Product DesignUX ResearchUI Design

Savely

An iOS A/B test: does saving alongside other people actually make people save more?

Role

UX Designer

Timeline

Nov 2022 – Dec 2022

Tools

Figma, Maze, Octalysis framework

−6–8%

Savings setup completion under gamification — why I reverted

−7%

First-week activation with leaderboards + family nudges

Flat

Retention — no lift despite the added social pressure

A/B

2 variants: control vs. Octalysis-driven gamification

Overview

Savely is a micro-savings app that sets money aside as people go about their day, without them having to think about it. This study tested one idea with an A/B test: would gamification — leaderboards and family-based nudges — get people to save more through social motivation?

Challenge

North Star

A first-time user trusts Crumbs with recurring money because nothing on screen makes them hesitate.

The study aimed to understand users’ saving habits and how they manage their money. We used a simple questionnaire covering four areas: mobile usage patterns, mobile-banking familiarity, money-management behavior, and awareness of their children’s spending. The goal was to get a high-level view of how they handle their finances.

Key findings

Users need a clearer picture of their cash flow—how income and expenses change over time.
Saving is a priority, and they want more control over family-related expenses.

Outcome & Consequence (critical learning)

The goal was to determine whether an incentive-driven app design could encourage users to save more. I developed two versions: Version A (Control A), a standard design, and Version B (Gamified Concepts), which incorporated gamified, incentive-driven elements. I applied the Octalysis framework, which is structured around eight core drives that influence user behaviour. These drives help shape the gamified elements to evoke specific emotions, guiding how users engage with the app.

What I tested

Leaderboards comparing savings progress
Family-based nudges encouraging collective saving behavior
Messaging framed around shared goals and visibility

Primary metrics

Savings setup completion
First-week activation

Decisions & Trade-offs

Designing the experiment, not just the screens.

Testing a social-accountability idea cleanly meant trading signal strength for privacy and rigor. Each row is the call, what it cost, and why it held up.

DecisionWhat it costWhy it was worth it

Make social accountability the core bet

Sharing financial behavior raises real privacy concerns.

The test targets the one lever most likely to move savings behavior, not a safe tweak.

Clean A/B split on shared progress

Smaller samples, so significance comes slower.

Results are trustworthy enough to actually ship on — rigor over speed.

Group visibility stays opt-in

Weakens the social signal the test measures.

Consent is preserved, so the feature can survive contact with real users and reviewers.

Gentle nudges over binding pledges

Softer behavior change for far lower risk.

Users are not punished for slipping, so drop-off from guilt stays low.

Measure consistency, not totals

Undervalues large but irregular savers.

The metric rewards the habit the product is trying to build, not one-off deposits.

F01

Sustainability, folded into the trip

Problem

Green features usually live in a tab nobody opens, so eco-driving stays an afterthought instead of shaping the trip.

Decision

Carbon, charging and Green Credits sit inside the live trip and vehicle views — footprint tracked as you drive, charging found in a tap, and credits earned for every eco-mile.

Tradeoff

More on the trip and vehicle screens to keep legible, and a rewards economy to maintain behind Green Credits.

Outcome

Target: eco-driving becomes the default path, not a setting people have to go looking for.

F02

Pricing you can trust

Problem

Payment is where rental flows lose people — a single moment of doubt about the total reads as a hidden fee and turns into a drop-off.

Decision

Pricing, billing terms and confirmation are cleanly separated: a live cost breakdown, protection and add-ons chosen explicitly, and a running total that stays legible at every step.

Tradeoff

More steps between choosing a car and paying, in exchange for a total the user always understands and controls.

Outcome

Target: fewer checkout drop-offs, with the final price never a surprise.

F03

A guided rent-or-lease choice

Problem

Renting versus leasing is the hardest call a new user makes, and a bare side-by-side comparison offloads all of that decision fatigue onto them.

Decision

A short guided questionnaire reads usage and priorities, then recommends renting or leasing with a clear match score and the exact reasons why — a decision made with the user, not dropped on them.

Tradeoff

A recommendation lands before the user has weighed both options themselves, so every result shows its reasoning and a one-tap way to compare.

Outcome

Target: less decision fatigue at the first big choice, and more confident bookings.

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