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Never in the course of history have so many customers been so analyzed.

The latest entrant is Boston-based Cintell, which is today launching its first product, a platform to automate the creation of customer personas for B2B sales. The platform has been in beta testing with a couple of dozen company customers, and it’s free to use for a limited time.

Customer personas, common from the Mad Men days of advertising through countless website design sessions, address the type of consumer you’re targeting as if she were an actual person.

The opportunity: Persona-making for businesses selling to other businesses is still largely a manual process conducted by consultants using surveys and the like, Cintell CEO and cofounder Apparao Karri told me.

Cintell’s platform generates a profile of your ideal customer by chomping through three main data sources. There’s primary data from the client company, such as surveys from Cintell’s online tool or through live interviews.

There’s third-party data purchased from providers about the target market in question, such as IT directors. And finally, there’s behavioral data about how that type of user employs your product or system, such as your website.

Woven together and sifted, this creates an analytical model that the company calls — yes — Cintelligence, from which a specific persona is created.

At launch, the platform writes the persona to a shareable digital file. Integration with a marketing automation platform like Marketo is in the works, where the persona’s attributes — reads Businessweek and The New York Times, for instance — can automatically match with actual customer or prospect profiles in a company’s customer relationship management (CRM) system.

Interestingly, a startup called MentAd announced last week its system for the automatic creation of segment personas, and the implementation of ad campaigns based on them, for B2C sales.

MentAd takes a business’ first-party data about its customers — such as name, email, and purchase history — and marries it with data from other sources to create enriched actual profiles. It then uses machine learning to find common characteristics between, say, all the customers who bought Shoes A and ones who bought Shoes B.

Those common customer characteristics — such as married mothers, aged 22 to 35, who shop at Trader Joe’s and listen to NPR — are analyzed for their possible return-on-investment from an ad campaign. If they look like a good bet, MentAd then conducts an online ad campaign directed at those types.

While MentAd builds segment personas based on commonalties, Cintell creates its personas both from commonalities — when there is supporting data — and from on abstractions derived from data, such as for personality insights.

Do we still need abstracted personas?

A key question is whether, in this age of massive and fast segmentation, personas using abstractions are still useful.

After all, they originally emerged so that advertisers and marketers could have a general sense of the likes and dislikes of the customer they wanted. But now, oceans of accessible data enable marketers to pinpoint actual behavior and preferences — and, potentially, to segment audiences by type into small groups.

CMO and cofounder Katie Martell told me that personas with abstracted components are still useful in the B2B world. They help marketers “engage with a select group of people,” she said, such as the complex buying committees that are common. Also, she noted, B2B doesn’t have the kind of massive data from transactional sales and other interactions that B2C can use for its automated segmentation.

Even more importantly, Martell said, personas offer more than just distillations of hard data. They can provide buying motivations, for instance, which can only be obtained through surveys and such.

“Personas are like snowflakes — [they’re] unique to each company,” she said.

Karri added that the key differentiator separating his company’s platform from, say, the lead scoring systems of Lattice Engines and 6sense is “the human element.”

“Lattice doesn’t give you info about buyer’s motivation” or the ecosystem surrounding the buyer, he said.

That company’s platform, Martell added, “is trying to predict. What we’re doing is improving the initial strategy, the planning for how to segment your data and where to put the content.”

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