by Geoff Pofahl
In the COVID-19 era, rates of unemployment skyrocketed to unprecedented levels at breakneck speeds. Others were furloughed, faced reduced hours and pay, or were informed of across-the-board pay cuts. For many of those whose take-home has not yet been impacted, feelings of vulnerability and uncertainty are real and will likely persist for the foreseeable future.
All of these circumstances result in at least one common outcome — a much greater awareness of and sensitivity to price levels and changes. The U.S. Bureau of Economic Analysis price index showed beef, poultry and egg prices increasing 10-20 percent in the February-June period, fanning the flames of already elevated price sensitivities.
What happens to these sensitivities post-pandemic? No one can know with any certainty, but recent research shows retailers anticipate dealing with the issue — in fact, a recent study found that 72 percent of retailers stated shopper price sensitivity would remain high for the foreseeable future. What can retailers be doing now and in the longer term to adjust to the realities of transformed price sensitivity when historical data has lost much of its relevance?
The Power — and Necessity — of Science-Driven Pricing
Innovative retailers are increasingly aware that manual, human-led processes are not adequate to the task. By turning to advanced science algorithms and recommendations, retailers can ensure that they have real-time insights into shopper demand signals and sensitivities, and steer clear of the unconscious biases that affect human analysis and recommendations, no matter how well-intentioned the stakeholders are.
Retailers say they are ready to embrace this approach, though the market is in relatively early phases of adoption overall. The same study found that 60 percent of retailers say they are focused on putting AI-powered pricing in place, but today 90 percent say they have manual or only semi-automated processes for pricing, promotion and markdown.
At the same time, market basket patterns that had been well-established have shifted dramatically as families rely on home cooking and baking and less on dining out and purchasing prepared foods. Less-frequent trips to grocery stores dictate a stronger reliance on long shelf-life foods, and unpredictable supply chain disruptions tempt shoppers to remain in pantry-loading mode. Shopper price expectations have been proven to vary by channel, and the pandemic has driven shoppers to online adoption at an accelerating pace — another critical factor that retailers must adjust for.
Structuring for the Win-Win
The value of science-based pricing, promotion and markdown optimization tools extend beyond the compelling benefits of delivering prices based on comprehensive, current insights into price sensitivity and elasticity. Abrupt changes in consumption patterns, preferences and behaviors have caused traditionally stable KVIs to diverge wildly from past patterns.
In light of these observations, the power, speed and accuracy of productized KVI analysis provide the best way for retailers to stay abreast of what items their shoppers care most about so the retailer can deliver meaningful prices on those items, while also being aware of where in the assortment retailers can safely recover margins to sustain a healthy business — thus delivering the elusive win-win approach to pricing across the assortment. Shoppers traditionally loyal to a certain brand may have been led by availability outages or increased budget consciousness to focus more on store brands or item substitutions, so assortment assumptions should also be revisited with increased frequency, optimally at the zone or even store level.
Fortunately, the scalable, elastic and domain-focused science available to retailers today enables all these approaches to staying relevant to shoppers while fending off competitors. With AI-based science, retailers can respond to changing market, shopper and competitive conditions with prices and promotions that keep pace with the speed of the dynamic retail environment.
Our Best Guesses aren’t Good Enough
Well-meaning and highly experienced pricing and merchandising teams and executives can and do speculate about how shopper behaviors will unfold in both the near-term and the post-COVID era, whenever that turns out to be. But predicting the future with any accuracy is a huge challenge in an era with no historical precedent. Whether science confirms our assumptions or refutes them, it provides invaluable objective, current insights, and prescriptions.
Harnessing science means pricing teams are freed up from manual, repetitive tasks and able to access easily consumable science to conduct what-if scenario planning, monitor recent shifts in demand signals, and automate processes to focus on an exception-based model that speeds responses and earns ongoing shopper loyalty. Innovative retailers are leading the way by embracing these capabilities, now more than ever. Retailers who hesitate too long will risk falling out of tune with shoppers and losing the ability to compete successfully.
Geoff Pofahl, Ph.D. is the Head of Science at DemandTec. Geoff has more than 10 years’ experience leading global data science teams to create innovative AI-based solutions leveraging deep retail pricing and promotion domain knowledge. He joined DemandTec from IBM, where he was Principal Data Scientist. Earlier he was Principal Data Scientist at Revionics, a retail price optimization provider, and a data science consultant to Nielsen’s Perishables Group.