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The State of B2B Revenue in the Age of Intelligence

2023 Annual Report: Unlocking The Revenue Potential of AI for B2B

Revenue teams can access more information than ever before. So why haven’t their jobs gotten any easier? Find out now.

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Cover Image — 2023 Annual Report: Unlocking the Revenue Potential of AI for B2B.

Chapters

Chapter 1

Introduction

Chapter 2

The State of B2B Revenue Production

Chapter 3

Challenge #1: Buyers Prefer to Remain Anonymous​

Chapter 4

Challenge #2: The Buying Process is More Complex

Chapter 5

Challenge #3: Revenue Teams Need to Be Increasingly Aligned & Efficient

Chapter 6

No Longer A Novelty, AI is A Necessity

Chapter 7

Where to Start: Practical AI Applications Across the Revenue Team

Table of Contents

Chapter 1

Introduction

For the past two decades, businesses have amassed exabytes of data — that’s several billions of gigabytes — that could revolutionize every aspect of how we work.

Yet sellers and marketers are struggling to work effectively. Despite working harder and being armed with new tools and limitless information, they’re still missing pipeline and revenue targets. Why?

We’ve reached a data tipping point. B2B revenue teams now have so much data, they can’t make sense of it all. What should have been a path to clarity has instead become a convoluted journey of noise and guesswork. 

The outcomes for B2B revenue teams are dire: low-quality pipeline, lackluster conversion rates, and a shocking amount of go-to-market (GTM) waste — a combined $2T per year, according to Boston Consulting Group

We’ve now realized that information is easy to collect — but without the ability to convert it into usable intelligence, it doesn’t do much good. And distilling massive amounts of data is more than human brains, spreadsheets, and a disparate collection of apps can accomplish.

Intelligence is what you get when you turn data into insights that drive revenue growth. It’s what empowers marketers and sellers to take meaningful actions, like:

  • Creating hyper-targeted, personalized interactions with buyers who prefer to conduct research anonymously
  • Uncovering prospective customers (aka “accounts”) that are in-market and interested in what you’re selling
  • When contacts self-identify through form-filles, acquiring clues about their engagement

Gleaning intelligence from this epic volume of data can’t be accomplished by tweaking systems and processes that have worked in the past. It requires sophisticated technology built to handle massive amounts of data. Namely, it requires the help of artificial intelligence (AI). 

Without AI, it’s nearly impossible to predictably grow revenue in today’s modern buying environment, which by the day is growing more anonymous, complex, and prone to waste and misalignment.

Armed with AI, revenue teams have an unprecedented opportunity to make sense of the virtually unlimited depth and breadth of information at their disposal. It extracts invaluable signal from all that noise.

To understand the true impacts of AI on revenue production, we analyzed the performance of 420 6sense customers who have used big data and AI to power their revenue teams for four quarters — enough time to fully implement the tools and accurately measure their effects. These customers ranged from early-stage startups to Fortune 100 companies.

This report will demonstrate what we found based on real customer data. It will offer suggestions for how revenue teams can use AI to:

  • Create high-quality pipeline
  • Convert that pipeline to revenue, and
  • Reduce waste across the GTM process

Let’s take a closer look.

Key Findings

Chapter 2

The State of B2B Revenue Production

After a decade-plus of growth at all costs, B2B revenue teams now face a new era of belt-tightening. Economic uncertainty brought on by the pandemic, global instability, and widespread inflation ushered in a demand for intelligent, predictable, and profitable growth — often with reduced or flat budgets.

Meanwhile, during that decade-plus of growth, buying behavior changed in key ways that led to a phenomenon we call “revenue team inflation” — the ballooning of sales and marketing resources required to garner the same results. Much like buying the same items at the grocery store now costs more than it did a few years ago, achieving the same revenue results we’ve long enjoyed now takes considerably more time, effort, and expense. (At 6sense, for example, it now takes us twice as many touches as it once did to progress an account to a stage 2 opportunity.) 

Another challenge has also arisen: Marketers and sellers must be in more places at once to effectively engage with prospects. McKinsey reports that B2B buyers are using more channels when researching potential vendors — up to 10 channels in 2021, compared to five in 2016. And yet, of 18 potential buying signals that reveal buyer activity in these channels, 88% of marketers report using fewer than half.

