Why Intent Data Misses Most of Your Market

Why intent data misses most of your market 

The dashboard looks healthy. Cost-per-lead is on target. Intent-sourced pipeline is converting. Every number is green, and nobody in the room is asking the one question that matters: what is happening to the pool these programs draw from? Because intent data only sees buyers who are already researching. The rest of your market, the 95% who will need what you sell but aren’t looking yet, sits outside its detection range entirely. That’s not a flaw in the tool. It’s a reason to stop treating the tool’s view as the whole picture. 

Intent data is an instrument, and instruments only detect what emits a signal. 

An intent system detects behavior. A topic surge, a site visit, a content download, a competitor comparison. Where there is behavior, there is a signal to read. Where there is no behavior, there is nothing to detect, and the absence of detection is not evidence of absence. 

This is the distinction the whole argument rests on. A buyer who has not begun researching emits nothing an intent system can observe. No surge, no visit, no registration. They are not ranked low in your data. They are not in your data. 

A low-priority account is a decision you made. An undetectable account is a decision the tool made for you, by never surfacing it. When a demand generation strategy runs entirely on captured signals, nobody in the room decided to write off most of the market. The detection range decided for them. 

How much of your market the instrument can’t see, and where that number comes from 

Roughly 95% of business buyers are not in the market for a given product at any one time, which leaves about 5% in-market in any given quarter. That figure gets quoted constantly. It’s more convincing when you see where it comes from, because it isn’t a survey opinion, it’s arithmetic. 

Professor John Dawes of the Ehrenberg-Bass Institute derives it from how often companies actually buy. As Dawes explains, corporations change principal service providers such as their bank or law firm about once every five years on average. A five-year replacement cycle means roughly 20% of buyers enter the market over a full year, and something like 5% in a given quarter. Turn it around and 95% aren’t in the market right now. Ehrenberg-Bass’s own summary puts it plainly: up to 95% of business clients are not in the market for many goods and services at any one time. 

The number moves with your category, which is the useful part. Compute your own instead of borrowing Dawes’s. If your buyers replace their solution every two years, then 50% enter the market annually and about 13% sit in-market per quarter. A longer cycle pushes the in-market share down. Either way, the majority is out-of-market on any given day. 

Intent platforms arrive at the same place from the other direction, typically surfacing only a single-digit percentage of target accounts as in-market in any given quarter. Two methods, one answer: the share of your market that any detection system can see at a given moment is small. That isn’t a flaw in any particular platform. It’s the shape of B2B buying. 

Why capture-only looks efficient right up until it doesn’t 

Capture-only programs look efficient because of where the measurement is taken. Last-touch attribution credits the final interaction before conversion, and a capture program owns that interaction almost by definition. So its measured efficiency is partly an artifact of standing at the finish line and taking credit for the race. 

Meanwhile the pool of in-market buyers who already recognize you is either being replenished by creation activity or it isn’t. When creation is doing its job, a healthy share of the 5% who go in-market each quarter already know your name, and capture converts them cheaply. Cut the creation work, and that pool thins out. Cost-per-lead drifts up, and nobody can explain why from the dashboard alone. 

The delay is what makes it invisible. The average B2B buying cycle now runs about ten months (per the 2025 B2B Buyer Experience Report, down from 11.3 the year prior), so a cut in creation spend shows up as a demand problem three or four quarters later. By then the spreadsheet blames something else: a soft quarter, a pricing change, a rep who left. You can starve the top of your market for a year and read it as an execution problem in Q4. 

What preference formation does to the timing argument 

By the time intent data flags an account, the buyer has usually already picked a favorite. Forrester’s 2024 Buyers’ Journey Survey found that 92% of B2B buyers start their journey with at least one vendor already in mind, and 41% begin with a single preferred vendor before any formal evaluation. The 2025 B2B Buyer Experience Report puts a finer point on it: 94% of buying groups had ranked their shortlist in order of preference before making first contact with any seller, and the vendor ranked first goes on to win about 77% of deals. 

Sit with what that does to a capture-only motion. The ranking happens before sellers are involved, drawing on whatever the buyer already knew about the category. Intent data can only detect the account once behavior starts, which is at or after the moment the shortlist is being ordered. A program built purely on capture is competing for a position that was largely assigned earlier, by memory and familiarity it had no hand in building. 

This is a timing argument, not a branding one. The problem isn’t that your brand feels unloved. The problem is that the vendor ranking that predicts most wins happens in a window capture cannot observe. If you’re not present before the trigger, you’re showing up after the field has already been set. 

What creation activity is actually doing to a buyer who isn’t shopping 

Creation activity builds category association and memory ahead of a trigger, so that when the trigger finally arrives, you’re already in the consideration set. That’s the whole mechanism. It isn’t persuading an out-of-market buyer to buy today. Dawes is clear that you can’t move someone in-market who doesn’t have a need. What you can do is make sure that when their need appears, yours is a name they reach for without effort. 

