How Much of Our Revenue Did Marketing Actually Make?

Ask a marketing director how much revenue marketing made last year and you will get a number. Ask the sales director and you will often get a smaller one. Ask the finance director and often he will show a complicated set of emotions depending on how the revenue and EBITDA numbers look at the time:

If they are good, he will sometimes look dazzled, like he’s seen a wonderful magic trick. If the numbers are bad, he will more often than not look frustrated, yet still just as mystified.

All three are reasonable people looking at the same business.

Marketing-sourced revenue is the revenue from deals where marketing made the first qualified contact, under a definition both Sales and Marketing have agreed. Most companies cannot state theirs. That is why three directors give three different numbers.

When I was at a telco software company, our entire universe was about 400 big accounts. Once we landed a mega deal. It had come in through an event, then been nurtured by us in the email channel for nine months. Then it closed.

It turned out the salesperson who closed it was old friends with the man who signed. The relationship went back years, offline of course, so no way of tracking or confirming that. But the deal went to Sales. A cynic may say that this was more the work of politics than attribution.

This has happened many times in my career in B2B software. It happens often enough that HubSpot built a method for it: the Sales and Marketing Service Level Agreement, or SLA. 

Both teams sign one page. Marketing commits to a number of qualified leads a month, defined in writing. Sales commits to contact each one within a set time and work it a set number of times. Every month the two numbers are read out together.

HubSpot’s final ingenious addition is a Judicial review: any disputed deal goes to a C level leader (above the political fray) who runs neither Sales nor Marketing, usually the President, COO or CFO, who looks at the evidence and rules.

I have recommended it at several companies since I first read about it over six years ago. It has never been implemented, and I have never seen it run anywhere, even though it comes closest to solving the problem.

The reason is not complicated. An SLA makes both teams measurable by someone else, and nobody who is winning the current argument wants a referee. It’s the Sales/Marketing equivalent of gerrymandering.

Why is the marketing revenue number usually wrong?

Because there are two systems and two definitions, and nobody owns the join.

Marketing lives in a marketing platform. It knows who opened an email, clicked an ad, came to a webinar. Sales lives in the CRM. It knows who was called, who got a proposal, who signed. The two systems are supposed to talk to each other. 

In my experience, across a dozen B2B companies, Sales and marketing alignment is on a spectrum; never perfect, rarely terrible, but that continuum is flexible and rapidly changing. It needs constant maintenance to keep it on the 'healthy' side.

The first time I saw this properly was at a Fintech software company in 2013, where I ran marketing automation on one system while Sales ran the pipeline on another. A customer could be a hot lead in one and a stranger in the other.

When the systems disagree, each side counts its own way. Marketing counts everyone it touched. Sales counts everyone it closed. Both numbers are true, but neither tell the whole story. 

That’s why in B2B you need constant honest communication (addressing the brutal facts in your funnel) to keep all your numbers healthy: from lead to sale conversion rates, to cost per sale, to Return on Ad Spend, and so on.

What does the real number look like?

Different from both of the ones you were given, and findable depending on the systems you have in place – perhaps immediately, perhaps longer (maybe a week, say, for a poorly integrated platform).

The method is not clever. You take every closed deal, you look up every contact on it in the marketing system, and you write down what marketing did to that person before the deal was opened. Then you do the same for every open lead. It is tedious. It is also the only way to get a number that both directors will sign.

What are three levers I can pull right now to improve my campaign peformance?

None of them needs new budget. Each need one decision from the top and a fortnight of someone's time.

How do you find leads lost between marketing automation and the CRM?

Lever 1: find the customers you have already paid for.

This is the cheapest revenue in the building. The marketing money that produced it has already been spent.

Every company I have worked in had leads that were generated, paid for, and then lost between two databases. The fix is a rule, not a project: once a fortnight, every contact in the marketing system is matched to the CRM, and anything that does not match gets a name next to it.

If you do want the bigger fix, it is worth it. The question to ask: when did we last check that every lead we paid for reached a salesperson? If the answer is a date, good. If it is unclear, or only gives you a partial picture, this is your first lever.

How do you get Sales and Marketing to agree on lead definitions?

Lever 2: make Sales and Marketing count the same thing.

The argument about whose lead it was is a definition problem, not a personality problem. But it is also a big picture systems issue. If we are not clear about definitions, then we are also unclear about incentives, and most importantly, accountability. It has a one-page fix, and it is the SLA from the top of this piece.

Write down once what counts as a marketing-sourced deal and what counts as marketing-influenced. Get both directors to sign it. Then stop arguing about individual deals. Where you can, replace the argument with a score. That is the real test of a definition. Not whether it is fair, but whether the sales team changes its behaviour because of it.

The revenue effect is large. Once both teams count the same way, marketing stops generating volume (‘MQLs’ for their own sake in a performative manner) to win the argument and starts generating leads Sales will work. At a fintech scale-up where I made this change, the cost of a sales demo fell eightfold in a year. Same budget, same team, different definition of a good lead.

The question to ask: do Sales and Marketing have one written definition, signed by both, and does Sales use it?

How should a B2B company reallocate its marketing budget?

Lever 3: spend where the last ten wins came from.

Most marketing budgets are set by last year's budget. The better question is where the last ten customers actually came from, by name, and whether the money is going there.

I would caveat that by saying – sure, you need to look at the longer-term trends, not least because the large data sets show statistical significance. (Bayesian statistics, though, is the primary approach well-suited for working with small data sets – as in your last ten deals).

The deal-by-deal check answers this for free. What I see usually is that a handful of behaviours predicts  a won deal far better than the rest: repeat visits to the website, a recent email click, a look at the pricing page. The campaigns that produced those behaviours were not the biggest. They were the ones aimed at a short list of accounts rather than a long list of names.

I have seen this at every size of company. At a telecoms billing software company, a year of work on one named account became a five-million-pound annual contract. At a B2B software company a decade later, moving paid media from broad targeting to a named-account list halved the cost of a lead. At a martech scale-up, the two largest customers of the year came from targeted outreach to a handful of global brands, not from the campaign with the biggest budget.

The lever is to move money, not add it. Take ten percent from the channel that produces the most leads and put it into the channel that produced the most customers. They are rarely the same channel.

