Saturday, September 19, 2026

B2B Intent-Data Attribution: 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. 

Wednesday, August 19, 2026

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.

Sunday, July 05, 2026

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

Account-Based Marketing: fishing with spears

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

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

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.

ABM: fishing with spears

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.

Friday, March 06, 2026

Why CRM Systems Drift Into Disorder

Understanding CRM Data Entropy in Complex Systems

“These systems were working fine a few months ago. Why are they going wrong?”

This question is common in any organisation running large, interconnected data systems. At first glance, it seems reasonable: if a platform functioned well before, why not now?

But CRM and marketing systems are not like cars—you can’t simply “service, refuel, and go.” Their behaviour is closer to that of ecosystems than that of machines.

Why Systems Decay

A key distinction with CRM platforms is that they are not purely technical. They are socio-technical systems: a fusion of software, integrations, workflows, incentives, and—most unpredictably—human behaviour.

The Gremlins Metaphor

Engineers in WWII jokingly blamed mysterious aircraft malfunctions on “gremlins.”
It was a way to acknowledge that complex systems fail for reasons that aren’t immediately obvious and often emerge from subtle interactions. Crucially, it also bolstered morale by avoiding blaming any one individual for the failure.

CRM systems behave similarly. It’s rarely “one big issue.” It’s the quiet accumulation of tiny mismatches, workarounds, and human shortcuts.

Was It Ever Truly “Working Fine”?

A system that appears stable may in fact be held together by ad-hoc patches, legacy logic, or assumptions that only worked under lighter data loads.

Like a bridge that seems sturdy—but once traffic increases, stress fractures emerge.

        

We have this exact same issue around the corner from my house, at Hammersmith Bridge, London - closed to all traffic except pedestrians and cyclists for the last seven years for these very reasons.

Initially, the bridge was strong. But as traffic increased, small structural weaknesses began to show — bolts loosened, joints flexed, stress fractures appeared. This important connecting bridge in the UK has not functioned for seven years now. 

The Role of Entropy

The second law of thermodynamics states that systems tend toward disorder unless energy is continually applied to maintain structure.

CRM ecosystems follow the same principle. Even if perfectly configured on day one (they never are), entropy creeps in through:

  • Human behaviour
  • System complexity
  • Time

These forces push the system toward disorder unless actively countered.

The Beehive Model: When Systems Work

A healthy beehive functions because:

  • Roles are clear
  • Communication is consistent
  • Inputs are reliable
  • Activity flows are coordinated

In CRM terms:

  • Leads flow correctly
  • Fields are completed consistently
  • Deals follow standard paths
  • Dashboards reflect reality

Information moves cleanly through the “colony.”

When the Hive Breaks Down

Entropy emerges through small, seemingly harmless actions:

  • Required fields skipped
  • Inconsistent tracking parameters
  • Manual deal creation
  • Logic edited without documentation
  • Data duplicated
  • Attribution overwritten

Individually trivial.
Collectively destabilising.

This mirrors research across socio-technical systems: micro-errors compound in non-linear ways, producing instability that no single actor intended.

Complexity Magnifies Entropy

Modern revenue stacks include:

  • CRM platforms
  • Marketing automation
  • Intent data systems
  • BI tools
  • Sales engagement platforms

Every integration introduces risk:

  • ID mismatches
  • Sync failures
  • Schema drift
  • Conflicting definitions

The more integrated the ecosystem, the faster entropy accelerates.

Time as a Force of Disorder

Even without major changes:

  • Definitions evolve
  • Teams rotate
  • New fields accumulate
  • Legacy data lingers
  • Integrations layer on top of integrations

The result is gradual “data model drift”—the system you have is no longer the one originally designed.

Below: Dashboard of a Lockheed Martin F-35 Lightning II fighter jet 

Dashboards Are Instruments, Not Reality

A dashboard is to a business what cockpit instruments are to a pilot: essential, but only a representation of reality—not reality itself.

