AI Is Changing the Rules of Customer Data. Here’s What Wins Now.

Customer data has become one of the most valuable assets a company can own. But AI is changing what gives that data value in the first place, making judgement about what the data means as important as the data itself.

For years, companies fought to acquire more customers, believing scale alone would deliver a lasting advantage. As AI makes it easier to interpret and act on customer data, the source of competitive advantage is shifting. Increasingly, it depends not simply on many customers companies have, or the amount of date on those customers, but on whether they are collecting the information that gives AI genuinely new insights.

According to Raj Venkatesan, a professor of business administration at the University of Virginia Darden School of Business, owning customer data still matters. But what separates leaders from laggards is no longer how much data they possess, but what AI allows them to do with it.

Customer data is not enough

Customer data remains a source of competitive advantage, but it is no longer enough on its own. Increasingly, that advantage is being forged by companies that are using AI to unlock its true value, Venkatesan says. AI has dramatically raised the returns companies can generate from customer data, via making far more precise and personalized marketing possible, he explains.

“AI turns vast amounts of first-party data into predictions, allowing companies to understand and serve customers as individuals rather than broad segments,” Venkatesan says. “By continuously learning from the data customers generate, AI allows companies to anticipate customer needs rather than simply react to them.”

And the returns from that are already showing up in marketing budgets. Nearly three-quarters of advertisers expect AI to boost media spending over the next year, while one in three believe it will boost return on ad spend by more than 10%, according to McKinsey.

AI is becoming a prediction engine

If AI is changing the value of customer data, it is also changing what companies can—and should—do with it, Venkatesan says.

The technology’s top strength is not generating content, though that is very much happening. Brands are already using it to create advertising copy, images and video at a brisker clip, and at far lower cost.

But not every experiment has worked: McDonald’s pulled an AI-generated Christmas advert in the Netherlands last year after it was ridiculed online. The fast food giant described the episode as an important lesson, as it explores how best to use the technology.

For Venkatesan, content generation is only part of the story. He says AI’s best value for marketers comes from making better predictions about customers. “At its core, AI is a prediction engine. “It helps companies answer three critical questions: Who is the right customer? What’s the right message? And what’s the best way to reach them?”

Prediction, in turn, is only as powerful as the data behind it.

“The more relevant customer data AI receives, the more accurate its predictions become because it continuously learns and refines its understanding,” says Venkatesan.

Netflix is a case in point: the streaming platform uses AI to comb through data drawn from its 325 million users to spot patterns in viewer preferences that would otherwise remain hidden, helping managers make decisions about what content to commission and recommend.

Venkatesan adds: “The goal is to make every marketing decision as relevant as possible to each individual customer.”

From customer data to customer knowledge

Owning customer data is only half the battle, though. Companies also need the tools and expertise to turn it into customer insights, which then drive product recommendations, personalized marketing and pricing.

“Getting your data house in order means bringing together clean first-, second- and third-party data so AI can process it, identify patterns and predict what customers are likely to want and need,” Venkatesan says.

Competitive advantage is won by what companies know about their customers, not simply the data they hold. “When companies combine strong customer data with the ability to analyze it, they build something far more valuable: customer knowledge,” says Venkatesan.

“The companies that turn that customer knowledge into better marketing decisions are the ones that outperform their competitors, with more effective marketing and stronger business performance.”

Where marketing strategy is headed

AI is changing why companies invest in technology. Data and AI capabilities matter because they help companies understand customers better and make better marketing decisions.

“AI is blurring the line between inside-out and outside-in strategy,” Venkatesan explains. “Companies build internal capabilities in data and AI for one reason: to generate a deeper understanding of their customers.”

Where companies still get it wrong

However, many companies are still not doing this very well, he adds. “The biggest constraint isn’t always a lack of customer data. It’s understanding what you still don’t know about your customers.”

In other words: many companies still underestimate the commercial value of customer insight. “Too many companies still treat customer insight as a marketing function. It should inform far more of the decisions they make across the business,” Venkatesan says. 

“The companies pulling ahead are treating those insights as a source of competitive advantage rather than simply a cost of doing business.”

What executives should do next

The practical implication of all this for marketing leaders is a simple one: building a lasting data advantage requires patience as well as technology. Venkatesan says: “Companies need to be willing to invest for the long term and accept that the returns won't always come immediately.”

Alongside that, it’s also going to be key not to confuse collecting data with creating value, as many firms still do, he adds. “Data and information are the foundation of better decisions and new opportunities. But having more data is only a condition for success.”

Ultimately, Venkatesan argues that success rests on three pillars: “There are three resources that matter most in a data-rich world: customer data, the capability to analyze it, and the customer knowledge that analysis creates.”

This article draws on the insights from the book "The AI Marketing Canvas: A Five-Stage Road Map to Implementing Artificial Intelligence in Marketing," by Rajkumar Venkatesan and Jim Lecinski.