Low-quality pipeline, lackluster conversion rates, and some serious go-to-market (GTM) waste. These are three major issues for B2B revenue teams today, according to the 6sense report, Unlocking the Revenue Potential of AI for B2B. Yes, a lot of data is available to help these teams, but is it being used? Is it even usable? It is with the help of predictive and generative AI, and 6sense CMO Latané Conant explains how.
A few stats to start The basis of the 6sense report is a study of 420 of the company's customers over four quarters. This looked at customers using the platform's AI tools versus a group that didn't use AI to understand the impact of AI on revenue.
Here are a few stats from that study when using AI:
- Deal sizes are 2x+ bigger
- 91.3% faster deal velocity
- 123.6% less effort to make $1 million
- 34 opportunities are required to get $1 minion in revenue (without AI, it took 76)
Conant walked me through some key challenges revenue teams face and how AI helps overcome them.
Only three percent of website visitors complete a form
We can have the 'gate versus ungate content' debate until the cows come home, but the numbers speak volumes that no one can deny. People don’t fill out forms. Conant quoted research that says only three percent of buyers will complete a form, and companies do not know how to capture and analyze (or use) the data from the other 97%.
Marketers put a lot of work into creating a great website experience - content, personalization, and advertising to drive people to it- yet they aren't capturing activity and working with it. Conant said it's a good "truth bomb and reality check." And it's a minimum AI use case, she said. Use AI to understand your web traffic.
There are also many other channels where potential customers hang out and show intent to buy your product. Gartner states that only 17% of the buyer journey is spent on the website. So not only can AI analyze website traffic, but it can also pull in and analyze intent signals from other channels to provide a more complete picture.
Only 10% of your TAM (ICP) is in-market at any given time
Companies define their Total Addressable Market (TAM) and then run campaigns and other activities to either reach out or bring these companies to them. But only 10% of your TAM is looking for the company's product at any given time.
Now, it's not surprising that only a percentage of a company's TAM is in-market. What is surprising is that 78% of that 10% isn't in a company's CRM. Sales and marketing are not actively tracking or engaging with a huge chunk of those in-market accounts. Another use case for AI.
The buying teams are growing
We know that B2B sales are done by buying teams. But those teams are growing in size - up to 23 people on a team, according to Gartner. And it's not only that the team is growing; it's impossible to know everyone on the team. And it's not only knowing who is on the team but who are the most important team members to engage.
AI can plot the journey of an account and then translate this information into follow-up plays. It can tell you which buying team members are influential and which are more likely to be involved in the deal cycle. The AI consumes tons of data to predict the best approach to move forward.
Conant said that every quarter, 6sense analyzes won deals to figure out what's required to win a deal. She recommends looking at deals by segment and product. They have found that for enterprise customers, it takes 13 engaged contacts to win a deal, and they can see what their roles and responsibilities are. They also know there are, on average, 122 website visits across those personas and what campaigns and content they consume. So they know what's required to choreograph success.
B2B inflation is a serious issue
Conant said that revenue teams are given the same, or less, budget and told to do more. But she said it's much harder today to do the same things they did two or three years ago. More people are on the buying team, and more touches, channels, and personalization are required.
When you use AI, it takes 7x less effort to get to $10 million in revenue, according to the 6sense study. Conant said:
We need an antidote to more. I hate do more with less. Nobody wants to do that. So how do we work differently and that's where using AI in the prioritization, using AI to help make sure that your data is accurate and enriched, using AI to be more personalized, that's where you start to say, Oh, okay, we can take the same team and get over this inflation.
This is also where Conant sees generative AI filling in the gaps that Sales never gets to. You can use generative AI to perform webinar follow-up and respond faster to inbound hand raisers. It can help reach people on buying teams faster, becoming that first level of interaction via conversational email and passing on the right accounts and contacts for BDRs and AEs to connect with on the phone. She said:
How do we make sure that we're touching these precious signals that the buying team is giving us at the right time? And that's where the AI assistant that I think Marathon Health talked about came into play because then they could augment their sales team with something that's just in time. That's cueing off of the signal from the prospect, knows their intent keywords, knows their timing, knowing what pages of the website they've been to, and can then craft that very relevant message and go ahead and get meetings for a salesperson. And so I call that autonomous pipeline development.
Making sales more human through AI
It sounds silly to say that AI can make BDRs and AEs more human, but that's what Conant is saying. Many people complain about BDR emails because they often miss the mark and seem pushy or impersonal. AI can take on that part of the role and elevate that element of the sales function. Conant said:
If you think about how many signals a buying team is putting off, it's a lot for a human to take in. They need to know the persona. They need to know all of their research patterns, they need to know their intent keywords. They need to know their psychographics, and their posting on social. They need to know where they are in the buying journey. Ideally, they know their technographics, so if they're on a competitor. Plus, they need to know all 6sense case studies, all of our data, and put that together in about a minute.
Conant explained that you train the AI on your case studies, stats, personas, and intent keywords and then let it send that first email and handle the replies. Now, the BDR or AE can spend more time talking to the right accounts and personas on the phone (yes, Conant said more phone time) and engaging on social.
6sense follows this process, and Conant said they found that outbound generated opportunities are converting the same way inbound is. Usually, the conversion is much lower, she said. But with AI sending relevant messages quickly and the team being able to get on the phone, outbound is becoming much more effective.
The 6sense report found that Sales teams spend 72% of their time on non-selling activities, often switching between up to ten tools to prospect. That's a lot of data spread across many tools, some of which likely don't talk to each other.
This is why predictive and generative AI will play a key role in sales - it can do the work necessary to understand the right accounts and contacts faster, even going as far as doing some of that first-level work. And then, it can advise BDRs and AEs on what to say and do because it has captured and analyzed all the data. As long as that data is clean and accurate, integrated, and the AI constantly learns, it will know more than a human does.
We need to look at AI as an assistant that can help us do our jobs better and put in place all the guardrails and checks necessary to ensure it's working correctly. If it's done properly, we have more opportunities to bring what Conant called our "x-factor" to the table, whether that's in marketing, sales, or support.