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.
Take for instance breakfast cereals. The category’s role varies across markets, across channels and across chains. In mature markets like North America or Europe, breakfast cereals might be a destination category. On the other hand, in Asian markets where the penetration of breakfast cereals is relatively low, the category is more likely to be a routine category.
One approach to understanding how to create effective promotion plans is to consider different scenarios. For instance:
A summary of the scenario outcome from the Profit Analysis reports is provided in the above exhibit. The analysis is sourced from the Plannogrammer an online training facility for category managers, trade marketers, and retailers in consumer markets.
Plannogrammer supports a collection of simulation and analysis platforms such as Promotions and Space Planner for optimizing space and promotion plans, 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.
Reverting to the profit analysis, Scenario III, an extreme example of turf protection, yields the maximum gains in volume, but at the expense of profits. A key point to note is the assumption that the model parameters do not change despite the entrance of a competing retail chain. While the new entrant will certainly affect market dynamics, the current information on elasticities and cross-elasticities is the only estimate we can work with.
While Scenario II is the most profitable, Scenario IV, which relies of promotional support from manufacturers, provides the best outcome in overall terms.
Importantly take note that the profit analysis does not account for the gains in sales and profits of other product categories, through the additional store traffic generated by the category on promotion. The retail chain would benefit from any increase in shopper traffic yielding gains in sales and profits across product categories over and above the gain/loss in the promoted category.
Market Intelligence and Data Visualization
Custom designed, interactive, automated, online dashboards that allow for the integration and visualization of market knowledge from diverse sources, in a manner that makes it easy to access and digest.
Consumer Analytics, Loyalty and Consumer Panels
Analysis of continuous individual/household level (customer level) behavioural data to address business issues.
Scan Track and Retail Analytics
Dashboards and analytic solutions for reading/analysing retail audit/scan track data; and techniques for analysis of continuous outlet level transaction and shopper data to address business issues.
Retail Measurement Service - Processing and Visualization
Automated data projection, data processing and data visualization system for retail measurement services.
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.
Quantitative Research, Customer Satisfaction Research
Custom designed solutions for data processing and reporting with a working example of an interactive, automated online dashboard for Customer Satisfaction Research.
Marketing Analytics Practitioner’s Guide,
Destiny Marketing Simulator,