The dashboard provides important signals such as altitude, speed, heading, but it is not the sky, the weather, or the terrain itself. 

Instruments must be monitored, calibrated, questioned, and cross-checked. No pilot assumes the sky looks exactly like the dial.

Dashboards can provide powerful insights, but they should never be treated as the entire picture of what is happening in the business. 

And just as in aviation, the most effective organisations combine instrument readings with context, culture, and human insight to understand what is really happening.

The same principle applies in organisations. Leaders evaluating CRM outputs should adopt a similar mindset.

The Core Insight

CRM systems are not static assets.
They are living organisms that require constant:

  • Maintenance
  • Alignment
  • Definition clarity
  • Human behavioural guidance
  • Technical calibration

Without active energy input, the system naturally drifts toward disorder—data entropy is not a failure of people or platforms, but an expected property of complex socio-technical systems.

The Reality for Leaders

Managing these systems requires constant vigilance, thought, testing, imagination, planning, and long-term strategy, just as the rest of the business does.

Below: Solving Data Disorder, Managing Organisational Culture

Because left unattended, complex systems drift toward disorder. 

How you manage this complex system will depend a lot on your 'problem-solving culture'. If you look at the matrix above, and according to Jim Collins, author of 'Good to Great', only about 5% of organisations sit in the ideal top right-hand quadrant. 

But essentially, CRM/Marketing Automation/Analytics systems are no exception to the second law of thermodynamics - they drift into disorder, naturally. 

Like any complex system built on human inputs, software integrations, and evolving processes, they naturally accumulate entropy over time.

Which means the question is never whether disorder will appear. That is a given.

The real question is who is paying attention when it does. A Strong culture with psychological safety, populated by teams with diverse cognitive styles, will solve these issues faster and more effectively than others. 

Saturday, January 31, 2026

The Real Edge of Private Equity: Active Ownership

I’m a big fan of Scandinavian thrillers, especially the original The Girl with the Dragon Tattoo. So when I walked into the auditorium at the London School of Economics, I had the strange feeling I was looking down at three lead actors from a Nordic noir drama.

The speakers were Ulf Axelson, Professor of Finance and Private Equity at LSE; Per Strömberg, Professor of Finance at Stockholm School of Economics and LSE; and Kurt Björklund, Founder and Executive Chairman of Permira, with roughly $50bn under management.


What followed was one of the clearest, data-driven explanations I’ve heard of why private equity (PE) ownership so often outperforms public equity, and where its limits lie.

Why Private Equity Outperforms: Start with the Data

The first half of the lecture was led by Per Strömberg and focused squarely on the evidence. Rather than starting with anecdotes or ideology, he began with productivity data across countries and firms.

In Germany, for example, fewer than 1% of firms accounted for roughly 65% of positive productivity growth over the period studied. Most firms contribute little. Some actively destroy value.

This matters because private equity does not rely on averages. Its entire model is built around identifying, creating, and scaling outliers.

       

The Mechanism: How PE Actually Creates Value

Strömberg argued that the performance gap between PE-owned and publicly listed companies is not primarily due to regulatory arbitrage or tax advantages, though those exist at the margin.

The core driver is active ownership.

Drawing on both academic literature and operating evidence, PE value creation can be grouped into three broad mechanisms:

1. Governance engineering

PE owners are not distant shareholders. They:

  • Sit on boards
  • Hire and fire management
  • Set incentives tightly linked to value creation
  • Intervene early when performance slips

This sharply reduces classic agency problems between owners and executives.

During my MBA at Northeastern, one of my finance professors specialised in corporate governance, and I conducted research on shareholder activism. One theme emerged repeatedly: public-company executives often optimise for personal incentives that diverge from shareholder value.

Below: PE-owned companies are rigorous in selecting customers that add value

PE ownership compresses that gap. In the same way that active shareholders hold senior leadership to account, Private Equity owners can step in to ensure the company is run as efficiently as possible. 

