We recently completed two recent research studies, one surveying retailers with RIS News and one surveying shoppers with EnsembleIQ and Progressive Grocer, that yielded very timely insights into the retail landscape and shopper behaviors during COVID – and beyond. Some of the findings quantified trends we all would have assumed were measurably underway, such as an abrupt shift to online channels and heightened shopper price sensitivity across every retail sector.
But there were other more surprising findings. It was startling to learn that three in four shoppers – 74% – said that during COVID-19, they had encountered retailers charging prices that they believed were arbitrary or unfair. Put another way, a large majority of shoppers believe retailers were price gouging.
“Most retailers do not have the science-based tools required to keep up.”
Now, I don’t think any of us believe that a huge swath of responsible retailers was deliberately price gouging during the pandemic but at end of the day it is their perception. There were undoubtedly bad actors stockpiling items such as face masks, hand sanitizer and toilet paper, and then unloading at jacked-up prices in online marketplaces, but most retailers are acutely aware of statutes forbidding and penalizing price-gouging sellers in times of emergency. The root problem is that most retailers do not have the science-based tools available designed to keep up with the new shopper that has emerged due to the impact of COVID. These retailers lack the ability to dynamically pick up on changing demand signals and shopper sensitivities in the moment as once-stable KVIs – those items that drive a disproportionate amount of price perception – began to seesaw and shift at unprecedented speeds.
“Refreshing KVIs has become a matter of ongoing, dynamic analysis and response rather than a consultant-driven one-off reassessment exercise once every year or three.”
We observed many retailers who use DemandTec leveraging our ability to give them real-time insights into shopper price sensitivities by channel, and down to the store-item level, and reveal the items where prices are most important to shoppers right now. For these retailers, refreshing KVIs has become a matter of ongoing, dynamic analysis and response rather than a consultant-driven one-off reassessment exercise once every year or three. With a combination of real-time price sensitivities and competitive elasticity algorithms, these retailers know where to systematically change their pricing, and where they needed to price aggressively to keep shoppers engaged and attract new shoppers who are abandoning competitors that are less in tune with customers. Equally important is that they also know exactly where in the assortment they can safely recover margins to sustain a long-term healthy business. Ultimately these retailers do a better job of gaining market share, supporting their price image, and achieving their desired business results.
In contrast, we know of other retailers who take a more limited view of pricing, relying instead on experimenting with prices using limited A/B testing on target shopper sets that look at alternative price points on the same product. This presents several risks. First, the limited set may not be the optimal set to test. Second, the limited nature of a test set means that the sample size is likely too small to carry true statistical significance. Third, these tests are run on actual shoppers using the so-called ‘vote with your wallet’ methodology. If your test set contains some of your dedicated, loyal shoppers and a test price proves disastrously off-base, you run the risk of losing forever the very shoppers you value most. And finally, A/B price testing tells you only that one price resonates with those sample shoppers more than a different price, but it does not tell you anything meaningful about competitive elasticity, shopper price sensitivity, or cross-item effects. In other words, if you begin with two sub-optimal price guesses and test them against each other, all you learn is which price is less sub-optimal. It’s easy to see how this approach can lead to prices that undermine price image and create devastating disconnects with competitive positioning and business targets.
“trust is hanging by a hair, random price experimentation is too risky for strategic retailers.”
That’s a much more limited view of the world than true AI science-generated price recommendations, which fully factor in real-time demand signals, shopper price sensitivities, KVIs, competitive factors, business goals and price strategy. At a time when shoppers feel their wallets have shrunk, they feel they have been a victim of unfair or arbitrary pricing, and 61% of shoppers have stated they are more price sensitive than pre-pandemic, trust is hanging by a hair, random price experimentation is too risky for strategic retailers. Our recent study with EnsembleIQ found that price sensitivities among shoppers, already high pre-COVID, are more elevated now and that shoppers expect that price will be even more critical to them a year from now, post-pandemic. In this environment, retailers must ensure that every price they present is relevant to their shoppers to sustain a healthy business.
The pandemic is expected to last for several months, but the impact it’s made upon the shoppers will last forever. Retailers ignoring the fact that pricing within today’s Retail environment no longer works work with yesterday’s lead times and technology is sure to join the growing list of failing business.