Most retailers use one of three baseline models:
These models are easy to implement, draw on accessible data, and don't require large analytical capacity. They may also prove sufficient for replenishing everyday sales stock. But they are inadequate for evaluating promotions.
We put each of the models to the test estimating baselines for 4 different item types, for which baselines could be reliably determined by independent means.
Figure 1 shows how sales volumes vary over time for each item type.
Figure 2 shows how much each model under or overestimates baselines for each item type.
It is difficult to predict demand. Tastes and preferences aren’t static. Products go in and out of vogue. Some products, like ginger shots and protein puddings, display steep preference trends few could have predicted. Other products flop when nobody thought they would.
External factors affect supply. Climate, for instance, affects harvests, makes commodities rarer, drives up prices, and stifles demand. New products routinely enter the market, for which retailers have little or no data against which to benchmark baselines.
Retailers run thousands of promotions across hundreds of stores each week. This generates a vast amount of data that needs to be processed, analysed, and acted upon in very short order. Most retailers simply cannot keep up.
Merchandising teams’ toolkits are not up to task. Excel was not built for this kind of volume and complexity. Consultants run pricing exercises on dead data sets, but can’t offer tools for operational use. Enterprise software providers offer tools, but they’re outdated, unwieldy, and often overly technical.
Retailers know strikingly little about their promotions, even though a major chunk of their revenue is generated by them. Bad baselines are the main culprit.
We repeated our experiment, but this time included Formulate’s proprietary baseline algorithm.
Figure 3 shows the results. Formulate accounts for changes in supply and demand, and it scales across the entire assortment. It is also between 2 and 10 times more accurate than other models at estimating baselines.
Formulate is between 2 and 10 times more accurate than other models at estimating baselines.
Take a seasonal item like ice cream selling 1000 units a week. The HIST model has a 26% error margin, is off on average by 263 units, and estimates a range of 737 to 1263 units in sales. Formulate, on the other hand, is off by just 50 units, and estimates a range of 950 to 1050 units.
As a buyer how many units should I purchase? Too few, I risk underestimating sales, and miss out on profit. Too many, I risk overestimating sales, decimate my margins and create waste.
The wider the range, the harder it is to plan. The harder it is to plan, the more likely a scenario with either waste or shortage. One scenario is bad for the environment. The other is frustrating for customers. Both are costly for retailers.
By using Formulate retailers can double the business impact of their promotion decisions by:
At a company level, this translates into a profit improvement of up to 10%; a major profit contribution for such a high-volume, low-margin business.
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