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Conversion Audit

A conversion audit is a systematic review of a website, landing page, or funnel to identify the obstacles preventing visitors from completing a desired action.

In depth

A conversion audit combines quantitative signals (analytics, heatmaps, event tracking) with qualitative inputs (session recordings, user surveys, support tickets) to build a prioritized list of issues. Auditors typically score findings by impact and effort, then map each one to a hypothesis that can be tested. The goal is not a long PDF of complaints but a short, ranked backlog of experiments the team can actually run.

The most common pitfall is auditing in a vacuum: flagging "the button is the wrong color" without tying it to a measurable drop in a real step of the journey. In a quiz-funnel or lead-qualification workflow, a good audit follows the visitor from ad click to landing page, through each quiz question, and into the lead-capture and result steps, so you can see exactly where qualified prospects abandon and why.

Example in practice

A B2B SaaS growth lead runs a quarterly conversion audit on a Pivix quiz funnel and finds that 38% of visitors abandon on a 9-field lead-capture form. The audit recommends cutting the form to email plus company size and moving it after the score reveal; the team ships the change and watches completed leads climb from 120 to 190 per week.

Frequently asked questions

How long does a conversion audit take?

A focused single-funnel audit usually takes one to two weeks, including data collection and a prioritized findings report. Larger multi-page audits can run three to four weeks because they require deeper session-recording and survey analysis.

What data do I need before starting?

You need at least a few weeks of analytics with funnel events, plus heatmaps or session recordings if available. Qualitative inputs like exit surveys and support tickets dramatically improve the quality of recommendations.

Is a conversion audit the same as A/B testing?

No. An audit diagnoses problems and generates hypotheses, while A/B testing validates whether a proposed fix actually improves the metric. Audits typically come first and feed the testing roadmap.

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