FOR SHOPIFY DTC BRANDS · $5M–$20M REVENUE

Find out what's actually causing your conversion loss — and what to fix first.

Your dashboards describe what's happening. ConversionQuest isolates the cause, quantifies the lost revenue, and ranks the fix to ship first.

See a sample analysis →

A finished analysis. Yours to explore.

SAMPLE_DIAGNOSTIC.pdf
01Executive summary
02Funnel diagnostic
03Behavioral personas
04Hypothesis backlog
05Sprint roadmap
06Experiment readouts
07Multi-touch attribution
08On-site search performance
09Device & channel intelligence
10LTV cohort analysis
11Causal analysis
+8 more sectionsOpen sample →
WHY THIS WORKS

The rigor most CRO tools skip

We tell you when we don't know. Most tools won't.

Causes, not correlations.

Most tools show you that mobile converts lower than desktop. ConversionQuest isolates whether your mobile checkout is the actual cause, and quantifies the revenue impact. We use the same family of statistical methods published economics researchers use to separate cause from coincidence: Double ML for causal estimation, propensity overlap testing, Rosenbaum sensitivity bounds, and causal forests for heterogeneous effects.

Methodology: Double ML (Chernozhukov 2018), trimmed DML (Crump 2009), causal forest (Athey & Wager 2019), Rosenbaum bounds (Rosenbaum 2002).
Demoted

We say when we don't know.

When the data isn't strong enough to support a causal claim, we say so, and downgrade the finding to "pattern worth investigating." Every analysis carries an overall data confidence grade and a per-finding causal status. Most tools present every chart at the same confidence level. We don't.

Reproducible by design.

Same data, same findings, every time. The analytical pipeline is deterministic. Re-run last quarter's analysis tomorrow and get the same answer. Verified by automated regression tests on every release.

How it works

Three steps. Under an hour, not weeks.

01

Connect your data

Upload exports from the tools you already use: Shopify, GA4, Klaviyo, Meta Ads, Google Ads, Hotjar, PostHog. PII is stripped at intake. No integrations to wire up, no data team needed.

02

The pipeline runs the analysis

A custom analytical pipeline maps your funnel, segments your customers behaviorally, isolates the actual causes of your conversion losses, and prioritizes the fixes. Uses formal causal estimation, statistical confidence grading, and the same methods published economics researchers use.

03

Get answers your team can act on

You get a full diagnostic report with findings, methodology, and recommendations, plus a plain-English summary on top so you don’t have to be a statistician to act on it.

WHAT YOU GET

The diagnostic a strategist needs a stats team to produce. In under an hour.

One page from Section 11 of the actual deliverable, applying the method from above. Numbers redacted on the public sample.

SAMPLE_DIAGNOSTIC.pdf — Section 11 · Causal AnalysisSection 11 of 19
Finding 11.1
Treatment: Mobile vs. desktop device · Double ML · all preconditions passed

Mobile checkout is the actual cause of the conversion gap.

Grade AConfirmed causeShip priority #1

Mobile sessions convert ██████ below desktop. After controlling for the variables below, the gap survives, meaning the mobile experience itself is the cause, not lower-intent mobile visitors. Estimated annualized revenue at stake: ██████████.

Controlled for
Channel mixReturn-visit behaviorPDP timeSession week

Implication: the friction concentrates at the checkout shipping and payment stages, where elevated form-field edits and rage-click signals indicate cognitive load on the small-screen interface.

Effect size
█.██ pp
Recoverable / yr
$ ███K
ICE score
█.█
Method · 95% CI
—
↳ Method: Double Machine Learning (Chernozhukov et al., 2018). All preconditions passed: treatment overlap, sample size, and sensitivity checks. Cross-fitted estimation on cleaned cohort excluding anomaly-flagged sessions. Continues into Section 11.2 →
Ask ConversionQuest
Why did you rule out channel mix as the cause?
Channel mix was included as a control in the DML estimation. When we condition on channel mix and re-estimate the mobile-vs-desktop effect, the gap remains at ██████with the same confidence interval. If channel mix were the driver, conditioning on it would have collapsed the effect toward zero. It didn't.
Numbers redacted on the public sample. Run on your data to unlock.

Every analysis includes 19 sections like this one. Funnel diagnostic, behavioral personas, hypothesis backlog with ICE scoring, sprint roadmap, attribution synthesis, plus a plain-English summary and a built-in AI companion that knows your data.

“Can I just ask Claude?” General LLMs will accept your data, sample it, and write a plausible narrative. We run formal causal estimation on the full set, with sensitivity bounds, propensity overlap testing, and reproducibility checks.

Even at a 1M token context, general-purpose LLMs sample or summarize when running computations on large datasets. They don't run propensity testing, sensitivity bounds, or reproducibility checks.

FOR CRO TEAMS AND GROWTH AGENCIES

Run rigorous diagnostics across your portfolio.

If you're a growth or CRO agency, ConversionQuest gives your strategists a senior-grade diagnostic on every client engagement, in hours, not weeks. Differentiate your retainers with auditable analysis. Retain the implementation revenue. White-label exploration welcome.

Diagnose every retainer client with the same statistical rigor.
Walk into QBRs with the same methods published economics researchers use.
Retain implementation revenue without giving up the diagnostic layer.
Email us about agency partnership →
First cohort — limited spots

Apply for the pilot.

Three questions. If you qualify, we reply within 24 hours with intake instructions and what's included.

PILOT FIT CHECK · 3 QUESTIONS
Q1
Q2
Q3

~3 design partners this quarter. DPA, AI Use Policy, and Terms shared on intake.