Marketers want to invest where they’ll get the best ROI on revenue. But without accurate attribution, it’s impossible to make truly data-driven decisions about where to allocate budget. Attribution is complex—there are multiple models, and what works for one company may not be the best fit for another. The real goal is to understand what’s driving revenue, pinpoint which channels are working (and which aren’t), diagnose where in the funnel things are breaking down, and identify opportunities for optimization.
One of our clients faced this challenge firsthand. They relied on a broken attribution model that mistakenly assigned credit to outdated or incorrect sources, leading them to misallocate marketing spend and undervalue high-performing campaigns. Their existing setup made it difficult to see the true impact of marketing efforts, creating friction between sales and marketing teams over who was driving revenue. They needed a clear, reliable way to track attribution-without relying on error-prone manual inputs.
The Challenge
The company was using a first-touch attribution model that didn’t reflect how revenue was actually generated. Their system attributed all opportunities to the original source of a lead, even if years had passed, meaning they were crediting outdated marketing efforts instead of recognizing the most recent, impactful touchpoints.
In addition, their attribution process lacked automation, requiring AEs and SDRs to manually select the source of new opportunities. This led to inconsistent and often incorrect data entry, which fueled internal disputes between sales and marketing over who was responsible for generating pipeline.
As a result, marketing efforts that played a major role in revenue generation were undervalued, leading to misguided budget allocations and a misalignment between teams. The company needed a more accurate, automated way to determine which channels were truly driving revenue and inform future investment decisions.
The Solution
Our approach focused on eliminating attribution blind spots and ensuring marketing efforts were accurately recognized. We overhauled the attribution framework, replacing the outdated first-touch model with a structured, logic-based approach that prioritized the most recent, relevant touchpoints. This ensured that opportunities were credited to the right source, enabling more accurate measurement of marketing performance.
To further reduce inconsistencies, we introduced automation that pulled the last marketing touchpoint within a six-month window directly into Salesforce. By eliminating reliance on manual AE and SDR data entry, we minimized human error and prevented misattribution. This provided a clearer view of how marketing efforts influenced pipeline generation, resolving disputes between sales and marketing.
With a more transparent and data-driven system, the company was able to reallocate budget to high-performing channels and refine go-to-market strategies based on actual revenue impact. By tracking opportunities more accurately, they unlocked new insights into campaign effectiveness, ultimately driving better investment decisions and stronger alignment between teams.
The Approach
Learn more about our approach on how we solved this for our client below:
Audit & Discovery: Conducted a deep dive into the client’s attribution setup in Salesforce to identify inconsistencies, manual gaps, and misattributed revenue.
Identified Key Issues: Found that opportunities were being incorrectly credited to outdated lead sources due to a first-touch model and manual AE/SDR selection.
Designed a Waterfall Attribution Model: Established a structured sequence to assign attribution logically:
-
Channel-Sourced Opportunities: If created by the channel team, credit goes to channel.
-
Marketing Attribution: If a contact had responded to a marketing campaign within six months, credit goes to marketing.
-
SDR Attribution: If an SDR created the opportunity, it was assigned to them—unless marketing had a recent engagement.
-
AE Attribution: If no other criteria applied, credit defaulted to the AE.
Implemented Automation: Created a new last campaign responded date field in Salesforce, ensuring opportunities were automatically attributed to the most recent marketing engagement rather than relying on manual selection.
Ran Parallel Testing: Monitored outputs from the new vs. old model to identify discrepancies, refine automation, and ensure accuracy before full rollout.
Refined Reporting & Insights: Provided clear dashboards for sales and marketing teams to view real-time attribution data, improving alignment and decision-making.
The Impact
$4M+ in Pipeline Reattributed: Previously unrecognized marketing efforts were correctly credited, providing a clearer picture of true revenue drivers.
Budget Allocation Based on Data: Marketing could confidently allocate budget to high-ROI channels, optimizing spend and campaign effectiveness.
Time Savings: Eliminated manual AE/SDR attribution selection, reducing errors and saving hours per week on attribution corrections.
Improved Collaboration: Sales and marketing alignment improved, reducing disputes over opportunity sourcing and fostering better strategic planning.
Increased Attribution Accuracy: New opportunities now follow a consistent, automated attribution model, reducing errors.
Request a free consultation to see how we can help you better understand where to spend your marketing budget based on data.
