Agentic analytics · for search & funnel analytics teams
Reprice on the real cause. Price gap, or a search bug.
When look-to-book falls in a market, Bicycle ranks price competitiveness against a ranking regression, on multi-city queries, so your team reviews instead of rebuilds.
Cursor multiplied what one engineer could do. Bicycle does the same for your analysts.
No credit card required
Detect
Explain
Review
The operating model is breaking
The force multiplier for your search & funnel analysts.
Faster onboarding, fewer repeat investigations, and decisions backed by evidence, with your team governing every definition, answer, and action.
Without Bicycle
With Bicycle
repeat
The same search and funnel questions come back to your queue every week
Look-to-book slips, click-through dips, search-to-payment stalls, and you re-cut the data, rebuild the drivers, and reassemble the evidence from zero.
reuse
You build each investigation once, then reuse it
Tune the driver tree once; Bicycle reruns it every time look-to-book moves, with the evidence already attached.
sprawl
Every new market and route adds more to model
More query types, more KPI definitions, more dashboards, more ad-hoc "why is look-to-book down?" asks landing on your queue.
one model
New markets inherit one governed model
New markets and routes inherit approved KPIs, patterns, and cause paths instead of starting from a blank cut.
shadow
Revenue management builds shadow analytics while it waits
Side spreadsheets and ad-hoc queries on look-to-book and pricing, with no lineage, no review, no shared definition.
self-serve
Revenue management self-serves inside guardrails
They get answers with lineage on the KPIs they own; you keep definitions, permissions, and actions governed.
blamed
You own the number but get blamed for the delay
Accountable for whether the look-to-book number is right and for clearing the queue, at the same time.
both
Trusted answers, delivered faster than the asks arrive
The recurring queue moves faster than the questions come in, and every published answer is defensible.
The capabilities
One governed system, six capabilities behind every answer.
Set up once, run continuously. Each capability is reviewable and governed by your team, so the business self-serves on a number it can trust. Pick one to go deeper.
Capabilities work together in one continuous loop. Detect → Explain → Act → Learn.
The playbookBuild · Tune · Govern
Your team builds the intelligence layer once. Bicycle runs it, tunes it, and lets you govern it as it scales.
Buildthe trusted foundation
→
Tunethe intelligence layer
→
Governself service and safe actions
A day in the lifeFirst pass by Bicycle · the call is yours
Retail investigation queue
The morning review starts with the first pass done.
Look-to-book dropped on multi-city queries. Bicycle has already checked the segments, ranked the likely causes, attached the evidence, and kept the definition governed. Your team reviews the answer, publishes the story, and tunes what should happen next time.
Look-to-book · multi-city queries · weekendTop segment below baseline. Affected segment and evidence already assembled for analyst review.
Queue · alerts needing review
HighLook-to-book droppedMulti-city queries · weekend2.3%-1.8pp vs baselineTop segmentMulti-city queries · weekend$58Kat riskSearch Engineeringowning team8:42 AM
MedAdd to cart rate downAll devices · Paid traffic5.6%-0.7pp vs baselineTop segmentPaid traffic · US$31Kat riskGrowth Marketingowning team8:15 AM
MedPayment success rate downAll devices · Credit cards92.1%-1.2pp vs baselineTop segmentCredit cards · US$44Kat riskPaymentsowning team7:02 AM
LowInventory in-stock droppedShoes · 3 warehouses94.3%-0.3pp vs baselineTop segmentShoes · West region$12Kat riskInventoryowning team6:55 AM
+ 4 alerts · 1 high · sorted by revenue at risk
SnoozeOpen investigationTriaged to one segment · multi-city queries · weekend
2.3% conversion
baseline 4.1% · ▼1.8pp · returning users · high volume
1 of 6 segments affected · 5 within expected range
Likely driversranked · confidence
Dynamic pricing variance >20% on multi-city combosprimary76%
Ranking algo v2.3 relevance regressionsecondary55%
Competitor pricingruled out
Recommended actionsscoped
Create a ticket on Jirascoped
Alert #retail-revenue on Slackscoped
Roll back ranking algo v2.3preview
scoped · previewed · reversible · logged
+ Detected 8:42 · first pass 8:43 · top segment multi-city queries · weekend
Adjust modelApprove & publishImpact overview
2.3%
conversion rate · -1.8pp vs baseline 4.1%
1.24Maffected users
5.83Msessions
Top affected segments
1
Multi-city queries · weekend · one market
-1.8pp2
Multi-city queries · weekend · West
-1.2pp3
Multi-city queries · returning users
-0.9ppEvidence attached
Funnel trendPDP to purchase
Channel mixpaid vs organic
Price competitivenessvs comp set
Ranking releasev2.3 @ 2:15 AM
Ruled out · not drivers
Price changesno overlap with window
Promo changeseligibility unchanged
Competitor pricingunchanged
New vs returningboth affected equally
Definition governed · Source read-only · sources fresh
+ Reviewed by analyst on duty · 8:51 AM
Send backConfirm evidenceYour conclusion84% confidence
Look-to-book dropped for users running multi-city queries in one market, primarily driven by dynamic pricing variance above 20% on multi-city combos. A ranking algo v2.3 relevance regression is a secondary contributor.
Recommended next steps
1Roll back ranking algo v2.3 for multi-city queries.
2Monitor look-to-book and price competitiveness.
3Re-test ranking and re-enable with a relevance guardrail.
Attach & sharegoverned
Impact summary
Evidence bundle
Next steps
Share with
Ecom leadershipEcommerce OpsGrowth Marketing+2
+ Published to Slack · #retail-revenue · 8:54 AM
Save draftApprove & publishBicycle applied your approved playbook.
Detected pattern
Pricing variance
→
Applied playbook
multi-city look-to-book
→
Likely driver
Dynamic pricing variance
→
Recommended action
Roll back ranking v2.3
Caught 74 min earlier · 7:28 AM vs 8:42 AM on the first run
No re-investigation · pattern auto-applied, analyst review optional
+ Memory: travel look-to-book investigation playbook · run 2
ReviewView playbookMore roles on Bicycle · same loopYour team's KPI · your screens
Different teams own different metrics. Bicycle keeps the investigation governed.
Move from this search and funnel view to the travel workflows around booking-funnel conversion and supplier bookability.
Vibe Analytics
Build your first governed investigation.
Start with one recurring metric in your queue: why checkout conversion, payment success, or activation moved. Bicycle watches the governed KPI, checks likely causes across the warehouse, payments, app & web, and events, and hands your team a reviewable first pass.
What to bring
A recurring investigation: checkout conversion, payment success, search conversion, or activation rate.
An approved read-only path to the relevant sources: the warehouse, event streams, app & web, payments, or ad networks.
The KPI definition, default segments, and reviewers who decide what becomes trusted.
What we do
Configure the investigation path around your KPI, segments, and evidence rules.
Monitor device, OS, channel, region, and segment movement.
Prepare a first-pass cause analysis with evidence, ruled-out paths, and governed definitions.
No credit card required · analyst-reviewed · governed from day one