What is statistical significance? What does it really mean?

We considered calling it the State of ABM and Demand study, and the contents of this report would justify that name. For a similar report last year, we opted for “Buying Signals Study.” Then, our focus was on raising awareness of the need to utilize the full array of signals to identify buyers.

But that still missed the mark. What truly matters are the last two words of the prior paragraph:

identify buyers.

That is the first hurdle marketers must surmount. As marketers, we obsess over “engage and convert.” But, if we pursue the wrong accounts or look the other way as potential buyers file through your digital doorways, we will waste time and budget.

In this report, we first map the terrain of B2B buyer journeys. Buyers are showing us what they want if we have the eyes to see. We know what those journeys are like. Then, we catalogue the steps and strategies B2B marketers use to engage with them. Spoiler alert: the steps and strategies don’t always intersect with the journeys they are meant to track.

Whatever you are doing to identify your next best customers today, it almost certainly isn’t what you will be doing by the time your employer refreshes your laptop. We invite you to stay with us throughout the coming year as we guide you through the landscape of B2B’s best and worst practices and the recommendations that follow from them.

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Chapter 2

Introduction

Our recent report, Buyer Experience Study, revealed that just one in three individuals who will go on to become your customer will fill out a form to see your content. If you put content behind a gate, you are ensuring that two-thirds of your most devoted audience will not see it.

We also know that serious buyers do not speak with vendors until they are 70% through their buying journeys.

In other words, most buyers won’t fill out your forms or talk to you while they are researching your solutions.

But it is what buyers accomplish while shopping undercover that should alarm B2B organizations the most: they decide what they are going to buy and from whom.

The buyers in our recent Buyer Experience Study told us that they do not simply respond when sellers reach out. Instead, they bide their time, waiting to engage until they are ready as a buying group. When they are ready, they initiate that first contact 83% of the time.

What does ready mean? Your buyers told us that their first direct interaction – which they initiated – was with the vendor that ultimately won the business 84% of the time.

Because this first direct interaction happens more than two-thirds of the way through the journey, there can be only one conclusion: buyers know who they are going to buy from when they begin engaging with sellers directly. Buyers decline conversations with sellers until 70% through their journeys, because they want to decide what to buy before talking to sellers, and it takes that long to get there.

That is the context for this report.

Our 2022 Buying Signals Study revealed that B2B organizations relied primarily on the old-familiar – form-fill leads. Knowing that sales must be made prior to the first buyer-seller interaction, we surveyed 546 marketers in late 2023 to see if this had changed.

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Chapter 3

The B2B Buyer

B2B Purchases Are Made in Groups, Leaving A Wealth of Signals for Marketers

Recent studies, including our own, consistently highlight the collaborative nature of B2B purchases. Our 2022 Buying Signals Study and 2023 Buying Experience Study together surveyed more than 1,000 B2B practitioners and buyers and found the average buying group size to be 10 members. The current research buttresses this understanding, revealing that as solutions get more expensive, the teams that buy them get larger. In fact, nearly 40% of the reason one buying team is bigger than another is due to average selling price (ASP).

Figure 1 shows that marketers know that buying groups expand with solution costs. Results from our recent Buying Experience Study validate this understanding. In that study, we asked buyers to tell us how large their buying groups were and compared that data to the data collected on buying group sizes from the current study.

Marketers’ View of the Buying Group Largely Tracks with What Buyers Report

As Figure 2 below illustrates, buying group sizes that marketers report align closely with those reported by their buyers (the correlation coefficient for the two responses was r = .94, an exceptionally strong correspondence). Marketers clearly understand the sizes of the buying groups they target. It is notable, however, that marketers tend to underestimate the size of buying groups for lower cost solutions. As in prior years, we also found that Director-level responders had more accurate – which in most instances means slightly higher – estimates of buying group size.

In summary, our findings underscore that B2B buying is a team effort, with group sizes averaging just under 10 members, a number that increases with the size of the deal. This reaffirms our previous research and emphasizes the collective decision-making process in B2B purchases.

Why Large Buying Teams Are the Key to Understanding and Identifying Buyers

In our 2023 Buying Experience Study, we found that a typical 10-member B2B buying team had 160 digital and human-mediated (e.g., calls, direct emails, in-person meetings) over the course of their buying journey, and more than 4,000 across all channels.

The most effective B2B revenue teams exploit these signals to know which accounts are in-market and require marketing or sales attention. And it is not just a designated scout or two from each buying team that visits vendor websites. Buying team members from the corner offices to the smallest cubicles told us that they have double-digit interactions with each vendor being evaluated.