This approach can also work on the campaign level.

The question to ask: where did our last ten (for B2B Enterprise), or a hundred (for smaller transactional selling) customers come from, and does the budget match that list?

Three questions to ask on Monday

You do not need to understand the systems. You need to ask three questions and insist on a date, a name or a number in reply.

1. When did we last check, deal by deal, that marketing's revenue number and sales' revenue number describe the same customers?

2. Do Sales and Marketing have one written definition of a marketing-sourced deal, signed by both, and does Sales use it to decide who to call?

3. Where did our last ten customers come from, by channel (and Campaign), and does the budget match that list?

4. Are you regularly disagreeing over whether a deal 'comes from' sales, or marketing?

If all four come back with a clear answer, you have a commercial engine most companies would envy. If any come back with a pause, you have found your lever. 

And if the answer to the final question is yes, you know why. 

Nobody who is winning the argument wants a referee. 

That is a decision only the person above both teams (President/COO/CFO) can make - Certainly not the Head of Sales, nor the Head of Marketing.

But as I've said many times, most of these problems don't come from tech (but tech is the easiest scapegoat to blame, for sure), they come from politics, systems, and above all, Governance.

Frequently asked questions

What is marketing-sourced revenue? Revenue from deals where marketing made the first qualified contact, under a definition Sales and Marketing have both agreed. Marketing-influenced revenue is wider: any deal marketing touched along the way.

What is a Sales and Marketing SLA? A one-page agreement. Marketing commits to a number of qualified leads a month; Sales commits to how fast and how often each one is worked. Both numbers are reviewed together monthly.

How do you reconcile marketing automation with the CRM? Match every contact across the two systems on a shared identifier, flag the ones that do not match, and give each unmatched record an owner. Fortnightly is enough.

Why do Sales and Marketing disagree about leads? They measure different things. Marketing counts engagement; Sales counts readiness to buy. One scoring model, adopted by Sales (and by ‘adopted’ I mean they need to believe in it, like a priest does in Jesus), closes the gap.

How often do marketing automation and the CRM disagree? More than most leaders expect. Daily, at some companies, weekly at others, rarely just monthly, and never have I seen them never disagree.

What is the fastest way to increase marketing-sourced revenue? Find the leads already paid for and lost between systems, agree one definition of a good lead, and move budget toward the channels that produced the last ten customers.

About the author

Rudy Parker is a Senior Demand Generation Manager with 14 years in B2B SaaS marketing (Cybersecurity/Fintech/Martech/Telco Software). 

What has intent data got to do with Account-Based-Marketing?

Recently a Business leader asked me ‘What has intent data got to do  with Account Based Marketing?’ and it got me thinking – even today, ABM is a very misunderstood strategy. When Damien Seaman, well known B2B Insurance (Currently working with Marsh, and KPMG) Content Strategist, and I ran a round table on this, it became clear;

You’d perhaps expect heads of Marketing, and Demand Generation at large and midsize SaaS organisations to be ‘on top’ of their Account based marketing strategies. But many freely admitted:

• We have those strategies in place but are not getting very far with them.
• We don’t even really understand the value this approach can drive, let alone how to get started.

But the evidence is in – ABM does work on many levels, not least of all with that critical Sales/Marketing alignment need to win large deals with big accounts faster

In my business, using an ABM approach to secure enterprise customers for large deals, you need a highly targeted 'spear fishing' approach, that begins with account selection.

For net new accounts, I usually use a highly targeted paid channel like Linkedin to drive initial contacts, and Hubspot/Salesforce/Marketo/Eloqua to nurture via marketing automation, sometimes alongside retargeting ads (to get your contacts when they are ‘in market’ – only 5% are at any one time).

Here's an example of an ABM campaign, for a Salesforce partnered SaaS CPQ Solution (Online Publishing/Telcos/Utilities), I built out of my CMO's ask 'to run roundtables to speed up MQL to Sales rate'

At that company we had ultra targeted ICPs, TAM's and Countries; Only a specific Job title (like CIO, CFO, or CRO), alongside a set target list of companies, at certain countries, would be accepted as an MQL/SQL by the Sales team. 

But going back to that Marketing Executive’s question ‘How does intent data fit in to all this?

Account-Based-Marketing requires marketing and sales to work as a unified unit rather than in silos. Intent data acts as a single source of truth that both teams can trust.

When I was part of the implementation of our Intent Platform 6sense, one of our biggest challenges was reconciling our Sales peoples ‘Target account/Pursuit list’ and the reality of what we in Marketing, were seeing:

• Sales may want to set up sales opportunities with fifty companies
• But how many of those fifty want to speak to us?

That, in a nutshell, is how intent data can help. Not only does it give you a highly granular view of what each contact at every account is doing in terms of engaging with your company – pages viewed, emails clicked, social media sites and partner sites visited...

But, it also gives you an outstanding ’30,000 ft’ arieal over view of those accounts. Where are they in the sales funnel? And not just net new, but existing prospects, opportunities being worked by sales, and even current customers, or returning ones:

• Prospecting: Identifying and researching companies that match your ideal customer profile to build a pipeline.

• Qualification: Evaluating a prospect's budget, authority, and needs to ensure they are a viable fit.

• Research: Deep-diving into the prospect’s business and mapping out their internal decision-makers.

• Presentation: Demonstrating your product via a tailored pitch that directly addresses the prospect's unique pain points.

• Proposal & Negotiation: Submitting a formal contract and working through pricing, terms, and legal adjustments.

• Closing: Securing final procurement approvals and getting the contract signed to officialise the deal.

• Onboarding & Retention: Handing the account to customer success to ensure seamless adoption and future growth.

Without reliable, comprehensive intent signals, your Sales success at many of these stages will be severly limited, and without those, you cannot drive forward with an outstanding Account based marketing approach.

High quality Intent data won't always score the goals for you, but it will help to set them up almost every time. 

Additionally, most of the marketing suite of tools you use will work more effectively on campaigns if you understand intent data for your company.

For example, Clay – everyone has access to this tool. But only you have access to your own data. So if you combine your data with Clay, rather than tapping out on return on investment (diminishing returns), you will continually grow the effectiveness of Clay for your organisation.

