i-Invest — Full Case Study

Role: Product Manager

Duration: 2025 (short tenure, pre-Moniepoint)

Product: i-Invest — Investment Fintech Platform

Team Scope: Engineering, Design, Support

Background

i-Invest is an investment fintech platform giving retail investors access to financial instruments — savings products, money market funds, and more. I joined for a short period with a specific mandate: find ways to improve the existing version of the product, and improve user satisfaction. I was not brought in to build new features for the sake of it. I was brought in to make what existed work better, and to give users a voice inside the product.

The Problem

The product had existing users but no structured mechanism for understanding how satisfied they were or what was frustrating them. Issues were surfacing reactively — through support tickets, app store reviews, or word of mouth — rather than being caught proactively before they affected retention. CSAT was not being tracked systematically. Bug prioritisation was not grounded in user impact data.

Additionally, the product's investment catalogue was limited. Users who wanted access to money market funds — a highly requested instrument type — could not find them on the platform.

Discovery: Letting Users Tell You While They Are Still in the App

Rather than running external interviews or sending surveys separately, I designed the discovery mechanism into the product itself.

I introduced an in-app feedback survey — a lightweight CSAT prompt that appeared at relevant moments in the user journey, asking users to rate their satisfaction level with the product. The routing logic was deliberate:

• If the rating was low or negative — the user was routed to a support link to raise a ticket about their specific issue. This captured the problem while the frustration was fresh, and gave the support team structured input rather than vague complaints.
• If the rating was high or positive — the user was prompted to leave a review on the App Store or Play Store. This turned satisfied users into a source of organic social proof without requiring any additional effort from them.

This was not just a satisfaction measurement tool. It was a feedback funnel that separated users who needed help from users who were happy — and gave each group the right next step.

The complaints and issues captured through the low-rating routing were then analysed and used to prioritise bug fixes. Which issues appeared most frequently? Which were causing the most friction at which point in the user journey? This analysis drove the fix prioritisation — and it was more defensible than gut instinct or whoever shouted loudest.