As the CEO of a retail analytics company, I can attest to the erratic patterns we are seeing in store traffic and conversion trends. Historical trends are out the window, and now the key is to remain flexible and monitor trends by day and even by hour. The key consequence of all this is that the interpretation of the data has become significantly more nuanced and complicated, so on top of everything else retailers are dealing with, they also now have to be even more careful with how they use and apply insights from their data.

The only thing predictable about consumer behavior during the pandemic is the unpredictable nature of it. However, harnessing the power of consumer shopping behavior, coupled with third-party market insights remains a valuable part of merchandising, assortment planning, pricing, promotion strategies, and execution.

The normal forecasting and buying models have to take into account the blips of unpredictability we are experiencing. What may have been perceived as an outlier has become the norm for the moment, with consumers leveraging digital commerce channels, spending less time in-store, and making very value-driven shopping decisions. It has become increasingly critical for retailers to have a holistic view of their consumers across all shopping channels.

Agility, flexibility and adaptability, powered by valuable consumer insights, will help win in today’s environment.

COVID-19 has made all the predictive models obsolete – that much is obvious. But what’s less apparent is the long-term impact. It will be months or more before shopping behaviors stabilize, which means that all the data being collected now is of limited value. The status quo is out as an option, and there is no new normal yet. AI models themselves will likely need recalibration. The one upside is that COVID-19 resets everything to zero, so retailers that have stayed away from analytics and modeling have time to get into the game – if they act quickly.

The COVID-19 pandemic is an event much like a hurricane in respect to how it’s impacting retail; there have been severe commodity market cost changes, etc., but the difference is that this will last a lot longer than a hurricane or other natural disasters. Retailers (and the software vendors who depend on this data to perform analytics) will need take into account this period when planning next years budgets, sales and labor projections.

So does this mean we are on the cusp of a return to the merchant who knows what sells and relying less on AI? I thought the whole point of AI was unmatched prediction-making ability.

Any model that relies on data patterns over time is presently useless. The big question for marketing research is, what will shopping patterns be like when everything is back to normal (if that ever happens)? Predictions run the gamut from “no differences from pre-pandemic shopping” to “everything is totally different.” I favor the former, which suggests that while purchase forecasting models may not be so useful, snapshot-based models will return.

If nothing else, the data sets that result from COVID-19 will be invaluable for potential future calamities. COVID-19 may seem like an anomaly as we’re living it now, but it also serves as a cautionary case study for any future events.

The forecasting models from prior seasons are pretty much useless. So are year-over-year comparisons. There are demand forecasting techniques that – use recent months’ data, forecast, compare actuals, and refine. This requires more business intuition, assumptions and factoring in supply constraints all at once.

Even then, it is very likely to overshoot on some and have stock outs on others. To overcome this, businesses have to actively think of substitutes to offer and promote.

it’s a double whammy — the predictability of shopper behavior is significantly dented, but so is the predictability of supply. Retailers are going to have to relearn how to fly by the seat of their pants, you know like in the “good old days” of retail. Not so good, is it …

The true COVID-19 effect will not be known until the customers who are waiting to return goods post-COVID-19 flood retailers. Long term, so many variables are in play. Schools opening? Vaccine? Work from home? The list is long. Marketers are going to need to do a deep dive into the data to make sense of new shopper behaviors. Socio-marketing seems in order.

The challenge is that pre-COVID-19 customer insights are of seriously limited value due to the disruptions of shopping habits, the formation of new ones and the new offerings and changes made by retailers and other brands. This all poses new challenges for organizations on top of what largely challenged them pre-COVID-19.

On a recent blog post of ours, we shared that “In our work, we see companies with more data than actionable insights. But we also see smart clients embracing the reality that things have changed and recognizing their pre-COVID insights are increasingly irrelevant. They’ve already reset their management dashboards to gather benchmarking data and analysis to see how far they’ve fallen and what customer segments have emerged due to the ‘new normal’.”

Count analytics and insights as just another one of the challenges that retailers face in addition to everything else!

COVID-19 has not been around long enough to truly have a formidable impact on retail data. COVID-19 is a five-month phenomenon, not even two quarters and certainly not a full retail year.

Reacting to COVID-19 after five months is premature and ignores all of our current, historical retail data. We cannot be so reactive in this dynamic, online retail environment, simply because there is a new force that changes our very short term perception of the omnichannel and online environment.

We can accommodate the current changing environment, recognizing that it is a transition and flexibility is critical.

When consumers clear shelves of products, the brand has no real understanding of actual demand in the supply chain. Did their products sell out because they were desired, or because they were the only option left?

The problem for consumer goods companies is that brand and product affinity goes out of the window during a panic buy or out-of-stock scenario. When there’s only one brand of toilet paper (or soda) on the shelf, consumers are forced to grab whatever is available.

Quotes pulled from this piece: “Pepsi Reveals the Future of Direct to Consumerism.“

It has absolutely rendered the process more challenging but not impossible. There are many trends and buying patterns, etc. that are erratic through the pandemic and will continue to be, but aligning your retail store shopper data together with your e-commerce data and trying to understand attributes can help guide your planning. No one has a crystal ball and that is why it is critical that retailers and manufacturers use this time of uncertainty to truly collaborate, share data and work together in order to emerge stronger!

The traditional models are essentially broken at this point. Relying on LY and LLY just won’t cut it going forward. Many of the currently closed and/or severely impacted locations will need to be treated like new stores. The reverse of that is true for those businesses like grocery who have had a big increase in sales. They will need to temper that surge next year when their historical movement comes into play in their models.

The big gains in eCommerce sales will also need to factored in. I believe we have increased the online sales adoption rate by 3-5 years in some segments of retail like which will have long lasting implications and hasten adoption of solutions like micro-fulfillment in semi-dark or dark stores.

Some data, like panic buying at the outset of the pandemic, is anomalous and can be ignored. Other data, such as the more dramatic behavioral changes among Millennials and Gen Z than among Boomers, can be segmented. But the most reliable data comes from identifying trends that have accelerated. Three years of transformation happened in three months, as Accenture’s CEO has pointed out. This includes online-to-store fulfillment, home delivery, and subscription services. Data in these areas is more robust and predictive on the path to a post-COVID economy.