All Buying Groups Conduct Online Research, But Most Remain Anonymous

Consistent with all prior research both by 6sense and others, marketers once again told us that approximately 3.7% of their web visitors fill out forms to view content. The Interactive Table below shows form-fill rates by factors such as industry, average selling price and more. While you’ll see slight variations in form-fill rates with these filters, most are not statistically reliable. We’ll soon see that the only meaningful difference in form-fill rate is between organizations that practice account-based marketing and those who do not.

There is critical context to add from other research, which indicates that when visitors come from accounts that can be identified through de-anonymization techniques, just 15% to 20% of these visitors fill out forms (PathFactory). Further, in our recent Buying Experience Report, buyers reported that only 30% filled out forms on the websites of vendors they brought from.

While individuals from identifiable accounts clearly complete forms at a higher rate than others do, 70% to 80% of these visitors remain anonymous, even to the companies they eventually buy from.

We will discuss the use of strategies such as outbound, inbound, and Account-Based Marketing (ABM), below. Here, however, it is worth noting that marketers that employ an account-based strategy earned a higher form-fill rate. This might be due to the focused account targeting and messaging inherent in ABM strategies.

As shown in the chart below, ABM marketers report an average form-fill rate of 4%, roughly half a percentage point higher than those who do not (3.4%). This difference is statistically reliable. It is not, however, likely to make a meaningful difference in pipeline and revenue creation for those doing ABM compared to others.

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Chapter 4

How B2B Organizations Attract Buyers: Go-To-Market Strategies

In our research, we queried marketers about their use of three specific go-to-market (GTM) strategies.

Inbound Marketing: An approach aimed to attract and engage potential customers to reveal themselves to vendor organizations by offering valuable content and experiences.
Outbound Marketing: Methods that involve proactively reaching out to an audience through channels such as email, direct mail, and outbound prospecting to promote products or services.
Account-Based Marketing (ABM): A targeted strategy that aligns marketing and sales on highly targeted engagement with high-value accounts, tailoring inbound and outbound tactics to address the target accounts.

In our sample, 43% of marketers practice ABM, whether on its own or combined with inbound, outbound, or both.

Because many respondents reported a mix of strategies, we classified participants into five categories based on whether they exclusively employed a single go-to-market strategy or combined it with others.

ABM Only: The marketing organization exclusively practices Account-Based Marketing (ABM/X).
Inbound Only: The marketing organization exclusively practices inbound marketing.
Outbound Only: The marketing organization exclusively practices outbound marketing.
ABM Blend: The marketing organization practices ABM along with either inbound marketing, outbound marketing, or both.
Traditional Blend: The marketing organization practices both inbound and outbound marketing.

As Figure 6 shows, the most common strategy involved ABM plus either inbound, outbound, or both (ABM Blend). Together, the two categories of blended strategies accounted for 51% of respondents. Despite ABM Blend being the most common strategy, ABM-only practices were the least common.

These results suggest that ABM strategies have been widely adopted but are rarely the only way an organization goes to market. Later, we will explore the conditions, tactics, and outcomes associated with the adoption of ABM.

Marketers Are Equally Satisfied with Inbound, Outbound, and ABM

We asked marketers to rate their level of satisfaction with each of the three main GTM strategies. These results are given in Figure 7 below. Despite variation in how organizations combine the three major approaches, B2B practitioners reported statistically identical, high levels of satisfaction with each.

Account-Based Marketing (ABM) Adoption

Across the board, approximately 43% of organizations have an ABM practice. We looked across industries, company funding (private-equity, venture capital, public, etc.), annual revenue, solution prices, and buyer company size (small, medium, large), but found no meaningful patterns that predict which types of organizations employ Account-Based Marketing (ABM).

The Types of Account-Based Marketing (ABM) that Marketers Employ

As we’ve seen, 43% of B2B organizations use an Account-Based Marketing (ABM) strategy, whether on its own or combined with inbound marketing, outbound marketing, or both. Of these, we wanted to understand the type of ABM approach they use (see descriptions below).

Although the current study did not yield sufficient data to address this question, insights into the types of ABM practiced by marketers today can be gleaned from the responses of 650 B2B marketers in another survey we conducted in January 2024 (for more information on this study please see the Methods section).

One-to-one ABM: Revenue teams target individual high-value accounts with customized strategies tailored to each account’s unique needs and characteristics.
One-to-few ABM: Marketers target a larger set of similar accounts, employing personalized strategies for tailored marketing efforts.
One-to-many ABM: Revenue teams target clusters or segments of accounts with shared characteristics, such as their industry, region, or size. Messaging and tactics are customized to the segment, but not to the specific account level.

