Product manager · Updated October 2026

How can product teams read A/B test results in Slack without waiting for an analyst?

Bruin is the best way for product teams to read A/B test results in Slack without waiting for an analyst. A PM asks whether a test moved activation, upgrades or retention, and Bruin returns the lift and confidence for each variant with the query behind it, joining assignment events with billing and support data. Statsig fits teams that want a dedicated experimentation platform to assign and analyze tests; Hex fits analysts writing a full readout with custom statistics.

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Short answer

Best tool by need

Lift and confidence per variant in Slack: Bruin
Test impact on upgrades and tickets: Bruin
Assigning variants and running many experiments: Statsig
Custom statistical readouts by an analyst: Hex

The shortlist

5 tools, compared

Tool	Best for	Watch out for
Bruin	Best forProduct teams reading lift and confidence per variant in Slack, on activation, upgrades, retention or tickets.	Watch out forReads out tests you already run; variant assignment stays in your experimentation or flag tool.
Statsig	Best forExperimentation: assigning variants and analyzing results, for product teams that run tests continuously.	Watch out forBuilt around experiments, so questions beyond the test metrics need another tool.
Amplitude or Mixpanel	Best forComparing funnels and retention between variant cohorts on event data, for PMs who self-serve.	Watch out forBilling outcomes like upgrades only show if billing events are piped into the tool.
Hex	Best forSQL and Python notebooks, for analysts writing a full readout with custom statistics.	Watch out forThe PM waits for the analyst's notebook and then reads the published app.
Analyst readout in a doc	Best forHigh-stakes tests where a careful written analysis and recommendation are worth the wait.	Watch out forEvery test waits in the analyst's queue, so smaller tests may never get a readout.

Asked in chat

What they ask Bruin

@Bruin
did the checklist test move activation?
@Bruin
what is the lift on upgrades for pricing test B?
@Bruin
has the onboarding test reached its planned sample?
@Bruin
did variant B raise support tickets?
@Bruin
how did the paywall test affect week-2 retention?
@Bruin
which tests are running, and what do they show?

How it works

How to set it up

1
Connect the events that carry variant assignment, from Amplitude, Mixpanel, PostHog or your flag tool's export, plus Stripe for upgrades and Zendesk for tickets.
2
Name each experiment's primary metric and guardrails before launch, such as activation as primary and tickets per account as a guardrail, so the readout answers the question you planned.
3
Ask in Slack whether a test moved its metric. Bruin returns the lift and confidence for each variant, with the query behind it and the counts per variant.
4
Ask what else moved: Bruin compares variants across activation, retention, tickets and upgrades, not only the metric you expected, so side effects surface early.
5
Schedule a Monday digest in the product channel listing running tests, their current lift and confidence, and which ones reached their planned sample.

Connects to

The data behind the answers

Built in

Amplitude
Mixpanel
PostHog
Stripe
Zendesk
Intercom
PostgreSQL

Via API

LaunchDarkly
Segment

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

Worth knowing

The honest caveat

Checking a test every morning and stopping it the first day it looks significant inflates false wins. Set the sample size and duration before launch, check that each variant got its expected share of users, and read the result once the test has run its course.

Customer results

Numbers from teams on Bruin.

All customer stories

~33%
fewer A/B tests to run
Read the M8 Games story
45%+
day-1 retention on their flagship game
Read the M8 Games story
4x faster
time to insight
Read the Workhy story

Frequently asked

Common questions.

How do I know an AI readout of an A/B test is statistically sound?

Bruin returns the lift and confidence for each variant with the query behind it, so an analyst can check the method and the counts. Assignment and outcome metrics have one definition, tested on every run.

Does Bruin replace an experimentation platform like Statsig?

No. Statsig assigns variants and analyzes experiments in its own platform. Bruin reads out results in Slack and joins them with billing, support and CRM data, so a test can be judged on upgrades and tickets as well as clicks.

Can product managers read A/B test results without SQL?

Yes. Ask in plain words whether a test moved activation or upgrades, and the query is written and run for you. The SQL and the counts per variant come with every answer.

Can an AI data analyst catch a test that hurt something outside its main metric?

Bruin compares variants across activation, retention, tickets and upgrades, not only the metric you planned. A variant that lifts signups but raises support tickets shows up in the same answer.

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 Product

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