Insights at the Speed of Engagement: 3 Ways to Jumpstart AI-Based ABM Today

September 28, 2021 | Blog, Resources

A journey of a thousand miles begins with one step, as the saying goes. Companies launching ABM initiatives shouldn’t let data challenges delay their first steps — especially if they’re armed with AI-powered technology that can help build on and improve what they have as they get started. 

Perfectionism can prevent companies from starting new ABM initiatives, according to MRP’s Pierre Custeau, who discussed the role of AI in a recent “ABM Conversations” podcast. But today, companies are better served by launching with the customer profiles they have than waiting for their data to be perfectly sanitized, he said.

That’s because advances in artificial intelligence (AI) enable ABM platforms to synthesize existing data, yield accurate predictive insights, and help marketers identify which data is most meaningful — all while collecting more information as campaigns roll out, thereby creating an increasingly-beneficial cycle, Custeau told “ABM Conversations” host Yaag Neshwaran.

Companies often attempt to solve data gaps by using more tools to gather more data from new channels: teams now use an average of up to 29 marketing tools to execute and track activities. But the more tools they use, the more manual labor is typically involved knitting those systems together. 

That’s because companies often skimp on foundational ABM platforms that can unify and centralize data across sources and teams, Custeau said. These platforms are now more powerful than ever thanks to AI, which can help teams optimize their ABM campaigns within shorter timeframes thanks to data optimization capabilities. AI-driven technology can:

Connect the data you have and infer what you don’t

Within a centralized platform, AI can tabulate multiple incoming data streams, organize and parse it, Custeau said. Not only that, but given the right business rules, machine learning can make accurate inferences that compensate for data missing from customer profiles. 

Custeau used weather as an example. In Canada, if it’s sunny outside in January, it’s logical to infer that the temperature is very cold, as the thermometer usually drops when clear. If it’s sunny in June, the temperature is probably hot. In both cases, the temperature was inferred based on the time of year and the sunny skies overhead. 

Uncover new data from unstructured sources.

Enterprise B2B companies tend to rely on one-to-one account management, which means sales team members are often communicating with customers via individual emails and telephone conversations. The contents of these one-to-one interactions are rarely captured in ABM customer profiles, but AI can now parse these unstructured interactions into meaningful data that can be factored into predictive insights and used to personalize campaigns. Tools can extract topics, sentiment, and specific details from email text and voice recordings, and even capture whether an email was forwarded, giving marketers a new incoming data stream. 

Once companies begin using AI and ML to capture unstructured data, Custeau said, they can learn from the results and craft scripts, email templates, and other tools to help orchestrate interactions and improve the quality of information being conveyed. 

Derive insights at the speed of engagement. 

Not only do teams that lack a unified platform expend resources with manual data management; the process also takes too much time. Weekly or even monthly reconciliation and reporting can result in missed opportunities, Custeau said. Customers engage on their own terms and skip “steps” on the customer journey; their decision-making context is changing too rapidly for manual processes to keep up. 

For example, if a customer on the company website consumes content, marketing and sales teams need to knit that data point together with contextual information such as the location of the viewer, whether they’re likely to be on a buying team, and whether the team has had other recent interactions to indicate that the time is right for a follow-up call — and all those calculations must be made and the recommendation relayed in time for a sales rep to pick up the phone within a half-hour of the website visit when the prospect is likely to be most receptive to a conversation, Custeau said. Data management and processing powered by AI can deliver those recommendations in real-time so that coordinated efforts are timely — delivering vital engagement feedback to the B2B seller and more value to the potential customer. 

Listen to the full podcast for more insights on the role of AI in ABM, and schedule a demo today to see how MRP’s AI-driven enterprise solution can kick-start ABM initiatives.

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