Figure 8 below represents the proportion of marketers with an ABM practice that engage in each type of ABM. For example, the most common type of ABM practice is one-to-few.

Chapter 5

Do Marketing Teams Recognize Buying Groups?

A Strong Majority of Organizations See and Respond to Buying Groups, Not Just Leads

In B2B transactions, relying on a single form-fill to identify in-market prospects is ineffective. It fails to differentiate between casual browsers and active buying team members. When multiple individuals from the same group research similar solutions concurrently, that is a more reliable indicator of a potential buyer. The likelihood of a buying opportunity increases with each additional visitor from the same account engaging in this behavior.

To understand if organizations see and act on this indicator, we asked whether they prioritize accounts from which they have received multiple leads. A promising 74% of organizations indicated they do prioritize accounts with multiple leads, a notable rise from 61% found in our 2022 study. This indicates a growing awareness of active buying processes.

Figure 9: In the figure above, ‘Buying Group Prioritizers’ refer to marketers who recognize when they receive multiple form-fill leads from the same account and prioritize that account ahead of those where there is less evidence of an active buying process. 'Buying Group Blind' refers to marketers who are unaware when they receive multiple form-fill leads from the same account, and 'Buying Group Aware' refers to marketers who recognize when they receive multiple form-fill leads from the same account but do not prioritize such leads over a single lead from another account.

Prioritizing Buying Groups Is Associated with Better Financial Performance

Bolstering the case for prioritizing buying groups, we found that marketers who prioritize buying groups report 4% better financial performance compared to those who don’t. These findings indicate that simply knowing when buying groups are present without acting does not yield any benefits. Prioritizing buying probably does not by itself cause better financial perform. However, prioritizing buying groups is systematic of more advanced organizations.

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Chapter 6

Tools and Data Sources

Data and Tools Used to Identify Accounts & Contacts

Organizations have a variety of data sources, both internal and external, to draw from when identifying accounts and contacts to target as prospects. In addition, numerous tools exist to help organizations refine their selection of accounts and contacts. These tools range from spreadsheets that can be filtered to tools that use predictive analytics/artificial intelligence (AI) to identify ideal customer profiles.

Marketers Use Many Data Sources, But None Are Universally Adopted

Figure 11 below reveals that just over half of marketers use each of the data sources listed for account acquisition, but none are used by more than 43% for contact acquisition. While most sources are used by at least half of the participants for account identification, they are less likely to be used for contact data. For instance, less than 38% use MAP for contact data, and under 30% use CRM. CDPs and 3rd party data providers are more common for contact data, but their usage doesn’t exceed 43%.

Typical marketers use three to four different sources for account and contact information.

The reliance on external sources suggests that marketers find the quality and/or quantity of their internal sources to be inadequate.

Marketers Use Many Tools for Selecting Contacts and Accounts, But None Are Universal

In addition to these data sources, marketers utilize tools offering sophisticated analytics and filtering to select the best accounts and contacts. These tools are distinct from pure data sources. Often, data will be sourced elsewhere and loaded into the tools discussed below for further processing.

Stronger Performing Organizations Utilize More Data Tools

We found that marketers with ABM practices generally use three tools, whereas others use two. Notably, companies with better financial performance use between four and five tools. Lower-performing companies tend to use only two to three tools. This indicates a correlation between a company’s investment in buyer identification tools and their financial performance.

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Chapter 7

Buying Signals

Identifying In-market Buyers: Buying Signal Usage and Utility

As we have seen, marketers have a variety of tools available to identify potential prospect accounts and contacts. However, as our prior research has demonstrated, only a small fraction of accounts – even those that are targeted to be a good fit – will be in-market for a solution at any given time. To optimize efficiency, marketers need to identify the small subset of good-fit accounts that are actually in-market.

Fortunately, the intelligence about which accounts are in-market is available to be harvested. As B2B buying teams comb the internet in search of solutions to their business problems, they have thousands of digital interactions. These interactions are catalogued, matched to the accounts from which they come, and are made available to B2B vendors in the form of 3^rd-party intent data. Combined with traditional signals such as email and ad clicks, this offers mountains of information to help B2B providers identify in-market buyers. We explored if revenue teams are leveraging this abundance and their perception of its usefulness, using a list of 23 distinct buying signals.

B2B Marketers Don’t Agree on Much, But Still Rely Heavily on Form-fills

Website form-fills remain the most utilized signal. As described earlier in this report, we know that most web visitors – even those that will make a purchase — don’t fill out forms. More importantly, buying team members don’t engage directly with sellers until 70% of their buying journey is complete, and only after they have formed strong opinions and even chosen a winner.

