The perfect snack recipe calls for a few crackers, a scoop of peanut butter, a piece of chocolate and a hint of big data. That’s right, big data has made its way into the food industry. It seems as if no industry is safe from the data influence and in this case, big data may offer answers to meet the ever changing needs of snacking.
Food companies face an interesting dilemma when trying to reach the needs of their consumers. When it comes to food, or in this case snacks, tastes and preferences vary so much, it’s hard to have one single strategy that meets everyone’s needs. Instead, businesses are forced to come up with a wider range of products and approaches, which is no easy task.
Americans love snacking, running to the fridge or pantry multiple times a day to satisfy their cravings. Snacking is a multi-billion dollar global industry, making it fiercely competitive and lucrative. Among the more active in this category are millennials, who account for the largest percentage of snackers. And while companies are actively trying to target millennials, it’s easier said than done. They range in age from 14 to 32, and come from different backgrounds with different taste buds. In the United States alone there is incredible diversity in this age group, meaning a one-size-fits-all approach to snacking will end up in the trash, if it’s even lucky enough to make it off the shelves.
Enter big data. Companies are looking to use the vast amounts of information on consumer behavior to figure out which products to make, and where to focus their efforts. The right data and analytical tools can help manufacturers understand taste preferences and which retailers cater to which consumers. Overall, everyone benefits when big data is in the equation. Manufacturers are able to get their products to the right locations, retailers see an increase in sales, and consumers have easier access to the snacks they prefer. Meaning, companies can determine which snacks to sell in specific areas. For example, if a U.S. company specializes in making Indian and Pakistani snacks, they should focus their efforts on metropolitan areas with a high South Asian population, like New York or Chicago. Or, a GPG company may use a wide variety of retailers to sell their product and so they’d like to know who shops where, and send different snacking options accordingly.
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Big data can also help manufacturers become more reactive to changing tastes and snacking trends. Data will help determine why certain products are popular, increasing the likelihood of future product launches. Also, it’ll help determine which ethnic foods are popular with the mainstream, and which are not. Relying on big data analytics will help companies better understand the snacking and overall food landscape.
Here are a few ways technology is helping manufacturers and retailers uncover the snacks consumers crave.
1. Know Your Audience
As mentioned earlier, big data analytics can help manufacturers and retailers uncover who’s buying what. This will help them develop and stock products tailored to current demands. Companies can’t expect to survive by guessing what people are looking to buy. Instead, it’s far better to learn from what people are buying now, how that alters with age, and how future products can be tweaked to fit changing demands. Companies can learn what specifically people are drawn to, and create similar snacks that satisfy these cravings.
2. Monitor In-Store Experiences
How are consumers shopping? What draws them into stores and influences their purchases? Today’s consumers are also mobile users. Are retailers offering mobile options to enhance the shopping experience? Using loyalty programs and other incentives can help companies gain data while also offering something back. Companies need to learn what works and what doesn’t, then implement the right changes to make sales.
3. Cognitive Computing is Essential
Improvements in technology and big data trends have given rise to improvements in machine learning. The sheer volume of data is growing exponentially, and companies are looking for faster speeds and real-time analytics. Cognitive computing combines machine learning and artificial intelligence to go beyond data mining and provide actionable insights.
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