Agentic analytics · for payments analytics teams
Reroute before the volume is lost. The cause before risk escalates.
When approval rate drops on an acquirer, Bicycle checks where it moved, ranks the issuer-cohort decline against the routing rule, and attaches the evidence, 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 payments 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 payment questions come back to your queue every week
Approval rate slips on an acquirer, a decline cohort spikes, settlement lags, 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 approval rate moves, with the evidence already attached.
sprawl
Every new acquirer and corridor adds more to model
More gateways, more KPI definitions, more dashboards, more ad-hoc "why did approval drop?" asks landing on your queue.
one model
New acquirers inherit one governed model
New acquirers and corridors inherit approved KPIs, patterns, and cause paths instead of starting from a blank cut.
shadow
Payment ops builds shadow analytics while it waits
Side spreadsheets and ad-hoc queries on approval and decline rates, with no lineage, no review, no shared definition.
self-serve
Payment ops 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 approval-rate 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.
Approval rate dropped on one acquirer. 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.
Approval rate · Acquirer B · EU debitTop segment below baseline. Affected segment and evidence already assembled for analyst review.
Queue · alerts needing review
HighApproval rate droppedAcquirer B · EU debit2.3%-1.8pp vs baselineTop segmentAcquirer B · EU debit$58Kat riskPayment ops + Riskowning 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 · Acquirer B · EU debit
2.3% conversion
baseline 4.1% · ▼1.8pp · returning users · high volume
1 of 6 segments affected · 5 within expected range
Likely driversranked · confidence
MCC risk re-scoring, thresholdsprimary81%
Routing rule drift, EU debitsecondary47%
Fraud spikeruled out
Recommended actionsscoped
Create a ticket on Jirascoped
Alert #retail-revenue on Slackscoped
Update routing rule, EU debitpreview
scoped · previewed · reversible · logged
+ Detected 8:42 · first pass 8:43 · top segment Acquirer B · EU debit
Adjust modelApprove & publishImpact overview
2.3%
conversion rate · -1.8pp vs baseline 4.1%
1.24Maffected users
5.83Msessions
Top affected segments
1
Acquirer B · EU debit · US
-1.8pp2
Acquirer B · EU debit · EU
-1.2pp3
Acquirer B · high-volume merchants
-0.9ppEvidence attached
Funnel trendPDP to purchase
Channel mixpaid vs organic
Issuer cohortapproved vs declined
Routing changeEU debit @ 2:15 AM
Ruled out · not drivers
Price changesno overlap with window
Promo changeseligibility unchanged
Fraud spikeunchanged
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
Approval rate dropped on Acquirer B for EU debit, primarily driven by merchant category risk re-scoring that tightened MCC thresholds. Routing rule drift on EU debit is a secondary contributor.
Recommended next steps
1Update the routing rule to fall EU debit back to Acquirer A.
2Monitor approval rate and decline reasons by acquirer.
3Re-balance routing and set an approval-rate 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
Approval-rate drop
→
Applied playbook
acquirer approval rate
→
Likely driver
MCC risk re-scoring
→
Recommended action
Update routing rule
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: acquirer approval-rate 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 payments analyst view to the data engineering team that keeps the same approval-rate answer trusted as it scales.
Payments
Payments AnalyticsApproval rate and the recurring investigation · you are here
Payments Data EngineeringRate and pipeline trust: real movement vs artifact
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