This powerful cumulative approach will both drive potential buyers at the top of your funnel, whilst securing sales contracts at the bottom.

Without good intent data, this effective strategy will be limited. Secondly, an 'arms race' has been going on with data tools like Clay, Apollo, and Zoominfo (along with a plethora of others, like Blitzap) for many years, which AI has escalated.

So in short, you will not only differentiate your self from all your competitors using ‘AI slop’ and be truly revolutionary in having enriched contacts, and accounts, unique to you. This approach will ensure larger and increasing financial returns on your marketing ABM marketing campaigns.

One question I get levelled at me a lot is ‘Hey Rudy, these are great insights, but how can I actually apply them in my business?’

And I get that – due to a strange brain, and even stranger upbringing, I have a passion for analysis.

But I know that most are not that interested in ideas, unless they result in actual money - usually the more money, the greater the interest.

So for that reason I recently attended a Demandbase workshop on how to use AI, to speed up your ABM campaigns, and make them more effective. And the results are proof that it is working now.

Not just from me but other attendees there, using it effectively, like the Head of Marketing at State Street Bank, and Fujitsu Global Brands.

The core message cut across all of it: in ABM, early adopters of AI will win, and the winners will be the ones who feed AI good context and good data — not the ones with the flashiest tool:

● You can cover 10× more accounts, with 10× more personalisation than was possible pre-AI.

● An ABM campaign that used to take six months can be stood up in roughly a month.

Frequently asked questions (FAQs)

What is the difference between intent data and lead scoring? Lead scoring is a rule that assigns points to behaviours. Intent data is the behaviour itself. A score is only as good as the evidence behind its rules, and most scores were set by assumption (this is why companies consistently over value actions like email opens). Test the rules against won deals before trusting the score.

Do you need 6sense or Demandbase to use intent data for ABM? No. First-party intent from HubSpot, Marketo or Salesforce, joined to your CRM outcomes, is the most reliable intent data you will ever own. 

Third-party platforms add reach across the open web and are worth it once the first-party join is working. As John Blackmore, Global Director of Demand Generation, at Cybersecurity SaaS company Field Effect, put it, "6sense on its own is nothing super. When you pair it up with another tactic, it's one plus one equals three."

How much revenue does intent data add? In the cases above, intent directly sourced about a quarter of opportunities at one cybersecurity firm, lifted every tactic 10–20%, and sized at £0.5–1.5m a year of additional revenue across one portfolio through better hand-off and prioritisation. The honest answer is a range, and the floor is the number to budget on.

Why does ABM fail even with an intent platform? Because sales does not act on it, or because marketing never checked which signals predict revenue. Rocio Sasson, VP of Demand Generation at Cybersecurity company Checkmarx points to the same cause: the tool was bought, the operating rhythm with sales was not.

Which intent signals matter most in B2B? Return visits on a later day, session depth and recent pricing or product-page views. Early email clicks help in the first week and fade by 30 days. Form submissions, brochure downloads and open counts predict little once you control for return visits.

How does AI change intent-led ABM? AI lets a team cover roughly ten times the accounts with the same personalisation and stand up a campaign in a month instead of six. It does not improve the underlying data. Teams with clean, joined first-party intent compound the advantage; teams without it produce more of the same noise.

Driving High ROI Revenue from Insights.

I trained as a lawyer before I ever worked in marketing, and it shapes how I approach data perhaps even more than what I learnt in my Statistics or Financial Analysis classes during my MBA.

A courtroom doesn't accept a claim because it sounds plausible — it wants the chain of evidence, unbroken, from source to conclusion. I bring the same discipline and clarity to my business meetings.

Currently we are innundated with data. It's growing exponentially: roughly 400 million terabytes of data are created every single day at current rates, and IDC's estimate is that global data volume is doubling roughly every three to four years.

"90% of the world's data was created in the last two years"

Ironically, just at the time when we are absolutely over-whelmed by data, scepticism about that very data is at an all-time high. 

I think this is an area in marketing where a typical background data expert (let's say a Maths graduate who then got a masters or phd in statistics) will often run into trouble. Firstly, will their senior leadership audience understand them? Secondly, even if they do, will they believe them?

AI has enormous untapped potential. A key area that has been used in my sector it automation, by GTM 'engineers' for example. But a realm that I have discovered that I think has even more potential, and I have capitalised on is AI's ability to connect and convert disparate platforms data. (Harvard Business School Article)

Despite these advances, research suggests that senior leadership often lacks trust in the validity of AI driven data insights. According to a recent Financial Times Survey, three quaters of leaders don't know what data to trust. 

That's why I always include methodology (for example, Statistical Significance of my findings, explained in a clear, non-technical manner), and back up my data insights with reasoning, and evidence, particularly when the outputs rely heavily on AI driven (for example in Claude) conclusions. 

And of course the benefit of having worked in B2B Marketing and before that in Sales, ensures that my experience 'reality checks' my conclusions, and ensure that our 'actionable recommendations', as in the diagram above, are achievable (Alongside my Platform knowledge, & understanding of how teams operate).

'The elephant in the meeting room' 

The elephant in the room when working with Marketing and Sales data, is the levels of inaccuracy, gaps, and mismatches (especially across different platforms). In my experience, this is commonplace across a range of B2B businesses, and also across sectors; 

When I worked in Fintech, Cybersecurity, Marketing software, we all experienced these challenges. When I hosted an ABM roundtable last year, heads of marketing at SaaS businesses all rated trust in and reliability of data as paramount.

That's why I always check my insights. And one rule I stick by, which I first heard from Jeff Bezos of Amazon is that:

 "when the data and stories don't match, I trust the stories."

I am not going to be 'that guy/girl' who stubbornly doubles down on the numbers, when all the other signs are telling me otherwise.

That's where a discipline like Economics, which I studied at various levels, is useful. Since Economics uses data, and sometimes complex maths to come to conclusions. 

But the best Economists realise that metrics will only ever measure part of the picture. The real world which Economics captures is complex, and certainly not straightforward, or rational, in the sense of a chemical formula, or a mathematical equation. 

Behavioral Economics, which is the closest discipline to Marketing, captures that idea most effectively. I recommend reading 'Predictably Irrational' by Dan Ariely to see my point. 

