Conversion Research Audit

Page problems become clearer when no single clue is treated as the whole story.

We compare analytics, session recordings, heatmaps, accessibility checks and technical health. Every finding keeps its source and a confidence rating, so the team can see what's well supported and what's still a hunch. The result is a ranked register ready to feed the next hypothesis. Your page "should convert better" and you need a sourced account of what's actually wrong before anyone proposes a fix.

You get a rated friction-and-opportunity register for the page that's ready to become the next hypothesis.

A researcher cross-referencing session recordings, heatmap data, and accessibility notes on one page

Some of the 500+ brands we've worked with

See all references
  • Memorial
  • İstikbal
  • Capital Dergisi
  • HangiKredi
  • Joker
  • Gusto
  • GS Store

How the work runs

Compare the clues before calling anything a finding

Analytics, recordings, accessibility checks and technical evidence all enter the same register, but they don't carry equal weight. AI can group approved notes and flag contradictions. The research owner decides what the combined evidence can support.

How we hold ourselves to it

  • Every clue gets a source and a confidence rating before it counts as a finding
  • Quantitative and qualitative evidence are read against each other
  • The audit ends in a ranked register a hypothesis can start from
  • A thin data pipe is logged as a finding in its own right

  1. Pull the quantitative baseline

    Read page-level and funnel-step analytics: entrances, exits, on-page events, device and channel splits, and any existing segment cuts, so every later clue has a number to sit next to.

    Baseline table of entrances, exits, on-page events and device and channel splits, with broken tracking flagged.

    AI assist
    Compiles the baseline metrics into a single sourced table and flags gaps or broken events.
    Human gate
    The CRO strategist confirms the baseline is trustworthy before qualitative work starts.
    Owners
    CRO strategist + analytics owner

  2. Layer in qualitative evidence

    Watch a representative sample of session recordings, read heatmap and scroll-depth data, and pull any existing survey or support-ticket themes tied to the page, logging how many sessions or responses back each observation.

    Qualitative log of recording, heatmap, survey and support themes, each with the session count behind it.

    AI assist
    Clusters qualitative notes by theme and counts how many sessions support each one.
    Human gate
    The research owner checks that clustered themes aren't overreading a handful of sessions.
    Owners
    Research owner + session analyst

  3. Check accessibility and technical health

    Run an accessibility pass against WCAG success criteria and check load performance, script errors, and cross-device rendering to surface friction that behavior data alone can miss.

    Accessibility and technical findings mapped to WCAG success criteria, load performance and cross-device rendering.

    AI assist
    Runs the accessibility and performance checks and flags criteria that fail or sit borderline.
    Human gate
    An accessibility-literate reviewer confirms each flagged issue is real before it enters the register.
    Owners
    Accessibility reviewer + engineering

  4. Cross-check every clue against the others

    Take each observation from the steps above and ask what else would have to be true for it to be the real story. A heatmap cold zone that matches a broken lazy-loaded image points to the bug as the likely explanation.

    Competing-explanation notes for each observation, separating a behavioral finding from a bug.

    AI assist
    Flags observations that contradict each other or that only one evidence lane supports.
    Human gate
    The CRO strategist resolves flagged contradictions before anything gets rated.
    Owners
    CRO strategist + research owner

  5. Rate and rank the register

    Score each finding on evidence strength (how many independent sources back it) and estimated opportunity size, then order the register so the next reader knows exactly where to look first.

    Register scored for evidence strength and opportunity size, ordered for the next reader.

    AI assist
    Drafts the ranked table from the approved findings and their scores.
    Human gate
    The research owner signs off on the ranking before it ships.
    Owners
    Research owner + CRO strategist

  6. Hand off with sources attached

    Deliver the register with every finding traceable to its underlying data, such as a recording, heatmap segment, analytics query, or accessibility rule, so the next hypothesis has evidence to support it.

    Handoff register where every finding links to the query, clip, heatmap segment or accessibility rule behind it.