To influence buyers earlier, revenue teams need to identify interest before form-fills occur. This involves two underutilized methods: de-anonymized web traffic and 3rd party intent (for more information on these topics, see “Moving on from Lead-Centricity”).

B2B’s Underutilized Early Warning Systems: De-anonymized Web Traffic & 3rd-party Intent

With technology readily available today, typical B2B companies can identify the accounts from which half or more of their (non-bot) anonymous traffic emanates. Doing so dramatically increases a marketer’s understanding of which accounts are showing interest.

However, our survey found that only 31% of marketers de-anonymize their web traffic, with a mere 11% finding it useful. This indicates a profound gap in marketers’ understanding of the value of unmasking anonymous web visitors.

The second early warning system for marketers comes from 3^rd-party sources such as intent data, product reviews, and social media. While over 40% of marketers use leads from product review sites and social media, less than 25% find them useful. Despite being a rich intelligence source, only 30% of marketers use third-party intent data, with only 12% of users finding it useful.

Readers may browse the table below to examine how the characteristics of provider organizations, such as the cost of their solutions, their industries, and many others influence their use of buying signals.

Overall Signal Utilization Is Low Across B2B

The data show that most organizations use only a fraction of the signal types available to help the identify buyers. Most organizations use between five and six signal types out of the 23 we asked about. Worse yet, as presaged above, marketers do not find the signals they do collect particularly useful. None of the signals were found to be useful by more than 35% of users, and most signals were found to be useful by 20% or fewer of marketers.

Below, we examine how critical factors such as go-to-market strategy, annual revenue, company funding type (private, public, etc.), and buyer segment impact or do not impact the variety of signals that marketing organizations collect.

Blended GTM Strategies Drive More Buying Signal Usage

Not surprisingly, the strategies a company employs to identify buyers influence their use of buying signals. Companies with multiple strategies employ more signals than those with just a single strategy.

Private Equity-Backed Firms Tend to Use More Signal Types

Private equity-backed (PE-backed) firms tend to gather more signals compared to their counterparts in companies with other funding structures. Marketers at private, public, and venture capital firms, on the other hand, use a statistically equivalent number of signal types.

Small to Mid-Size Enterprises Collect More Signal Types

Surprisingly, small to mid-size organizations report using more types of signals than larger companies do. Those with revenue between $50M and $250M gather 8 to 9 types while those above $250M collect around 5 to 7 types.

Classifying Signals to Understand What Marketers Want From Them

With most signal types in use by 40% or fewer of marketers, and most marketers using between just 5 and 6 types of signals in total, we were not able to identify any clear combinations of signals that marketers tended to employ as a group. There are no canonical “signal stacks.”

However, many signals share characteristics. For example, some signal types (e.g., form-fills) point to the activity of individuals who are identified by name. Others (e.g., de-anonymized web traffic) can only point to an account that is demonstrating interest. Likewise, some signals point to actions by groups from the same account (e.g., 3^rd party intent), while others indicate the behavior of individuals (e.g., syndicated content leads).

By categorizing each of the 23 signals along four dimensions, we were able to gain a better understanding of what marketers value in buying signals. Whereas analyzing the list of 23 specific signals yielded relatively few insights into what marketers value, this new perspective allowed us to identify clear patterns in marketer preferences.

In the tables below are definitions of the four dimensions, along with utilization and utility ratings for each.

	Raw	Derived
Defintion	Signals acquired and used in the form they are received (e.g., form-fill lead).	Signals in which an operation to either one signal or a combination of two or more signals to produce a new signal (e.g., a form-fill to which a score has been applied to enable prioritization).
Average Collected	5.3 signals	1.7 signals
Percent Considered Useful	49%	45%

	Anonymous	Identified
Defintion	Signals used to identify anonymous behavior (e.g., third-party anonymous intent, anonymous visitors to a website).	Signals for which the identity of the individual is known (e.g., webinar registrations).
Average Collected	2.1 signals	5.0 signals
Percent Considered Useful	46%	50%

	Individual	Group
Defintion	Signals that reveal the presence or actions of a single person (e.g., form-fill, email open).	Signals that are telling of group behavior (e.g., third-party intent, software review site account-level report).
Average Collected	5.4 signals	1.5 signals
Percent Considered Useful	50%	46%

	Received	Acquired
Defintion	Signals that are received directly from provider systems (e.g., freemium downloads, form-fills).	Signals that are acquired from another party, which originally received or created the signal (e.g., syndicated content leads, third-party intent).
Average Collected	4.1 signals	2.9 signals
Percent Considered Useful	44%	52%

No Surprise: Marketers Like Signals Where Individuals are Named and Received Directly into Their Systems

Marketers clearly depend on signals that identify individuals, are received in their own systems, and used in the form they receive them. In other words, marketers still want form-fill leads or other signals, such as syndicated content leads, and product review website leads that point to specific people.