These days, we must also market and sell to more individuals for each deal. Buying groups, which were about 6 to 10 people a few years ago, now include 14 to 23 people, according to Gartner.

Buying Group Size

The phenomenon of 'revenue team inflation' means it now takes considerably more effort to achieve the same results than it did a few years ago.

This has made life harder for all members of the revenue team. Take BDRs (Business Development Representatives), who work under intense pressure to generate new business opportunities and meet targets within a fast-paced, highly competitive environment.

These professionals say their jobs have only gotten harder. In fact, only 30% of them achieved 90% or more of their quota goal in the past year, compared to 34% the previous year — a clear indication that BDR productivity and effectiveness are slipping.

The three main reasons B2B revenue teams are facing these challenges are:

Let’s delve further into these challenges and explore how AI can help with each.

Chapter 3

Challenge #1: Buyers Prefer to Remain Anonymous

In the past two decades, as the buying process has increasingly morphed into a digital one, sellers and marketers adjusted to reach their buyers. One way was by “gating” educational content and requiring visitors to fill out a website form with contact information before they could access the material.

Buyers got wise to this lead generation tactic — especially after they were bombarded by unsolicited emails and unwanted phone calls. Suddenly, buying teams weren’t big fans of marketers and sellers. Today, less than 3% of B2B buyers today identify themselves to vendors by filling out a form. 

That means that 97% of the buyers that revenue teams work so hard to drive to their websites are going unnoticed, even as they’re actively showing that they’re in market and primed to buy. 

With dramatic behavior changes like these, revenue teams can’t rely on old marketing tactics like form fills, blast emails, and cold calls. In order to even know who’s in market to buy what they’re selling, they need visibility into what we call the “Dark Funnel™” — all the many thousands of websites, social networks, and channels across the digital universe where buyers conduct their research anonymously. 

The Dark Funnel is a trove of buyer intent information that revenue teams historically haven’t been able to access. This invisible data is essential for modern selling and marketing — the kind that rapidly moves accounts through the sales funnel. 

One main source of Dark Funnel data is a company’s own website. Accessing that data requires de-anonymizing web traffic in order to attach buying signals to the accounts that are sending them. And yet, recent 6sense research found that only 26% of B2B organizations de-anonymize their web traffic. 

Yet another truth about how B2B organizations buy is that they conduct only a small percentage of their buying journey on vendor websites. According to Gartner, the percentage of time spent on vendor websites is only 17% of the buyer’s journey itself. Because buyers evaluate multiple vendors, any given B2B provider is likely to see only a fraction of that 17%. 

Other places buyers conduct their research anonymously — and leave behind important signals of intent to buy — include:

  • Industry publications
  • Blogs
  • Social networks
  • Influence outlets
  • Product review sites like G2 and TrustRadius
  • And more…

The intent signals buyers leave behind in these channels brim with incredibly useful and actionable intent data. The game changes when you can combine these disparate intent signals into a cohesive picture that informs the entire revenue team.

So in addition to de-anonymizing traffic on their own sites, revenue teams need intent data from third-party sources to paint a complete picture of which accounts are in market for what they’re selling. And also importantly, they can determine who isn’t ready to buy.

No more than 10% of the accounts in a company’s Ideal Customer Profile are in market to buy at any time. AI reveals these highly valuable (if fewer) accounts that deserve meaningful engagement. 

Of course, knowing that an account is in market only gets you so far. You also need to be able to act on that information. Doing so requires having up-to-date buyer contact data ready at just the right time. But our research shows that revenue teams don’t even have contact information for 78% of companies that are in-market for their solutions. It’s no wonder so many teams struggle to create high-quality pipeline.

How AI helps: Identify hidden buyer activity, automatically enrich CRM

All that data is an essential first step in seeing who’s in market for what you’re selling … but it’s only part of the equation. 

It’s not easy to combine data from these hundreds — and sometimes thousands — of sources. For starters, intent data providers package and deliver their signals in different formats, using different methods for deriving meaning from those signals. For instance, some provide raw feeds of the interactions that they deem is “intent.” Others deliver a simple score. Still others use sophisticated methods for identifying rises and dips in activity related to a topic.

These differences can make the data very difficult to combine, interpret, and act upon.

With AI, revenue teams have an unprecedented ability to develop high-def images of their buyers and use that image to sell and market effectively.