Classical Economics assumes that all actors behave rationally. Anyone who has worked at a company for any length of time knows that this is not always the case. 

One of my favorite Economists, and an LSE Professor (Tooke Professorship of Economic Science and Statistics), Friederich von Hayek, said: “The fact that we can statistically ascertain certain magnitudes does not make them causally significant.”, and also,

"Data gives us precision about what we have measured. It doesn't necessarily give us completeness about what is happening." 

(Hayek's 1974 Nobel Memorial Lecture, "The Pretence of Knowledge," delivered 11 December 1974 at the Stockholm School of Economics/). 

And using calcuations in Sales and Marketing is the same. That philosophy informs my thinking. You should never confuse mathematical accuracy with insights in the real world. The narrative must make sense on multiple levels, not just with metrics.

Applying the theory

The rule I apply everywhere now: a piece of marketing engagement only counts as having influenced a deal if it can be shown to have happened before that deal existed in the sales system. Not correlated with it. Not "around the same time." Provably prior. 

It sounds obvious written down. In practice, most attribution models quietly skip this step, which is how marketing ends up either overclaiming credit it didn't earn or — more often, in my experience — underclaiming credit it did.

Applying that discipline to one reallocation case built the evidence to correctly move a six-figure sum of revenue from "sales-sourced" to "marketing-sourced" in the CRM — not by arguing harder, but by showing the paper trail. 

Recovered value like this, across a handful of cases, has added up to significant  revenue that existed the whole time; it just wasn't being credited to the work that produced it.

Scoring leads on what actually predicts a sale, not what feels intuitive

The second half of this work is prospective rather than forensic: instead of proving what already happened, building a model of what's about to happen.

The instinct in most marketing teams is to score leads on things that feel like effort — email opens, form fills, general activity. In practice, across the datasets I've built models on, those signals are close to noise. 

What actually separates a lead who converts from one who doesn't is a narrower set of behaviours: depth of engagement (not whether someone visited, but how many sessions and how many pages), job function, and 

— the single strongest predictor: How quickly sales actually makes contact once the signal appears. A high-intent lead left untouched converts below the average rate. The lift isn't in the lead. It's in the handoff.

Once you build a model around that and route the highest-scoring leads to the right rep on the same day rather than into a generic nurture sequence, you're not doing more marketing. You're doing marketing with a better sense of where the ball actually is.

The takeaway

If there's one thing I've learnt in my career so far, it's that "the dashboard says" is not evidence. It's a claim, ideally a powerful one (if your dashboards are effective). But it should never be the one and only source of truth, in the same way you would never fly a plane by only looking at your control panel. 

None of this, the reconciliation, the scoring model, the evidential discipline, means anything until it becomes a decision. Data and trends are the evidence. They are not the verdict.

That's the step most data people skip. They lay out the exhibits, build the chart, cite the methodology, and expect the room to convict itself. 

In my experience, arguing your case, is the part of the job nobody teaches a data analyst. And in the AI data world, this could end up being the most important data skill of all.

Not proving what already happened, or predicting what's about to. Standing in front of a leadership team who have been burned by an over-claimed dashboard, or a plausible-sounding AI summary that fell apart under questioning.

And making the case anyway — with evidence to support it.The data and the trend analysis gets you to the room. The argument is what gets you the agreement. 

The President's Problem: Faster Shortlists, Slower Decisions — A Leadership Playbook for AI-Led B2B Buying

              

Every company I’ve worked at, and according to research also, most companies, are struggling with the same issues – falling conversion rates, and higher costs per lead. Those two powerful forces can absolutely pull a campaign apart, so I think it is worth exploring them in detail.

If I can break these two problems down into what’s always been a factor, and what is changing, I believe that will be the simplest way of getting to the root of the issue. 

In addition, I will demonstrate what actions you can take to alleviate these problems, and even, capitalize on these trends; to ensure that you are riding the wave of the major changes that are happening, rather than fighting upstream against them.

First off, why are high quality leads getting harder to deliver? There’s a myriad of reasons for this. One of the key problems is the constant war for people’s attention. Back in the day, there used to be a few key ad channels, and a sure fire way of delivering from them; Create new prospects through organic search, with email, and google and linkedin ads. Then the Sales Development Representative would close out the first meeting, and pass the promising ones to sales to seal the deal.

I’m sure you can see the problem here; Nowadays there’s a plethora of platforms and channels that your prospects are scouring for information, as well as connecting with like-minded communities on: from Quora for in-depth b2b discussions, to review sites like G2, and Truspilot, to video channels, Podcasts (every thought leader seems to have one now), not to mention, Reddit, X, Facebook, Meta, Instagram/Tiktok (Yes, B2B companies are really using both these days, I’ve worked at some of them). 

Then there’s private communities like Whatsapp, Skool, Linkedin groups, Slack, Circle (this is a big one in Cybersecurity), Shopify community, AWS, Hubspot, Salesforce Trailblazer, GitHub discussions, blogs (like mine) and other niche channels.

Audiences have become ultra segmented (not in the traditional way by job title, country, company size or sector) but by the way they find and absorb information. And that is before we’ve even mentioned AI. 

Of course, once you secure a new prospect, the fact that it's harder to reach them directly by phone these days doesn't help the sales team either. The window in which sales can influence a prospect has been narrowing for many years now.

What can you do about this problem? I would use both feeback, qualitative research, and data, to discover what are the five or six most powerful channels (in terms of how your attribution model shows they drive sales revenue) and double down on them.

Secondly, I would ensure that you create content that is feeding directly into AI search engines. There’s plenty of research to show that you can accomplish this fairly simply and effectively: 

For B2B, AI will draw from key channels like Quora, reddit, Wikipedia, Linkedin, and surprisingly, your own guides, if they are well written, authoritative, and provide real answers. 

On the one hand, b2b content teams seem to often lack the experience or understanding of their audience to double down on these opportunities. I suggest spending time with prospects to understand what their business challenges are, and even what they do and how they spend their time. 

Often B2B content is too focused on what their company does, and who they are. Perhaps because I worked in sales and as as sales development rep at the start of my career, the needs of the prospect are always central to my messaging. Too many companies fall into the age-old trap of selling features, not benefits. 

