How AI can help CPGs define the new normal at the shelf after the pandemic

Time, accuracy and scale: a modern view

The disruptions in consumer demand caused by the Coronavirus pandemic this spring revealed new realities for brands and their retail partners. Panic-buying caused out-of-stocks to soar in sensitive categories, exposing a brittleness in the just-in-time supply chain that trading partners were unprepared to confront.

For CPG brands, the consequence is that shoppers currently are far less likely to find and purchase the brands and items they prefer. Instead they are much more likely to purchase the items they can get. This assault on loyalty means executing at the shelf and preparing for new fact-based conversations about space and assortment have never been more important.

This paper presents a point of view that AI based machine learning tools have potential to help brands take positive action to find their way back to the new normal at the shelf.

Shelf presence matters more than ever

As more households have turned to online ordering for grocery products during the present crisis, the problem of substitutions has come into sharper focus. A recent study by Acosta, a grocery marketing agency, found that 28% of online grocery shoppers made their first-ever online orders in March 2020. Eighty-eight percent experienced out-of-stocks, and of those shoppers, 47% accepted a substitute for half or more of their unavailable items.

Similarly, recent store tracking report by marketing agency SFW found that 86% of shoppers who encountered recent OOS had tried a substitute brand. Ominously, 43% of those shoppers indicated they now prefer the newly-tried brand.

Shelf space changes are coming

Current shortages (and anxieties) have led shoppers to experiment much more freely with brand alternatives and digital-versus-store shopping behaviors. Collectively, their choices have exposed certain categories to be under- or over-allocated. Space, adjacency, assortment – all these fundamental merchandising dimensions are likely to be revisited in coming months, as trading partners come to terms with the implications.

We can hypothesize that recent substitution behaviors and shifting loyalties will reveal categories that have been chronically over-assorted. Much of the foundation work of category planning may need to be refreshed in light of new insights. This sets up a period of intense competition among brands to defend and re-justify their places on the shelves.

Robots and AI tech generate shelf data

Well before the onset of the pandemic, retailers were already experimenting vigorously with robotic shelf-scanning devices and camera sensors to capture shelf conditions in real time and address out of stocks. Best-of-breed solutions use Artificial Intelligence and machine learning to interpret captured images and deliver rapid and reliable information on shelf status.

Walmart has led the charge in this area, with a commitment to deploy shelf-inventory scanning robots in 1,000 stores by year end. The present disruption at retail could help open the door to more widespread daily use of AI and robotics by retail industry to support execution. All stakeholders, including brands, will need to reckon with the new torrent of shelf data that are becoming increasingly available to your partners and/or competition. As the industry recovers and begins to put its house back in order, brands will want to take initiative in this area to preserve or even enhance their position on retail shelves.

How can brands lead the way?

There is already strong precedent for this. Snap2Insight has been working with leading brands including Clorox, and Starbucks, providing them with valuable analytics before and during the crisis. The solution interprets shelf images collected from retail robots (or crowdsourcing or merchandisers) and converts them into actionable data using machine learning. The resultant information can be used monitor on-shelf availability in your category and detect trends that justify changes in space and facings.

Accurate, timely data will be the key asset brand marketers need to drive better execution and fact-based category planning. Brands who come to the conversation with clear analyses of retail conditions and shelf productivity will stand out as value-added partners as this recovery process proceeds.

Collecting this information store-by-store has been a perennial challenge. Anecdotal reports from your field merchandisers are unlikely to deliver the quality of data to support timely decisions or justify which items merit additional space. For best possible impact you will need to adopt new tools which bring speed, scale, and accuracy.

Defend Your Shelf

In sum, unless brands maintain a continuous clear picture of your product presence at shelf, they cannot take effective action to create lasting advantages. A winning approach will use AI shelf analytics to measure both the “what?” and the “so what?” in terms of out- of-stock and share of shelf.

Snap2Insight recommends brands to explore our ‘AI shelf tracker’ package that analyzes images from 200 or more stores in three waves across a period of 3-6 weeks, to measure SKU-level out-of-stocks for your brand and category-level out-of-stocks. Shelf analyses, including share of shelf and shelf placement are presented in a live dashboard and PowerPoint. Full results will be available within two weeks after data collection ends.

What will you gain: Overall out-of-stock rate and dollar loss, which can enable your brand to hold retailer, merchandisers accountable. Rapid discovery and fixes for out-of-stocks. Better leverage in the discussion to rationalize assortment and fair share of shelf.

If you deliver the right data, even non-captains will earn a seat at the table in the realignment to come.