Find the leak. Recover the revenue. Learn the pattern.
When a promoted style stops converting, Bicycle pinpoints the SKU and size, explains it from your ads and support signals, and routes the recovery to the team that owns it.
No credit card required
- 1Key M and L sizes out of stock on the hero SKUhigh
- 2Ads still promote the full size rangecontributing
- 3Gorgias tickets mention the missing sizescontributing
A dashboard shows the drop. Bicycle finds the leak, recommends the recovery, and learns if it worked.
Start with the leak you can recover this week.
Two Bicycle agents cover the Shopify Plus fashion funnel: Revenue & Conversion Recovery for the leaks before the sale, Post-Purchase Experience for the breaks after it. Pick one and watch it run, Detect to Learn.
A Shopify leak not listed here? Book a demo and we will map it.
A dashboard shows the drop. It never finishes the investigation.
Sales dip. The owner becomes the analyst.
The dashboard shows the decline. On a lean Shopify team the owner or operator still checks orders, inventory, campaigns, checkout, returns, and payments by hand, across six tools.
- Owner checks orders and top products
- Ops checks availability and fulfillment
- Marketing checks campaigns and AOV
- Someone checks checkout and payments
- No one is sure which moved first
Bicycle finds the leak the moment the KPI moves.
It detects the drop, isolates the affected SKU and size, explains the cause from Shopify, ads, and Gorgias evidence, recommends the Klaviyo recovery, and learns whether it worked.
- Detect the movement, ranked by impact
- Test business and technical causes together
- Attach the evidence, rule out the rest
- Recommend the safe action to the owner
- Learn the accepted cause for next time
Trained on the KPIs, patterns, and root causes of Shopify commerce.
Six capabilities, set up once and run continuously. Business teams move faster, analysts keep governance, everyone trusts the number. Pick one to go deeper.
Multi-factor cause analysis
"What caused this?" turns into a three-day thread across ops, growth, and a developer, and the answer still arrives without evidence.
What Bicycle does
Open the cause summary. Ranked business and technical causes. The likely driver is named, with confidence and impact.
See the evidence. App releases, shipping changes, discount stacking, payment attempts, and fulfillment, with the data attached.
Know what was ruled out. The negative findings close the debate before it starts.
What it looks like for your team
"Mobile checkout dropped 12%." Cause: Shop Pay failing on iOS since the 9.40 checkout update. Ruled out: pricing, inventory, promo, traffic mix.
Capabilities work together in one continuous loop. Detect → Explain → Act → Learn.
Business teams need answers fast. Analysts ensure conclusions are trustworthy.
Same governed intelligence underneath. The Shopify agent for the teams who own the number, the controls for whoever owns the data, even when that is the same person.
See what changed, why, and what is safe to do.
The move finds you, ranked by revenue impact, with the cause and the next step attached. No dashboard hunt, no three-day ticket.
Trading through marketplaces or your own stores as well? Start from the wider retail view.
Govern the definitions, evidence, and safe actions.
Analysts validate the KPI, the evidence, and the recovery. On a lean team the owner gets the same governed answer without an analyst in the loop.
The agentic analytics layer on top of your stack.
Your stack stays the system of record. Bicycle turns Shopify, ads, and support signals into alerts, triage, stories, and governed Klaviyo recovery actions on top of it. No rip-and-replace.
Frames the question, orchestrates the investigation, writes the story, recommends the next step. It reasons.
Detects movement, ranks drivers, computes confidence, forecasts. The numbers are calculated, not generated.
And the rest of what you run: Shopify data · ads · support tickets · email · warehouse
Bicycle runs on top of the Shopify connectors you already use. What's next: Phase 2 adds inventory forecasting, churn, and RFM (ERP/WMS/3PL, CRM).
How is Bicycle different from what a Shopify team already runs?
Dashboards are useful for overall business trends, reporting, executive summaries, and recurring visibility. Bicycle complements dashboards: the dashboard tells you revenue dropped; Bicycle tells you why and what to do next.
| What the tool does after a KPI moves | Dashboards | AI chatbots | Warehouse AI | Bicycle |
|---|---|---|---|---|
| Notices the move without being asked | Only when someone opens the right view. Thresholds fire on the total, not on one product or one channel. | No. The owner or operator still has to suspect the dip and phrase the question first. | Queries can be scheduled on warehouse tables, which most lean Shopify teams do not run. | Watches revenue, conversion, AOV, availability and returns continuously, down to product, channel, device and account. |
| Ranks the likely causes | No. The chart shows the decline. Orders, inventory, campaigns, checkout, returns and payments are still checked by hand, across six tools. | Generates a plausible explanation. Tests no drivers in parallel and rules nothing out. | Writes the query once you decide which metric, which window and which drivers to ask about. | Tests stock availability, campaign mix, price and promo, checkout friction and payment rejects in parallel, ranked by revenue impact. |
| Shows the evidence, and what it ruled out | The underlying views are there. Assembling them into an explanation is manual. | No lineage attached, so teams quietly re-check the number in a spreadsheet. | The SQL is visible. Shopify, ads and support systems outside the warehouse are not. | Definition, lineage, source query and the drivers ruled out, attached to the finding. |
| Recommends the next step, and who owns it | No. At best a link through to another view. | Stops at the answer. Choosing and running the next step stays with the operator. | A warehouse computes. It does not act. | Recommends the next step with the affected products, channels and estimated revenue impact, ready to run in the tools you already use. |
| Keeps the action governed | Read-only, so there is nothing to govern. | Where actions exist at all, they sit outside scope, approval and rollback. | No governed path to Slack, a ticket, or a rollback. | Every action scoped, previewed, approved, reversible and logged. |
Bicycle runs on top of the stack you already report from. Your dashboards, warehouse and BI stay the system of record.
Building it yourself is the fourth option. What the other 90% costs →
Start with one KPI in your Shopify stack.
Bring one revenue-critical KPI, connect a trusted source, and watch Bicycle turn a prompt plus your data into a Shopify analytics agent. Zero to a working agent in about 15 minutes.
Grow into the full product when your team is ready.
How does a Shopify store find out why revenue dropped?
Bicycle watches store KPIs continuously and catches a drop on one variant or campaign before total revenue moves. It tests stock, ads and support signals together, names the likely one with the evidence attached, and says what it ruled out. Bicycle recommends the recovery; you approve it before anything runs.
Shopify itself, your catalogue metadata, Meta and Google Ads, and the support and email tools you already run, Gorgias and Klaviyo among them. Bicycle connects directly, so a lean team can start without a warehouse or a data engineer.
Shopify, ads, CRM, inventory, orders, support, fulfilment and payments, plus the catalogue and pricing data in your Admin. Nothing has to move into a warehouse first, which is the point for teams who do not have one.
Days, and without a data hire. The commerce pack models SKU, variant, campaign and channel before you start, so you connect the store, review what Bicycle proposes, and adjust it. You approve, edit or reject every suggestion.
You do. When a variant stocks out or a campaign stops converting, Bicycle recommends the next step, a back-in-stock and substitute flow in Klaviyo for example, and flags merchandising. Nothing runs until you have previewed and approved it.
The one where the average is hiding the problem. Variant-level revenue is the usual answer: a store can hold total revenue flat while a best-selling size or colour has quietly stopped converting, and the storefront number never shows it.
Test ride Bicycle on a Shopify KPI.
Bring one KPI that matters. We will show how Bicycle detects the move, explains the cause, and recommends the next step on top of the stack you already run.
No credit card required