For most consumers, shopping and buying experiences have been forever changed by Covid-19. A year ago, most shoppers would not have predicted that their grocery, drugstore, home goods and even alcohol purchases would unrelentingly migrate online. Options from shipping and same-day delivery to click-and-collect curbside pickup and buy online pick up in-store (BOPIS) were forced into virtual ubiquity by a do-or-die pandemic imperative. Safety, speed, and convenience accessing goods, whether in-store, curbside, or online, has become more important than ever, as has keeping up with ever-shifting consumer demand.

In the same way that online services needed to evolve rapidly overnight, so too do the analytics that understand and predict demand and figure out on the fly what categories are needed, when and in which locations. Previously, it was the category manager’s job to look back on historical and seasonal trends which would, presumably, allow them to predict with a decent measure of accuracy what would come next in consumer demand, and to prepare for upcoming supply needs. Covid-19 has upended any predictability in this and other industries, and has proven that, in the world we live in today, nothing is certain, and there is little in the future you can base off the past. In an environment where consumer shopping habits have changed so radically, retailers need a better way to assess rapidly changing demand and manage inventory. The shopping experience has changed; so too must our strategy.

The bright side is that we have all the tools we need: data. Using real-time, granular-level insights to drive category management decisions, retailers and brands have everything they need to develop the right protocols and proactively prepare, delivering on consumer demand.

Historically, these tools and their data have all lived in different places: One tool for supply chain reports, working to complement a separate tool tracking inventory management and product promotions, and then there’s always external data from third-party providers as well. In today’s environment, however, it is no longer possible to make critical decisions in a timely and effective way across departments, within channels and categories, or amongst trading partners, especially when stakeholders are operating off data from multiple, disjointed sources. It is certainly not possible to make quick decisions to respond to new customer behaviors as they change day-to-day if analysts must sort through various spreadsheets to understand what is happening in real-time. When you are stuck working with fragmented, higher-level data, you are severely limited in your ability to analyze and react, let alone effectively provision for what is to come.

But what if supply chain operators were able to monitor current inventory at the shelf level alongside their product and category management teams and respond proactively, for example, as supplies run low? This would require harmonized data in one place, integrated with the whole supply chain. A single source of truth is essential for providing as close to real-time insights as possible, while cross-organization collaboration and communication enables even more agility. This is where the future of analytics resides.

Delivering on the demands of today’s shopper, who now constantly questions all their shopping habits, brands and preferred retailers, is nearly impossible for brands to manage on their own. The shared visibility of end-to-end supply chain analytics and co-developed strategies is the only way forward if retailers are to be flexible, and it is that flexibility that will determine their ability to respond to ever-changing consumer needs and habits.

Access to this kind of data has in the past year especially become the dividing force between successful retailers and brands, and those that are struggling to keep up. At the same time, the current environment is bringing to light existing deficiencies and opportunities to address evolving demands that may have otherwise been missed. Retailers without the foundations of successful data-driven category management in place will be challenged to respond to customers quickly and efficiently enough to survive in the long run.

Knowing what the customer will want, however, is only half of the battle. For this new strategy to be truly successful, a much more informed, collaborative strategy needs to be developed to not only address shifts in demand for the types of products consumers need and want, but also to consider when or where they will want them, and which avenues can deliver on demand the fastest. Granular and localized demographics, region, geography and climate data, as well as news and especially public health situations is necessary. Only through this understanding will retailers, from the beginning of the supply chain to the end, be able to optimize today’s results while also preparing for tomorrow’s uncertainties. 

If alternative sales, early warning indicators, and supply chain data are integrated, as a category manager, you’d know where demand changes are occurring at large and will be able to evaluate whether your current inventory is equipped to meet those changes with real-time stock availability and future orders. From there, managers can plan inventory adjustments; if it is too late, those same data tools will be able to suggest suitable alternative products for customers, increasing satisfaction and loyalty.

Retailers that invest in their data infrastructure right now will have the information they need to adapt and respond to consumer demand faster than their competitors and capture customer loyalty in the long run. The consumer trust and confidence built during these “unprecedented times” will last long after the pandemic has been resolved.