Relying on UTM link clicks for B2B influencer campaigns is a failing strategy, as social platforms penalize external links and users rarely convert directly. Instead, use a combination of time-series analysis (correlating campaigns to signup spikes) and self-reported attribution on forms to get a more accurate picture of an influencer's impact.
Related Insights
Applying a single attribution model, like last-touch, to all channels is a mistake. It undervalues top-of-funnel activities and can lead to budget cuts that starve the pipeline. Instead, measure each channel based on its intended outcome and funnel stage.
Go beyond simple affiliate tracking by using vanity URLs (e.g., brand.com/creator) to tag incoming users. This allows you to analyze the long-term value and performance of different creator cohorts months or even years later, informing future partnership decisions.
Standard attribution models, even multi-touch, fail to credit influential, non-clickable touchpoints like a child watching a Netflix show that inspires a purchase. This "Hot Wheels Problem" highlights the need to account for view-through attribution and the full, often hidden, customer journey.
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.
A common attribution error is assigning all sales to paid marketing activities. In reality, most brands have a strong "baseline"—sales that would occur even without marketing. Accurate measurement requires modeling this baseline first, then attributing only the incremental lift from campaigns.
To prove business impact beyond vanity metrics, define success by aligning with key departments *before* the campaign starts. Executives want pipeline, product wants trials, and customer success wants retention. This prevents a disconnect where marketing celebrates impressions while leadership asks about revenue.
Direct attribution models are flawed because platforms like Google and Facebook use tracking pixels to claim credit for sales that would have occurred anyway. Smart marketers are returning to older methods of measuring lift from campaigns rather than relying on misleading platform data.
Moving beyond basic attribution, LinkedIn's new Conversion Lift Testing tool measures the causal impact of campaigns. It compares conversions between an ad-exposed group and a control group that saw no ads, allowing marketers to determine the true incremental value generated by their advertising.
Instead of chasing perfect attribution, recognize that customers will explicitly tell you how they found you. At Drift, prospects on sales calls would frequently mention being fans of their podcast. This qualitative data from the front lines is often the most direct and powerful measure of brand impact.
Solely judging marketing by last-touch attribution creates a false reality. This narrow metric consistently favors predictable channels like search and email, discouraging investment in brand building and creative storytelling that influence buyers throughout their journey. It's a losing battle if it's the only basis for decision-making.
