Instead of debating multi-touch attribution, first identify the single, independent event that caused a sales rep to engage a prospect. This "trigger" (e.g., demo request, MQL score) reveals the true efficiency of your GTM motions, which is a more fundamental problem to solve.
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Don't just measure SDR calls and emails. Systematically track the *reason* for outreach—the sales trigger. Was it an intent signal, a form fill, or cold outreach? This crucial data reveals which initial signals actually lead to the best outcomes and deserve more investment.
Many marketing teams invest in attribution tools hoping to justify spend, but these platforms can't provide clear answers if the underlying engine is inefficient. You must first diagnose and fix how your leads convert into meetings before attribution data becomes meaningful.
The company's overall win rate was low (6-7%) and decreasing. Analysis showed this decline mirrored a drop in marketing 'signals' (e.g., event attendance, content downloads) before an opportunity was created. This provided a clear data link between mid-funnel marketing activities and sales success.
By measuring success on 'last lead source,' the company was incentivized to pour money into paid search for product trials—a clear final touchpoint. This model blinded them to the higher value of other lead types and actively discouraged investment in demand creation activities that build brand and generate higher-quality leads.
Traditional funnels jump from a marketing signal (like an MQL) to an opportunity, creating a blind spot. They miss the 'Engagement' period of initial interaction and the 'Prospecting' phase of active sales pursuit. Ignoring these stages makes it impossible to diagnose performance issues or identify improvement levers.
Top-performing companies are abandoning traditional metrics like MQLs. They now focus on understanding the entire prospecting process—from lead creation to BDR/SDR engagement—to generate stronger pipeline, higher win rates, and more revenue with less wasted effort.
A modern data model revealed marketing influenced over 90% of closed-won revenue, a fact completely obscured by a last-touch attribution system that overwhelmingly credited sales AEs. This shows the 'credit battle' is often a symptom of broken measurement, not just misaligned teams.
Ditch MQLs. For sales-led motions, measure marketing on qualified pipeline (deals converting at >25%). For PLG motions, measure 'activated signups,' where users hit their 'aha moment.' This aligns marketing with quality and revenue, not volume.
To identify which events actually drive business, analyze your last 5-20 closed-won deals. Look for recurring, time-bound triggers that you didn't create. This data-driven approach provides clarity on where to focus your efforts, revealing the organic drivers behind your biggest successes.
AI now enables the tracking of every customer touchpoint, including interactions outside of marketing-controlled channels. This provides a complete view from first contact to close, finally solving the long-standing challenge of accurate marketing attribution and ROI measurement.
