Saturday, October 10, 2026

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


Recently a Marketing 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/Insurtech 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 by far the best highly targeted paid channel Linkedin to drive initial contacts, and hubspot/Salesforce/Marketo/Eloqua to nurture via marketing automation, sometimes alongside Linkedin, Google, or Microsoft retargeting ads (to get your contacts when they are ‘in market’ – only 5% are at any one time).

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

ABM 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 strong, 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.

Additionally, most of the marketing suite of tools your use will work much more effectively on ABM 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, in terms of driving potential buyers at the top of your funnel, and securing sales contracts at the bottom.

Without good intent data, this will be impossible for you. 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 unique to you contacts, but you will get 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 and make more effective your ABM campaigns. 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. 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 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's seven years of ABM at Checkmarx and my own experience at Tricentis point 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.

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.

Sunday, June 28, 2026

The 272-Day Problem*: Why Your B2B Sales Cycle Keeps Getting Longer, and What to Do About It

Image above, source: Demandbase

I’ve spent almost my entire career in b2b sales and marketing, most of it in software. And I am seeing changes in the business that are unprecedented, and are hitting companies like a perfect storm.

- Plummeting conversion rates and ROAS on advertising, compounded by the already existing issue of often very small target audiences (as compared to B2C)

- Difficulty in attributing revenue, since a sale involves such a flywheel of both marketing and sales collateral and channels, that sometimes it would take a marketing analytics genius to unpick what caused that sale.

- The continual march of new technologies, like ABM platforms, Intent, and list building tools, AI – the game changer, and now, often hard to fathom AI hybrids like Clay (try explaining that to someone unfamiliar with it – ‘its like a cross between zoominfo and seamless AI, except it also ingests data from lots of other intent and list building tools like bombara’).

- The unrelenting pressure, which I’ve never seen before in my entire career, to prove ROI on every single action. In the 'roaring' 1990’s you could throw money around to get sales, these days CFO’s want military precision in the way marketing (and sales) budgets are allocated. 

- Falling lead to sale conversion rates, which sometimes can accompany declining average order values at the same time – in countries or sectors with low or negative growth, for example. 

Yes, B2B sales has certainly changed dramatically in the last thirty years. Some of those changes have been slow and incremental, for example the changes in business intelligence, from using magazines and trade publications to research prospects (yes, that did happen in the ‘olden days’ as my 17 year old son Jack calls any time pre-2000), to basic computer research (but google was very basic back then), to the adoption of tools like zoominfo and discoverorg, that provided you with both list building and business intelligence features – this would take us from the 1990’s, up until say 2018.

Others have been dramatic and disruptive – with the adoption of AI, which hit us all hard at the start of the 2020’s. Continuing that business intelligence example,  where now where you can get a full breakdown on a prospect in seconds using a variety of AI tools. 

But possibly the most alarming change, and one that is baffling teams across the world, from small startups, to huge behemouth companies, is the lengthening of the sales cycle 

Dreamdata** just dropped their 2026 LinkedIn Ads Benchmarks Report, and the data confirms what we already felt:

  • The B2B sales cycle is getting longer, more complex, and more demanding.
  • The average B2B customer journey now takes 272 days — up from 211 last year*
  • Each deal involves 10 stakeholders and 88 touchpoints across 4 channels.
  • And 81% of that journey happens BEFORE a prospect ever enters the sales pipeline.

That means buyers are spending roughly 7 months doing their own research before they ever talk to you. This aligns with other research I’ve read that show that 80%+ of the sales evaluation is completed before a b2b prospect even talks to your sales person. With AI that number will no doubt increase. 

So what is going on, and why?.... and I’m sure an even more important question you are asking: How can I fight this trend, and start to shorten our sales cycles?

