In Part 1 of our “Data-Driven Selling” blog post series, we outlined why it’s so important for manufacturers to have food industry data analytics in 2023.
In this post, we drill down to highlight specific key data available to food manufacturers selling to retailers. More importantly, our Director of Perishable Sales and Business Development, Erin McCulloch-Crume, shares how these critical insights can help food manufacturers hone their selling strategies to maximize sales.
Why food industry data analytics are so important in 2023
As a quick recap of Part 1, the retail food industry currently faces many unprecedented and/or complicated issues. These include rampant inflation, global supply chain issues, the war in Ukraine, unpredictable weather and disease.
As a result, many food retailers have seen a decline in traffic and sales. So, given the numerous factors impacting the food industry today, it’s more important than ever for manufacturers to use food industry data analytics to their advantage.
Critical data food manufacturers should obtain and leverage
“Data helps food manufacturers assess things at a more granular level,” Erin said. “It can help them figure out what’s working – and what’s not – how to position their brands and where to focus their efforts.”
According to Erin, here are five powerful insights that food industry data analytics can offer manufacturers.
Shopper buying habits
Understanding shoppers’ buying habits at the grocery store can be very enlightening.
For example, recent data shows that each generation shops at the supermarket differently. For example, Baby Boomers make a greater average number of trips to the store but spend less per trip, while Millennials are making fewer trips but spending more per trip.
In addition, food industry data analytics show that as shoppers age, they become more deal-driven. This is especially true for older shoppers buying natural products.
“Having data that shows shoppers’ buying habits can lead to more effective marketing strategies, personalized shopping experiences, and ultimately, increased customer loyalty,” Erin said.
Syndicated data offers insight into how products are performing compared to competitors in their category. It also signals up-and-coming categories, and other beneficial information that food manufacturers can use to their advantage.
“Category performance data is especially helpful for food manufacturers that are developing products and formulating sales strategies,” Erin said.
For example, data may show a particular category is experiencing tremendous demand. Food manufacturers may want to consider developing a product for that category to capture a piece of the market. At the same time, having data that shows a category is losing popularity may signal to food manufacturers it’s time to rethink selling a product there.
Food industry data analytics can show which specific product attributes are most popular among different types of shoppers.
For example, according to SPINS’s “Big Trends 2022” report, 77% of all grocery shoppers believe sustainability is important when selecting products. Further, Millennials and Generation Z are largely driving demand for certain sustainability attributes, such as those related to the environment.
Another example is diet-specific attributes, such as Keto-friendly, plant-based and gluten-free.
“Having data that shows which attributes are the most popular can positively impact food manufacturers’ decision-making when it comes to product development and packaging,” Erin said. “They’ll know what is most important to their customers and can prominently display those keywords on the package.”
It’s important manufacturers have food industry data analytics that break down information according to each retailer where the product is sold.
“Each retailer’s goal is to meet the needs of their specific consumers,” Erin said. “And each retailer’s target consumer might look a little different. It’s important to focus on solutions-based selling that offers products that fit the needs of the consumer.”
Even if a product is selling well in total at retail, that doesn’t mean it’s selling well at each individual retailer. That’s why retailer-specific data is important: it will show exactly where a product is selling well, and perhaps where it isn’t.
“Having this customer-specific data helps manufacturers identify voids and can influence the pricing matrix and promotional strategies to determine what works at each specific retailer,” Erin said.
Trends in Total U.S., Natural Specialty Channels and Club Channels
Manufacturers that have data showing retail food trends in Total U.S., Natural Specialty Channels and Club Channels can have a competitive advantage when selling to retailers.
“When we work with our manufacturer clients, we’re always selling the why to retailers,” Erin said. “Why the customer needs the product, why the product fills a void in the store, etc. And it’s best when we can sell the story through data.”
For example, consider a food manufacturer that can show through data that their natural product is performing very well at a natural specialty store. They also happen to know that a non-natural-specialty store is looking to grow their natural department. The manufacturer can use the data showing where the product is performing well in their attempts to sell to the other retailer.
Stay tuned for our third and final post in the “Data-Driven Selling” blog series. In it, we’ll cover how food manufacturers can get the food industry data analytics they need to maximize their sales to retailers.