How AI Can Transform Customer Centricity in NPD

The Decline of Traditional Customer Research

For decades now, the prevailing wisdom surrounding how to handle strategic decisions and consumer-centric product development has been to talk to your consumers.  They are the end-users of your product or service and it makes a lot of sense to listen to their needs and try to craft solutions that solve those particular pain points.  This can be done through surveys, interviews, focus groups, and indirectly through collating the feedback that you receive through all your consumer service channels.  This information is then used to create prototypes and this is further validated by way of a consumer panel or consumer test. This is what traditional consumer research has been for a long time now.

While still a valuable way of generating key insights, it seems that the value of this traditional research is waning in our modern world. The mercurial Steve Jobs was the pioneer in this thinking, saying:

“Some people say, give the customers what they want.  But that’s not my approach.  Our job is to figure out what they’re going to want before they do. People don’t know what they want until you show it to them.”

Of course, it’s easy to dismiss this as the uttering of a once-in-a-generation product designer but there are kernels of truth there.  The consumer landscape has never been more dispersed and customer behavior is always changing. Kari Aldredge, from McKinsey, says:

“Consumers don’t always know what they want, and they can’t predict how their behavior will change.  So traditional consumer research – which asks consumers how likely they are to purchase something – is becoming less relevant or reliable than actual data in the market.”

Great companies are able to read the trends in their industry and create solutions that consumers hadn’t even dreamed of yet – thus putting them ahead of the curve.  That’s why it’s important to not only rely on what customers are telling you directly but also to look at their behavioral data to understand their needs on a much deeper level.

The Decline of Loyalty

Another trend that we see in the modern business landscape is that customer loyalty is not nearly as prevalent as it used to be.  Thanks to the internet, global competition, and the idiosyncrasies of the new generations, companies must fight every day to retain their customers. People are more likely than ever to try new brands or new products, and they’re more likely to change their habits and purchasing behavior based on influencers and sentiment on social media.

This makes customer retention much more challenging which is a problem because the costs rise quickly. Fred Reichheld, from Bain & Company, says:

“Attracting new customers can cost companies roughly 5 times more than retaining existing ones.” 

This is a staggering statistic and points to why it’s so important to keep your existing customers. Today’s customers are much more open to innovation than they ever have before and the battlefield continues to get more crowded, and this fragmentation is coming at the expense of large players. 

But if this is the case, and you can’t rely on brand loyalty, how do you retain them? The answer, once again, is to read the data and understand what that customer is going to need in the future. If you can’t rely on loyalty, you have to over-deliver in terms of the product-market fit that you’re able to achieve.  You need to be so good that they can’t ignore you. And to get there, you have to have a firm grasp on the objective data that shows how consumer behavior is shifting.

Using AI for Customer Centricity

The only way to get sustainable results is to put customer preference data at the top of your priority list and build robust systems around collecting and analyzing it. Mining online data can give you a quick understanding of what the current sentiment is on the ground, and then advanced analytics can be used to evaluate online ratings and reviews to take things one step further.

This is where machine learning and artificial intelligence can be so powerful. If you utilize the right technology, you can train your algorithms to spot consumer patterns in advance of your competitors and steer your organization in the direction of where the industry is going.  This strategic decision-making goes a long way to maximizing customer retention, not through branding campaigns, but through a vision for the future that is based on actual behavior.

Practically, a solution like digital twins can really help here.  A digital twin is a virtual simulation of a customer that can be used to test various consumer theories and make predictions about buying intent and likeability. Here at Foodpairing, we help our clients create these digital twin systems, which rely on advanced AI, to help them capture the consumer data that they need to make smarter decisions.

Typically, you can get millions (or even billions) of data points that can be transformed into actionable insights for your product development team. Think of it like having a consumer panel in your pocket. This gives you the information that you need to adapt to changing market circumstances and maintain your customer centricity initiative throughout all your processes.

Foodpairing has been digitizing consumers and chefs for more than 10 years. By combining data from the Foodpairing tools like FlavorID, chef apps and public data like recipes and social media, Foodpairing has successfully digitized consumers with a diverse demography.

If this sounds of interest, be sure to get in touch today and let us show you how you can leverage AI to improve your customer centricity.