continuous search for even a sliver of competitive advantage, manufacturers are examining how their organizations interact with stores – through merchandisers in a Direct Store Delivery environment, sales calls in a traditional warehouse model, or, increasingly, through broker representatives – who are in the stores on a consistent basis, day after day, week after week. Manufacturers are looking to create consistent, reliable, repeatable, ways to capture the collective observations from these in-store visits to provide new insights into in-store performance.
 

In one example, a manufacturer leveraged a field force management solution to ensure its merchandising force collected consistent data during each retail detailing call. The solution provided pre-call data regarding outlet performance, directed data collection during the call, and summarized call data so the rep could focus on moving to the next outlet.
 

Another organization created an iPad-based solution to enable its internal merchandisers to accurately and consistently collect in-store merchandising, display and pricing information at the store level, using an intuitive, ‘drag and drop’ process for both their brands as well as their competitors. This data is then aggregated at the store-, account-, market- and chain-level to detect changes in competitor behavior. Perhaps more important, the solution also provides insights into local store performance which, in turn provides greater understanding of trade promotion performance.
 

A third organization is collaborating with its broker organization to drive store-specific interventions. Using POS data from its Demand Signal repository, together with store-specific promotional demand plans, the manufacturer is able in near-real-time, to detect performance issues and gaps; for example, lower than expected performance on a new item, a lack of scan pattern detail expected from a promotional display, or a non-scan suggesting a stock-out. This organization routinely models the economic value of addressing the outage, prioritizes which are most important, and directs the broker merchandisers to address the most impactful opportunities.
 

Leading manufacturers are leveraging existing opportunities to capture in-store conditions, in a consistent, fact-based manner, in order to create competitive advantage. How can your organization tap into the tremendous strategic and economic potential from understanding in-store performance?
 

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