Wallie

Wallie (formerly known as Rewards Wallet) is a fintech for hyperlocal deals. Wallie allows merchants to offer personalized deals to users based on their shopping patterns to drive higher foot traffic and sales. Wallie also smart suggests users which credit cards to use for any transaction in order to maximize their rewards points. The best part – Wallie can do this all without needing any credit card details from the users! Wallie is currently part of Y-Combinator’s Startup School Program, that picks select companies for mentorship and advice.

I would encourage you to check out the FAQ’s section on Wallie’s website here – http://getwallie.com

Background

Wallie started off as Rewards Wallet rising from my personal pain point of lack of apps to track credit card rewards and cashback. I carry several credit cards in my wallet and it was difficult to track what rewards programs they each offered in order to get maximum benefits out of those programs e.g. 3% cashback on groceries by Amex BlueCash, 5% cashback on department stores by Discover, $19.99 comprehensive car rental insurance per booking on Amex and tens of others. I was looking for an app that integrated easily with my cards but didn’t ask for my card details. I wanted the ability to opt-into sharing my financial transaction details rather than that being the only way. All the apps I came across asked for that up front. Plus, they didn’t really offer proactive location-based alerts, that would suggest the best credit card when I am near a store I had specified as “interest point”.  Even the ones that offered something remotely similar didn’t do it well. So I decided to research whether there was an opportunity gap in the market for such a product.

The Process (in somewhat brief)

Following is a list of things we did to design and build Wallie

  • Market Research
    • Usage stats research on credit cards in use, user demographics, spending patterns, trends across demographics
    • Competitive analysis of similar products on the market using Multi Attribute Utility Theory (MAUT)
  • User Interviews
    • Crafting user personas based on the above research
    • Conducted several hour long interviews with at least a handful of users from each of the personas
    • Online survey based on the personas
  • Storyboarding
    • Brainstorming for “Hot Ideas” based on the user interviews and opportunity gaps in the market
    • Storyboarding uses cases discovered through user interviews
  • Wireframing
    • Based on the storyboards, created product wireframes using Balsamiq
  • User Feedback
    • Went back to the users we interviewed with wireframes to ask for their feedback and more input
  • Vision Document
    • Based on the understanding so far in the process, crafted a vision document for Wallie
  • Medium-Fidelity mocks
    • Designed mocks based on wireframes and user feedback
  • User Feedback
    • Another round of user feedback, this time on the mocks
  • Product Requirements Document
    • After establishing a good understanding of user’s needs and workflows, created a detailed product requirements document outlining the technical and non-technical features of defined product
  • Engineering Estimates and Schedule Plan
    • Based on the PRD, created a detailed list of user stories along with their engineering estimates and scopes
    • Created release plans and product roadmap breaking it down into MVP, Fast Follow and Future Releases
  • Marketing and Sales Strategy (including defining success metrics)
    • Identifying partners, customers and potential revenue model for Wallie
    • Established growth and expansion strategy with detailed resource allocation and timeline
  • Launch Plan
    • Devised a detailed launch plan based on product roadmap, marketing and sales timelines

Some examples

Medium-Fidilety Mocks