What’s more, interpreting anonymous traffic on a company’s own site isn’t always straightforward. For instance, companies that offer multiple solutions must figure out which solution visitors are interested in before they can market or sell effectively. 

So, yes, the data is essential. But without a way to intelligently combine, integrate, and use all these sources of data to generate insights, it’s just information. And we’ve already mentioned that this informational noise drowns out the all-important, relevant “signal” that revenue teams actually need to sell effectively and survive. 

This is where AI comes in. Making sense of this volume of data requires the advanced pattern-matching capabilities that only AI can provide. AI can digest and analyze those troves of Dark Funnel data, as well a company’s own historical sales data, to recognize patterns that signal when a prospect is ready for engagement. In doing so, AI predicts the right time for sellers to begin direct outreach to prospects based on their behavior and engagement patterns. 

This is obviously a win-win for sellers and buyers. Sellers are more efficient and effective. Buyers receive helpful, meaningful engagement only when they’re ready — and not before. 

AI also dynamically enriches and acquires contact records at exactly the time the revenue team needs it for the most effective outreach. This means there’s no need for team members to spend time manually enriching every account currently in the CRM, and there’s also no need to enrich accounts that aren’t being contacted anytime soon.

Instead, revenue teams can automatically target their acquisition budgets toward enriching and refreshing data on the right accounts at the right time. (This “right time” can be defined by a certain thresholds of predictive scores, fit matches, or other criteria.) Costs are controlled. Engagement efforts are optimized.

Case Study: How Bynder Increased Outbound Pipeline 250% with AI 

Bynder is an industry-leading, cloud-based solution for organizations that want to easily manage their digital content. Customers include Five Guys, Groupon, and Zendesk.

The Challenge

Bynder wanted to diversify its pipeline by upping the marketing team’s outbound efforts. While the company understood its total addressable market (TAM), its BDRs needed a better understanding of their prospects’ behavior and buying propensity.

The Solution

Bynder used an AI-based revenue platform to uncover and analyze buying signals from many sources to accurately predict which accounts to engage at the right time. BDRs then narrowed their focus on the accounts that were likely to result in revenue, empowering them to hit and exceed quota, quarter after quarter.

The Results

  • 2.5x increase in outbound pipeline 
  • ROI within four months
  • Increased BDR productivity and quota attainment
  • Improved alignment across revenue team and between EMEA and North America

“By Q4 in 2021, we already paid back the cost of the tool for the upcoming two years,” said Marko Ivanov, Demand Generation Manager, Bynder.

Chapter 4

Challenge #2: The Buying Process is More Complex

Unlike in B2C selling (which targets individuals), B2B revenue teams conduct business with a group of individuals on a buying team who work together to evaluate and acquire solutions for their organization. As noted earlier, those teams have grown in size to up to 23 known individuals.

More people means more everything.

But beyond the known individuals on a buying team — the usual suspects who are copied on emails and who attend meetings — many other people engage in the buying process by conducting research and contributing perspectives behind the scenes. 

A 2023 study from PathFactory found that small and midsize organizations involve 30 to 35 distinct individuals in the buying process. The same study found that for large companies, those numbers swelled to well over 100 individuals. These continually growing and changing buying teams mean marketers and sellers must address the personal concerns of a growing number of stakeholders and personas.


More people means more everything — emails, content, number of personas to address, activities to track, touches, etc. — which makes it exponentially harder to focus on and reach the people who matter the most.

How AI can help: Navigate buying complexity

Buying journeys are far more complex than back in the days when journey maps were  handwritten and tacked to an office wall. With multiple buyer personas continuously entering and leaving the conversation at different stages of the sales process, navigating today’s buying journey requires orchestration that operates across function, tactic, and lifecycle.

AI can plot these journeys and translate them into easy-to-follow plays, which marketers and sellers can use to their advantage.

We know that multi-threaded accounts — those in which multiple contacts are engaged simultaneously throughout the marketing and sales process — are more likely to close than those that aren’t worked with this approach. 6sense’s own data shows that:

  • Won deals in our strategic segment have an average of 9 engaged contacts, versus 6 for Lost deals
  • In our enterprise segment, Won deals have an average of 7 engaged contacts, compared to 4 for Lost deals

AI-driven insights empower revenue teams to multi-thread accounts much earlier (and more easily) in the buying journey. By identifying influential buying team members based on their roles, content consumption, and engagement, AI provides revenue teams a deep understanding of the buying team. It elucidates which personas are:

  • Most likely to be involved in a deal cycle
  • Who else on the account should be looped in, and
  • Whether marketing is engaging with leads in ways that create sales opportunities

This assist from AI means that by the time marketing passes an account to sales, all the right stakeholders have the information they need at that stage in their buying journey — which accelerates the sales process and yields more successful, higher-value deals.