    AI assist
    Compiles the final register with source links attached to every finding.
    Human gate
    The client owner reviews the register before it's used to write a hypothesis.
    Owners
    Client owner + research owner

What lands with your team

The evidence behind the ranking

Each finding keeps the material that supports it and the confidence rating the reviewer gave it. That lets the next person challenge the conclusion without starting the audit again.

  • Quantitative baseline table

    Entrances, exits, on-page events, and device and channel splits for the audited page, with any broken or missing tracking called out explicitly.

    Accepted when

    Every audited page has entrance, exit, event and split figures, and any broken or missing tracking is named rather than silently omitted.

    Cadence: At audit start

  • Qualitative evidence log

    Session-recording and heatmap themes, each tagged with how many sessions support it and linked back to the source clips or maps it came from.

    Accepted when

    Each theme states how many sessions support it and links back to the clip or map it came from.

    Cadence: During the audit

  • Accessibility and technical findings

    WCAG-mapped issues plus technical and performance findings that behavior data may not identify clearly.

    Accepted when

    Each issue cites the WCAG success criterion or technical check it failed, not a general impression.

    Cadence: Per audit

  • Ranked friction-and-opportunity register

    Every finding scored for evidence strength and opportunity size, ordered so the next hypothesis has an obvious, defensible place to start.

    Accepted when

    Every entry carries an evidence-strength score, an opportunity estimate and a source link, and the order is defensible to the next reader.

    Cadence: At audit end

Before the work starts

When this work is the right next step

Without a reviewable register, a page audit becomes an opinion contest. Everyone has a preferred theory about why a page underperforms, whether it concerns the headline, form, price, or image. A documented register gives the team a common basis for judging which theory has the strongest support. Without it, the strongest opinion can set the fix before the evidence is resolved.

A good fit when

  • The page draws enough traffic for session recordings and heatmaps to show patterns, but nobody has compared them with the analytics baseline.
  • Several plausible explanations compete for the page's weak conversion, yet no source-backed register shows which one has the strongest evidence.
  • Your team needs to challenge each finding after handoff, so every entry must keep the recording, query, heatmap segment, or WCAG check behind it.
  • Three screen recordings are being used to describe all visitor behavior.
  • Heatmap "cold" zones get read as broken without checking what's actually rendered there.
  • An accessibility barrier may be affecting a segment, but nobody has logged enough evidence to rate the finding.
  • The stakeholder with the strongest opinion sets the fix before the register is reviewed.

Better handled as other work when

  • The problem is already documented with its source and confidence, so the next useful step is a falsifiable hypothesis and test design.
  • Traffic is too low for recordings or heatmaps to reveal a stable pattern, and the resulting register would overstate a handful of sessions.
  • Analytics tracking on the page is broken or missing. That needs to be fixed before it can serve as an audit input.

People who own a single channel

Paid Search, Paid Social, CRO, and Programmatic each run under a named owner at Zeo. The consultants below are matched to the channel this page is about, so you can see who you'd actually work with.

Content we've produced on this topic

Videoby İlker EmirWays to Increase Conversion Rate | Ilker Emir, ZeoArticleby Zafer YıldızConversion Structure & Conversion Creation in Google Analytics 4

Tools we use

Tools behind this work

  • Crazy Egg

    splits the click evidence by traffic source, not just by position on the page

  • axe DevTools

    runs the accessibility pass and is honest about what it can and can't verify automatically

  • Google Analytics

    pulls the quantitative baseline the register is built on top of

Get the competing theories onto the same page

Review what the page evidence really supports

Analytics, recordings and existing findings often point in different directions. We'll compare them with accessibility and technical checks, then rank only the conclusions that hold up.
Review the evidence

The questions we hear most

Those tools supply inputs. The audit reads them beside analytics, accessibility, and technical data, then rates how well each finding is supported. Every entry keeps its source and confidence level. A human reviewer decides what the combined evidence can support.