App founder / growth lead · Updated October 2026

What tools do consumer app companies use for business analytics?

Bruin is the layer that ties a consumer app stack together: it loads subscriptions, attribution, events and store reports for every app into one warehouse model and answers across them in Slack. The usual stack has one tool per layer: RevenueCat for subscriptions, Adapty for paywall tests, AppsFlyer or Adjust for attribution, Amplitude or Mixpanel for product analytics, Firebase for event tracking, and App Store Connect and Google Play Console for store reports. Each answers inside its own layer; Bruin answers questions like CAC per paying subscriber that need several.

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Short answer

Best tool by need

Subscriptions across stores and web: RevenueCat
Install and campaign attribution: AppsFlyer or Adjust
Product events and funnels: Amplitude or Mixpanel
Warehouse pipelines across every app: Bruin
Questions that cross layers, in Slack: Bruin

The shortlist

6 tools, compared

Tool	Best for	Watch out for
Bruin	Best forAnswer and pipeline layer: loads subscriptions, attribution, events and spend for every app into one model and answers in Slack.	Watch out forKeeps RevenueCat and your MMP as sources; it does not replace in-app purchase handling.
RevenueCat	Best forSubscription layer: in-app purchase infrastructure with MRR, trial conversion and churn charts across stores and web.	Watch out forSubscription data only; acquisition cost and in-app behavior come from other layers.
Adapty	Best forPaywall layer: A/B tests on paywalls and pricing, with subscription analytics attached.	Watch out forResults are paywall-level; campaign ROAS needs attribution and spend joined.
AppsFlyer or Adjust	Best forAttribution layer: credits installs and in-app events to networks, campaigns and creatives.	Watch out forCounts follow each MMP's attribution windows, so they differ from store and network totals.
Amplitude or Mixpanel	Best forProduct analytics layer: what users do in the app, from onboarding funnels to retention and feature use.	Watch out forBilling truth sits in the stores, so revenue events here can drift from payouts.
App Store Connect and Google Play Console	Best forStore layer: official downloads, proceeds, refunds and subscription reports from Apple and Google.	Watch out forTwo separate consoles with different report formats, timing and currencies.

Asked in chat

What they ask Bruin

@Bruin
how do RevenueCat proceeds compare with App Store Connect?
@Bruin
which app has the best trial-to-paid this month?
@Bruin
what is CAC per paying subscriber by network?
@Bruin
do users who finish onboarding renew more often?
@Bruin
what share of new subscribers came from the web?
@Bruin
how many refunds did Apple issue last week?

How it works

How to set it up

1
List the owner of each layer per app: subscriptions, paywalls, attribution, events and store reports, and note which one finance uses for revenue.
2
Connect RevenueCat or Adapty, App Store Connect, Stripe for web, AppsFlyer or Adjust and Amplitude or Mixpanel, loading into BigQuery, Snowflake or Bruin's warehouse.
3
Build one user table that links app user IDs, store transaction IDs, web customer IDs and attribution IDs, so every layer joins on the same person.
4
Define revenue, trial-to-paid, renewal and CAC per subscriber once across apps, then compare with RevenueCat charts for one closed month.
5
Move cross-layer questions to Slack and schedule a weekly portfolio brief that ranks each app on trials, conversion, renewals and spend.

Connects to

The data behind the answers

Built in

RevenueCat
Adapty
App Store Connect
Stripe
AppsFlyer
Adjust
Amplitude
Mixpanel
Google BigQuery

Via API

Google Play Console
Firebase

Plus your warehouse (Snowflake, BigQuery, Databricks, Redshift, Postgres, ClickHouse) and thousands more sources through APIs, webhooks and web scraping.

Worth knowing

The honest caveat

Store reports, RevenueCat and Stripe convert currency and record refunds on their own schedules, so revenue never matches to the cent. Choose one source of truth for revenue per platform before wiring the stack together, and document it next to the definition.

Customer results

Numbers from teams on Bruin.

All customer stories

20+ apps
on one platform, run by one data engineer
Read the NativeMinds story
Up to 5x less
cost than their Fivetran stack
Read the NativeMinds story
Daily → hourly
Slack alerts, reporting to executives in near real time
Read the NativeMinds story

Frequently asked

Common questions.

Why don't App Store Connect, RevenueCat and Stripe revenue numbers match?

Store reports arrive after fees, refunds and taxes on their own schedule, RevenueCat and Stripe count transactions their own way, and currencies convert at different rates. Bruin keeps one rule per platform and shows where the sources differ.

How does Bruin keep subscription revenue consistent across apps and platforms?

Revenue, trial-to-paid and renewal are defined once and applied to every app, store and web checkout. Each answer shows the query and sources, and checks run on every load, so a late store sync is flagged instead of skewing the number.

Does Bruin replace RevenueCat or the MMP in a consumer app stack?

No. RevenueCat keeps handling in-app purchases, and AppsFlyer or Adjust keep attributing installs. Bruin reads their data and answers the questions that need several layers at once, like CAC per paying subscriber.

Is subscriber data safe in a consumer app analytics stack with Bruin?

Yes. It is SOC 2 Type 2 attested and ISO/IEC 27001:2022 certified, subscriber data stays in your warehouse, and access follows the roles you set. Bruin never uses that data to train AI models.

Keep reading

What is Bruin?

Bruin is an open-source, AI-powered data platform. One project covers the whole path from source to answer: data ingestion (with ingestr), SQL and Python transformations, quality checks, column-level lineage, a data catalog and scheduling. On top of that data, an AI data analyst answers questions in Slack, Microsoft Teams, Google Chat, WhatsApp, Discord, Telegram, email or the browser, and builds dashboards from a prompt.

Pipelines are plain SQL, Python and YAML files in your own Git repository, and the work runs inside your own warehouse: Snowflake, BigQuery, Databricks, Redshift, Postgres, ClickHouse, DuckDB and others. The Bruin CLI is open source under Apache 2.0 and runs on a laptop, in CI or on-prem. Bruin Cloud is optional and adds scheduling, the AI data analyst, governance, SSO and audit logs.

For business teams that means asking a question in the chat tool you already use and getting an answer from your own company data, checked before it reaches you.

Replaces: Separate tools for ingestion, transformation, orchestration, data quality, cataloguing and BI
Open source: Bruin CLI under Apache 2.0; ingestr source-available under the Functional Source License
Your data: Stays in your warehouse; pipelines stay in your Git repository
Security: ISO/IEC 27001:2022 certified and SOC 2 Type 2 attested
Used by: Data teams at Lessmore, StarBerry, NativeMinds, SCG, M8 Games and Fabrikatör

Why Bruin for Consumer apps

Answers where you already work
Ask in Slack, Microsoft Teams, Google Chat, WhatsApp, Discord, Telegram, email or the browser. There is no new tool to learn and no ticket to file with the data team.
Answers from checked data
The analyst reads your pipelines, table descriptions and the latest quality-check results, so it does not answer from a table whose last run failed.
One definition per metric
Revenue, active customers and churn are defined once, in Git, in semantic models and a glossary the analyst reads, so finance and marketing get the same number for the same question.
Built on a real data platform
Bruin also loads and models the data. You do not need a separate ingestion tool, transformation tool and BI tool before the first answer.

How it works

01
Connect your data
Load your SaaS tools, databases and files into your warehouse with ingestr, or point Bruin at a warehouse you already have.
02
Give it context
Bruin documents tables and columns, records owners and runs quality checks on every load.
03
Ask
Ask in Slack, Teams or the browser. Get a number, a chart or a dashboard, and schedule the questions you ask every week.

How the AI data analyst works Read the docs