The effectiveness evidence points the same way, with an honest caveat. Analysis of the IPA Databank by Les Binet and Peter Field for the LinkedIn B2B Institute puts the optimal B2B budget split at roughly 46% brand building to 54% activation. In their B2B data, 10 percentage points of extra share of voice generates about 0.7 points of market-share growth per year, and fame-driven campaigns produced 2.2 very large business effects against 0.7 for activation-focused ones. 

Treat those figures as directional, because the B2B subset of that databank is small and the campaigns skew toward larger advertisers. The direction is what holds up across every read of the data: sustained presence with the out-of-market majority pays off later, and cutting it to fund more capture borrows growth from a future quarter. Different signals decay at different rates too, which changes how long that presence keeps working. 

How to measure creation without pretending it’s capture 

Applying capture metrics to creation activity guarantees the wrong conclusion, because you’re measuring a slow effect with a fast ruler. Ask a brand campaign for its cost-per-lead this month and it will look like a failure, because that isn’t what it does or when it does it. Judge creation on capture’s timeline and you’ll cut the thing quietly holding up your pipeline. 

Three measures fit the mechanism instead: 

1. Share of voice against your defined category. Are you more present than your share of market would predict? Excess share of voice is the leading indicator Binet and Field tie to future growth. It tells you whether you’re building the pool or drawing it down. 

1. Unaided and aided recognition inside the target account set. Not the whole market. The specific accounts you’re trying to reach. Track whether the people in those accounts can name you before you contact them, because that recognition is the asset that wins the pre-contact ranking. 

1. The share of new opportunities that arrive already knowing your category position. When a fresh opportunity opens and the buyer already places you correctly against alternatives, creation did its work upstream. That proportion, rising over time, is creation converting on its own timeline. 

One question puts the whole problem in context: is your capture program actually finished? Most teams are running an incomplete one and calling the gap a creation problem. Before you conclude that creation isn’t working, it’s worth checking whether your capture motion has a designed measurement framework behind it, or whether it’s just collecting signals without a plan for what to do with them. We cover the common reasons intent data stops producing results in a separate piece. 

The instrument is fine. The field of view is the problem 

Intent data does what it does well: it catches the 5% who are ready and converts them efficiently. The problem was never the tool. It was treating the slice it can see as the whole market, then wondering why pipeline thins out three quarters after you stopped feeding the top of it. A demand generation strategy that reads what buyers are telling you now and builds memory with the ones who haven’t spoken yet is the one that still has a pool to draw from next year. 

Our own campaign data makes the same case with numbers. Across Digitalzone programs, accounts engaged by two or more tactics surface 72× more buying-committee members than single-tactic accounts. Every one of those multi-tactic accounts converts a lead, compared to 9.6% for single-tactic accounts, where 97% stay at the lowest qualification tier. Creation and capture aren’t competing budget lines. They compound: the accounts that see both are the ones that produce pipeline. 

That’s the loop worth building: create demand with the out-of-market majority, capture it when they raise their hand, and learn enough from each cycle to do it better next time. If you want help reaching the buying committee inside your target accounts before the shortlist gets written, talk to Digitalzone about Programmatic Nurture. And if you want the evidence base under all of this, our Dimensions of Demand Gen 2026: Buyer Report surveys what buyers and marketers actually do. 

FAQs 

What’s the difference between demand creation and demand capture? 

Capture catches the buyers already shopping: search, retargeting, intent-sourced outreach. Creation reaches the ones who aren’t shopping yet, building enough familiarity that you’re in the consideration set when their need appears. One harvests existing demand. The other builds the pool that capture later draws from. 

Why can’t intent data find out-of-market buyers? 

Intent data detects behavior such as topic surges, site visits, and content downloads. A buyer who hasn’t started researching produces none of that behavior, so there’s no signal for the instrument to read. They aren’t ranked as low-priority accounts; they’re absent from the dataset entirely, which is why a capture-only strategy structurally overlooks them. 

Where does the 95:5 rule actually come from? 

Professor John Dawes of the Ehrenberg-Bass Institute derived it from B2B interpurchase cycles. If companies replace a given service provider roughly every five years, then about 20% of buyers are in-market annually and around 5% in any quarter. The ratio shifts with your category’s buying cycle, so a shorter cycle means a larger in-market share. 

How do you measure demand creation if it doesn’t convert like capture? 

Use measures that match its slower mechanism instead of cost-per-lead: share of voice against your category, unaided and aided recognition within your target account set, and the share of new opportunities that arrive already knowing your category position. These track whether you’re building mental availability ahead of the buying trigger. 

Should a B2B demand strategy still invest in intent data? 

Yes. Intent data is a strong instrument for the small share of accounts actively in-market, and demand capture converts them efficiently. The problem isn’t using it; it’s assuming its field of view is the whole market. A balanced B2B demand strategy pairs capture for the ready 5% with creation for the 95% who will be ready later.