This is how so much marketing outreach looks like today - - ‘Whizzletech is the leader in fintech solutions (it’s clearly not) and we want to make the world a better place (also hard to prove). We have x number of products that do c things'. 

Very little, or nothing about the prospect, who they are, what they do, what are their hopes, dreams or fears, and most importantly, what their problems are, or how your company solves them. 

Where you can really make a positive impact and drive sales requires you to understand at deep level: What is their actual business objective? — revenue, growth, cost reduction, efficiency, market share, retention, etc. 

What would get them fired? (‘What keeps you up at night?) What would get them promoted? (A quantum leap in the efficiency with which they do their job). You get the picture. 

Ultimately you are also looking to find the prospects who are BANT qualified, and who have a 9 or ideally 10/10 problem. You will struggle with those who think your solution is merely ‘a nice to have’. 

But many companies have driven themselves out of existence trying to fool themselves into believing a lot of buyers with 5 or 6 out of ten pain points, need their product now, and will buy in this quarter (yes, I have worked at one or two of these, as well, during my career).

Focus on the prospects who have the greatest need of your solution, when they are in market (only 5% of them will be at any one time), with a targeted personalised message (that solves their pain), in the channels that they operate in. 

Finally, I will add that AI is creating a massive opportunity here. It is like the first years of Google when only 5 or 10% of the market understood and capitalised on the changes, by running those first basic advertisements. 

If you can crack the AI code now, and keep innovating, you will likely reap a massive growing annual harvest from it. 

I recently attended a day long session, organised by Demandbase and Turtl on Account based marketing with AI. The good news is that even with limited resources, experts on AI and ABM will enable your company to:

• Cover 10× more accounts, with 10× more personalisation than before.
• Run an ABM campaign that used to take six months in roughly a month.                                        

For those who manage to harness AI, they will cut down on the lengthening b2b sales cycle, and improve their conversion rates significantly. AI is condensing the vendor selection process. 

If you can categorically position yourself as the leader, and continue to persuade, your prospect won’t be felled by the two biggest reasons why they end up as closed lost (Insufficient Budget, and No decision, which are often the same objection).

How you get there is another challenge in itself. Gartner's newest CMO Spend Survey has a chart that should worry anyone setting a marketing budget this year. 

Marketing spend as a percent of revenue sits at 7.8 percent in 2026, up from 7.7 percent last year. Functionally flat. That number is also 18 percent lower than it was four years ago.

Here's what makes that flat line matter: 70 percent of CMOs in the same survey say becoming an AI leader is critical to their 2026 plan. 

Only 30 percent say their organization is actually ready to scale AI capabilities. 

Fifty six percent say their budget isn't enough to deliver on this year's strategy at all. Perhaps your best bet is to focus on those who are most passionate about solving these problems – who are prepared to take well calculated risks to achieve your goals?

Certainly it seems like with diminishing or flatlining budgets, and exponentially growing demands, many CMO's need to make some radical changes to accomplish their goals in the next five years.

Now that we’ve covered off on the lead generation challenges, let’s move on to the biggest conundrum today: 

Why are lead to sales conversion rates falling, and sales cycles lengthening, at the decision stage?

Again, AI is partly to blame for this trend. And those who solve this problem will also be in the prime position to drive higher revenue, with stronger conversion rates, and more efficient sales funnels. 

The best way I can describe the change is this: the funnel has been pinched at the top and stretched at the bottom. AI has collapsed discovery, comparison and elimination into a single step — prompt, compare, recommend — and a shortlist now forms fast, often before a seller ever hears from the buyer. 

You would think that would make everything faster. It hasn’t. It has made the start faster and the finish slower.

Faster to a shortlist. Longer to a signed decision. If you take one idea from this piece, take that one, because nearly every falling conversion rate I have investigated recently decomposes into those two halves: the buyer did their choosing before you knew they existed, and then their organisation took months to let them act on it.

Why the end of the sales cycle is slowing down

There are four drivers, and any one of them alone would stretch a sales cycle. Together, they are where all the elapsed time went.

1. The buying group has roughly doubled — and gained an external layer. Gartner puts six to ten decision-makers around a complex purchase, each arriving with four or five pieces of research of their own. 

Forrester’s State of Business Buying now counts thirteen internal stakeholders plus nine external influencers — call it twenty-two people around one decision — with nearly nine in ten purchases spanning two or more departments. Every extra person is another calendar, another set of concerns, and another chance for someone to say ‘not yet’.

2. Finance has become a stage, not a signature. According to G2’s 2026 Buyer Behavior Report, finance involvement in buying decisions leapt from 31% to 46% in a single year, and nearly half of buyers have watched a CFO veto a deal that had already been approved — rising to 54% where there is a dedicated AI budget. 

Procurement now sits as a decision-maker in over half of buying cycles, engaged from the start rather than stamping the end. The uncomfortable implication: if your business case cannot survive a finance review you are not in the room for, you do not have a business case.

3. The payback window shortened while the approval lengthened. Three in four buyers who have been through a late-stage veto now expect positive ROI within six months of signing, and they push for contracts of under twelve months at more than double the usual rate. Read that as a single message from the market: prove it faster, and let me commit for less.

4. The real competitor is no decision. Dixon and McKenna analysed 2.5 million recorded sales conversations for The JOLT Effect and found that 40–60% of qualified pipeline is lost not to a competitor but to no decision at all — and 56% of those losses stem from indecision and fear of getting it wrong, rather than genuine attachment to the status quo. 

Forrester finds 86% of purchases stall at some point. Which brings me back to those two closed-lost reasons I flagged earlier. In my experience, ‘insufficient budget’ is rarely a price objection; it is usually a business case that did not survive an internal conversation you never saw. 

Budget losses and no-decision losses are mostly the same loss wearing different labels — and that matters enormously, because indecision is addressable in a way a genuine budget freeze is not.

Put the four together and the lengthening cycle stops being a mystery. Cheap, AI-assisted looking gives your champion more options and less proprietary insight, and the organisation compensates with scrutiny.

Ironically the compression at the top may well be causing the expansion at the bottom. Which means the response has to work both ends of the funnel at once — and they usually need different tools (though I have one idea that solves both problems together).

