Retail tracking data is essentially 3-dimensional — product, market and time (refer to Exhibit 28.11), and each dimension has a hierarchical structure. For instance the product dimension can be broken down to category, segment, sub-segment, manufacturer, brand, variant and item.
The following are some metrics or facts supported by the retail index:
Consider the example in Exhibit 28.12. Brand X is handled by shops A, B and C; its numeric distribution therefore is three out of four or 75%. Its weighted distribution is the total weight of shops A, B and C in terms of category sales, which is equal to 50% (5 + 20 + 25). Note also that the brand’s weighted distribution (50%) is the same as the trade share of shops A, B and C, which handle brand X.
Unless otherwise specified, distribution is weighted in terms of category value sales. Defined as a percentage of where money is spent on the product category, it reflects the quality of distribution.
Considering that brand X’s weighted distribution (50%) is lower than its numeric distribution (75%), one may conclude that the quality of the brand’s distribution is relatively weak. In comparison brand Z with 50% numeric and 70% weighted distribution is handled by stores that contribute more to category sales.
Occasionally categories are weighted in terms of ACV (i.e. sales value of all categories sold by store). This is advisable in case of small, new/growing categories with few brands. For such categories, ACV weighted distribution provides a better reflection of the quality of distribution.
In the context that a product handler may run out of stocks, what is required, is a clear distinction between in-stock distribution, out-of-stock (OOS) distribution and loss of distribution.
Consider Exhibit 28.13, which depicts a brand’s incidence of purchase and stocks over four time periods. The brand has in-stock distribution in January and February, and it has out-of-stock (OOS) distribution in March. The brand lost distribution in April because there are no sales, no purchases and no stocks — it did not exist in the store at any time during the month.
Now suppose there was some closing stock in March, and as before, no purchases in April and no stocks by end of April. In this case (Exhibit 28.13b), the status in March changes from OOS distribution to in-stock distribution.
Is this store still considered a non-distributor in April?
No, because the stocks at the end of March are opening stocks for April. These stocks would have sold during the month. So, in this scenario, the store is a handler that is experiencing stockouts for the product.
As it capture the most fundamental market information, retail tracking data is essential to formulating marketing strategies and sales plans. Its scope encompasses all of the following areas:
Analysis and interpretation of this data, in the context of sales and distribution, is the topic of Chapter Sales and Distribution.
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Suite of interactive, online dashboards that seamlessly integrate retail and consumer data sources in a manner that makes it easier to glean insights.
Suite of dashboards to visualize/analyse retail scan data.
The Plannogrammer is an experiential learning facility for category managers, trade marketers, and retailers in consumer markets. Ideally suited for hybrid learning programmes, Plannogrammer imparts hands-on training in the planning and evaluation of promotions and merchandising.
It supports a collection of simulation and analysis platforms such as Promotions and Space Planner for optimizing space and promotions, Plannogram for populating shelves and merchandising, a Due To Analysis dashboard that decomposes brand sales into the factors driving sales, and a Promotion Evaluator to evaluate the volume, value and profit impact of promotion plans.