If you imagine how a food and beverage company creates new offerings, your mind likely fills with images of white-coated researchers pipetting flavors and taste-testing like mad scientists. This isn’t wrong, but it’s only part of the picture today. More and more, companies in the space are tapping AI for product development and every subsequent step of the product journey.

At PepsiCo, for example, multiple teams tap AI and data analytics in their own ways to bring each product to life. It starts with using AI to collect intel on potential flavors and product categories, allowing the R&D team to glean the types of insights consumers don’t report in focus groups. It ends with using AI to analyze how those data-driven decisions played out.

“It’s that whole journey, from innovation to marketing campaign development to deciding where to put it on shelf,” Stephan Gans, chief consumer insights and analytics officer at PepsiCo, told VentureBeat. “And not just like, ‘Yeah, let’s launch this at the A&P.’ But what A&P. Where on the shelf in that particular neighborhood A&P.”

A new era of consumer research

When it comes to consumer research, Gans likes to say that “seeing is the new asking.” Historically, this stage of product development has always been based on asking people questions: Do you like this? Why don’t you like this? What would you like? But participants’ answers aren’t as telling as we’d like to think. They might not really care because they’re paid to be there, or they might just be trying to be nice. They might also be sincere in the moment, but it doesn’t mean they’ll still be excited about the product three years after launch.

“People will give you all sorts of answers,” Gans said. “It’s just not very close to what is ultimately driving their buying behavior.”

To uncover more telling insights PepsiCo can channel into product roadmaps, the company uses a tool called Tastewise, which deploys algorithms to uncover what people are eating and why. Also used by Nestlé, General Mills, Dole, and other major consumer packaged goods companies (CPGs), the AI-driven tool analyzes massive quantities of food data online. Specifically, Tastewise says its tool has monitored more than 95 million menu items, 226 billion recipe interactions, and 22.5 billion social posts, among other consumer touchpoints.

By collecting data from all these different sources — which represent what people are voluntarily talking about, searching for, and ordering in their daily lives — Gans says his team “can get a really good idea as to what people are more and more interested in.” For example, it was findings from the tool that gave PepsiCo the idea to incorporate seaweed into a flavored savory snack. The company brought it to market as Off The Eaten Path, and long story short, Gans said it’s been a top seller since.

“If you would’ve asked consumers, ‘tell me what your favorite flavors are and let us know what you think would be a great flavor for this brand,’ nobody would have ever come up with seaweed. People don’t associate that typically with a specialty snack from a brand. But because of the kind of listening and the outside-in work that we did, we were able to figure that out through the AI that’s embedded in that tool,” he said.

Data-driven social prediction

Taking another angle to insights, PepsiCo also leans heavily on Trendscope, a tool it developed in conjunction with Black Swan Data. Rather than analyze menus and recipes, it focuses exclusively on social conversations around food on Twitter, Reddit, blogs, review boards, and more. The tool considers context and whether or not the conversation is relevant to the business; it measures not only the volume of specific conversations, but how they grow over time. Gans says this allows the team to do what they call “social prediction.”

“Because we have done this over and over and over again now, we can actually predict which of the topics are going to stick and which are just going to kind of fizzle out,” he said.

The pandemic, for example, caused a massive spike in interest around immunity. By using Trendscope, PepsiCo determined that specifically for beverages, the interest is here to stay. About six months ago, the company acted on that insight when it launched a new line of its Propel sports drinks infused with immunity ingredients.

From idea to a shelf near you

Once the products are developed, there’s still plenty for AI and machine learning to do. Jeff Swearingen, who heads up PepsiCo’s demand accelerator (DX) initiative, said the company uses the technology in agriculture and manufacturing, which has helped reduce water consumption. Sales and marketing, his domain, also leans heavily on AI. He said the company started “moving very quickly” in 2015 by building big internal datasets. One has 106 million U.S. households, and for about half of that, he says the company has first-party data at the individual level. There’s additionally a store dataset of 500,000 U.S. retail outlets, as well as a retail output dataset, he says. Both his and Gans’ teams use the data to engage core consumers in “uniquely personalized ways,” from customizing retail environments to online ads.

