Find the fashion revenue leaks before the season closes.
Bicycle watches sell-through rate, return rate, markdown depth, and size availability across the styles and channels 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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- 1Core sizes sold out in week 2, fringe sizes still at 40%high
- 2Stock concentrated in low-demand stores and channelscontributing
- 3First markdown held while the season clock rancontributing
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 fashion specialty 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 fashion specialty problem not listed here? Book a demo and we will map it.
A dashboard shows the drop. It never finishes the investigation.
Sell-through misses plan in week 4. The manual chase begins.
The weekly trade report shows the miss on Monday morning. The team still rebuilds the size mix, the store split, and the markdown history by hand, merging exports from two systems, while the season clock runs.
- Buying checks the size curve on the affected styles
- Allocation checks which stores are holding the stock
- Merchandise planning rebuilds the weekly sales and stock sheet from two systems
- Ecommerce checks returns and product page performance
- Nobody agrees whether to mark down or move the units
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.
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.
Start with one KPI in your fashion stack.
Bring one revenue-critical KPI, connect a trusted source, and watch Bicycle turn a prompt plus your data into a fashion analytics agent. Zero to a working agent in about 15 minutes.
Grow into the full product when your team is ready.
Test ride Bicycle on a fashion 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