Auto-Reload for a Wallet Users Kept Leaving
Accrue × Westside Market • 2025
Users claimed the discount, made one purchase, and disappeared. I studied 15 retention loops and designed auto-reload around what worked.
Jump to solutionProduct
Westside Market wallet (Accrue embed SDK)
Team
1 Designer, 1 PM, Product leadership review
Timeline
Sep 2025 · One-week design sprint
Role
Research, Strategy & Design
THE CLIENT
Accrue × Westside Market
Accrue builds white-label co-branded wallet and loyalty platforms for enterprise retail brands. Westside Market (WSM) is a regional grocer that uses the Accrue wallet for loyalty and payments.
By September 2025, the WSM wallet had run for months. Users claimed the welcome discount, made one purchase, then vanished. The product team needed a way to bring them back without touching the reward economics. The fix had to come from design and behavior, not money. The team handed me a one-week sprint to design auto-reload, the retention tactic with the most leverage. To do it right, I started with the research.
PROJECT GOALS
- 1Design auto-reload as the headline retention tactic
- 2Pull users from first purchase to second without changing the reward economics
- 3Surface the cost of inaction without crossing into dark patterns
- 4Recommend a posture, not just options
THE PROCESS
Research
Days 1–3Studied 15 consumer apps that retain users (Starbucks, Duolingo, Sephora, ClassPass, Nike SNKRS, and ten others) across 7 dimensions each. The repeating patterns gave me a formula to design against.
Design & Recommendation
Days 4–7Designed three auto-reload directions with explicit tradeoffs and two companion patterns. Recommended the trustworthy angle as the primary surface, with selective urgency layered on top.
Research
Days 1–3Studied 15 consumer apps that retain users (Starbucks, Duolingo, Sephora, ClassPass, Nike SNKRS, and ten others) across 7 dimensions each. The repeating patterns gave me a formula to design against.
Design & Recommendation
Days 4–7Designed three auto-reload directions with explicit tradeoffs and two companion patterns. Recommended the trustworthy angle as the primary surface, with selective urgency layered on top.
THE PROBLEM
Users treated the wallet like a one-time discount.
Onboarding worked. Users claimed the welcome reward, made a first purchase, then disappeared. Between purchases, they forgot the wallet existed. I needed to give them a reason to come back, and auto-reload was the tactic with the most leverage.
Activity sparkline: gap between sessions, employee vs regular user overlay
~15 days between actions
Actions in the wallet averaged about two weeks apart, and most of that activity came from employees beta-testing. Regular users opened the app to buy something, then closed it.
Funnel: signups → first purchase → second purchase drop-off
Discount hunt, not habit
The welcome reward got users in. Nothing kept them. After the first purchase, users treated the wallet like a coupon and closed it.
Constraint diagram: locked economics, open design surface
No changing the reward schema
Economics stayed locked. Whatever I designed had to work inside existing earn rates and rules. My only lever was attention.
Re-entry map: 5 moments after first purchase, with sentiment labels
Tap dots to explore
Five moments where users might come back
I mapped every re-entry point: balance increase, purchase complete, reward expiry (30/7/1 day), drops or campaigns, purchase cancelled. Each carried its own emotional state and needed its own treatment.
THE RESEARCH
15 apps. Three patterns that mattered most.
I studied 15 consumer apps that pull users back, dissected across 7 dimensions each. Three patterns stood out as directly applicable to auto-reload. The full grid is in the gallery.
Starbucks Rewards
Starbucks Rewards: stars expiry + bonus challenge screens
Stars expire after 6 months. Bonus challenges ('buy 3 drinks in 5 days for 75 stars') create urgency windows without crossing into pressure.
Duolingo
Duolingo: streak counter + 'streak freeze' notification
No tangible rewards. Pure streak psychology. The Duolingo streak is loss aversion stripped to one screen.
ClassPass
ClassPass: credit balance + expiry warning
Engineered sunk-cost retention. Users paid for credits, the credits expire monthly, and users book classes to avoid the waste.
One pattern showed up across the 15 apps. I distilled it into a four-part formula and used it to design auto-reload: [Trigger] + [Auto-action] + [Loss frame] + [Perk]. The formula outlived the sprint and became the team's retention reference.
AUTO-RELOAD: THE DESIGN
Three postures. Two supporting patterns. One recommendation.
