The previous part of this series discussed at length about the linearity of FMCG sales within a month for retail sales management and defined metrics to measure the skew. In this part we would look at how this linearity varies over different categories.
Below is a chart depicting the IMS values for 10 different categories.
As it is evident, there is huge variation among the categories. Cosmetics and Men’s Grooming categories show very high IMS which means that their peak period sales is around 3.5 to 4 times that of the bottom period sales. This clearly shows that for promotional activities, not every day is equal for these categories. Any activity should coincide with the peak period in order to reach the maximum possible consumers.
For Biscuits and Beverages categories the IMS values are just over 1.5. This would mean that the peak period sales are only just about 50% higher than bottom period sales. This shows pretty even distribution of sales throughout the month.
The charts depicting the sales trend by date for these categories are as follows.
As seen in the IMS values, Cosmetics and Men’s grooming show markedly different behavior when compared to other categories. There could be some similarity in the shopper behavior and purchase motivation between these two categories which also makes them distinct from other categories.
Can this information be used somehow by the retailers or brands? How about optimization of visual merchandising expenses? By identifying the peak period of purchase for categories like Cosmetics and Men’s grooming, a temporary burst of promotional activity covering more stores will surely be much more beneficial than steady activity across lesser number of stores. And by identifying lean periods for categories like Biscuits and Beverages, corrective action could be taken to improve the offtake.
Retailers can use this information to design something like a designated stocking area which would stock those products whose peak period is currently on. This would improve the offtake of these products by reaching out to maximum consumers.
Also, through this analysis categories with similar behavior can be identified and stocked together by retailers for maximum shopper attention or benchmarked to each other by marketers for achieving superior distribution.
One more important application of this analysis could be analyzing individual brand’s performance and comparing it to lead competition or rest of the category. By studying the deviations in the patterns, inferences can be drawn on the consumer perception of the brand vis-à-vis lead competition or rest of the category. This could be especially useful in tracking the performance of a newly launched brand to aid in corrective action.