AI conversion optimization that ships fixes, not just findings
Traditional CRO stalls between the insight and the implementation: the analysis says “fix the PDP,” and the ticket sits in a backlog for a month. WorkDaddy runs the whole loop — find the leak, stage the fix, get your approval, verify with a test.
Conversion optimization fails in the handoff. An analyst or a dashboard identifies a drop-off; a designer mocks a change; a developer schedules it; weeks pass; the context is gone. WorkDaddy collapses that pipeline into one accountable loop between two agents. The Analyst agent reads your GA4 data daily and pinpoints where sessions leak — which step, which template, which device, which traffic source. The Builder agent turns the highest-impact finding into an actual page change: a staged edit on your Shopify theme or a pull request on your headless storefront. You review the exact diff before it ships, and an A/B test or before-after measurement verifies whether the fix worked. No fabricated projections — real changes, real measurement, your final call on everything.
Diagnosis first: where your funnel actually leaks
The Analyst agent doesn’t start from best-practice checklists; it starts from your data. It walks your GA4 e-commerce funnel — view item, add to cart, begin checkout, purchase — and segments every drop-off by device, traffic source, landing page, and product category. The output isn’t a dashboard you have to interpret. It’s a ranked list of specific, located problems, each with the evidence attached and an estimate of how much revenue flows through the broken step. That ranking decides what the Builder works on first, so effort follows impact instead of hunches.
Funnel step analysis segmented by device, source, template, and product category
Leak ranking by the revenue exposure of each drop-off, not by generic severity scores
Anomaly alerts when a release, app update, or theme change moves conversion
Evidence attached to every finding, so you can check the reasoning yourself
From finding to staged fix
When the Analyst flags that mobile users abandon on a slow, cluttered product page, the Builder doesn’t reply with advice — it prepares the change. That might be restructuring the buy box above the fold, simplifying a checkout field, clarifying shipping and returns messaging, or fixing a broken variant selector. On Shopify, the change is staged against your theme; on headless, it arrives as a pull request. Every fix comes with the Analyst’s finding attached, so you always know why a change is proposed, not just what it does. Dashboards tell you what’s wrong; this loop ships the fix.
You review every change before it ships
Your store’s voice, merchandising, and brand judgment stay yours. Each proposed fix lands in a review queue as a before-and-after diff with a plain-language rationale. Approve it, request an edit, or reject it — rejections teach the agents your preferences, so proposals converge on your standards over time. This review-first model means you get the speed of an always-on optimization team without ever waking up to a homepage you didn’t sign off on. When specific change types have earned your trust, you can put them on auto-pilot while keeping review on the rest.
Verification: the test decides, not the opinion
A shipped fix is a hypothesis until the data confirms it. Where traffic supports it, the agents set up an A/B test and let it run to significance; where it doesn’t, they run a clean before-and-after comparison with the confounders documented. Either way, the result is written back into the shared record: what changed, what moved, and how confident the measurement is. Losing variants get rolled back. Winning patterns inform the next round of proposals. Over months, this builds something most stores never have — an honest, cumulative record of what actually converts for your audience.
A/B tests where traffic volume supports statistical confidence
Documented before-and-after measurement where it doesn’t
Automatic rollback of changes that measurably hurt conversion
A permanent experiment log: hypothesis, change, result, decision
How it works
Set the goal
Point WorkDaddy at the outcome — reduce checkout abandonment, lift mobile conversion, fix a leaky PDP template — and connect GA4 and your storefront.
Analyst finds, Builder stages
The Analyst locates and ranks funnel leaks from your real data; the Builder turns the top finding into a staged page change with the evidence attached.
You review and ship
Each fix arrives as a diff with a rationale. Approve it to ship, or reject it and the agents recalibrate. Nothing touches your live store without you.
Verify, then automate what’s earned it
Tests confirm or roll back each change. As the win record grows, put low-risk fix types on auto-pilot and keep your review time for the big swings.
What you get
A weekly digest of funnel leaks ranked by revenue impact, each with evidence attached
Page and checkout fixes shipped as reviewed changes to your live storefront
A/B tests or documented before-after measurement on every meaningful change
Automatic rollback of variants that measurably hurt conversion
A cumulative experiment log of what was tried, what won, and why
Mobile-specific findings and fixes, since that’s where most stores leak worst
Handled by these agents
Frequently asked questions
How is this different from a CRO audit or a heatmap tool?
Audits and heatmaps end at insight — someone still has to design, build, and ship the fix, and that handoff is where most CRO programs die. WorkDaddy’s Analyst produces the insight and its Builder teammate stages the actual page change for your approval, then a test verifies it. The loop closes instead of stopping at a PDF.
Will it redesign my store without permission?
No. Every change is a staged edit or pull request you review first — you see exactly what will change and why before it ships. Auto-pilot is available, but only for change types you explicitly enable after the agents have built a track record you trust, and you can revoke it anytime.
What if my store doesn’t have enough traffic for A/B tests?
The agents adapt the verification method to your traffic. With enough volume, they run proper split tests to significance. Below that threshold, they ship one change at a time and measure before-and-after with confounders documented — slower and less certain than a controlled test, and the agents say so rather than overstating confidence.
Does it work with my existing A/B testing tool?
WorkDaddy can run experiments natively on Shopify and headless storefronts, and it can coexist with an existing testing setup — the agents will avoid overlapping tests on the same templates. Results are read from GA4 either way, so measurement stays in a system you already trust and can inspect.
Keep reading
Conversion rate optimization
Find and fix the steps where shoppers drop off.
A/B testing
Verify every change with a controlled experiment.
Product page optimization
Ship PDP improvements as reviewable diffs.
Playbook: improve mobile conversion
A step-by-step plan for the device where stores leak most.
Put the team to work on your store
Connect your storefront, analytics, and email stack, set a goal, and let the agents run the work end to end. Start on the free plan with your own model key.