---
title: Markin + LaunchDarkly
url: https://markin.ai/integrations/launchdarkly
category: Experimentation and feature flags
description: Connect Markin to LaunchDarkly: execute growth hypotheses through feature flags and read each one against a holdout.
---

# Markin + LaunchDarkly

> Feature flags as an execution channel for growth hypotheses.

**Job:** Roll out product treatments per customer, with a measured verdict.

A Markin and LaunchDarkly setup uses flags as the execution surface for product-side decisions: Markin decides the treatment per customer, LaunchDarkly delivers it, and the outcome is read against a holdout.

Some of the largest ARPU changes are product changes, not messages. Treating a flag as a channel puts them in the same queue and under the same measurement discipline.

## What can you ask Markin to do in LaunchDarkly?

- How do I target feature flags by customer value?
- Can LaunchDarkly deliver a personalised experience per user?
- How do I measure the revenue impact of a feature rollout?
- How do I run a product experiment with a proper holdout?

## What Markin does in LaunchDarkly

- Read flag definitions and current targeting rules.
- Decide the treatment per customer segment.
- Write targeting membership for the chosen treatment.
- Keep a randomised holdout excluded from the rollout.
- Track exposure events keyed to the decision ID.
- Read revenue and retention outcomes back per treatment.
- Recommend rollback when the read turns negative.
- Report incremental revenue per feature.

## The growth work behind LaunchDarkly

### Read the estate

- Rebuild the revenue baseline from orders, payments, refunds and credits.
- Derive behavioural features from event history without a new pipeline.

### Find where revenue is leaking

- Watch ARPU by cohort, plan, market, channel and tenure for drift that clears noise.
- Detect churn risk building in a segment before it shows in the monthly number.

### Explain why

- Separate mix effects from real behaviour change.
- Rank drivers by how much of the movement each one accounts for.

### Write hypotheses worth funding

- Attach the evidence and the assumption each one depends on.
- Rank the portfolio by expected value, not by seniority.

### Decide per customer

- Cap discount and margin exposure at the level finance agreed.
- Attach a plain-language reason and an expiry to every decision.

### Execute in the tools you already run

- Open the work as a draft for approval where a human should sign off.
- Recompute idempotently so a replay never double-sends.

### Prove it caused the revenue

- Refuse to call a result that has not cleared the evidence standard.
- Publish the readout in the same place for every experiment.

### Retire, govern and hand over

- Retire programmes automatically when they stop beating control.
- Re-test assumptions that decay, such as price sensitivity and seasonality.

Full catalogue: [Everything a growth team does, running every day.](https://markin.ai/growth-work)

## Activation patterns

- **Decision API.** The surface asks Markin for a decision at render time and receives one action plus its reason, with a defined fallback.

## What Markin reads from LaunchDarkly

- Flag definitions and exposure logs

## What Markin writes back

- Per-user targeting decisions with holdouts preserved

## Used together with

- [optimizely](https://markin.ai/integrations/optimizely)
- [amplitude](https://markin.ai/integrations/amplitude)
- [web-and-mobile-sdk](https://markin.ai/integrations/web-and-mobile-sdk)
- [snowflake](https://markin.ai/integrations/snowflake)
- [stripe](https://markin.ai/integrations/stripe)
- [mixpanel](https://markin.ai/integrations/mixpanel)

## Related

- [Solution: revenue-expansion](https://markin.ai/solutions/revenue-expansion)

## FAQ

**How does Markin connect to LaunchDarkly?**

Decision API. The surface asks Markin for a decision at render time and receives one action plus its reason, with a defined fallback.

**What does Markin read from LaunchDarkly?**

Flag definitions and exposure logs.

**What does Markin write back into LaunchDarkly?**

Per-user targeting decisions with holdouts preserved

**Do we have to move our data to Markin?**

No. Markin reads from your warehouse, product events and operational systems in place, on your compute, under the access rules your data team already set. Nothing is copied into a separate customer base and there is no vendor-side profile store to migrate off later.

**Does Markin replace our engagement platform or CDP?**

No, and it should not. Your engagement platform keeps the channel, the templates, the deliverability and the governance. Your CDP keeps identity and consent. Markin adds the layer neither has: deciding which action deserves to exist for each customer, and proving it against a holdout.

**What if the system we use is not listed?**

The four activation patterns cover almost everything: attribute write-back, triggered event, decision API and direct surface rendering. Any system that exposes an API, accepts a table, or can read a warehouse column can receive decisions. New connectors are built during deployment, typically in days.

Source: https://markin.ai/integrations/launchdarkly