The front end: win the shortlist you cannot see

Three numbers describe the new front end. 75% of US B2B technology buyers now complete the purchase journey in twelve weeks or less, against eleven months in 2024. 82% have sourced software recommendations from an AI chatbot in the last two years. 

94% of buying groups rank their preferred vendor before they ever speak to a seller — with that preferred vendor going on to win around 80% of the time. The shortlist is the new first meeting. 

Yet McKinsey finds only 19% of firms are actually implementing generative AI use cases for buying and selling — which is precisely the Google-in-2003 land grab I described above. Here is where I would start:

Shift the discipline from SEO to GEO (generative engine optimisation). Ranking is no longer the game; being retrieved and cited is. That means structured, machine-readable content — specification pages, honest comparison tables, question-and-answer guides that give real answers — written so a model can parse and quote them. This is the same point I made about your own guides feeding AI search at the top of this piece, now with a commercial reason to fund it.

Invest in third-party proof, not just your own claims. Review sites (38%), like Trustpilot, G2, and Clutch.co have just overtaken AI chatbots (37%) as the top shortlist-shaping source, and the models themselves lean heavily on third-party corroboration. Reviews, analyst mentions, community threads and peer evidence are now retrieval assets. Budget for earning them the way you budget for paid media.

Run a generative listening audit, then repeat it monthly. Harvard Business Review’s 4C framework is the best structure I have seen: Coordination of the narrative across functions, Citability of content, Credibility of the sources citing you, and Calibration — auditing how you actually appear in AI answers. 

GSK ran roughly 6,000 prompts across nine decision points and discovered they ranked first on broad prompts but fourth on the specific prompt where they believed they were strongest. 

You cannot fix a shortlist you cannot see. Most companies have no visibility here at all, which is itself the finding.

The back end: shorten the approval you cannot control

Once you are on the shortlist, treat it as the starting line, not the finish line. Everything from here is about helping twenty-two people say yes — most of whom you will never meet.

Arm the champion. Your contact is one voice among many, and most of the decisive conversations happen without you in the room. So build every late-funnel asset to be forwarded without a rep present: self-contained, evidence-led, and written for the sceptic who receives it, not the fan who sends it.

Write the one-page business case a finance director can approve. Your numbers, framed to a six-month payback rather than an annual horizon, with verifiable references attached. Given that finance is now a stage in nearly half of deals, this single page will do more for your conversion rate than another nurture sequence ever will.

Such a guide will also drive improved AI search rankings; provided it is well written, and informative. 

Offer phased or flexible commitment where the full ask cannot clear the gate. Buyers are demanding shorter terms and outcome-based structures at double historical rates. A smaller yes that survives the CFO beats a bigger yes that dies in the veto.

Triage ageing deals instead of waiting on them; win probability decays sharply the longer an opportunity sits still. Build a weekly review of anything stalled beyond thirty days at stage and treat it as an indecision problem — per The JOLT Effect, your job at that point is to help the buyer decide, not to keep helping them buy: narrow the options, make a recommendation, and take ownership of the risk of acting.

And keep the front-of-funnel discipline from the first half of this piece. Concentrate on the five or six channels your attribution model proves drive revenue, on the roughly 5% of your market that is in-market now, and on the prospects with a nine-or-ten-out-of-ten problem — because a doubled buying group and a CFO veto will kill a ‘nice to have’ every single time.

The funnel has not stopped converting. It is converting somewhere you cannot see, and stalling somewhere you lost control of. Win the shortlist you cannot see; shorten the approval you have limited influence over. 

Do both, and the two forces I opened with — falling conversion rates and rising cost per lead — start running in your favour, while your competitors are still fighting upstream.

Credibility Is the Spearhead: What working at fast growing B2B SaaS companies on Enterprise ABM Taught Me About Winning Big Accounts

In a recent interview, Jeff Bezos, founder and long-time CEO of Amazon, made a point that has stayed with me.

Everyone, he said, asks, “What is changing in my industry?” But the far more interesting and useful question is, “What will stay the same?” You can build a business around the answer to that.

Two things, in my experience, will never change. The first is that companies will always want to reach larger, often enterprise accounts for big-ticket purchases. The second is that success in that pursuit will always depend on finding curious people: the ones forever exploring new ways of doing things, adopting new technology, and adapting to a business world that refuses to sit still.

With that framing in mind, consider Account-Based Marketing. ABM has existed, in substance, since B2B selling began. But it has been through many permutations — and several name changes: from “Major Account Selling” in the 1950s, to “Target Account Marketing” (TAM) in the early 1990s, to the “Strategic Marketing to Named Accounts” that I was practising at Visual IQ and Zscaler through the 2010s. The label “Account-Based Marketing” only took hold around 2015, pioneered by Jon Miller, founder of Marketo and later Demandbase.

Whatever you call it, ABM is here to stay. The newest chapter in this long-running need — to penetrate enterprise businesses at many levels and across many parts of an organisation in order to secure large recurring deals — is the application of AI.

The AI inflection point

Next week I’ll be attending a workshop with Demandbase built around exactly this question: how do we turbo-charge and extend an already strong understanding of ABM using AI? In my experience, AI can 10x — or more — what you’re already achieving in this realm. But the point is not that machines replace the craft; it’s that they amplify it.

The AI revolution is about leveraging and accelerating the best of what humans can do — by teaching machines how to perform tasks, and then using AI as an “Iron Man” suit to accomplish our goals, together as one unit.

— Teresa Barreira, CMO at Publicis Sapient (and a fellow Northeastern University MBA alumna)

That is the right mental model. The judgement about which accounts matter, why they matter, and how to reach the humans inside them remains stubbornly human work. AI simply lets you do far more of it, far faster, and with far better signals.

This is not a new interest of mine. About two years ago, content strategist Damien Seaman and I convened a virtual roundtable with leaders across B2B SaaS — CMOs, heads of demand generation, and others — to examine how this account-based approach was proliferating. 

A year later, in August 2025, I worked alongside AI go-to-market experts like Jasper Ruijs (the organiser), including senior leaders from Adobe and Semrush, together with Clay and ABM specialists, to understand how AI was reshaping the way large B2B deals get done: “fishing with spears” — precise, one-to-one marketing — as opposed to the “fishing with nets” typical of smaller, more transactional B2B.

