How AI Helps B2B Marketers Prioritize Accounts

How AI Helps B2B Marketing Prioritize Accounts

By Praveen Balla
Vice President, Production, True Influence

Data-driven B2B marketing and sales involves more than amassing volumes of data. It’s also about finding meaningful patterns in all that information to help understand and anticipate your best customers’ needs and decision drivers. Artificial Intelligence (AI) is rapidly changing every aspect of how B2B marketers find those answers.

AI takes analytics to the next level, learning as it sifts through mountains of first- and third-party behavioral data. Through propensity modeling and predictive analysis, AI finds fresh customer traits and habits that indicate a prospect is ready to buy.

Let’s take a quick look at how AI is reshaping some of the fundamentals of B2B marketing and sales. Some of these transformations are already well under way; over the next five years AI will have completely changed the way we execute these core activities.

AI and Account-based B2B Marketing 

Here at True Influence, most of our customers come with a target account list, and my first question is always, “How did you build it?” Is it an old list sitting in a CRM, perhaps based on stale data, or is it just limited by core assumptions?

I’ve found that the main way AI can B2B marketing cast the net a little wider, particularly at the early stages of engagement, is by finding accounts in other verticals who share many of the same traits as your own business or the company you work for. 

If you offer a healthcare-focused software tool, can it also be applicable for manufacturing or defense? I recently worked with a leading software manufacturer on a programmatic campaign and found a whole new audience segment – focused on an entirely different platform – based on patterns we found via AI creating an aha! moment for our customer.

You won’t be able to find these account-based marketing (ABM) opportunities in your first-party data, or even third-party data you’ve likely employed to date. But they’re out there, if you have access to internet-wide data and analytical tools in place to identify them.

What to Ask Vendors about Contact Acquisition and Targeting 

Any programmatic or outbound B2B marketing partner can append an industry or job role to your campaign criteria to meet a desired gross number of contacts reached. That’s the easy part. After all, there are only so many accounts that match your core firmographic criteria, and most database partners will have that general email and targeting info covered.

Before signing a contract, be sure to ask this important question. How does the provider use AI and both contact-level and account-level attributes to build out audience segments that are actually likely to buy what you are selling?

Here at True Influence, we use AI-powered intent data to find accounts that are in-market based on purchase research activity. We also use machine learning and AI in our Identity Graph Triangulation™ to map these signals to individual contacts. We couple these with a massive database of 400 million B2B contacts that we are constantly scrubbing for quality.

Our True Influence Marketing Cloud™ employs AI to project responses from your segments, including those times when you need to expand your criteria a bit. There’s simply no reason to blindly send content syndication emails or spend valuable ad dollars on display advertising in hopes of getting some limited responses. 

AI and the Most Important Variable for Predictive Lead Scoring

Predictive lead scoring has been a hot topic in AI for a while. As with campaign targeting, AI-driven lead scoring is still largely defined by your core understanding of which leads actually result in booked revenue. AI simply allows you to enhance those metrics with intelligence (such as intent) that aren’t readily apparent with standard analysis of your first-party data.

I tend to think that the most important variable AI can expand for lead scoring is timing, particularly when it comes to general market conditions. This interesting article notes that one of the biggest risks for AI-powered lead scoring is major shifts in market conditions, which won’t necessarily evidence themselves in direct response data.

AI-Powered Sentiment Analysis Increasingly Important for B2B Marketing

AI-powered sentiment analysis is becoming increasingly important in lead scoring and most other aspects of B2B sales and marketing. Distilling how prospect accounts feel about their own markets and businesses is critical in weighting any explicit activity on which you score leads. A whitepaper lead in a hot market may merit a call from inside sales, while a stagnant spending climate might indicate a wait-and-see approach.

Social media is the leading source for sentiment analysis today and will likely continue to be so. (I’ll add that industry-wide intent trends are also an important timeliness factor in lead scoring.) Social sentiment analysis is near-real time. There’s so much social content posted each day that you don’t have to rely on historical data, which ages quickly when it comes to market trends.

Social is perhaps the most “human” source for this kind of intelligence. Decision-makers will often explicitly say they are confident or wary on social media. As smart as AI has become, it’s much harder to infer emotional factors from simply search intensity. This intent intelligence is critical, particularly if you want to score leads you’ve acquired from outside your core verticals.

As a footnote, the piece on lead scoring I mentioned earlier noted that predictive lead scoring systems can sometimes be a “black box,” and that sales needs an overview of why the lead is qualified before an algorithm just pushes it into their queue. 

B2B marketing teams who work with intent-based leads have a way to deal with this. They build understanding within the sales team and other stakeholders by educating them about the unique qualities of those AI-driven leads.

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