Why do retail profits remain low despite revenue growth? Tight profit margins are an infamous challenge across supermarkets and grocery stores. Typically as low as 1%-3%, they make these businesses among the least profitable industries in the U.S. Even the transition to online grocery shopping, which triggered a very respectable 9.5% growth in revenue in 2020, did not affect a similarly positive trend in profits, as online orders racked up losses to the tune of -70%.
In a hyper price-conscious, post-pandemic world, retailers and supermarkets are fast learning that pricing is right up there with customer experience when it comes to competitive differentiation. However, hitting the sweet spot in retail pricing is far from easy — mostly because a lot of supermarkets fail to take into account the whole picture when analyzing price sensitivity, while at the same time keeping an eye on maintaining healthy margins. Retailers, therefore, need to build a much larger, clearer picture in terms of who and where to target promotions and discounts effectively to maximize their margins.
So, unless your demand forecasting and marketing effort adopt a coordinated strategy to price optimization, chances are you’re going to pay in the form of margin and market share.
Demand data at a local, global, SKU level can reveal a wealth of insights.
While customers the world over today are savvy and capable of zeroing in on the best deals, their priorities are different — so much so that customer behavior analysis and accurate customer segmentation are critical to predicting future buying decisions. In other words, accurate demand forecasting plays a crucial role in telling you just what and how much you should stock to cater to demand while simultaneously maximizing margins.
Therefore, simply stocking up on the cheapest subtype may not necessarily yield expected returns. Wastage by way of spoilage, deadstock, etc., is just as harmful to retail margins and can end up taking entire chunks out of the bottom line. In a world of rapidly depleting buffers, this perhaps weighs in more heavily when it comes to narrowing down on demand forecasting accuracy.
It stands to reason that the ability to forecast demand at an SKU level can prove instrumental to planning how to cater to it. With the right demand data, you can plan exactly what and how much to produce to address demand while at the same time optimizing sales and operations.
What can retailers do to run optimal pricing and promotions for products? Retailers and supermarkets that price their products effectively leverage their point-of-sale data to coordinate and optimize their pricing and promotion strategies, leading to significantly higher margins. This can be done by following three simple steps:
• Do a detailed study to determine price sensitivity. A lot of retailers, while aware of the importance of determining price sensitivity before making pricing decisions, make the mistake of generalizing customer behavior by segmenting at a higher level than required to accurately determine sensitivity. For instance, product category (need/luxury), seasonal changes, and purchasing frequency are common factors that help determine price sensitivity, but equally important are factors such as local access, delivery time, and shipping costs — besides individual preferences. Considering the sheer scale of supermarket inventory, artificial intelligence (AI) is the only feasible solution to execute this exercise in the quickest, most error-free manner possible on a continued basis.
• Execute highly targeted promotions. Buying behavior changes based on priorities, which means that someone who frequently purchases scented candles may snap up a bundled offering (of the same product), while someone who rarely buys them may be motivated to buy a single candle because it’s available at 50% off. Thus, a precise segmentation based on buyer persona would help run highly targeted campaigns based on data such as browsing patterns and frequency, purchase history and price sensitivity.
• Link and optimize pricing and promotions. Once you’ve determined your pricing and promotion strategies, it’s time to test them against a backdrop of each other, as well as other critical factors such as shipping costs and delivery time. Often, in a bid to offer free, expedited shipping to customers, supermarkets end up incurring obscenely high shipping costs themselves — not to mention following equally unoptimized delivery schedules — with the end result that something that started out as a high-margin sale in fact incurred a loss by the time it was delivered. The best approach to identify the optimal strategy is to draw up a price-promotion matrix and extend it to include other relevant determiners. Adapt your strategy to different permutations and combinations of buyer behavior.
Online retail is exploding. On the upside, that means you have all the data you need. As the grocery business becomes more competitive than ever before (shopping online means that customers have the pick of the lot when it comes to finding the best deals), the good news is that you can start raking in the kind of data that the likes of Amazon has been gathering for decades. This essentially provides you with everything you need to understand customer demand and buyer behavior and use the insights to inform dynamic pricing and promotion decisions. Of course, this is only possible if you automate the whole process and leverage AI to accurately predict demand in the near term.
But think of it this way: What’s the alternative? To have a category manager increase the price of a product due to a hike in supply prices, only to have a marketing manager instead offer the product at 33% off as part of a promotion campaign aimed at clearing stock? Painfully counterproductive, not to mention appallingly unprofitable. Who would choose to fly blind when your data is simply a click away?