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Big Data Guest

For better snacking, big data is the secret ingredient

Nutella Jonathan Lin FlickrNutella Jonathan Lin FlickrImage Credit: Jonathan Lin/Flickr November 6, 2014 6:30 PM
Stephen DeAngelis, Enterra Solutions 0

Snacking is officially a craze. More than nine out of 10 Americans snack daily — a quarter of them three to five times every day, according to a recent Nielsen survey. But it’s not just people in the U.S. who are sneaking a bite. Snacking is now a $374 billion global industry, and it’s growing 2 percent annually.

Europeans spend nearly $170 billion annually on snacks, while the Asia-Pacific region accounts for $50 billion, and Latin America for $30 billion. The U.S. is right behind Europe at $120 billion in annual sales.

Among the snackers most attractive to manufacturers and retailers are millennials. Already representing 14 percent of the population, millennials are expected to have $2.45 trillion in annual spending power by 2015 and exceed baby boomers’ spending by 2018. But manufacturers are quickly learning that millennials can’t be treated as a homogeneous group. After all, millennials are commonly defined as anyone born between 1982 and 2000, meaning everyone from a 14-year-old high school student to a mature 32-year-old.

That’s why capitalizing on the snacking trend and catering to the tastes of millennials will require a new approach. Demographics, customer information and feedback, and other data are impacting product development at an unprecedented pace. As a result, many of these companies are turning not only to big data analytics to find patterns and actionable insights, but to cognitive computing to react more quickly to shifting tastes and increasingly unwieldy piles of data.

Uncovering the patterns in changing tastes

Armed with the right data, big data analytics tools can help manufacturers understand changing taste preferences and which grocery stores cater to niche consumers. It’s a win-win-win situation for manufacturers, retailers, and consumers. Manufacturers can place the right products in the right locations, retailers can drive sales, and consumers have increased access to the products they prefer. This is true around the globe.

Big data also helps manufacturers react more quickly to changing tastes. For example, although many Hispanic foods have gone mainstream in the U.S., other ethnic foods remain popular only in niche markets. By turning to big data analytics, manufacturers can better understand the emerging food landscape.

A new paradigm for CPG manufacturers

For many manufacturers, however, this will mean a fundamental change in how they operate. Through 2015, 85 percent of Fortune 500 companies will be unable to use big data for competitive advantage, according to Accenture. While technology has long played a role in improving supply chains and other processes, many of these companies need to rethink the role of technology, using it more to engage customers than eke out new efficiencies.

What this means is creating what’s been termed a “data supply chain” — essentially making sure that data doesn’t lie fallow in a silo, but is actively circulated throughout an organization so the company can more easily mine that information for new directions.

This is especially important when it comes to millennials, who are arguably the most connected generation in history. By compiling all the data that millennials offer, from their social media feeds to their supermarket loyalty cards to mobile phone data, manufacturers can better position themselves to tap into the growing clout of this demographic and their various snacking preferences.

How CPG manufacturers mine data to pair snacks with their audience

While the most popular snack right now is chocolate (don’t worry, it’s followed closely by fruits and vegetables), tastes vary by region, age, income, and other factors. Some millennials work full time while others are still in school. Some live with their parents while others live with their significant other. Here are five ways technology is helping manufacturers, retailers, and consumers uncover the snacks and tastes they most desire.

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.

Stephen DeAngelis is president and chief executive of Enterra Solutions.


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