In addition, AI digests every account’s firmographictechnographic, and psychographic details, as well as insights from prospect keyword research, web page visits, and campaign activity. These millions of datapoints — which are far beyond a human’s ability to process — are distilled into insights that enable revenue teams to make every conversation relevant and effective.

AI can plot buying journeys and translate them into easy-to-follow plays, which marketers and sellers can use to their advantage.

For the conversations themselves, AI (specifically generative AI) dramatically enhances revenue teams’ ability to multi-thread efficiently. Rather than expecting sellers to stay on top of the activities and progress of the dozens of individuals involved in a buying team and engage them with relevant outreach at just the right time, AI can do the heavy lifting.

Here’s a prime example of how AI can move the needle for a B2B revenue team. We know that AI tracks each individual persona’s progress on the buying journey in order to understand and anticipate their needs. Layering on a tool like AI-powered conversational email, which uses intelligence to engage in limitless email conversations with prospects, dramatically increases the team’s efficiency. These back-and-forth email conversations provide relevant context, respond to questions, and loop in humans when necessary and appropriate. 

With conversational email, revenue teams can use AI to move prospects efficiently through the buying process, without overwhelming BDRs and sales teams. Companies using AI see nearly double the deal velocity, according to our research.

“It started as a solution to just generate emails. But now, conversational email AI assistants are fully executing campaigns and following up with leads that our SDRs didn’t have the bandwidth to go after," said Rich Fong, Senior Manager of Sales Development, Vendavo

Case Study: How Custom Truck One Source Saves 738 Hours of Work with AI

Custom Truck One Source is the first true single-source provider of specialized truck and heavy equipment solutions, offering a vast rental fleet, new and used equipment sales, and much more.

The Challenge

As Custom Truck One Source modernized its go-to-market strategy from largely in-person interactions to largely online, it needed to find a way for its small team to market and sell to the company’s 45,000 to 60,000 unique monthly website visitors. 

The Solution

Part of its strategy was to use AI-powered conversational email to reach more prospects. The revenue team launched campaigns targeting people in management-level roles for: 

  • Cross-selling 
  • Post-event follow-up 
  • Closed-lost revival
  • High-intent account conversion

The Results

In just five weeks, Custom Truck One Source’s use of AI-based conversational email:

  • Generated 132 qualified contacts
  • Saved 738 hours of work
  • Brought in $6 million in opportunity and pipeline
  • Led to $1 million in closed business 

“The amount of work conversational email does in a day would take someone on my team a week to do. It’s like having five more SDRs working simultaneously,” said Walker Smith, Manager of Sales Development, Custom Truck One Source

Chapter 5

Challenge #3: Revenue Teams Need to Be Increasingly Aligned & Efficient

With increasing demands on revenue teams, efficiency is more important than ever. Yet siloed data and technology, along with the resulting misalignment across revenue teams, results in:

  • Divergent sales and marketing goals 
  • Incompatible strategies and workflows
  • Poor-quality pipeline, which devolves into …
    • Finger-pointing
    • Job dissatisfaction, and 
    • Missed revenue targets

This generates a tremendous amount of waste from missed opportunities and misplaced efforts — amounting to an astonishing $2T per year, according to Boston Consulting Group.

“Our sellers love the AI-powered insights that allow them to prioritize outreach and focus on accounts at the right moment of time versus knocking on a door when no one is home," said Elizabeth McDonough, Senior Director of Demand Generation, Classy

Misalignment starts early on, with pipeline forecasting and measurement. A pipeline model the entire revenue team trusts is the foundation of predictable revenue, but too many teams work off different datasets and tech solutions, which makes it impossible to align on pipeline. 

Aligning on pipeline requires the entire revenue team to play from the same sheet of music — setting the same goals, tracking the same KPIs, and intervening together when things go off track.

Misalignment leads to poor-quality pipeline, finger-pointing, job dissatisfaction, and missed revenue targets.

