Last updated on September 26th, 2020 at 02:51 am
Monica Behncke wasn’t necessarily thinking about the potential of AI in B2B when she let her teenage son plan their vacation at an amusement park, but she was quick to recognize the parallels.
Speaking at last week’s SiriusDecisions Summit Canada in Toronto, the research firm’s VP and group director noted how amusement parks involve a bevy of choices involving the selection of rides, restaurants and amenities, ways to access the attractions and more. Similarly, those making enterprise purchases can be almost paralyzed with the variables to consider.
AI in B2B helps address this problem, she said, by helping organizations make use of more data, as well as seeing the connections within all that data.
“It’s a way of knowing your buyer and really understanding them beyond a title,” she said. “It’s also a way to better gauge performance of what’s working — or not.”
Some examples include automating processes, especially in repeatable areas such as contract renewals or approvals, as well as illuminating what Behncke described as “best-fit” market segments.
“We all have limited resources and energy and people. You don’t want to waste that in places that aren’t really a good fit, but it can be really hard to tell what is the good fit,” she said. “How do you know if (customers) are looking to buy? With more processing, they can look at deals won and lost and template the wins based on a profile AI creates.”
AI in B2B can also match third-party intent data to the segmented market, Behncke added. That means when those companies give off the right signals — such as web searches, third party analyst information requests and so on — a company can see that and increase marketing activity accordingly.
AI is also good at reading web assets and creating a taxonomy aligned to solutions, according to Behncke. The technology can assess repeat visitor interests and presents
“How many come to the web site — two people 10 times, 10 people that come two times? What content do they look at? How often do they come back?” she asked.
“How many conversations does a sales process include? How may different people? Over what length of time? What’s the length of your sales cycle? How many were initiated by marketing activities?”
Over time, Behncke predicted more organizations will use AI in B2B to build a “master buyer repository” that combines first party and third-party data to help drive better decisions. That only works, however, if you give the technology the right questions to answer. She suggested firms conduct an internal survey with their subject matter experts to get at those questions, and determine whether they are critical issues or merely “nice-to-know” kinds of queries. Then, ask whether you already have the answer.
“Also ask, ‘Are we fooling ourselves that we have a good answer?’” she suggested. Sales teams might say their buying cycle is 42 weeks, on average, for instance. This should be verified in order to ensure AI tools aren’t being wasted.
The results of such surveys can be prioritized with a “confidence index,” Behncke added, or an rating of importance that might be based on factors like whether the organization needs more data.
“The real power (of AI in B2B) is not necessarily in the headline-grabbing things,” Behncke said. “But it’s addressing the things that sales and product managers are struggling with every day.”