Firstly, Looking at the data across B2B (Saleshive) this is the pattern

What’s particularly interesting about this data to me is that it matches what I’ve seen in reality working at a variety of mainly b2b saas companies, from Zscaler, Visual IQ (Now part of Nielsen), and Hansen, working on average annual deal sizes of $1M -$5M, to working more recently at companies like Mention Me, or Mintago, where it was at the other end of the spectrum, $20,000-$75,000. But across the board I’ve seen slower sales cycles.

Why B2B Sales Cycles Are Stretching

Expanding Buying Committees: A typical complex B2B purchase now requires consensus from 6 to 10 distinct decision-makers (ranging from IT and security to finance and procurement). Every stakeholder added introduces another calendar, new objections, and internal misalignment

Intense Budget Scrutiny: Due to broader economic slowdowns, purchases that previously required a single manager's signature now need multiple sign-offs, often terminating in a strict review by CFOs or procurement teams

The "No Decision" Paradox: The fear of making a bad software or vendor decision has increased. Research shows that 40% to 60% of B2B deals end in "no decision" because champions cannot justify the business case internally, or because the team is overwhelmed by information.

What can we do about it? 

Gen AI is restructuring the entire B2B buying journey. AI is not just adding a new channel, but displacing the controlled channels (sales reps, distribution networks, owned media) that B2B go-to-market has always relied on. 

Buyers now use AI tools to discover vendors, compare options, and evaluate fit long before they ever talk to a salesperson, which means much of the influence happens before sellers even know a buyer exists. 

This means that the current ‘80%' of buying done before a prospect even talks to a sales person may move up to 90%, or even 95% over the next five years. 

Obviously, the sales person who can make the most of that 20%/10%/5% of influence they have at the end of the buying cycle is critical. 

If the sales person is not armed with all the buying information they need, they will loose out to better informed, and supplied sales people (even if those sales people are not as technically proficient or experienced).

How the funnel changes

The old funnel ran on controlled channels and a slow, resource-intensive evaluation phase. Buyers worked through offerings, use cases, pricing, and internal alignment over months. 

Gen AI is inverting the shape: the top widens because buyers can access a far broader set of vendors via AI synthesis, but options get eliminated much earlier and faster. 

75% of US B2B technology buyers now finish their purchase journey in 12 weeks or less, versus 11 months in 2024. That is a fast and dramatic compression. IDC's predicts that 62% of traditional B2B demand generation will be AI-led by 2028.

Buck the increasing Sales cycle trend with smart search strategies

You'll have no doubt spotted the contradiction. Everything above says B2B sales cycles are getting longer, yet here's IDC forecasting an AI-led buying surge that makes them dramatically shorter. Both are true. They're just describing different parts of the funnel.

The lengthening happens at the back end: more stakeholders, tighter procurement, CFO sign-off. That's structural and it isn't going away. But the front end, discovery, research, shortlisting, is where AI-led buyers move at a completely different speed. 

A buyer who once spent months researching now lets an AI assistant synthesise the options in an afternoon. The committee still takes its time; getting onto the committee's shortlist now happens in days.

That's the opening. The average cycle is lengthening, but a fast-growing pocket of AI-native buyers, concentrated in the US, where these shifts usually begin, is compressing the early stages hard. 

Get your content built for how those buyers actually search, and you don't just keep pace with the trend. You can pull your prospects through the slowest, most expensive part of the funnel before your competitors even surface as an option.

Two opposing truths in one market is a lot to hold at once. It feels like cognitive dissonance. But sit with it, and the contradiction dissolves: the cycle is lengthening and compressing at the same time, in different places, for different reasons.

From an SEO perspective, using a famous analogy, you don’t want to be the organisation producing the best buggy whips, as the age of the motor car begins. Yet so many organisations are stuck in the old models. 

McKinsey’s 2025 B2B Pulse Survey finds that only 19% of respondents are implementing use cases involving gen AI tools for B2B buying and selling.

