Marketing Performance Analysis and Recommendations
Analyze your campaigns, identify performance drivers, and get actionable recommendations to optimize your actions.
Monday morning, before the budget review
A marketing manager arrives with exports from Google Ads, LinkedIn Ads and the CRM open in three tabs, needing an explanation for last week’s drop in conversion rate before the 10am review. Reconciling the numbers by hand eats most of the morning, time that would otherwise go towards deciding what to do about it.
When a coincidence reads like a cause
A tool that spots a conversion drop coinciding with a creative change or a targeting shift offers a tidy explanation. It might just as easily reflect a seasonal effect, a public holiday, or a competitor’s move that happened at the same time. Delivered with confidence, that reading pushes teams to reallocate budget on the strength of a coincidence, and the error only surfaces weeks later, once the real driver reappears on its own.
Shared definitions, before the first dashboard
This depends on channels measuring the same things: a lead in the CRM has to mean the same thing as a lead on the ad platform, and enough history has to exist to compare genuinely similar periods. Without that groundwork, the gaps you see reflect differences in measurement rather than differences in performance. With too little history, or definitions that still shift from one quarter to the next, it is usually better to wait than to build recommendations on shaky ground.
Leads worth testing, a reallocation decided in the room
We cross-reference data across channels, surface factors that coincide with the variations observed, and rank them by plausibility rather than certainty. Each lead stays a hypothesis to test before any budget shifts; the reallocation itself gets decided in the room, with the marketing team.