How CPGs Can Use AI to Improve Retailer Relationships
When I was leading a CPG business several years ago, I had both consumers — the end users of the products in my portfolio — and customers — the retailers who sold those products to consumers. It’s sometimes referred to as B2B2C and it was the primary business model for CPG. Because I needed to develop partnerships with retailers in order to gain distribution and optimal shelf space for my SKUs, I worked hard to identify retailer pain points and solve them. With the advent of AI and cloud computing, many of those pain points are being solved with greater accuracy and speed than before. AI and Cloud allows CPGs to build stronger relationships with customers, amplifying efforts through the power of tech. Here are a few examples.
Stock out prevention
Good demand planning, especially for highly perishable products like dairy, can make a big difference in both minimizing excess production and ensuring retailers don’t experience stock outs. I recall a retail buyer who, despite the fact that a year had passed, remained disappointed by a major stock out. The prior year’s new brand launch had been highly successful, far exceeding expectations which led to the stock out. Because of this disappointment, the buyer was unwilling to increase shelf space for any of the parent company’s other brands, including my own. While minimizing excess production is the brand’s problem, there are few issues that upset retailers more than stock outs. The retailer has shelf space dedicated to your product and when you’re out of product, you both lose. With advanced AI, brands are able to manage demand forecasting with more accuracy, reducing stock outs.
Shelf space optimization
Category leadership is a privilege. It strengthens partnerships with retail buyers. When my brand was the category leader, we had an incredible amount of influence on shelf space planning. I led initiatives where my team’s sales leaders partnered with category management and insights to pitch shelf plans that, through intensive data analysis and modeling, were predicted to improve sales for the entire category. These highly data-driven projects lend themselves extremely well to the power of AI/ML in cloud computing. Cloud helps brands and retailers ingest more data from more sources and use sophisticated ML to identify and respond to changing conditions faster, driving increased sales for the retailer, the category, and brands.
AI-enabled personalization
I had managed to negotiate a massive promo opportunity with a major mass merch retailer and expected to move 25% incremental volume. The agility with which I can support such a promotion through my app, social, search, and brand sites across web and mobile would greatly impact the promotion’s success. Headless commerce, which eliminates the design and execution constraints that come with tightly integrated platforms, allows brands to do more by being nimble and fast while freeing time and resources. AI-enabled headless commerce APIs offer the ability to turbocharge personalization through next-level search optimization, product recos, and predictive preferences in an omnichannel environment with flexibility and at scale. It helps my promotion reach the right users, at the right place, at the right time with greater precision, delivering increased efficiency. The more impactful my promotions are, the happier the retailer.
Bottom line is this: retailer expectations have risen. As someone who led a brand that was a category leader, I can attest that retailers expect better intelligence and more actionable, valuable insights than ever before due to the continued increase in competition. As the P&L owner of a $300M flagship CPG brand, more than half my time was spent querying and analyzing massive (Nielsen, IRI, Dunnhumby, et al) data sets, combining data sets from siloed systems (campaign performance, shipment, budget data lived separately), calculating inventory based on consumption and shipment data, creating complex visualizations from scratch, identifying trends and patterns to create my forecasts, and tying everything together to understand business dynamics in order to devise and refine my strategy — all this work I did manually. I would have preferred to have spent that time understanding key drivers and insights to make business decisions and developing innovative growth strategies to improve sales for both myself and my retailers.
AI and cloud tech significantly reduces manual analytics and insights work, enabling business leaders like myself to focus where we drive the most value — innovation, strategy, and execution. Retailers are more likely to partner with those CPGs who do these things best. Embracing AI/Cloud is a must for CPGs looking to strengthen partnerships with retailers.
