---
title: How Young made onboarding an always-on experimentation programme
url: https://markin.ai/customers/young
company: Young
industry: Fintech
result: +31% activation
published: 2026-08-31
description: Young turned onboarding into an always-on experimentation programme with Markin: +31% activation on treated cohorts, measured against a randomised holdout.
---

# How Young made onboarding an always-on experimentation programme

> Young's onboarding converted signups into accounts but not into active customers. Markin turned activation into a continuous experiment: a per-customer path to the first meaningful transaction, tested against a randomised holdout, lifting activation 31% on treated cohorts.

## Results

- **+31%**, Activation on treated cohorts. vs randomised holdout, full measurement window
- **+16%**, 90-day ARPU. activated customers reaching a second product faster
- **6 days**, From hypothesis to readable result. previously a quarterly roadmap item

## At a glance

| Field | Detail |
| --- | --- |
| Industry | Fintech / consumer banking |
| Markets | Europe |
| Base | High-volume consumer signups |
| Motions live | Activation, product cross-sell, dormancy, KYC recovery |
| Stack | Warehouse, product events, Braze, in-app, support |
| First measured lift | Week 5 |

## The challenge

Young was very good at acquisition and much less good at what happened next. A large share of new customers finished signup, funded nothing, and were quietly gone within a month. The funnel looked healthy right up to the point where revenue starts.

Onboarding was a single sequence for everyone, changed a few times a year, and every proposed improvement competed for the same engineering roadmap. By the time a variant shipped, the cohort it was designed for had already lapsed.

Nobody could say which step actually caused activation, because nothing was measured against a control.

## What Markin did

- **Data & context, The first 30 days, event by event.** Markin reads signup, KYC, funding, card and transaction events as they happen, so the activation state of every customer is known in hours rather than in a monthly cohort report.
- **Intelligence, Activation as a hypothesis space.** Instead of one onboarding flow, the intelligence layer proposes and prioritises many small hypotheses about what unblocks this specific customer: a funding method, a missing KYC document, a first use case worth the effort.
- **Action, Continuous tests, retired automatically.** Each hypothesis runs as a live experiment against a holdout through Braze, in-app surfaces and support. Winners scale, losers are retired without a roadmap discussion.

## Experiments that ran

| Hypothesis | Segment | Action | Channel | Measured lift |
| --- | --- | --- | --- | --- |
| Funding friction, not motivation, blocks activation | Signups with completed KYC and no funding after 48h | Funding method matched to the customer's bank, one tap | Push + in-app | +21% funded accounts |
| A first use case beats a feature tour | Funded accounts with no transaction in 7 days | Single suggested first transaction based on declared intent | In-app | +31% activation |
| KYC drop-off is recoverable if we act inside the hour | Abandoned verification sessions | Contextual help on the exact failing step, not a generic reminder | In-app + support | +18% KYC completion |
| The second product should follow behaviour, not tenure | Active customers showing a savings pattern | Second product offered at the behavioural trigger | In-app + email | +16% 90-day ARPU |

## How it rolled out

1. **Week 0, Events connected.** Signup, KYC, funding and transaction events connected read-only. Historical cohorts replayed to size the activation gap.
2. **Week 2, First experiments live.** Three activation hypotheses running simultaneously, each against its own holdout.
3. **Week 5, First readable lift.** Activation reads +31% on the treated cohort. Two hypotheses are retired in the same week.
4. **Week 12, Always-on programme.** Activation, dormancy and cross-sell running continuously, with new hypotheses entering weekly instead of quarterly.

## In their words

"Onboarding stopped being a roadmap item. It's a programme that improves every week without asking engineering for anything.", Head of Growth, Young

## Stack Markin worked with

- [bigquery](https://markin.ai/integrations/bigquery)
- [amplitude](https://markin.ai/integrations/amplitude)
- [braze](https://markin.ai/integrations/braze)
- [web-and-mobile-sdk](https://markin.ai/integrations/web-and-mobile-sdk)
- [intercom](https://markin.ai/integrations/intercom)

## FAQ

**Does running this many experiments require engineering time?**

Not after the connection. Markin composes and launches each variant through the channels already in place, so a new hypothesis costs a review, not a sprint.

**How was the Young result measured?**

Every decision carries a randomised holdout. The lift quoted is the difference between the treated cohort and that control group over a full measurement window, not a before-and-after comparison.

**Did they have to move their data to Markin?**

No. Markin reads from the warehouse, CDP and product events already in place. Nothing is copied into a new system and nothing is locked in.

**Which team ran it day to day?**

The existing growth team. Markin does the analytical work, a fintech team of that size would need a dedicated data-science pod to produce, and routes every action into the tools they already operate.

**How long until the first measurable lift?**

A first motion goes live in about 30 days and the first statistically readable result lands inside the following six weeks. Scaled impact typically reads at 90 days.

## Related

- [Solution: growth-optimization](https://markin.ai/solutions/growth-optimization)
- [Industry: fintech](https://markin.ai/industries/fintech)
- [All customer stories](https://markin.ai/customers)

Source: https://markin.ai/customers/young