Big Data: The secret ingredient for food companies?

Published January 9, 2015   |   

All food companies face an interesting dilemma: How to understand the tastes and needs of their consumers? When it comes to food or snacks, tastes and preferences vary so much that it is hard to have one single strategy that meets everyone’s needs.

Besides, customers belong to all range of age groups and come from different backgrounds with different taste buds. So, how do they solve this problem? As one of the largest industries in the world, the food industry is adopting big data technology to better understand and serve customers.

Here are a few ways technology is helping manufacturers and retailers uncover the snacks consumers crave.

1. Know your audience

Big data analytics can help uncover who’s buying what products and better tailor future goods to their professed tastes. Guessing and assuming what the customer wants is no longer an option. What snacks are millennials buying now? How do snacking preferences change as millennials age? How does that correlate to future product plans?

2. Monitor the in-store experience

One in three grocery shoppers use a mobile phone while shopping. Whether to check prices or look up recipes, consumers are actively looking for engagement in stores. In fact, one study showed that nearly 73 percent of survey respondents said they wanted price comparison services on their mobile phones. The same study found that supermarket loyalty cards are a consumer-friendly way to pull data. Nearly two-thirds of respondents said they were fine with retailers using their shopping habits and purchase history to offer products and services as long as their data was safe.

3. Tap into social media

From YouTube stars to Instagrammers, millennials are making names and a living for themselves through social media. These influencers offer an opportunity to identify brand advocates to reach a specific audience on a specific platform. In addition, pulling in social media posts from consumers, especially brand interactions, can offer insights into consumer sentiment that can then be directed toward the production of new snacks.

4. Rely on cognitive computing

As the speed and volume of data continue to increase, machine learning will allow organizations to react more quickly to the insights contained in that data. Using ontologies that can discover relationships and aid understanding, machines can increasingly digest data, learn from it, and hone millennials’ taste preferences through ongoing interactions. Cognitive computing goes beyond data mining to provide actionable insights (i.e., real understanding).

5. Use data to engage

McCormick & Company’s FlavorPrint campaign is one example of cognitive computing targeting the tastes of consumers. FlavorPrint offers recipes based on taste preferences, available ingredients, and kitchen appliances. By learning a consumer’s preferences, the platform refines the recommendations over time, which doubled repeat usage of the site and increased the amount of time spent on the site ninefold.

As snacking continues to grow around the world, and as millennials’ buying power increasingly dominates all demographics, it appears the most important tastemakers of tomorrow are likely to be machines.

This article originally appeared here.