From Spray to Signal — LinkedIn Series on ABM & Signal-Based Marketing

The Spray-and-Pray Paradox

Most B2B marketing teams are still optimizing for reach in a world that rewards precision.

I’ve sat in enough QBRs to know the pattern. Someone presents a slide with impressions, MQLs, and a funnel that looks healthy from a distance. Then the CFO asks one question: how much of that pipeline actually closed, and from which accounts. The room goes quiet.

That’s the spray-and-pray paradox. You can hit every top-of-funnel target and still miss the number, because volume and revenue stopped being correlated the moment buying committees got bigger, longer, and more anonymous.

Here’s what nobody wants to admit out loud. A huge share of demand gen budget is still spent chasing anonymous traffic that will never convert, because the account it belongs to was never a fit in the first place. You can run the same nurture sequence against a 50 person startup and a 5,000 person enterprise, and both will show up as “engaged” in your MAP. Only one of them can actually sign a six figure contract.

I’ve run channel mix across five regions, and the pattern repeats everywhere. Paid social and paid search generate volume efficiently. What they don’t generate, on their own, is qualified pipeline from the accounts that matter to the business. Somewhere between the click and the deal, most of that spend evaporates into noise.

Account-Based Marketing isn’t a new idea. What’s changed is the layer underneath it: signal-based marketing. Instead of guessing which accounts are in-market, you now have behavioral, firmographic, and intent signals telling you exactly which companies are actively researching a category, right now, before they ever fill out a form.

This is the shift from campaigns built around personas to campaigns built around accounts. Not “marketing to IT directors in mid-market SaaS.” Marketing to the 40 named accounts your sales team actually wants to close this quarter, using the specific signals those accounts are showing.

Why does this matter for the P&L, not just for marketing ops? Because every dollar spent on an account with zero buying intent is a dollar of CAC inflation with no corresponding revenue. I’ve seen CAC drop by over a third simply by reallocating budget away from broad reach and toward accounts already showing intent signals. Not by spending less. By spending on the right targets.

There’s a deeper issue here too. Spray-and-pray campaigns train your sales team to distrust marketing. When SDRs get handed a list of “leads” that are really just anonymous form fills with no account context, they stop trusting the pipeline marketing hands them. That erosion of trust is expensive in ways that never show up on a dashboard. It shows up in how long it takes to get budget approved next quarter.

Signal-based ABM flips that relationship. When marketing can say “this account visited your pricing page three times this week, downloaded a competitor comparison, and has two stakeholders engaging with our content,” sales stops seeing marketing as a lead factory and starts seeing it as a source of qualified intelligence. That’s not a soft benefit. That’s the difference between a marketing org treated as a cost center and one treated as a growth partner.

The hard part isn’t buying an intent data platform. Any vendor will sell you a dashboard full of “intent signals.” The hard part is building the operating model around it: defining your target account list with real rigor, aligning sales and marketing on what a signal actually means for prioritization, and having the channel infrastructure to act on a signal within days, not weeks.

Most companies fail at the third part. They buy the signal, see it in a dashboard, and then run the same generic nurture sequence they were running before. The signal becomes a vanity metric instead of a trigger for action. That’s not signal-based marketing. That’s spray-and-pray with better reporting.

Over the next few posts I’ll break down what intent data actually is and isn’t, how to operationalize ABM across multiple markets without losing velocity, how to prove the revenue impact to a CFO who doesn’t care about engagement, and where signal-based marketing goes wrong when teams chase the trend without the discipline behind it.

If your funnel metrics look great and your pipeline conversations with sales still feel like a negotiation, you don’t have a lead gen problem. You have a targeting problem.

There’s also a budgeting consequence to this that rarely gets discussed openly. When a marketing org builds its plan around volume targets, it inevitably ends up optimizing for the channels that are cheapest to scale, not the channels that produce the best-fit accounts. Paid social CPMs are attractive. Programmatic display is easy to scale into six figures of spend in a single quarter. Neither of those channels is inherently bad, but when the planning conversation starts with “how many MQLs do we need” instead of “which accounts do we need to win,” budget gravitates toward volume by default, because volume is the easier number to hit.

I’ve watched this play out the same way across different companies and different markets. A team sets an MQL target for the quarter, the paid media plan gets built to hit that target as efficiently as possible, and by the time anyone checks which accounts those MQLs actually belonged to, the quarter is already over and the budget is already spent. Nobody planned to waste money on the wrong accounts. It just happens by default when the operating metric is volume instead of account fit.

Signal-based ABM forces a different planning conversation from day one. Instead of “how many MQLs,” the question becomes “which fifty accounts represent

This article was created with the assistance of artificial intelligence, in compliance with the EU AI Act.