AI in marketing: how AI helps marketers get ahead

This was a fantastic interview moment (I laughed out loud).

So, I asked this question: What can companies do to get the account selection right?

The answer was, “Cheat.”

I had a chance to interview Latane Conant, CMO at 6sense, about what it means to “cheat” by using AI and big data in marketing.

“The majority of the buying cycle is happening in what we call the dark funnel,” Latane said. “For marketing and sellers, we need different types of solutions driven by data to help us uncover that dark funnel buying journey.”

AI in Marketing Means Having Confidence in Data

Given that teams conduct buying (vs individuals), sellers can expect customers to do a high level of research before they even reach out.

“Only 13% of marketing and sellers have any confidence in their data,” Latane said.

This crisis comes to a head during 2 specific use cases: what happens after the sale and what happens when customers go dark.

1 — AI in Marketing can help you anticipate churn

“Typically, the second that a customer buys from you, they get cemented into a perception of what you do,” Latane said. But SaaS companies are rolling out features daily.

Smart marketers will use data to know if clients are researching something that you just dropped as a new feature. That account’s customer success leader could then educate that customer about what you offer.

Is the buying team researching competitive products? You’re probably already on your way out. “The reality is the churn scenario is almost too late,” Latane pointed out.

“Sometimes people don’t want to be upfront about their dissatisfaction,” she said. Data use can alert you to these scenarios when a customer won’t.

2 — AI in Marketing can help you eliminate ghosting

One of the most frustrating things for a salesperson is when communication goes silent—only to find out that they weren’t even in price negotiation. Those prospects were viewing the pricing pages of competitors.

Data can help with this one, too. “Knowledge is power. We need to know, as marketers and sellers and customer success leaders, that we’re not in control of the B2B buying journey,” Latane said. “We have to lean in and use as many insights as we can to help them make the best decision that they can.”

AI in Marketing Means Embracing Data

“I’m a believer in customer experience and employee experience—and those two things being critical for success,” Latane said.

And she said that we treat prospects like dirt. Wait. What?! Bear with me here:

What are the 3 things we hate?

1. Having to fill out a form before we get information
2. Irrelevant, spammy emails
3. Getting cold calls from sales

Yet we do these exact things to our customers:

1. Make them give up their identity before they can educate themselves.
2. Send 300 billion emails a day to just 7.7 billion people.
3. Arbitrarily declare a lead as qualified and chuck it over to sales.

“With rich data, we can deliver a much better experience—a smarter prospect experience,” Latane said. “If you crack that code—if you deliver that, you’ll win.”

Now, imagine yourself going in to add value by educating prospects on your pricing model instead of sitting there sweating about whether they’re going to ghost you or not.

Latane shared how she used to mistake ABM for tactics instead of strategy. “I got a potluck dinner of accounts, and we’d create super custom, amazing campaigns. But we didn’t use data and insights, so of course, it wasn’t all that successful.”

In other words, without taking advantage of the insights your data has to offer, it doesn’t matter how many custom landing pages you create. “If you don’t start with data and insights, sending cookies to their office isn’t going to work.”

Sometimes AI in Marketing Means You Cheat

Account selection is where most people fail, Latane told me. To get it right, you need to cheat.

“What I mean by that, is use AI,” she explained.

“Your sales reps are smart. I think I’m relatively smart. You’re smart. None of us is as smart as big data,” Latane said.

It’s going to take math, statistical accuracy, and data to have the best chance at targeting the right accounts right now.

How AI Identifies Intent

The 3 levels of intent:

1. First-party intent: people visiting your website
2. Second-party intent: people visiting review sites
3. Third-party intent: B2B publications people use to research

“The Holy Grail is to be able to bring in all of it and then be able to analyze it,” Latane said. (Which is the core of 6sense—an embedded CDP that analyzes and provides data-driven recommendations using AI.)

So in addition to using data to identify, consolidate, and suggest, marketers who use data should also be aware that not everyone works in corporate headquarters anymore.

“One of the things I encourage everyone to do when they’re looking at intent is to make sure you have your vendor do a match test,” Latane suggested. This will identify remote workers or people working outside of the US.

Latane ended by sharing the experience of how she became a CMO after many different roles, and she advised, “Believe in the company more than the role. You’ve got to pick a product that you believe in, a culture that you want to be a part of, and the right boss.”

For more interviews from the Data-Driven Marketer podcast, check us out on Apple Podcasts or at this link.