Find the retail revenue leaks before they cost you.
Bicycle watches conversion, availability, pricing, promo, and checkout KPIs across the segments that matter. When a number moves, it explains the likely cause and recommends the next action to the team that can fix it.
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- 1Availability dropped on top add-to-basket SKUshigh
- 2Competitive pricing widened on 3 categoriescontributing
- 3Search indexing lag on substitute itemscontributing
A dashboard shows the drop. Bicycle finishes the investigation and recommends the fix.
Start with a single revenue KPI critical to your business.
Bicycle comes with retail industry intelligence already inside. Pick one Bicycle-recommended KPI and watch it run: the KPI it watches, the causes it checks, the action it recommends to the owner, and what it keeps.
A retail problem not listed here? Book a demo and we will map it.
A dashboard shows the drop. It never finishes the investigation.
Search conversion drops. The manual chase begins.
The dashboard shows the decline. The team still checks inventory, catalog, pricing, search relevance, and regional patterns by hand, across five tools and four people.
- Category team checks top SKUs
- Replenishment checks availability
- Product checks search and indexing
- Analytics pulls city and store cuts
- Ops argues over who owns the fix
Bicycle starts the investigation the moment the KPI moves.
It detects the drop, ranks the affected keywords and cities, tests business and technical causes in parallel, attaches the evidence, and routes the issue to the owner with the next step.
- Detect the movement, ranked by impact
- Test business and technical causes together
- Attach the evidence, rule out the rest
- Route the safe action to the owner
- Learn the accepted cause for next time
Trained on the KPIs, patterns, and root causes of retail.
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 Slack thread across data, product, ops, and engineering, 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. Logs, deploys, pricing changes, supplier behavior, and search relevance, 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 4% on iOS." Cause: Apple Pay button failing since 9:42am, release v4.18.2. Ruled out: pricing, inventory, promo, weather.
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 retail agent for the teams who own the number, the controls for the team who owns the data.
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.
Selling through Shopify? DTC, B2B and wholesale get their own Shopify view.
Govern the definitions, evidence, and safe actions.
Analysts review first-pass causes instead of rebuilding them. Leaders give the business self-service inside one governed boundary.
The agentic analytics layer on top of your stack.
Your stack stays the system of record. Bicycle turns retail signals into alerts, triage, stories, dashboards, chat, and governed 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: warehouse · ecommerce events · catalog · pricing · observability · BI · Slack
Bicycle runs on top of your existing systems. The retail pack supplies the starting KPIs, causes, stories, and action paths.
How is Bicycle different from what a retail 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 category or city. | No. Someone has to suspect the drop and phrase the question first. | Queries can be scheduled on warehouse tables. You build and tune the detection yourself. | Watches conversion, availability, pricing, promo and checkout continuously, down to SKU, category, store and device. |
| Ranks the likely causes | No. The chart shows the decline. Inventory, catalog, pricing, search relevance and regional patterns are still checked by hand, across five tools and four people. | 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 availability, catalog, pricing, search relevance and release changes 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. Anything outside the warehouse is 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 routing the next step stays with the reader. | A warehouse computes. It does not act. | Recommends the next step to the category or replenishment owner, with the affected SKUs, stores and estimated revenue impact. |
| 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 retail stack.
Bring one revenue-critical KPI, connect a trusted source, and watch Bicycle turn a prompt plus your data into a retail analytics agent. Zero to a working agent in about 15 minutes.
Grow into the full product when your team is ready.
How does a retail team find out why a KPI moved?
Bicycle watches retail KPIs continuously and catches a move inside one segment before the company average shifts. It tests business and technical causes together, names the likely one with the evidence attached, and says what it ruled out. Bicycle recommends the next step; the team that owns the number approves it.
Search events, add-to-basket, orders, availability, pricing and catalogue, read from the ecommerce, inventory, order and BI systems you already run. Bicycle connects to them rather than moving them. One revenue-critical KPI, and a trusted source for it, is enough to begin.
The ecommerce stack, catalogue, inventory, pricing, order management, payment, fulfilment and support systems, plus the warehouse and BI you already report from. Bicycle reads from them and sends findings to the channels your team already works in. Your stack stays the system of record.
Days, not a quarter. The retail pack arrives with KPI definitions, detection patterns and cause categories already modelled, so setup is reviewing and adjusting what Bicycle proposes rather than building it from scratch. You approve, edit or reject every suggestion.
You do. When conversion or availability moves, Bicycle recommends the next step to the team that owns it, category or replenishment, with the affected SKUs, the stores and the estimated revenue impact attached. Every action is scoped, previewed and logged before anyone runs it.
The one that costs money fastest. Bicycle watches conversion, availability, pricing, promo and checkout KPIs, and most retail teams start with whichever is already in the weekly review: search-to-purchase conversion, or availability on the top-selling SKUs.
Test ride Bicycle on a retail 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