What marketers want varies depending on a variety of factors. Those factors can be used as filters in the interactive table below. We encourage readers to explore the interactive table below to understand how various go-to-market (GTM) strategies impact reliance on each signal category.

The Average Number of Signals Considered Useful by Characteristic Category

ABM

Inbound

Outbound

ABM

Inbound

Outbound

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Chapter 8

Challenges Marketers Encounter

Challenges Marketers Face Are Consistent, No Matter the GTM Strategy

Having noted above that marketers are underutilizing available signals, we were interested to see if there were particular challenges that prevented marketers from utilizing a greater range of signal types.

Our findings indicate that about half of marketers face each of the five challenges mentioned. These challenges were equally common across industries, companies of different funding types (private, public, venture-capital, etc.), and target buyer segments. They were also reported equally by marketers across seniority levels (explore the interactive table below).

It is notable that Budget is no more of a challenge than time, technology, process, or skill. This suggests that the low usage rate of signal types is more due to a lack of belief in or understanding of their value, rather than in the budget, time, technology, process, or skill required to utilize them.

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Chapter 9

Marketing Budgets

In our survey this year, we explored how organizations budget for marketing. Across our sample of marketers from a wide array of industries, company sizes and funding sources, marketers reported that their organizations budgeted 13.4% of their companies’ annual revenues for marketing.

This figure is higher than the oft-cited benchmark of 5% to 10% of revenue for B2B marketing. There are notable studies that have found quite similar results, however. Most notably, in a 2023 report by Deloitte, CMO’s reported that their budgets were 13.6% of their companies’ annual budgets. Lending further credence to the value we found, all industries surveyed reported statistically equivalent values, with Tech & Software being the exception at just 11.0% of revenue (see Figure 16 below).

Target Audience Influences Budget Allocation

We also explored whether other factors influenced how much was budgeted for marketing. We found that organizations that sell to accounting and finance departments enjoy substantially larger budgets (16.8%) compared to the overall average of 13.4%. In contrast, those selling to Operations/Engineering (9.1%) and Supply Chain (8.7%) departments were budgeted at a substantially lower level than their peers.

More Marketing Budget Yields Better Company Financial Performance

In our survey, we asked marketers to rate how their organizations performed financially over the past 12 months. Later in the survey, we asked about their budgets. A correlation analysis revealed that organizations that allocate more of their company revenues to marketing also report better financial performance. Not surprisingly, marketers in organizations that allocate more of their company revenues to Marketing also report higher satisfaction with their GTM strategies.

Inbound, Outbound, and ABM programs are Equally Budgeted

The marketers we surveyed indicated that Inbound, Outbound, and Account-Based Marketing (ABM) strategies each receive equivalent shares of organizations’ marketing budgets. According to our survey respondents, around 40% of the total marketing budget is allocated to these strategies, whether individually or in combination. This distribution is consistent with the observation that not all organizations adopt all three approaches; in fact, only 15% of the 546 respondents we surveyed utilize Inbound, Outbound, and ABM simultaneously.

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Chapter 10

Implications

This research shows that B2B marketers increasingly recognize interest from buying groups — not just individuals. This shift enhances the effectiveness and efficiency of B2B revenue teams. An additional benefit is that focusing on interested buying groups allows non-active individual form-fillers to engage with vendor content in peace.

We know that buyers don’t engage directly until they are 70% through their buying process, and when they do, they have already decided which vendor they want to buy from 84% of the time. To operate effectively in this reality, marketers must proactively identify and engage potential buyers long before that 70% mark.

To maximize visibility into active buying groups, marketers must invest in intent data and website traffic de-anonymization. There simply is no other way to see and influence early stage buying processes.

Marketers might very well object that they operate under constraints, primarily of budget and time, and so cannot take advantage of the available signals. However, while roughly half of the marketers we surveyed cited budget as a challenge to doing more, that leaves half of B2B marketers with budget to acquire more signals. These organizations need to move quickly to remove other barriers to productivity, whether those barriers be internal processes, skills, or re-prioritizing staff time.

Finally, the link between higher marketing budgets and improved financial performance underscores the need for adequate marketing funding. Marketers that are budget-constrained should use reports like this one and others cited in this report to advocate for more robust budgets, highlighting their impact on financial results and marketing effectiveness.