When sales and marketing rely on different datasets, work in different platforms, and track different metrics to measure success, it’s also very difficult to prioritize the right accounts and take the best actions to move buyers through their journey. 

Half of all sales and marketing leaders only somewhat agree or don’t agree at all on their target account list. Nearly 72% still focus on generating leads (MQLs/SQLs) instead of focusing on accounts, and half believe their lead-scoring processes don’t surface the best leads accurately. 

This means marketing wastes budget and time on campaigns that don’t deliver results, and salespeople spend too much time working junk leads.

Then there’s the inefficiency that comes from bloated tech stacks and disparate solutions. Frontline teams need to switch between as many as 10 tools to prospect, which slows the process of getting the information needed before reaching out to a customer and contributes to sellers spending 72% of their time on non-selling activities. 

Meanwhile, even with vast amounts of data available, sellers lack the insights they need to prioritize the accounts that are most likely to convert to revenue — those that are showing signs of interest readiness, through anonymous buying signals. As a result, they spin their wheels and watch their competitors close deals that they didn’t even realize were in market to buy.

BDRs Bear the Brunt

Business development reps (BDRs), also known as sales development reps (SDRs), are the lifeblood of any revenue organization. As the lynchpin between a buyer’s engagement with marketing and their connection to sales, BDRs are critical to turning marketing’s efforts into conversions, pipeline, and, ultimately, revenue.

BDRs are the frontline contact with new prospects and customers, so one would think companies would prioritize their success. And yet, they are often the ones to feel the most impact from misalignment and inefficiencies.

In a recent survey of BDRs, we found:

  • BDRs are spending nearly 3 hours per day on administrative tasks, taking away valuable time that could be spent on more productive sales activities. AI could lighten the load for them by taking over many of those admin duties.
  • In 2022, 76% of BDRs felt supported. In 2023, that number dropped to 64%. That has an impact on revenue: Unsupported BDRs are less likely to hit their goals.

The research is clear: Improving efficiency for BDRs by supplying them with the right supports and tools makes the entire revenue engine more effective.

How AI can help: Generate revenue efficiently

AI fosters alignment and improves efficiency across the entire revenue generation process, starting with a unified approach to pipeline forecasting and measurement. By aggregating go-to-market data and insights from multiple sources, including historical CRM data, AI models pipeline performance and forecasts an unbiased, accurate view of pipeline production. It can then recommend tactics to close any pipeline gaps in real-time.

AI models pipeline performance and forecasts an unbiased, accurate view of pipeline production. It can then recommend tactics to close any pipeline gaps in real-time.

AI also helps sellers and marketers align on the accounts that are most likely to result in revenue. By making sense of high-quality intent data, psychographic data, historical company data, and more, AI reliably uncovers net-new accounts that are showing signals that they’re likely to buy. And then it alerts sellers to the hottest accounts and contacts so they can prioritize their efforts and engage the most receptive people at exactly the right time. 

By combining the data and insights from every revenue function, AI makes for a more aligned, effective, and efficient marketing and sales process — without the need for an array of different tools to track down the data needed to market and sell effectively. Marketing understands where the buyer is in the journey, why they’re a good fit, and which contacts to engage. Sellers have visibility into the whole process and can layer in their own insights to create relevant and personalized conversations. 

By bringing revenue technology under one roof, AI integrates workflows, helps sellers combine learning from across all functions, and guides them toward the next best action to progress a deal as efficiently as possible.

An AI-powered revenue platform has helped us break down silos between sales and marketing and empowered us to become strategic partners,” said Megan Landisch, Marketing Ops Team Lead, Zywave

Case Study: How Marathon Health Used AI to Generate Millions in Pipeline

Founded in 2005, Marathon Health simplifies healthcare for employers by combining independent primary care with value-driven population health management. It operates health centers nationwide.

The Challenge

For years, Marathon Health risked missing out on millions of dollars of pipeline due to an overworked and understaffed go-to-market team. Capacity issues plagued their marketing and sales teams, resulting in significant gaps in their GTM process. While the company had first-party intent data, it didn’t have the capacity to convert that data into useful intelligence.

The Solution

Marathon Health added an AI-based revenue platform that would help them implement an omnichannel approach to their outreach, uniting all aspects of the buyer journey across all channels. They also added AI-powered conversational email to help the sales team engage more efficiently with prospects. 

