Everyone tracked clicks, Forrester tracked preference: why performance marketing lost the plot

It was a Monday morning pipelinepipelineAll active sales opportunities across the stages of the sales process, together with their combined potential value and probability of closing.View full definition → review. The marketing team had numbers for everything: click-through rates, cost per lead, MQLMQLA Marketing Qualified Lead (MQL) is a prospect whose engagement and fit signals indicate they are more likely to become a customer, justifying handoff toward sales.View full definition → volume, intent signal scores pulled from a third-party datathird-party dataData purchased from external aggregators, collected from audiences you don't own. It is bought or licensed rather than gathered through your own direct relationships.View full definition → provider. The VPVPA clear statement of the benefits your product delivers, the problems it solves and why customers should choose you over alternatives.View full definition → of Sales looked at the dashboard, looked back at the room, and asked the one question no metric on screen could answer: "Do these people actually want to buy from us, or are they just reading our content?"

Nobody had a good answer. That scene, or some version of it, plays out in B2B organisations everywhere. And it sits at the centre of Forrester's argument that performance marketing, as practiced by most B2B teams today, has reached a structural dead end.

Forrester's preference marketing argument, explained plainly

Forrester's core claim is not that measurement is bad or that paid mediapaid mediaVisitors arriving via paid ads or sponsored placements, where you pay a platform to display your message rather than earning visits organically.View full definition → is wasteful. The argument is more specific: the metrics that define performance marketing (engagement, intent signals, pipeline velocity) tell you how visible you are, not whether buyers prefer you over your competitors. Market preference, the state in which a buyer would choose you if all else were equal, is what actually drives revenue. And it is almost entirely invisible to the standard performance stack.

To close that gap, Forrester introduced what it calls the Preference Marketing Matrix. The framework combines two dimensions: market awareness (do buyers know you exist?) and market preference (do buyers want you when they're choosing?). A company with high awareness and low preference is, in Forrester's framing, not in a strong position regardless of how clean its attribution modelattribution modelA framework for assigning credit to the touchpoints that contributed to a conversion, so you can measure which channels and interactions actually drive results.View full definition → looks. Clicks and content downloads confirm that people found you. They do not confirm that people want you.

This is a distinction that most performance marketing infrastructure was never designed to capture. ROASROASReturn on Ad Spend (ROAS) measures the revenue generated for every unit of currency spent on advertising, calculated as revenue divided by ad cost.View full definition → tells you how much revenue a campaign produced relative to spend. It does not tell you whether the buyers who converted were already predisposed to choose you before they ever saw an ad. For teamsworking out what ROAS is actually measuring, this gap between attributed conversion and genuine causation is where the honest reckoning begins.

Why this moment exposes something bigger than one flawed metric

The timing of Forrester's argument matters. B2B buying committees have grown. The buying cycle has lengthened. Dark socialDark socialTraffic from private sharing (messaging apps, email, copy-paste) that can't be tracked by standard analytics tools, so it often gets misattributed as direct traffic.View full definition →, word-of-mouth inside Slack channels, peer recommendations on LinkedIn that never touch a UTM parameter: these influence preference formation in ways that performance dashboards systematically miss. A company can look like a performance marketing success story, with falling CPLs and rising MQL volume, while quietly losing preference share to a competitor who shows up well in conversations that leave no digital trace.

There is a parallel here with how marketing mix modelingmarketing mix modelingA statistical approach that estimates how each marketing channel and other factors drive sales, guiding budget allocation.View full definition → fell out of fashion in the 2010s when last-click attributionattributionA framework for assigning credit to the touchpoints that contributed to a conversion, so you can measure which channels and interactions actually drive results.View full definition → felt more precise, and is now making a significant comeback precisely because it captures the unmeasured. Teams doingserious attribution work across channels have already discovered that the model producing the tidiest numbers is rarely the one producing the truest picture.

The Forrester argument adds another layer: even a well-constructed attribution model only tells you what drove a conversion. Preference is what brought the buyer to the table in the first place, often weeks or months before any traceable interaction.

A useful case to consider is enterprise software, though Forrester does not reduce its argument to any single company. In mature categories where three or four vendors have broadly comparable capabilities, the decision often comes down to reputation and preference built over time through analyst relationships, peer recommendations, and brand presence at industry events. None of those touchpoints shows up cleanly in a ROAS calculation. The team that wins the deal will show a healthy ROAS. The team that built the preference that made winning possible will struggle to attribute it to any single campaign.

What CMOs should actually do with this argument

The first move is diagnostic, not tactical. Pull your pipeline data and ask what percentage of closed-won deals came from buyers who sought you out versus buyers you actively generated through paid demand channels. The ratio matters. A high proportion of inboundinboundA strategy that attracts prospects organically via valuable content (blog, SEO, social) rather than interrupting them.View full definition →, reputation-driven buyers suggests that preference is doing work your attribution model is not crediting. A very low proportion suggests you may be buying short-term pipeline at the expense of building the kind of market position that lowers CACCACCustomer Acquisition Cost (CAC) is the total sales and marketing spend divided by the number of new customers gained in a period. It measures how efficiently you grow.View full definition → over time.

The second move is to add a preference signal to your measurement architecture. Forrester's matrix is one approach. Quarterly brand preference surveys among your target ICPICPKey Performance Indicator, a measurable value that shows how effectively you're achieving a specific objective, tracked over time against a target.View full definition →, win/loss interviews conducted by a neutral third party, and share of preference tracked through analyst reports are all usable proxies. None of them is as fast or as granular as a click, but that is the point. Preference builds slowly and decays slowly. Measuring it quarterly is more honest than pretending a weekly ROAS report tells the full story.

The third move is the hardest: having the internal conversation about what performance marketing is actually being asked to do. If the goal is to generate short-term pipeline and justify budget through trackable attribution, performance marketing does that well. If the goal is to build a market position that compounds over years, a different mix is required, one that includes brand investment, thought leadership, and category-level messaging that cannot be optimised on a cost-per-click basis.

Forrester's declaration that performance marketing is dead is, like most analyst provocations, a useful exaggeration. What is genuinely in trouble is the belief that a clean dashboard of engagement metrics equals a healthy market position. The companies that figure out how to measure preference alongside performance will have a durable advantage over those still optimising click funnels while competitors quietly become the preferred choice. That gap is real, and it is widening.