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Chapter 11

Appendix

Methods

6sense Research surveyed B2B professionals to understand how they identify their target accounts and buying groups. The 2023 study expanded its scope from 169 respondents in 2022 to 546 B2B professionals and incorporating additional survey questions to explore how organizations allocate their budgets and the go-to-market strategies they employ to engage potential buyers. Conducting this survey annually enables us to track trends in B2B practices, providing a longitudinal perspective on changes over time.

The following charts provide a breakdown of our survey participants, highlighting demographic and firmographic information such as their industry, company size, and more.

Survey Sample Firmographics

Industry and Company Size Distribution

Solution Price by Company Size Distribution

A Note on Additional Survey Data Used

As noted earlier, there were limited responses to a specific question in our survey regarding the types of account-based marketing (ABM) strategies employed by marketers. However, we asked the same question to 650 marketers in another survey conducted early 2024. Thus, we used the data from that survey to provide insight into which types of ABM marketers are practicing today (one-to-many, etc.). About 20% of these marketers work in Technology, 15% in Manufacturing, 10% in Financial Services, 16% in Professional Services, 27% in Business Services, while the rest were spread across industries such as Education, Construction, Legal Services and more.

Signal Types and Their Characteristics

Signal Type	Individual or Group	Identified or Anonymous	Received or Acquired	Raw or Derived
Display Ad Clicks	Individual	Anonymous	Received	Raw
Display Ad Views	Group	Anonymous	Acquired	Raw
Scored Form-Fill Leads (MQLs)	Individual	Identified	Received	Derived
Form-Fill Leads	Individual	Identified	Acquired	Raw
Anonymous traffic, deanonymized	Group	Anonymous	Received	Derived
Syndicated Content Leads	Individual	Identified	Acquired	Raw
Social Leads	Individual	Identified	Received	Raw
Partner Referrals	Individual	Identified	Acquired	Raw
3rd Party Anonymous Intent	Group	Anonymous	Acquired	Derived
Product Review Site Leads	Individual	Identified	Acquired	Raw
Product Review Site Company Level Intent	Individual	Anonymous	Acquired	Derived
Email Opens/Clicks	Individual	Identified	Received	Raw
Demo Requests, Downloads	Individual	Identified	Received	Raw
Demo Usage (# users, product telemetry)	Individual	Identified	Received	Derived
Freemium Downloads	Individual	Identified	Received	Derived
Freemium Usage	Individual	Identified	Received	Derived
Virtual Event Registrations	Individual	Identified	Received	Raw
Live Event Registrations	Individual	Identified	Received	Raw
Contact Me Requests	Individual	Identified	Received	Raw
Cold Calling	Individual	Identified	Received	Raw
Direct Mail	Individual	Identified	Received	Raw
SEO Clicks	Group	Anonymous	Acquired	Raw
Web Chat (Live or Bots)	Individual	Anonymous	Received	Raw