The Results

After implementing an AI-based revenue platform, Marathon Health saw:

  • $66M in net-new pipeline
  • 211% in-market buying growth 
  • Higher-quality leads
  • Increased net-new sales

“We’re really just scratching the surface … look at how much more we can generate and grow. That has instilled a level of confidence in me to be able to go to leadership and say, ‘Let’s continue to do these wonderful things,’” said Troy Purdue, Director of Growth Marketing, Marathon Health

Chapter 6

No Longer a Novelty, AI Is a Necessity

In today’s economic climate, the grow-at-all-costs approach is no longer sustainable. However, the enormous depth and breadth of intelligence and technology that are now available still present B2B organizations with the prospect of dramatically increasing revenue productivity. 

With AI, organizations can capture and make sense of the ever-growing universe of buying signals, allowing them to turn the massive volume of information available today into actionable intelligence that reduces waste and increases revenue team efficiency. 

Even more, by employing AI to enhance revenue team intelligence, organizations dramatically reduce waste in marketing and sales processes. And, by focusing energies on the small fraction of companies that are in-market at any given time, organizations also create better buying experiences and better alignment with sales.

Customer data shows that the benefits of AI for revenue teams are undeniable. Let’s dig into our report’s findings. 

Report Findings: Proving the Benefits of AI for Revenue Teams

To understand how AI impacts revenue production, we analyzed the performance of 420 of our customers that use AI. These customers ranged from early-stage startups to Fortune 100 companies that had their models running on the 6sense Revenue AI platform for a minimum of four consecutive quarters between July 2021 and December 2022.

We chose this approach because four quarters is an ample amount of time for the platform to be fully implemented and outcomes to be monitored, which allows for a more accurate understanding of the platform’s full impact.

We used data from 6sense and customer systems to compare selling productivity when sellers worked opportunities that 6sense AI predicted were likely to become opportunities* versus opportunities where 6sense AI did not detect behaviors that would indicate that an account was legitimately in market to buy.

*To make this prediction, 6sense AI ingests and analyzes thousands of buyer signals, including multiple sources of third-party intent signals, anonymous web traffic, and form-fills.

Deal Velocity as a Measure of Production Efficiency

To assess selling productivity, 6sense analyzed customer deal values, conversion rates from opportunity to closed-won, and sales cycle times from opportunity to closed. Evaluating the three of these simultaneously provides a measure of how productive a selling organization is. 

A common metric that uses all three of these measures is deal velocity. Deal velocity is calculated by multiplying the number of deals being analyzed by the average deal size of those deals, then by the conversion rate of those deals to closed-won.

Finally, the product of those multiplications is divided by the average number of days to close. This results in a dollar value measure of selling productivity per period of time. As Table 1 below indicates, organizations produce nearly double the revenue per period of time when working 6sense-prioritized deals.

0 %
higher
deal velocity with AI

Deep Dive Into Deal Velocity

Deal velocity, also called sales velocity, is the speed with which deals move through the pipeline and generate revenue.

It’s calculated using the following equation:

Deal velocity is an important metric because it allows you to predict how much revenue you’ll generate in a certain period of time. It’s a useful measure of your organization’s health, your revenue team’s effectiveness, and your likelihood of hitting revenue goals.

Opportunities to $10M 

Another way to measure improvements to revenue productivity is to assess how many opportunities an organization would need to work to get to a target revenue number. We used a target figure of $10M to assess this and found that focusing on deals 6sense AI prioritized allowed organizations to achieve $10M in revenue while working less than half as many opportunities. 

In short, by utilizing the full universe of buying signals, 6sense customers more than doubled selling productivity. 

Table 1
Top of Funnel Accounts to $10M

Note: Figures above are for customers in their third and fourth quarters as 6sense customers. Deal velocity is calculated per 100 opportunities worked. Opps to $10M shows the number of Stage 1 opportunities an organization would have to work to produce $10M in revenue. Accounts to $10M shows the number of accounts an organization would have to work from top of funnel to achieve $10M in revenue.

Top of Funnel Accounts to $10M

These productivity savings extend back into the marketing organization, as well. As organizations use the full universe of signals to guide marketing spend as well, even more dramatic improvements are realized. 

When considering how many accounts are required at the top of the funnel to produce opportunities not prioritized by 6sense AI, they needed nearly 7x as many early-stage accounts to achieve $10M in revenue.