For the launch of Mountain Dew Rise Energy, for example, PepsiCo determined which consumers would be more likely than average to enjoy the drink, and then narrowed in further to determine a core target. The store data then enabled the company to figure out exactly which retailers those core consumers were likely to shop at and reach them with highly targeted “everything.” This includes digital media campaigns and content, as well as assortment, merchandising, and presentation.

“Because we have done this over and over and over again now, we can actually predict which of the topics are going to stick and which are just going to kind of fizzle out,” he said.

The pandemic, for example, caused a massive spike in interest around immunity. By using Trendscope, PepsiCo determined that specifically for beverages, the interest is here to stay. About six months ago, the company acted on that insight when it launched a new line of its Propel sports drinks infused with immunity ingredients.

From idea to a shelf near you

Once the products are developed, there’s still plenty for AI and machine learning to do. Jeff Swearingen, who heads up PepsiCo’s demand accelerator (DX) initiative, said the company uses the technology in agriculture and manufacturing, which has helped reduce water consumption. Sales and marketing, his domain, also leans heavily on AI. He said the company started “moving very quickly” in 2015 by building big internal datasets. One has 106 million U.S. households, and for about half of that, he says the company has first-party data at the individual level. There’s additionally a store dataset of 500,000 U.S. retail outlets, as well as a retail output dataset, he says. Both his and Gans’ teams use the data to engage core consumers in “uniquely personalized ways,” from customizing retail environments to online ads.

For the launch of Mountain Dew Rise Energy, for example, PepsiCo determined which consumers would be more likely than average to enjoy the drink, and then narrowed in further to determine a core target. The store data then enabled the company to figure out exactly which retailers those core consumers were likely to shop at and reach them with highly targeted “everything.” This includes digital media campaigns and content, as well as assortment, merchandising, and presentation.

“If you go back five years, if you were to walk into those 50,000 [targeted] stores, the assortment, presentation, merchandising, all of those things would probably look like the other 450,000,” Swearingen said, using sample numbers to make the point. “Now in those 50,000 stores, we’re able to truly celebrate this product in a way that recognizes the shopper that’s walking in that store.”

In regards to marketing, PepsiCo also uses AI to do quality control on massive amounts of personalized digital ads. Specifically, the company partnered with CreativeX to build algorithms that check each piece of advertising to make sure it meets an evolving set of “golden rules,” like that the brand logo is visible or the message still comes across with sound off. Gans said using AI is the only way they can do proper quality control when “you may end up making 1,000 [ads] to reach 1,000 different segments of consumers.” The company has invested “a ton” of resources into AI, he said, and will be investing more in the years to come.

Five years ago, the company was still relying on traditional broadcast advertising, according to Swearingen, who added that the new AI-enabled efforts are much more efficient. “There’s so much waste, number one, and you’re not customizing the message to those people that really love this proposition,” he said of the traditional route. “And now we’re able to do that.”

Maintaining human connections

When it comes to customer relations, PepsiCo, like many companies, is tapping natural language processing (NLP) to more efficiently help anyone who may call with a question, suggestion, or complaint. “Through a simple NLP-driven system, we can make sure that the person that you end up talking to already has the content that is relevant for you,” Gans said, noting that talking to a robot for 45 minutes would be “AI gone very wrong.”

It’s a good example of how the company is working to keep humans in the AI loop, which Gans said is “literally [his] favorite topic.” He feels that in integrating these technologies, it’s easy to become overly reliant on the data, which can’t always speak to people’s actual motivations. As an example, he referenced a recent Pepsi ad, which focuses on the shared human emotions of the pandemic and doesn’t feature any products.

“I’m always making sure there is both a data-driven and a human empathy perspective brought to commercial decision making,” Gans said. “That is a key role and the ongoing challenge for my team.”

Source: VentureBeat

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