Mobile-app analytics

Mobile-app analytics is setting up the collection and meaningful display of data about user behavior: which screens they view, which actions they take, where they get stuck and leave, how they return (retention), whether they reach target actions (conversions). We connect suitable tools (e.g. Firebase/GA4 or analogs), mark key events, build clear reports. Honestly about the main thing, this is key: analytics shows WHAT is happening but does not do the work for you — by itself it does not fix problems or raise metrics. Value appears when decisions are made by data and changes are introduced; without that, pretty dashboards are useless. We honestly aim for 'data → conclusions → actions' rather than collection for its own sake. Honestly about privacy and data gaps, this matters: data collection requires user consent (a privacy policy, consent banners), and on iOS the App Tracking Transparency system asks permission to track — many refuse, so data for such users will be limited or aggregated. This is a normal consequence of privacy, not a setup error — we honestly factor it into interpretation rather than passing off incomplete data as the full picture. Honestly about accuracy: mobile analytics is approximations and trends (samples, delays, discrepancies between tools), not accountant-precise figures. Honestly about the effect: we help understand the product and make decisions, but metric growth depends on what you do with those decisions. Honestly about access: access to the app code and analytics accounts is needed. An important boundary: this is behavior analytics; install-source attribution — 855, screenshot A/B — 848. Picture this: instead of 'we do not know what is wrong in the app' — a clear picture of behavior on which decisions are built. The base price starts from 20,000 ₽ (depends on the number of events and platforms).

• App decisions are made by feel, without data.
• It is unclear where users get stuck and why they leave.
• Analytics is not connected or collects junk without tagging.
• No understanding of retention and real conversions.

• Connecting analytics (Firebase/GA4 or analogs)
• Tagging key events and funnels
• Reports on screens, actions, retention, conversions
• Consent/privacy setup (a legal and store requirement)
• Honest interpretation (accounting for gaps due to iOS ATT/refusals)
• Aiming for 'data → conclusions → actions'
• A link with attribution (855)
• Handover and review of reports with you

• A clear picture of user behavior in the app
• Drop-off points, retention and conversions are visible
• Decisions by data, not by feel
• Honest accounting for data gaps (privacy/iOS)

• We define key questions and events; collect access
• We connect analytics, tag events, set up consent
• We build reports, honestly interpret with gaps in mind with you

• Will analytics improve my metrics by itself?

  No, and that is honest: analytics shows what is happening but does not fix problems or grow metrics by itself. It is a tool for decisions. Growth comes when conclusions are drawn from data and changes are made to the product. We set up analytics so it leads to actions rather than becoming pretty but useless dashboards.

• Why is data incomplete for some users?

  Due to privacy, and that is normal. Collection requires user consent, and on iOS the App Tracking Transparency system asks permission to track — many refuse, so their data is limited or aggregated. This is a consequence of privacy rules, not a setup error. We honestly factor these gaps into interpretation rather than pass off incomplete data as the full picture.

• Are the figures in analytics absolutely exact?

  No, honestly: mobile analytics is approximations and trends, not accountant-precise figures. Samples, delays, discrepancies between tools are possible. This is enough to see patterns and make decisions, but treating every figure as absolute truth is unwise. We work with trends and honestly note the margins of error.