Coverage means different things to different people. Generally, clients refer to it as the comparison between shipments volume from internal auditing systems and the volume reported by a retail tracking service like Nielsen provides. Subsequently, “coverage analysis”is an appraisal of the quality of the overall service including the sampling approach, collection methodology and data processing. In theory, the shipments data and the retail measurement data should be nearly identical. Sometimes a lagged effect helps to converge the two data streams.
In a recent conversation with the research manager in an emerging market, the kind of transferred demand versus incrementality insights was exactly what was needed to evaluate a series of assortment and pricing questions. However, she immediately pushed back on the opportunity to leverage the Middlegame CIA®platform. The percent of sales covered by the retail audit was less than 65 percent and the trend was not increasing.
There are several logical reasons for coverage to not be perfect. Many times, there are some channels that just won’t allow the retail service to audit them. As those become more predominant in the overall client business it can mean a coverage decline. Without accounting for lags, the pipeline effect can overtake the coverage analysis. This is particularly true for promotions. The audit parallelogram is a key player here in identifying shipments alignment.
A slightly more detailed discussion of this is probably needed in another post, but the parallelogram means those promotional shipments might be spread over two audit periods resulting in under-coverage during the promotion and over coverage after. Finally, transhipments can be an issue. Transhipments occur when products that aren’t supposed to move across sales regions do. In some instances, Nielsen has been able to identify this happening over national borders based on packaging. In the end, this will distort coverage.
The problem is that the metrics for the coverage analysis were wrong, so the research manager’s reluctance was misled. I asked her what the organization used the retail audit data for if she thought that the coverage was so poor in her opinion. She immediately said that the team was very comfortable with reporting market share from the audit. Market share is a relative metric, but they were looking at coverage from an absolute perspective. What if you looked at coverage from a relative perspective?
We immediately calculated the share of volume for each SKU in the portfolio versus the total portfolio volume across the each of the three-month moving totals (MMT) during the latest year of data. The 3MMT helped to smooth out the parallelogram effect. The share numbers were computed for both shipments and the retail audit. The results for all channels combined were correlated at the 0.96 level. We then segmented the data into the channels covered. In aggregate, the correlation only fell to 0.91. It wasn’t a lucky outcome. Middlegame has repeatedly seen these kinds of results across clients, categories, channels and countries.
The client was quickly convinced that the data was appropriate for the Middlegame CIA®platform and we got to work on addressing those assortment and pricing issues. The point is that we understand the importance of coverage analysis for reporting. It used to be a big part of my job in a past life. However, we need to look at coverage analysis for analytics slightly differently. It is probably summed up best with the “throwing the baby out with the bathwater”analogy, but there is something deeper there. Originally, the retail audit was only meant to report market share. We have pushed to provide absolute metrics as well and overwhelmingly, these tools pass the test. It is just as important to remember that they are pretty awesome at those core deliverables and the right analytics can immediately harness these tools.
Middlegame is the only ROMI consultancy of its kind that offers a holistic view of the implications of resource allocation and investment in the marketplace. Our approach to scenario-planning differs from other marketing analytics providers by addressing the anticipated outcome for every SKU (your portfolio and your competitors’) in every channel. Similar to the pieces in chess, each stakeholder can now evaluate the trade-offs of potential choices and collectively apply them to create win-win results.
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