Statistical Reporting

Finding	Statistical Test	Statistic	Significance Level	Effect Size	Sample Size
The average buying group size is 10 members.	Average	N/A	N/A	N/A	616
Larger deals involve more buying team members.	Correlation	R=.620	P<.001	Large	616
Average Selling Price (ASP) accounts for nearly 40% of what drives buying team size.	Linear Regression	R=.601	P<.001	R2=.36	573
Marketers report buying group sizes that closely match those reported by their buyers, with a correlation coefficient of r = .94, indicating exceptionally strong agreement.	Correlation	R=.94	P<.001	Large	616
Director-level respondents provided more accurate – which in most instances means slightly higher – estimates of buying group size.	ANOVA	F=5.290	P<.001	N2=.041	616
The average form-fill rate is 3.7%.	Average	N/A	N/A	N/A	574
Marketers using account-based strategies saw form-fill rates increase to 4%, about 0.6% higher than non-ABM users at 3.4%	T-test	T=2.17	P=.004	Cohen’s d=.085	573
43% of marketers reported practicing ABM, either independently or in conjunction with inbound, outbound, or both.	Frequency	N/A	N/A	N/A	574
51% of marketers use multiple Go-To-Market Strategies.	Frequency	N/A	N/A	N/A	573
Marketers rated their satisfaction with Inbound, Outbound, and Account-Based Marketing (ABM) equally.	Repeated Measures ANOVA	F=.436	P=.647	N2=.005	92
We found no meaningful patterns in the types of marketing organizations that employ Account-Based Marketing (ABM) based on industry.	Chi-Squared	X2=9.37	P=.095	N/A	573
We found no meaningful patterns in the types of marketing organizations that employ Account-Based Marketing (ABM) based on company funding.	Chi-Squared	X2=2.385	P=.496	N/A	573
We found no meaningful patterns in the types of marketing organizations that employ Account-Based Marketing (ABM) based on annual revenue.	T-Test	T=-1.023	P=.307	Cohen’s d=-.088	550
We found no meaningful patterns in the types of marketing organizations that employ Account-Based Marketing (ABM) based on solution prices.	T-Test	T=-.751	P=.453	Cohen’s d=-.063	573
We found no meaningful patterns in the types of marketing organizations that employ Account-Based Marketing (ABM) based on buyer company size (small, medium, large).	Chi-Squared	X2=3.441	P=.179	N/A	573
74% of organizations indicated that they prioritize accounts with multiple leads.	Frequency	N/A	N/A	N/A	552
Most marketers, even those not using Account-Based Marketing (ABM) practices, seem to recognize and give priority to buying groups, although the difference wasn’t statistically reliable enough to allow us to infer that trend exists among marketers outside of our sample.	Chi-squared	X2=14.23	P=.07	X2=14.23	552
We found that marketers who prioritize buying groups report a 4% better financial performance compared to those who don’t.	ANOVA	F=	P=.021	N2=.014	552
On average, marketers use three to four different sources for account and contact information.	Average	N/A	N/A	N/A	539
Our survey found that only 31% of marketers de-anonymize their web traffic, with a mere 11% finding it useful.	Frequency	N/A	N/A	N/A	546
While product review site leads and social media leads are used by over 40% of marketers, they’re deemed useful by only 25% or fewer.	Frequency	N/A	N/A	N/A	546
Third-party intent data, despite being a rich intelligence source, is employed by just 29% of marketers, with only 12% finding it useful.	Frequency	N/A	N/A	N/A	546
Most organizations only use between five and six signal types out of the 23 we asked about.	Frequency	N/A	N/A	N/A	574
Companies with multiple go-to-market strategies employ more signals than those with just a single strategy.	ANOVA	F=22.118	P<.001	N2=.135	573
Private equity-backed (PE-backed) firms tend to gather more signals compared to their counterparts in companies with alternative funding structures. Marketers at private, public, and venture capital firms, on the other hand, gather statistically equivalent numbers of signals.	ANOVA	F=2.623	P=.05	N2=.014	574
Small to mid-size organizations report using more types of signals than larger companies do. Those with revenue between $50M and $250M report collecting 8 to 9 types while those above $250M report collecting around 5 to 7 types.	ANOVA	F=16.425	P<.001	N2=.108	550
ABM practitioners are more likely to collect signals that are computed, such as account or intent activity scores, than those who do not practice ABM.	T-test	T=4.770	P<.001	Cohen’s d=.413	558
ABM practitioners are more likely to collect more anonymous signals than those who don’t practice ABM.	T-test	T=4.484	P<.001	Cohen’s d=.390	558
Marketers who practice Inbound marketing are more inclined to use signals that indicate the activity of identified individuals than those who don’t practice inbound.	T-test	T=3.46	P<.001	Cohen’s d=.312	558
Inbound marketers utilize raw signals (e.g., a form fill) more than peers that do not practice Inbound marketing.	T-test	T=2.98	P=.003	Cohen’s d=.269	558
Inbound marketers utilize computed signals (e.g., lead scores) more than peers that do not practice Inbound marketing.	T-test	T=2.37	P=.018	Cohen’s d=.213	558
The go-to-market strategy associated with the highest level of budgeting was those practicing ABM only, followed by those practicing Inbound and those practicing Outbound only.	ANOVA	F=12.815	P<.001	N2=.086	549
Organizations that allocate more of their company revenues to marketing also report better financial performance.	Correlation	r=.259	p<.001	Small	549
Organizations that allocated more of their company revenues to marketing also report higher satisfaction with ABM.	Correlation	r=.357	p<.001	Medium	232
Organizations that allocated more of their company revenues to marketing also report higher satisfaction with Inbound Marketing.	Correlation	r=.235	p<.001	Small	369
Organizations that allocated more of their company revenues to marketing also report higher satisfaction with Outbound Marketing.	Correlation	r=.256	p<.001	Fisher’s z=.262	321
According to our survey respondents, around 40% of the total marketing budget is allocated to these strategies, whether individually or in combination.	Average	N/A	N/A	N/A	574

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FAQ

The term “statistically significant” is used a lot. But, the word “significant“ in this phrase doesn’t mean what we normally mean by it. And there’s a lot of confusion and misunderstanding about what makes something statistically significant, how much data you have to have to get there, etc. Below, we try to cover it all.

