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Uncertainty is the New Normal for Retailers
Uncertainty is the New Normal for Retailers What we’re Excited to Discuss at NRF 2022 If 2020 and 2021 taught retailers anything, it’s that uncertainty
January 10, 2022
By John Kim, Data Scientist
When managing promotions, the primary end goal of grocers is to drive an increase in ROI. Though there are many tactics on how to achieve this goal, there are a few important secondary metrics that should not be overlooked. Two of those are cannibalization and halo effects. Advanced analytics and machine learning have created new opportunities for grocers to account for impacts that would have previously been ignored. Cognira has partnered with Kennesaw State University (KSU) in developing a halo/affinity effect model to allow for inclusion in broader forecasting methodologies.
Though you may have heard about cannibalization and halo effects, you may be wondering what they mean? Why are they a powerful tool for grocers? Throughout this article, learn more about cannibalization and halo effects, and why they are important for retailers when planning promotions.
Before we dig into the value of cannibalization and halo effects, it’s important to first understand what they mean.
Cannibalization is when a customer trades between similar products. This can be driven by many factors like the introduction of a new product, promotional incentivization, better product placement, etc.
For example, a new flavor of yogurt is introduced, and the store notices record sales for this particular product. The grocer now needs to figure out if the new flavor is bringing in new customers or if it is simply eating away the sale of other existing flavors of yogurt.
A halo effect is when a product causes a consumer to purchase another product.
For example, consumers may purchase the following items together: wine with cheese, hotdog with hotdog buns.
In order to determine if it is a halo effect, the grocer needs to make a distinction between causal and correlative relationships. Yes, Apples and Bananas are often bought together but is it because they incentivize the sale of one another or is it because people who buy apples are more likely to buy bananas because of their shopping patterns?
Imagine having the opportunity to pinpoint exactly why a consumer purchases a new product over their typical product, or why one product influenced a consumer to buy another product.
By understanding cannibalization and halo effects, grocers can do just that! But the benefits don’t just stop there. Here are a few advantages grocers should know about:
Better product performance analytics – When new products are released, or promotions are run, there is clear visibility into whether they’re bringing in extra revenue, or just eating away from existing products
Accurate promotion effectiveness – Grocers know the true performance of a promotion
Effective inventory management – Grocers are able to accurately project promotional impact across products and project inventory needs accordingly
Accurate promotion effectiveness – Grocers know the true performance of a promotion
Though the benefits above sound like a no-brainer it is not as simple as you may think to accurately detect cannibalization and halo effects.
We recognize that there is a two-part problem:
It can be difficult to identify a relationship, especially when deciding on the aggregation level – Do we do the analysis at a SKU to SKU level (i.e. 6-inch wheat hotdog buns vs turkey sausage) or at an aggregated level (i.e. hotdog vs hotdog buns).
While the two-part problem is near-impossible to solve manually, advanced analytics can help in collecting effective and accurate data. The grocery and retail industries are meaningfully different than they were 10 years ago but technology has kept pace and offers brands the opportunity to evolve their business with their industries.
John is a data scientist at Cognira. He found his passion for data science while working on a public health analytics project for the World Health Organization and decided to pursue his Masters Degree in Computational Data Science. He is currently working on various projects such as Monte Carlo Simulations and Halo/Cannibalism Modeling.
In his free time, John loves to play golf and is determined to find the best brewery in the metro Atlanta area.
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