Working the halo effect of store promotions

At any given time, up to one-third of a supermarket’s assortment is on promotion. Most of these promotions are meant to turn a standalone profit. So much for the theory. In reality, many promotions don’t turn a profit at all, or at least they don’t add nearly as much profitable revenue as retailers expect. To manage promotions according to their real ROI, retailers would have to be able to quantify the sales of non-promoted items caused by a promotion, the so-called “halo effect.” But so far, there has been no reliable method to quantify the halo effect of promotions. Even more sophisticated approaches to measure the impact of promotions have usually been limited to the items on promotion.

More than a decade ago, pioneers tried to quantify the halo effect of promotions with in-store surveys, but the results were so imprecise that category managers have been skeptical about any such efforts ever since. And even in today’s data-rich environment, many retailers still follow yesterday’s rules of thumb. Examples from grocery include:

We are not saying that such rules of thumb don’t apply at all. But data from Periscope By McKinsey shows that the net effects are often not nearly as big as many category managers think, and some rules may actually be false for a given retailer or a given store. The important point is that, without a reliable analysis of the halo effect, supermarkets will never know whether the promotion at hand yields a positive full-store ROI.

At the same time, we find that there are many hidden champions in a retailer’s assortment that would be well suited for ROI-positive promotions. But for want of reliable insights, many category managers tend to play it safe.

A holistic approach

Thanks to advances in statistical modeling, computing power and a growing base of ever richer transaction data, a new method to measure the halo effect of promotions is emerging. This enables supermarkets to assess past promotions from a full-store ROI perspective, as well as to optimize SKU selection for planned promotions.

For the first time, it is now possible to measure the impact of promotions on non-promoted products across a store’s entire assortment with sufficient reliability and precision. To what extent does the promotion trigger substitution, i.e., how many shoppers are buying the promoted product instead of their regular, full-price choice? How many additional shoppers does the promotion attract? And what is the influence of the promotion on average basket size? Do buyers of promoted items also buy other, regular priced items that they would normally not be buying?

In the past, the answers to these questions were a matter of experience, mixed with a fair amount of mythology and speculation. Now, these questions can be answered with scientific rigor, drawing on more data, more computing power and more sophisticated analytics than ever before. State-of-the-art models differentiate between baseline sales (pre-promotion), promotional sales (sales of promoted items) and full-store lift (incremental sales of non-promoted items). The foundational promoted item category models have to have very low forecast error to accurately assign full-store lift to category and item level promotions. Other features of this breakthrough include:

In effect, the latest generation of models provides retailers with the means to review the full-store impact of past promotions and to design future promotions to maximize true ROI, recognizing both the direct impact and the halo effect. Typically, it requires three to five years of past transaction data. The bigger the data base, the better. In most cases, the implementation of systematic measurement of the halo effect of promotions is part of a bigger transformation toward more data-driven decision making in retail. 

From myth to math

The objective of halo-effect modeling is to supply category managers with the facts they need to make better, higher full-store ROI decisions. Many of them, however, are skeptical at first. But once they see the value, they don’t want to go back to rules of thumb.

Supermarkets will benefit from putting rules of thumb to the test. Often, it turns out that only a small group of SKUs is fit to balance the objective to drive revenue with the objective to protect profitability, and this group of suitable items varies greatly across categories, regions and even individual neighborhoods. Also, consumption patterns and shopping habits change over time. This is why it pays to move from myth to math.

Retailers who adopt state-of-the-art full-store ROI, including halo-effect modeling, can often reduce their promotional spending by up to 10% or reallocate these funds to more promising commercial levers to achieve a full percentage point of margin expansion. For grocers with single-digit total profitability this is a very big opportunity. Building that into your everyday way of working via process, tools and central support can happen in as little as six months. 

Working with data-driven decision support systems trains category managers to think more critically about the real effect of promotions, and it prevents them from making excuses. Consumers want creative promotions that catch their eye and are aligned with their own shopping behavior.  The supermarkets that succeed will draw more customers, build brand loyalty and drive up profits.  It’s time to take a new data-driven approach to promotions.