A pilot study conducted by Digitas UK, a subsidiary of Publicis Groupe, examined B2B fintech payment solutions in the UK and U.S. markets. The findings revealed that more than 80% of the sources leveraged by LLMs originated directly from the brands themselves, such as Stripe, Adyen, PayPal, and Visa. 

Caitriona Gallagher, strategy partner at Digitas, said: “This makes sense because these brands have significant amounts of content on their sites to help support B2B buying journeys, including product comparison content and sector/audience led content".

So even if your industry is in a category that you don’t think warrants much of your own in-house quality technical content, AI will still be pulling search data for your prospects from the internet.

If you don’t have anything out there, content wise, your company will fall at the first discovery phase (Awareness/Consideration/evaluation) of your prospects AI driven buying journey. And as I already explained, the trend is moving to faster choice of vendor at the outset using AI research capabilities. 

If your content is not adapting to the new world, you could be left out in the cold, with your company not even being considered as an option at outset, let alone making it to the final decision stage, and eventual sale. 

When I first worked in sales over 25 years ago, there was complete information asymmetry on the part of the seller. The buyer had very little information to go on – trade magazines, perhaps some scraps of information on the then nascent internet. 

As time has passed, that balance has swung the other way completely. In the past, the sales person controlled perhaps 80% or more of the information that the buyer went on, but now, as discussed those numbers are reversed and may even move to 10% or less. 

We live in a world where the sales people have fast diminishing opportunities to make their mark on the prospect. So the sales person who is best informed will win most of the time. 

The Dark Funnel (AKA 'Iceberg'): Why 80% of Your Buyer's Journey Is Now Invisible to You

That is where tools like Demandbase, 6sense, clay, hubspot, alongside bespoke AI solutions, can make a decisive difference. These tools enable you to get into the heads of your buyers: 

Find out what problems they have (maybe even before they themselves can articulate them), what motivates them to buy, where they are in the buying cycle, and when the 95% of buyers finally jump to that sales nirvana of being ‘in market’. 

Some companies are sprinting ahead with these tools and methodologies; For example, in the Enterprise B2B SaaS markets I've worked, we started to transition  from the MQL-SQL-Sales model a long time ago, to the 'Buying groups' model 

This is where we recognise that it is (In Enterprise B2B) rarely one lead that drives a sale, but rather a group of decision makers - say, the initial sponsor, a mid management decision-maker, alongside, perhaps a CIO, a CFO, and a technology lead. 

To clarify - Retaining MQL as a metric, but adding an additional layer of insight around buying groups, for example on their intent dashboard.

Whilst many other companies are struggling to get the most basic data and insights, even from their own prospects and customers. You feel like saying 'hey, 2015 called and they want their Martech, and CRM stack back'.

- This joke originally came from the team at Akamai, who used to laugh at my wife, Catherine, still using a blackberry when everyone else was on Iphones already - I'm glad to say I was instrumental in securing that upgrade for her.

Peer to peer content, research, and review sites

When I purchase Hubspot CRM, and Marketing Automation for the UK and Italy arms of an international company just prior to its IPO, I was pretty nervous about making the right decision. 

There was a wide range of options available to us; from building in-house, keeping our existing, but rudimentary Pipedrive/Mailchimp infrastructure, or using combinations of Salesforce/Hubspot/Marketo/Dynamics and other providers out there. 

What helped me enormously in that critical final purchase stage, was the array of high quality peer-to-peer review sites, like trustpilot, google, and most importantly for me, G2  - the most professional and reliable of them all. That was in addition to reading the best research from companies like Forrester, Gartner and IDC.

I guarantee that your prospects will be doing the same. And the biggest trend of them all is the peer-to-peer content, which has skyrocketed - G2 crowd, Quora (I'm pretty active on that one), Linkedin, and to a lesser extent, reddit. These are also sites that AI use to drive their search answers. 

The best salesperson in the world won't sell your product as brilliantly as glowing customer recommendations. Win the reviews, win the revenue, and shorten your sales cycle.

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.