Changing the Narrative: Setting the Stage for Retail Recovery, Part I
At the start of 2020, the biggest challenges for the retail industry were issues such as market saturation, finding new ways for old and aging brands to stay relevant, and the impact of new technologies and innovations. No one anticipated the greatest challenge of 2020 — and beyond — would be a pandemic that shut down the world.
According to McKinsey, 80 percent of U.S. retailers shut down at least part of their business operations, and 44 percent closed their doors altogether during stay-at-home orders. Yet many of these respondents anticipate that, when stores reopen to full business, they will return to pre-crisis levels, and perhaps even be stronger. Those that could turn to e-commerce operations did so with some success, and this shift has many in the industry looking at a change in how they’ll do business going forward.
However, because most medical experts expect a second wave of COVID-19 and because they warn of other major public health crises in our future, retailers can’t just plan for the short term in a post-COVID world. They need to plan for survival during long-term shutdowns and stalled operations. The best way to approach this plan is with data-driven solutions.
Data-Driven Reinvention of Retail and the Customer Experience
While retailers are optimistic about the future, consumers are less so. Only 37 percent think there will be a quick rebound in the economy, and 44 percent said they were delaying purchases through the crisis due to an uncertain financial future.
Retailers can’t survive if consumers aren’t buying, but how do you attract people to make nonessential purchases if they aren’t confident when or if they’ll return to their job?
“Retailers can minimize current and future business impacts by identifying and executing on controllable activities,” Kelsie Marian, senior director analyst with Gartner's CIO Research Group, wrote for Retail Dive. “In the short term, they must identify and optimize existing technologies and business models. In the longer term, the focus should be on evolving business models and enabling transformational change with new and emerging technology.”
The driving factor that makes these technologies successful is how well they utilize data. During the worst of the pandemic’s shutdown orders, essential retailers struggled to meet the supply and demand of items. Toilet paper, cleaning supplies, hand soap and sanitizers, yeast and flour, canned vegetables, meat, and even bottled water have been nearly impossible to find in many locations. Retailers relied on the data of normal customer shopping habits and weren’t able to make the pivot necessary to meet increased consumer needs.
However, it wasn’t just retailers managing this shift; supply chains also had to adapt. As offices and restaurants remain closed, more emphasis will be on individuals making smaller purchases rather than on supplies sent to warehouses waiting for industrial orders. The challenge with data-driven models is that there's no history for this type of data analysis. While you might have been able to predict an increased need for toilet paper, no one anticipated millions of households would begin baking their own bread. With expectations that remote work will remain high and uncertainty around how other industries will recover, retailers can no longer rely on historical data to stock shelves.
In part two of this multipart series, I examine how artificial intelligence, machine learning, data infrastructure and management can help retailers adapt to the changing landscape.
Nick Jordan is founder and CEO of Narrative.io, the enterprise data streaming platform company.
Nick Jordan is founder and CEO of Narrative.io, the enterprise data streaming platform company. Nick founded Narrative in 2016 after spending nearly a decade in data-related product management roles including Yahoo!, Demdex (acquired by Adobe), and Tapad (acquired by Telenor). The author can be contacted on LinkedIn.