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Conversion

Kiosk conversion fell a fraction of a percent. Across hundreds of dimensions, where?

Bicycle watches kiosk conversion at every combination of location, device, software version and transaction stage, and finds the one behind a drop.

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Before

A small drop across a large fleet hides in the average. The cause is usually one combination, such as one network provider after one configuration change on one device, and finding it by hand takes weeks.

With Bicycle

Drops are found where they actually sit, the same day, with the combination that explains them.

What happens when a KPI moves.

  1. 01

    Detect

    Conversion falls at one combination of location, device, software version or stage.

  2. 02

    Explain

    The exact combination is named, such as one network provider after one configuration change on one device brand.

  3. 03

    Act

    Bicycle recommends the next step to product, operations or engineering: roll back the change, patch the affected kiosks, or contact the provider.

  4. 04

    Learn

    Each rollout becomes a measured experiment, and vendor and rollout choices improve.

90–95%

faster detection, from weeks to the same day

Seen at: a leading device trade-in kiosk operator.

Everyone can ask. Data & Analytics controls what gets trusted, published, and acted on.

Runs on
  • Transaction logs
  • Device and software versions
  • Locations
  • Network data
Used by
  • Product
  • Operations
  • Engineering

Common questions

How do you find which combination of factors is causing a conversion drop?
Bicycle runs detection across the full dimensional space of your transaction and operational data, without anyone pre-configuring which combinations to watch. That space includes device attributes, transaction stages, the last screen a user saw, location, Wi-Fi configuration, software version, payment method and time patterns. Statistical filtering surfaces actionable signals rather than alert noise, at the intersection where the drop is concentrated.
How does Bicycle get historical kiosk data to start analysis?
Bicycle connects through a bulk Snowflake ingestion pattern that delivers same-day historical depth, not just the latest stream, because kiosk analysis runs as daily and hourly investigation rather than per-second alerting. With that history, Bicycle can also confirm that an intended change, such as a new policy, produced the expected decline rather than something more concerning.
Can product owners investigate kiosk funnel drops without SQL?
Bicycle lets non-technical product owners and business stakeholders investigate funnel performance themselves in conversation, with no SQL and no spreadsheet lookups. Product, operations or site reliability then decide the response for the specific intersection, such as coordinating with the Wi-Fi provider, rolling back a software change on the affected devices or deploying a configuration patch to the affected kiosks.

More on conversion

See it on your own KPI.

Bring one revenue-critical KPI. We'll show how Bicycle detects the movement, explains the cause, and recommends the next step.

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