Grab Consumers’ Attention at the Shelf with AI

Did you know poor merchandising is responsible for 25% of lost sales? It might be time to rethink your retail execution solutions. 

With margins tighter than ever, in-store displays and promotions have never been more crucial to achieving profitable growth. Yet, even though the consumer goods (CG) industry spends $200 billion each year to promote products in stores, poor retail execution often fails to grab consumers’ attention. With 38% of in-store marketing plans carried out incorrectly, 36% of CG decision-makers see in-store merchandising as a key opportunity. We think future advances in AI will offer effective solutions to the retail execution issues that keep CG leaders up at night. 

Why use predictive and generative AI for retail execution?

You may already use predictive AI to forecast supply and demand. Predictive AI is programmed to carry out specific tasks, analyzing information to classify data or predict outcomes. Generative AI, on the other hand, has exploratory capabilities, which lets it look at your data – including the forecasts from your predictive AI – to create new content based on that data. This new content could include product photos, planograms, bills of material, promotional copy, and much more.

For example, predictive AI may tell you how many widgets field reps should order for their stores this year, but in the future, generative AI could also recommend the best brands and models of widgets to order, specify how many are needed for each store, and then design the endcap displays to house them in-store.

By using predictive and generative AI with your own trusted historical and real-time data, you’ll be able to more effectively get products on shelves and in carts to boost sales. And, field reps will thank you for making their jobs easier. 

Now, you might not see all of this retail execution functionality available today — we’re thinking big here! But AI innovation moves at light speed, and here’s where we believe the industry is heading. 

1. Keep tabs on your inventory to keep shelves stocked 

For CG leaders, keeping products on shelves is a persistent challenge: According to our research, 52% of leaders want better visibility into their inventory. AI-powered retail execution solutions could help.

Maintaining a smooth flow of goods from the warehouse to the store sounds simple. But sometimes, products aren’t delivered where and when they’re most needed. That’s where predictive AI could help. By analyzing historical data, market trends, and other variables like seasonal merchandise demand and promotions, it could calculate more accurate demand forecasts – by store or by region. With a better understanding of demand, you could optimize inventory levels and set the stage for more sales. 

AI could monitor your inventory levels at each location, considering factors like expiration dates or outdated promotions. Based on historical data and current inventory levels, it would automatically alert field reps when products need to be replaced or replenished, sending detailed checklists of products to remove and suggested replacements. Its recommendations would be informed by lead times, product availability, and supplier inventory to allow for automatic orders and delivery scheduling. Field reps can take immediate action without scheduling a store visit, saving them — and retailers — time and effort.

2. Design eye-catching, brand-centric store displays 

Have you ever run into the grocery store on July 3 for hot dog buns and left with spur-of-the-moment sparklers? That’s the power of advanced retail execution solutions. Effective merchandising keeps consumers spending and retailers happy. In grocery stores, for example, placing products on an endcap yields a 93% increase in exposure — a huge advantage since 62% of grocery store purchases are impulse buys.

Generative AI can help CG companies design displays and promotions by analyzing consumer behavior, product performance, and sales patterns to understand what attracts consumers. It can then create layouts of in-store promo materials based on that data. The suggestions can save merchandising and marketing teams time researching and creating rough layouts.

Based on factors like customer flow, visibility, and cross-selling opportunities, AI could suggest product placement that engages more customers and increases sales. It would also examine successful displays; optimal design principles; and customer preferences to recommend color schemes, shelf organization, and fonts to give your design team a head start. This saves a lot of legwork so designers can focus on more strategic and creative decision-making.

Still unsure of which design is best? Generative AI would help A/B-test designs by creating virtual store environments to see how a display affects engagement, sales, and conversion rates. 

Even with AI, you’ll need to keep your field reps in the loop. Ultimately, they are your eyes and ears in-store, evaluating competing merchandise and sharing what’s happening in the shopping aisles, so their input on final display decisions is always invaluable. But augmenting their expertise with AI could help them spotlight your products and increase sales in the long term.

3. Anticipate consumer wants and needs 

Imagine if, once your displays are in place, AI could use real-time data to help field reps make strategic decisions for product mix and display. For reps, it’s essential to get the right products in stores at the right time of year. Often, this means more than just analyzing historical sales data. It means understanding the regions in which they’re selling so they can consider local events, weather, and community preferences that might affect sales.

You could combine predictive and generative AI to analyze market trends while considering location-specific circumstances. For example, if a Milwaukee high school football team has made it to the state championship game in Wisconsin, there might be more demand for beanies and parkas sporting the school colors in stores around town. But if the team is playing a national championship game in Texas during a heat wave, those same stores may see an increased demand for baseball hats and t-shirts from Wisconsin residents who plan to trek to the game. By analyzing consumer demand and regional variations, AI could recommend the optimal product mix and automatically update demand forecasting, inventory management, and replenishment strategy.

In addition, AI would suggest shelf space allocation for products based on sales data and store layouts, product profit margins, and shopper behavior. This enhances visibility, improves sales performance, and keeps retailers and shoppers happy. Generative AI could also mock up the final shelf so reps can follow along, making the most of their store visits.   

Finally, you’d be able to add data sources like foot traffic sensors and heat maps to provide reps with real-time performance analytics. If your display fails to engage consumers, reps would use this data to make changes during their next store visit.

Improve retail execution solutions at every location

Prices and inventory might look different every year, but adding predictive and generative AI to your retail execution solutions could soon help reps make sure your displays and promotions are executed perfectly in every store. With better inventory management, display design, and real-time testing, you’ll win at the shelf with both retailers and consumers.