A simple approach to analysing promotions is to assess their impact in terms of volume, value and profitability. This is achieved by means of estimating the base volume, i.e., the expected sales volume in the absence of short-term causal influences such as promotions. The result is depicted by means of a baseline dashboard such as the one shown here, which utilizes a proprietary algorithm for estimating base volume. As can be seen from the dashboards, promotions and other causal influences, plotted alongside baseline and sales data, reveal the impact of these causal factors on sales.
Promotions can vary considerably in terms of impact. Some of them seem to work better than others; some items respond better to promotional influences; some promotions generate big gains, whereas others cannibalize; some promotions are profitable, others incur loss.
It is important, however, to appreciate that the impact of a promotion should not be gauged solely on the performance of an item, or a brand or even a category. Taking a blinkered view can be misleading both for the manufacturer as well as the retailer.
Consider for instance that an effective loss leader is unprofitable when view in isolation, and yet it may be highly profitable when assessed in terms of the retailers total business. For the retailer, promotions must be understood in the context of category roles and strategies, and in many instances you need to evaluate their overall impact through retail analytics and consumer analytics.
Baseline analysis is very useful because it quickly and inexpensively provides information on a large number of brands, in a manner that a layman is able to understand. It does not however, answer a number of critical questions, including the ones listed below:
Econometric response modelling of promotions can provide answers to all of the questions posed above. These models analyse data to establish the impact of each individual element of a promotion on sales. The sales response functions derived from these models yield estimates of discount elasticity of demand, discount cross elasticity of demand, and sales lifts due to displays, co-op advertising and other causal factors. It is possible to decompose sales into all of the elements contributing to the volume. Promotion response models can also forecast what impact a possible combination of initiatives will have on sales.
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Analytic techniques/dashboards for evaluating consumer promotions in terms of gains in volume value and profit; estimating discount price elasticity, price cross elasticity, decomposing sales, and applying due-to and what-if analysis.
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Marketing Analytics Practitioner’s Guide,
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