Per explained that the productivity and efficiency gains of Private Equity ownership (according to him, 2-3% higher than Public Equity, according to Kurt, head of a PE firm, it is closer to 6-7% higher), can be divided into three key categories:

Three types of engineering/tools

1. Governance engineering – being an active owner of the company

2 . Financial engineering – reduce cost of capital 

3. Become sector experts – can leverage networks to assist management

Well, that begs the question – why don’t other companies copy the behaviour of PE companies, to improve their performance?

According to Strömberg, this opens a “can of worms”.

First, PE performance may not be indefinitely sustainable. Funds have finite holding periods, typically six to seven years. Active ownership delivers diminishing returns once the biggest inefficiencies are removed.

However, within that limited time frame, PE seems to be doing better than ever. Exit value experienced a rebound in 2025, increasing 41 per cent to $1.3 trillion, the second-highest year on record. 

Second, PE capital is more expensive. While leverage can be cheaper than equity, the cost of financial distress rises sharply as leverage increases.

PE is not a universal solvent. It is a precision tool, effective under specific conditions.

An Operator’s Perspective: Kurt Björklund of Permira

The second half of the session (unrecorded) shifted from data to practice. Kurt Björklund described himself not as a financier, but as a “financial entrepreneur” and "Sector disrupter".

His framing was revealing.

Public equity investors, he argued, suffer from information asymmetry. Even large shareholders rely on periodic disclosures and carefully curated narratives.

PE ownership, by contrast, is built on information abundance:

  • Proprietary KPIs
  • Weekly operational interaction
  • Direct access to management and systems

Björklund was blunt: unlike asset managers such as BlackRock, he cannot afford to be wrong. Every investment must succeed. That forces extraordinary diligence and relentless focus post-acquisition.

He also warned about classic PE pitfalls:

  • Buyer’s curse in auction processes
  • Cyclicality of capital markets
  • The temptation to “take your eye off the ball” during exit processes

“In my business,” he said, “only the paranoid survive.”

Disruption, People, and the Role of AI

One of the most charged parts of the discussion came during the Q&A, where students (from the LSE, Imperial, Oxford, and Berkeley, USA) repeatedly asked about AI and job security. There were also several questions from analysts at various Private Equity firms.

Björklund acknowledged the anxiety, but did little to soothe it.

He described investments in complex B2B businesses where agentic AI, and improved automation have reduced headcount by orders of magnitude, particularly in areas such as KYC and compliance.

In one example, automation reduced a team from 5,000 people to 500, while increasing profitability. Many in the organisation were conducting relatively complex tasks, which could nevertheless be performed more effectively with AI and Automation.


Above - Top Target Universities (non-US) for Goldman Sachs. Source: Krugman Insights

His view was unsentimental: there will always be jobs for the very best, and the traditional path: An elite education, a top investment bank such as Goldman Sachs, and then a good Private Equity firm, remains viable. But the middle is being hollowed out.

Interestingly, he noted that older employees often adopt AI more effectively than younger ones, attributing this to psychological barriers to AI in younger workers. 

Perhaps it's also because you need deep experience in solving the problems, to ask AI the right questions? It's very easy to generate 'AI workslop' that drives no insight, and diminishes your credibility in the organisation. And that is no doubt from whence that fear emanates.


The recording was switched off halfway through the lecture, at which point the atmosphere in the room changed perceptibly. Kurt (The Chairman of Permira) smiled and said he would assume there were no journalists present, which meant he could now speak a little more freely than usual.

The professors, clearly enjoying the moment, joked that in Sweden, Kurt is known as “Superkurt”: the complete package: handsome, physically fit, wildly successful, and extremely wealthy.
Kurt laughed, didn’t deny it, and carried on.

Which confirmed something I’ve learned from working with private equity firms in the past: there is often remarkably little self-deprecation in the room, even when the person in question is a typically reserved and humble Swede.

Joking aside, this was one of the best lectures I've seen, unique in that it presented top-level insights from both the academic and 'real-world' perspectives.