Agentic analytics · for commerce analytics teams
Every variant watched. Every move explained. Before the weekly review.
The drift hides in one SKU, one size, one variant while the average looks fine. Bicycle watches them all, explains what moved with the lineage behind it, and governs the answers business teams act on.
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 commerce 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 Shopify questions come back to your queue every week
A variant slips, a size sells out, a hero SKU dips, and you re-run the ShopifyQL pull, 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 that variant moves, with the lineage already attached.
sprawl
Every new product and variant adds more to model
More SKUs, more sizes, more KPI definitions, more dashboards, more ad-hoc "why is this down?" asks landing on your queue.
one model
New products inherit one governed model
New SKUs and variants inherit approved KPIs, patterns, and cause paths instead of starting from a blank ShopifyQL pull.
shadow
Merchandising builds shadow analytics while it waits
Side spreadsheets and ad-hoc ShopifyQL on variant revenue and stock, with no lineage, no review, no shared definition.
self-serve
Merchandising self-serves inside guardrails
They get answers with lineage on the variants they own; you keep definitions, permissions, and actions governed.
blamed
You own the number but get blamed for the delay
Accountable for whether the variant-revenue 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.
Variant revenue dropped on a hero SKU. Bicycle has already checked the variants, 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.
Variant revenue · hero SKU · M and LTop segment below baseline. Affected segment and evidence already assembled for analyst review.
Queue · alerts needing review
HighVariant revenue droppedHero SKU · M and L sizes2.3%-1.8pp vs baselineTop segmentHero SKU · M and L sizes$58Kat riskMerchandisingowning 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 variant · hero SKU · M and L sizes
2.3% conversion
baseline 4.1% · ▼1.8pp · returning users · high volume
1 of 6 segments affected · 5 within expected range
Likely driversranked · confidence
M and L out of stock 3 daysprimary81%
Search maps to thin setsecondary38%
Discount rule changesruled out
Recommended actionsscoped
Create a ticket on Jirascoped
Alert #retail-revenue on Slackscoped
Suppress out-of-stock variantspreview
scoped · previewed · reversible · logged
+ Detected 8:42 · first pass 8:43 · top variant hero SKU · M and L sizes
Adjust modelApprove & publishImpact overview
2.3%
conversion rate · -1.8pp vs baseline 4.1%
1.24Maffected users
5.83Msessions
Top affected segments
1
Hero SKU · M size
-1.8pp2
Hero SKU · L size
-1.2pp3
Hero SKU · returning buyers
-0.9ppEvidence attached
Funnel trendPDP to purchase
Channel mixpaid vs organic
Variant availabilityin-stock vs OOS
Stock changeM and L OOS @ 2:15 AM
Ruled out · not drivers
Price changesno overlap with window
Promo changeseligibility unchanged
Discount ruleunchanged
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
Variant revenue dropped on the hero SKU in M and L sizes, primarily because M and L went out of stock for 3 days. Search mapping buyers to a thin-availability set is a secondary contributor.
Recommended next steps
1Suppress out-of-stock variants from the hero SKU listing.
2Monitor variant revenue and size-level availability.
3Re-rank in-stock variants and set an availability 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
Stockout impact
→
Applied playbook
variant revenue
→
Likely driver
M and L sizes OOS
→
Recommended action
Suppress OOS variants
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: variant-revenue 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 commerce analytics view to the other Shopify Plus workflows around conversion recovery, post-purchase, and campaign attribution.
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