I translated the formula into three auto-reload directions, each with explicit tradeoffs. Stakeholders picked a posture instead of arguing about copy. Two companion patterns ship alongside the auto-reload surface itself.
Trustworthy angle: auto-reload settings + first-load success screen
Safe & Trustworthy (Starbucks / Apple Cash model)
'When your balance drops below $10, we'll reload $25.' 'Never get stuck at checkout with an empty wallet.' Every reload includes a guaranteed prize. Users set it once. The behavior runs on its own from there.
Aggressive angle: countdown banner + 'lock in bonus' modal
Aggressive Loss Aversion (TEMU-inspired)
Flashing alert: 'Don't risk missing out, your balance is low.' Timer pressure: 'Set auto-reload in the next 24 hours to lock in your bonus prize.' The angle leans on urgency and FOMO. Converts faster short-term, risks feeling manipulative.
STEP 1 OF 3
Select payment method
Gamified angle: spin wheel + 'bonus spins for auto-loaders'
Gamified (Dunkin / Grab model)
Reload screen doubles as a spin-the-wheel. 'Skip reload = skip your chance to spin.' Auto-loaders get 2x spins each month. The angle adds variable reward, the slot-machine pattern in a softer skin.
Recommendation summary card: trust spectrum with chosen zone highlighted
The recommendation
I recommended shipping the trustworthy angle as the primary surface. Layer urgency tactics (expiry countdowns) on top where they inform a user. For a wallet that holds real money, the trust position beats the conversion play.
With/Without comparison card showing money state
Supporting pattern: 'With vs Without' card
Two states side by side at the decision moment. With Load: $25 → $26.25. Without Load: $25 → still $25, with a bold red 'Lost: $1.25'. Surfaces the cost of inaction in concrete dollars, without crossing into manipulation.
Dashboard module: cumulative missed-rewards counter
Supporting pattern: cumulative missed-rewards counter
Dashboard module: 'You've missed $18.40 in rewards this month.' Missing value over time hurts more than missing once. The counter updates weekly so the number moves.
ARTIFACTS
What the sprint produced.
Research grid, three auto-reload angles, two supporting patterns. The artifacts the team kept using after the sprint.
15-app research grid, full-width comparison
Research grid: 15 apps × 7 dimensions, the team's retention reference
Trustworthy auto-reload setup screen
Trustworthy angle, the recommended posture
With/Without comparison card mockup
With/Without supporting pattern
Cumulative missed-rewards dashboard module
Cumulative counter supporting pattern
Trust spectrum: tactics plotted from informative to manipulative
The line I drew between informing users and exploiting them
THE IMPACT
3
Auto-reload directions
Safe & Trustworthy, Aggressive Loss Aversion, Gamified. Three postures with explicit tradeoffs so stakeholders could pick the brand position, not argue about copy.
15
Apps researched
Each dissected across 7 dimensions. The comparison grid became the team's retention reference.
Trustworthy
The recommendation
I recommended shipping the trust posture, with selective urgency tactics layered where they inform a user. The conversion math pointed elsewhere; the product's actual brand pointed here.
Auto-reload is in development on the trustworthy angle. The 15-app research and behavioral formula became our standing retention reference, applied to the work that came next.
LOOKING BACK
Research produces value through specifics.
'Use loss aversion' is not a design direction. 'Cumulative dollars missed, updated weekly on the dashboard' is a design direction. The sprint mattered because the output was specific.
I chose the trust position over the conversion math.
TEMU's aggressive angle converts faster short-term. Starbucks's trust model holds longer. For a wallet that holds real money, I picked the trust position. The recommendation matched the product's actual brand and the user's expectations of a financial tool.
I drew a line on the trust spectrum.
Loss aversion can become manipulation. The sprint needed an explicit line for when urgency informs users versus exploits them. I plotted the tactics on a spectrum so later decisions had a reference instead of a vibe.
WHAT I'D DO DIFFERENTLY
I'd A/B test the three angles instead of recommending one from research alone. Even a directional read in this audience would have replaced my inference with data.
LOOKING AHEAD
Auto-reload is in development on the trustworthy angle. Next sprints: instrument the re-entry points, ship the cumulative-missed-rewards counter, test the With/Without comparison card at checkout.