As with everything AI touches, ABM is moving fast. Rather like the Red Queen in Through the Looking-Glass, you have to keep running simply to stay in the same place. Getting ahead of the curve takes even more talent, open-minded thinking, momentum, organisational backing, and investment.

Three stories from the field

I fear I’m getting too technical, so let me set the acronyms aside and tell some stories instead — because the principles are best seen in practice.

Visual IQ: 50 accounts, a pair of binoculars, and a category-defining survey

When I joined Visual IQ, in Boston, Massachusetts, in 2013, we had a crack team. On the sales side, most had been poached from Adobe by our Chief Revenue Officer, formerly head of sales at Omniture (which Adobe acquired). These were people with reams of experience closing multi-million-dollar-a-year accounts for digital marketing attribution with global names like TK Maxx, Walmart, Johnson & Johnson, Mastercard and P&G — where, incidentally, many of my fellow marketing MBAs had done their internships.

I learned an enormous amount about ABM from these people. Many times a week I’d sit down with a regional VP of marketing in the US, along with the VPs for Europe and APAC. We would draw up a list of the top 50 accounts they wanted to penetrate and debate the best way in: outbound calling? A physical promotion? An email campaign? LinkedIn InMail or sponsored content? Once the strategy was agreed, the hard, patient work of spear-fishing began.

The physical promotion is worth dwelling on, because it captures the essence of ABM better than any framework. Because we sold attribution — helping marketers see clearly — we had branded binoculars made, embossed with the Visual IQ logo, and sent them to the heads of marketing and digital at our 50 target accounts. 

In the US it did exceptionally well. The head of marketing at ESPN loved it, and it helped open the door to a roughly $1m deal. That is spear-fishing: a memorable, relevant, one-to-one gesture aimed at a named individual inside a named account.

Visual IQ also taught me the power of owning a category conversation. We published an annual State of Marketing Attribution survey report, built on the views of 500 CMOs. It generated a huge amount of SEO and some of the strongest leads we produced — because it made us the reference point for a question the whole market was asking. 

I’ve since replicated that playbook more than once, most memorably at a video-game advertising company where a segmented State of Video Game Advertising survey drew around 300 responses in what was essentially virgin territory, with tailored question sets for game companies, advertisers and agencies. The segmentation itself became a form of personalisation, and the response was excellent.

None of this was happening in a vacuum. The reason those spears landed was that the market already regarded Visual IQ as a leader. Forrester placed us in the Leaders segment of its Cross-Channel Attribution Wave — the analyst validation that made a cold outreach warm before a single word was exchanged.

The Forrester Wave™: Cross-Channel Attribution Vendors, Q2 2012 — Visual IQ positioned in the Leaders segment (Source: Forrester Research, Inc.).

What made the Visual IQ machine work was that ABM ran on two engines at once. The outbound engine penetrated named strategic accounts in defined regions; the inbound engine qualified the demand our category leadership and content were creating — web downloads, CMO reports, Forrester Wave enquiries, newsletter opens, referrals — and handed genuinely qualified opportunities to field sales. 

Marketing and sales weren’t two departments lobbing work over a wall; they were one motion. I still have the pipeline reviews from that period, and the discipline is striking: strategic accounts analysed for why prior efforts had won or lost, contact reach expanded through ZoomInfo (a list-building and sales intelligence tool I pioneered using at LMTech in 2011 and at Visual IQ in 2013), Salesforce and LinkedIn, and bespoke material built for specific verticals and named targets — never generic blasts.

Zscaler: the free security audit, and the confidence to be expensive

Zscaler taught me the same lesson from a different angle. Our whole proposition was cyber security delivered from the cloud — breaking companies free from the tangle of on-premise security appliances. Once again, we started with the biggest game. I worked at the Demand Generation HQ in Austin, Texas, before I moved over to the UK in 2015.

As at Visual IQ, I worked with some of the best salespeople in the business — typically from companies like Cisco, Palo Alto and Fortinet. Our ‘sales bible’ was The Challenger Sale, based on thousands of sales data points across hundreds of companies: a data-driven approach to enterprise sales success.

We drew up a list of 50 companies we wanted to penetrate and offered each of them something substantial: a free consulting engagement in which we would go in, examine their security posture, find the weaknesses, and hand back a report.

That offer is expensive to fulfil (up to $5,000 per company). You cannot make it to 5,000 companies; you can barely make it to 50. Which is precisely why account selection mattered so much.

We had to be genuinely confident that the accounts we approached were strong potential customers before we committed real consulting hours to them. Get the targeting wrong and you don’t just waste money — you burn your best asset, your experts’ time, on accounts that were never going to buy.

Here too, analyst standing did heavy lifting. When you walk into a global enterprise’s CISO office offering to audit their defences, the first unspoken question is “why should we let you?” Being named a Leader by both Gartner and Forrester answered it before we did. 

Gartner’s Magic Quadrant for Secure Web Gateways placed Zscaler firmly in the Leaders quadrant, alongside a very short list of credible names — and well ahead of the challengers and niche players.

Gartner Magic Quadrant, Secure Web Gateways, May 2015 — Zscaler positioned in the Leaders quadrant (Source: Gartner).

At Zscaler, in Europe, I also pioneered the use of Sales intelligence to aid us in spearing the top accounts. How it would work is this: We would identify five leads for Barclays Bank (A Key account). I would see in our Intelligence system, Discoverorg, that Barclays was looking to invest $5 million in various Cyber technologies this year. I would pass that on to the sales team, as well as crafting additional messaging around that proposition. 

Funnily enough, Discoverorg was acquired by Zoominfo (which I had been a big evangelist of before at several companies), a few years later. 

The through-line from Visual IQ to Zscaler is simple: in enterprise ABM, credibility is the spearhead. The binoculars, the free audit, the survey report — these are the conduit. But analyst recognition, category leadership and social proof are what let the spear actually penetrate. 

Both companies went on to strong exits — Zscaler to a landmark IPO, Visual IQ to acquisition by Nielsen — and in both cases the account-based motion was central to how the enterprise pipeline was built.

I think I’d be remiss if I didn’t also mention my time as Demand Generation Manager at Hansen Technologies. We provided CPQ solutions for the telco and media sectors. Our entire universe of accounts was under 1,000, and our average deal size was $1 million. It was a pure ABM play.

