Chapter 2: Not all promotions are made equal
2.6. Conclusions
The extant literature has investigated in detail sales promotion variation across products and retailers. Brand managers could therefore infer which brand sells more on promotion, and at which retailer. Yet, for a specific brand, they still cannot identify how many promotions are truly able to lift up sales, as they cannot pinpoint which ones are more effective and which ones are just wasted money. This is the focus of this study.
Indeed, every promotion stimulates sales in a unique way, as clearly visible when we look at the different heights of the deal spikes (Figure 2.2). Nevertheless, research on cross-promotion variation is still limited. We propose an “individual promotion” approach that allows us to capture in a more flexible way the effectiveness of promotion events and their drivers. This way, we obtain a much richer picture of how many promotions are truly effective, in comparison with models based on an average promotional parameter. In an
“average” approach, like a SCAN*PRO model, the researcher computes one average parameter across several promotion events. A (not) significant parameter may lead managers to (under-) over-estimate the actual number of effective promotions. With our approach, we show that such conclusions may be misleading, and result in spoiled arms or missed opportunities. For example, while an average approach shows that the promotional
activities of a focal brand are not able to stimulate sales, we may find that almost 40% of those promotions are actually effective. They would result in a missed opportunity if the manufacturers decide to abandon them. In contrast, 13% of the promotion events of brands for which the average model indicates a significant promotional effectiveness, are in reality spoiled arms, as an individual-promotion analysis indicates that they are not significantly able to increase sales. Furthermore, our approach highlights that only for 17% of the brands, all promotions are truly able to boost sales. This type of more detailed information is not possible with an average model.
In the second step of the analysis, our approach allows us to use promotion-specific (dependent and independent) variables, rather than averages computed across different promotions. In other words, we can utilize the exact number of weeks between two promotion events, the exact discount, etc., and not average frequencies, average discounts, etc. This is particularly important when dealing with variables directly controlled by manufacturers in the negotiations with retailers. For example, the promotional calendar is typically measured at the aggregate brand level (as frequency).
However, as mentioned above, the same deal frequency can correspond to very different promotional calendars (Figure 2.1), with promotions equally spread over time or concentrated in a limited time spam. With an average measure it becomes difficult, if not impossible, to capture the diverse consequences for the effectiveness of promotional activities of distinct promotional calendars. Similarly, the same average discount could correspond to different implementations. Three promotions with an average price cut of 20% could have been implemented with individual discounts of 20% each, or with a discount of 5%, 20% and 35%, leading to individual differences in their deal spikes, again not fully captured by an average model. Therefore, we highlight the importance of looking at a more refined measure, identified at the individual promotion level.
Moreover, we take into account the promotional calendar of competing chains, not considered by previous studies (e.g. Srinivasan et al. 2005). The fact that previous studies have long ignored the promotional calendar of competing stores may be due to a lack of data of competing chains, as the majority of work in the sales promotion domain tends to rely on data of a single retail chain (e.g. CVS for Ailawadi et al. 2006, Dominick’s Finer Foods for Srinivasan et al. 2004). An additional reason why this aspect may have been ignored so far could be that managers tend to ignore competing promotion events, and do not react to them (Steenkamp et al. 2004). We find that this practice may not be optimal. In particular, we show that promotion events at a competing retailer (for the same product) reduce significantly the effectiveness of sales promotions at the focal chain, not
only when they occur simultaneously (in the same week) but also when little time has elapsed between these competing events. Manufacturers, selling through competing retailers, should take this into account when planning their sales promotions.
These results provide some first evidence to support the existence of cross-chain effects. In fact, so far limited attempts have been made to capture the impact of cross-chain effects. While some studies report no signs of cross-chain effects (Bucklin and Lattin 1992, Vilcassim and Chintagunta 1992), others show that more than 80% of the households cherry-pick (Fox and Hoch 2005), suggesting that cross-chain effects may be significant. In this chapter, we capture the existence of cross-chain effects by means of the competitive promotional calendar (without explicitly modeling these effects; for a more formal approach see Chapter 4). In so doing, we provide some first empirical evidence that shows that cross-chain effects may reduce significantly the effectiveness of sales promotions, and therefore should no longer be ignored.