Statistical significance is reliability

As described above, statistical significance is a measure of how reliably a study’s findings represent the real world. It is literally a statement of the probability that a study finding represents the real world. For survey research, the standard for determining statistical significance is that we would expect to find the same result 95% of the time we replicate the survey with a sample drawn from the same population.

The word “significant” is often taken to mean “important” or “large” in everyday conversation, but there are many cases in which findings that are statistically significant are not meaningful, large, or important. In describing our findings, 6sense Research uses the word “reliable” instead of “significant,” because we think it is a more accurate description of this concept.

Significance is not importance

Statistical significance is a measure of how reliably a finding represents the population or real-world condition of interest. However, the word “significance” can be misleading. It can lead to the assumption that what is statistically significant is important or meaningful. In fact, many findings that are statistically significant are not really significant in any way that we would care about.

In 6sense research, when we encounter findings that are statistically significant but not important, we describe them as “not meaningful.” For instance, in our research of B2B buying processes we found that buyers reliably consume vendor content (e.g., website content, webinars) more than they read analysts reports, but only by 4%. This is an example in which we found a “statistically significant” finding that is not very meaningful.

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Sara Boostani and Kerry Cunningham

Sara Boostani is a marketing research analyst who’s passionate about using data to tell great stories. She transforms complex statistics into digestible narratives that drive B2B actions for 6sense and its customers. Kerry Cunningham is a thought leader in B2B marketing and is a former SiriusDecisions and Forrester analyst. He’s an expert in the design and implementation of demand-marketing processes, technologies and teams for a wide array of B2B products, solutions, and services. He’s also developed a wealth of expertise in the alignment of marketing and sales organizations

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B2B Buyer Identification Benchmark

Chapters

Chapter 1

Chapter 2

Chapter 3

Table of Contents

Summary & Author's Note

Author's Note

Introduction

The B2B Buyer

B2B Purchases Are Made in Groups, Leaving A Wealth of Signals for Marketers

Marketers’ View of the Buying Group Largely Tracks with What Buyers Report

Why Large Buying Teams Are the Key to Understanding and Identifying Buyers

All Buying Groups Conduct Online Research, But Most Remain Anonymous

How B2B Organizations Attract Buyers: Go-To-Market Strategies

Marketers Are Equally Satisfied with Inbound, Outbound, and ABM

Account-Based Marketing (ABM) Adoption

The Types of Account-Based Marketing (ABM) that Marketers Employ

Do Marketing Teams Recognize Buying Groups?

A Strong Majority of Organizations See and Respond to Buying Groups, Not Just Leads

Prioritizing Buying Groups Is Associated with Better Financial Performance

Tools and Data Sources

Data and Tools Used to Identify Accounts & Contacts

Marketers Use Many Data Sources, But None Are Universally Adopted

Marketers Use Many Tools for Selecting Contacts and Accounts, But None Are Universal

Stronger Performing Organizations Utilize More Data Tools

Buying Signals

Identifying In-market Buyers: Buying Signal Usage and Utility

B2B Marketers Don’t Agree on Much, But Still Rely Heavily on Form-fills

B2B’s Underutilized Early Warning Systems: De-anonymized Web Traffic & 3rd-party Intent

Overall Signal Utilization Is Low Across B2B

Blended GTM Strategies Drive More Buying Signal Usage

Private Equity-Backed Firms Tend to Use More Signal Types

Small to Mid-Size Enterprises Collect More Signal Types

Classifying Signals to Understand What Marketers Want From Them

No Surprise: Marketers Like Signals Where Individuals are Named and Received Directly into Their Systems

Account-Based Marketers Use More Anonymous and Computed Signals

Inbound Marketers Take a Surprisingly Broad Approach, But Still Favor Form-fills

Outbound Marketers Rely on Raw signals, Hampering Their Ability to See In-market Accounts

Challenges Marketers Encounter

Challenges Marketers Face Are Consistent, No Matter the GTM Strategy

Marketing Budgets

Target Audience Influences Budget Allocation

More Marketing Budget Yields Better Company Financial Performance

Inbound, Outbound, and ABM programs are Equally Budgeted

Implications

Appendix

Methods

Survey Sample Firmographics

Industry and Company Size Distribution

Solution Price by Company Size Distribution

A Note on Additional Survey Data Used

Signal Types and Their Characteristics

Statistical Reporting

FAQ

Statistical significance is reliability

Significance is not importance

Sara Boostani and Kerry Cunningham

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