I still remember our star sales engineer closing the Australian telco Telstra for $5 million ARR, where they would also be paying a $250,000 switching cost. That sales engineer, Pedro Jose, is now the CTO at Snowflake.

What the practitioners told us

I don’t want to leave the impression that ABM is a solved problem, or that my own experience is the last word. It isn’t. The roundtable Damien Seaman and I hosted brought together eight senior B2B marketers — from a cyber-security demand-gen lead to a portfolio CMO to a private-equity Chief Development Officer.

What struck me most from this session was how early most organisations still are on this journey, and how mixed the results have been even for experienced hands. A few themes emerged that map almost exactly onto what I learned in the field a decade earlier.

Intent is the modern equivalent of my top-50 list

At Visual IQ and Zscaler we built our target lists from a blend of engagement data and, crucially, sales-team feedback, confirming that accounts which looked engaged were also accounts sales agreed were worth an opportunity. 

I’ve always liked the Bezos line that when the stories and the data disagree, trust the stories. John Blackmore, who leads demand generation at a cyber-security firm, described the modern, instrumented version of the same instinct: rather than cold-calling phone books, his team listens for intent signals — someone researching endpoint detection, or evaluating a competitor.

Marketing and sales then insert themselves only into conversations that are already live. In his words, intent now accounts directly for around a quarter of his pipeline and lifts the efficiency of his other tactics by 10–15%. This is the same philosophy as my top-50 list, but with far more advanced technology and sensors.

The tool is the assist, not the goal-scorer

The most quotable insight of the day was also the most important, and it validated something I’d seen go wrong more than once. John — a Canadian, so the ice hockey metaphor is fitting — argued that ABM platforms like 6sense and Demandbase are not the killer app but the assist:

ABM is the assist. It’s not necessarily the goal-scorer, but it sets up all your goal-scorers in a great way to put the puck in the net. It can improve the efficiency of every tactic by 10 to 20%. It’s worth its weight in gold, even if it never delivers one sale for you.

— John Blackmore, Global Director of Demand Generation

To extend the analogy: in ABM, you want to skate to where the puck will be, not where it is, to paraphrase Wayne Gretzky’s famous line.

I’ve also lived the counter-example. At Tricentis, the team had the full Demandbase package, but left it on the shelf. Rocio Sasson, VP of Demand Generation at Checkmarx, described the same trap from her seven years using both 6sense and Demandbase: the platform is only as good as the cross-team effort behind it, and personalisation “takes a long time and still doesn’t guarantee success.” A tool bought and unused is worse than no tool at all, because it tells the organisation that ABM doesn’t work when in fact ABM was never really tried.

ABM is really a sales-and-marketing alignment strategy in disguise

This, for me, is the deepest point, and it’s the one my Visual IQ pipeline reviews prove out in retrospect. Every roundtable participant with real success traced it back to alignment. Blackmore holds two standing weekly meetings with two different sales teams purely to interrogate lead quality — are these good, do you like them, who do you actually want to talk to? 

He put it memorably: you’re not buying 6sense; you’re buying collegial alignment, and the tool is simply the expression of it. Rocio was blunt about the failure mode: however beautiful the asset, if sales won’t work the leads, the whole effort collapses.

This reminds me of Jim Collins, of Good to Great fame, who made a similar point about using technology. Collins said that in his analysis of top-performing companies, technology was not even in the top ten of the most important factors driving their success. It was the utilisation of technology to enable other high-performing functions — so, just like Teresa Barreira’s ‘AI as Iron Man suit’ analogy.

John Blackmore’s point also matches my lived ABM experience exactly. I’ve produced thousands of leads that fell to the floor because sales wouldn’t pick them up. The reverse — the two-engine Visual IQ motion where marketing qualified and sales closed as a single unit — is what actually produced million-dollar accounts. ABM, done properly, forces that alignment because the model simply cannot function without it.

Personalise the message, not just the list

Damien shared the campaign I still think is the gold standard of spear-fishing: a Canon campaign targeting C-suite executives at listed companies across six European countries.The team printed each target’s annual report, found the passages where the company itself flagged document-management pain, and hand-wrote tailored messages on Post-it notes placed at exactly those pages — each package arriving under a cover letter from Canon’s country head, peer to peer. 

The result was an 80% response rate across more than 100 accounts. The lesson isn’t the Post-it notes; it’s that the value proposition to different personas for the same product is genuinely different, and the personalisation has to reach the message, not just the mailing list.

There is far more in the full write-up — including a candid debate on whether LinkedIn produces real pipeline or only brand awareness (referencing the notoriously long LinkedIn lead sales cycle), the mechanics of preferential paid-search bidding on target-account segments, and geo-fencing as an alternative to trade-show spend. I’d encourage anyone serious about ABM to read it in full: B2B SaaS Leaders ABM Roundtable.

Where this leaves us

Put the field experience and the roundtable side by side and the pattern is hard to miss. The fundamentals of ABM have not changed in decades: pick the right named accounts, reach the right humans inside them with a message that speaks to their specific pain, and make marketing and sales a single motion rather than two teams.

Spear fishing, with AI

What has changed is the instrumentation. Where I once built a top-50 list from engagement data and a weekly conversation with a regional VP, today’s intent platforms surface that signal continuously and at scale. And where personalising a hundred accounts once meant hand-writing Post-it notes, AI now makes genuine one-to-one relevance achievable across thousands of them.

That is the opportunity in front of us, and, per Blackmore’s hockey metaphor, we are still in the early, high-advantage days of learning to use it well. We are also in the early stages of AI, and so those two nascent approaches are combining to produce an effect that is both hard to replicate and potentially a quantum leap in sales and marketing performance for those rare companies able to harness them both effectively.

John Blackmore, a veteran marketer, likened the state of ABM today to the early days of Google Ads. The combination of formidable intent-capturing platforms — Clay, HubSpot, Demandbase, 6sense — with fast-developing AI means the opportunities in this field are enormous.

But the platforms are available to everyone; the advantage was never the tools. It belongs to the companies with the talent and judgement to aim them, and the discipline to make marketing and sales fire as one.