Planning in a Pandemic: How AI-Powered Analytics Can Help Retailers Today

Consumption and shopping behaviors have changed dramatically, leaving retailers with a challenging and dynamic planning process ahead — months after the start of the pandemic. Further complicating things, historical data is losing relevance, and it’s unclear which new behaviors will stick.

Retailers must now juggle hundreds of constantly changing variables and answer questions like:

• With “66 percent of shoppers prefer[ing] mobile self-scanning and self-checkout,” should retailers invest now, and to what degree?
• Is this category growth caused by increased demand or temporary pantry-loading?
• How can retailers adjust their plans and investments to maximize growth in channels like online and curbside pick-up?

In order to answer these questions (and many others), retailers must plan with multiple scenarios in mind, zeroing in on the most likely possibilities as certainty grows. Predicting demand will also require more flexibility, as retailers operate against recent, rather than historical data. In an ideal state, teams can map out likely scenarios, monitor real-time performance, and pivot accordingly.

To achieve this level of dynamic planning, retailers need business agility and precise, cross-functional collaboration, as well as fast insights that enable crucial decision making.

Manual analytics processes that take days and weeks to produce insights are no longer sufficient, especially when category and analytics teams are strapped for personnel and resources. Silos, too, prevent retailers from understanding the full picture of business performance and generating return on investment from syndicated data. As a result, growth opportunities are overshadowed by this lack of visibility and cohesion.

Artificial intelligence-powered analytics provide retailers with a new approach to planning — one that's enabled by automation.

AI is inherently dynamic. It works much faster than human analysts, and it doesn’t approach data with preconceived notions about what happened in a category. Instead, it determines root causes and drivers, or the “how” and “why” behind category, brand and business performance. This process takes seconds or minutes compared to the days or weeks of a manual process.

Specifically, the process works like this: machine learning algorithms exhaustively analyze all first party and syndicated data sources, testing every data combination to determine drivers in a category or brand. As a result, AI analytics is unbiased, an essential capability when retailers are dealing with the unknown. By analyzing shopper, loyalty and panel data in tandem, AI analytics creates a single source of truth for different departments and functions.

As a result, teams can operate against the same insights narrative (produced via natural language generation), making decisions based on the complete context of various business functions.

For example, it’s one thing to know that private label grew by 70 percent due to an increase in demand. It’s another thing to know that private label grew faster than the category because of increased penetration as a result of higher promo intensity in a specific pack size. The ability to understand the “why” behind growth is critical because it helps teams determine the actions that will generate the greatest ROI.

Simply put, an insights narrative tells the whole story.

From there, retailers can construct plans based on exhaustive data analysis, rather than intuition, gut feelings or shaky predictions.

Essentially, AI analytics is capable of automating category analysis and other workflows (brand and market share analysis, for example). This automation occurs in seconds or minutes, meaning retailers can access their data in real time rather than learning what happened in Q4 at the start of Q1. This speed promotes proactive decision making and agility — essentials for long-term success.

In addition, this automation enables better cross-functional collaboration. AI can examine many different data sets (e.g., shopping, consumption, media, etc.) to understand the root causes behind demand. As a result, teams can make decisions from a single source of truth to close the gap against uncertainty.

In many ways, the digital transformation is already well underway as retailers adapt to e-commerce, online delivery, and more. Now, retailers must update their own tech stacks to support category and analytics teams with planning around the pandemic and beyond. With AI analytics, plans can be fueled by data and insights rather than constant uncertainty.

Ada Gil is product owner at AnswerRocket, a tech company specializing in AI-powered data analytics software for companies belonging to industries like CPG, retail, financial services, and more.