Benefits from AI Benefits By Deal Size

To understand whether the performance gains hold for B2B organizations of varying deal sizes, we grouped 6sense customers into four bands by deal size. While results vary across the deal size bands, organizations in all deal sizes experienced substantial productivity gains. 

“By focusing on a data-driven approach and hyper-targeting specific accounts we have successfully increased the effectiveness of our campaigns without increasing resources. Simply put, we stopped fishing in the ocean and now use a fish tank,” said Shannon Pritchett, Head of Marketing, hireEZ

Small Deals (<$25k)

For deals less than $25k, using 6sense AI resulted in 2x bigger deal sizes and 33.1% higher deal velocity. Without AI, it took almost twice as many opportunities to get to $10M, and 3.5x as many top-of-funnel accounts. (See Table 2)

Table 2
Metric
6s
Not 6s
Effort Difference
Deal Size
$12,221.81
$5,981.33
2x better
Opps to 10M
2,889
5,476
89.6% more effort to get to $10M
Accounts to get to 10M
34,393
156,464
354.9% more effort to get to $10M
Deal Velocity per 100 opps
$15,210.18
$11,430.25
33.1% higher deal velocity

Source: 6sense

Medium-sized Deals ($25k-<$50k)

For deals between $25k and $50k, using 6sense AI resulted in 2.3x higher deal sizes and 24.7% better deal velocity. Without AI, 84% more opportunities and 3.4x as many top-of-funnel accounts were required to reach $10M. (See Table 3)

Table 3
Metric
6s
Not 6s
Effort Difference
Deal Size
$44,668.80
$19,322.42
2.3x better
Opps to 10M
1,094
2,018
84.4% more effort to get to $10M
Accounts to get to 10M
13,027
57,648
342.5% more effort to get to $10M
Deal Velocity per 100 opps
$24,273.40
$19,461.85
24.7% higher deal velocity

Source: 6sense

Large Deals ($50k-<$150k)

For deals between $50k and $150k, using 6sense AI resulted in 1.4x bigger deal sizes and 42% higher deal velocity. Without AI, 52.7% more opportunities and 2.7x as many top-of-funnel accounts were required to reach $10M. (See Table 4)

Table 4
Metric
6s
Not 6s
Effort Difference
Deal Size
$92,185.40
$67,491.30
1.4x better
Opps to 10M
468
714
52.7% more effort to get to $10M
Accounts to get to 10M
5,568
20,400
266.4% more effort to get to $10M
Deal Velocity per 100 opps
$48,499.23
$34,152.82
42% higher deal velocity

Source: 6sense

Jumbo Deals (>$150k)

For deals over $150k in value, AI’s effects on revenue productivity were especially pronounced. For these “Jumbo” deals, using 6sense AI resulted in 1.9x bigger deal sizes and 230% higher deal velocity. Without AI, 181% more opportunities and 5.6x as many top-of-funnel accounts were required to reach $10M. (See Table 5)

Table 5
Metric
6s
Not 6s
Effort Difference
Deal Size
$403,091.31
$214,265.15
1.9x better
Opps to 10M
128
359
181.3% more effort to get to $10M
Accounts to get to 10M
1,521
10,269
575.2% more effort to get to $10M
Deal Velocity per 100 opps
$184,434.36
$55,816.08
230.4% higher deal velocity

Source: 6sense

Chapter 7

Where to Start: Practical AI Applications Across the Revenue Team

Companies that already use AI in their sales and marketing processes — and those just getting started — can implement changes a step at a time to improve revenue performance, without further taxing overwhelmed revenue teams.

Here are just a few examples of ways to use AI in sales, marketing, and revenue operations for each stage in the AI adoption process.

For most companies, embracing AI can be an evolution, not a sudden light switch. There are opportunities for teams of all sizes and readiness levels to bring AI into their revenue-generation practices.

But the research is clear: embracing AI is no longer an option or a luxury. It’s a necessity. And its promise is undeniable — nearly 2x deal velocity and deal size, half the effort to produce the same results, and $17k more revenue for every opportunity worked, to name a few.

Also clear: Now is the time to adopt AI throughout your revenue generation processes. In this report, we have highlighted the key ways organizations are already adopting AI. Undoubtedly, one or more of the use cases highlighted here will apply to your organization.

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The 6sense Team

The 6sense Team

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