Case study

When I joined, they had just launched to the public after a year testing the product with hundreds of beta users (later discovered to mostly be friends, family and investors but more on that later).

1 month after launch we had only a handful of users…

Both paid traffic campaigns and our TikTok influencers had delivered very little volume. We have 3 months left to hit our 10,000 MAU target for funding.

Fortunately, I had introduced a culture of weekly user testing, mostly async using Maze to balance speed with validation. Although unmoderated isn’t as effective as f2f it shed a light on some of the riskiest assumptions that had never been challenged which was critical.

Primarily, the critical assumption that people would want to ask for help saving towards their financial goals.

Even more crucially, if we were to hit our growth targets with an R Number of 8, the assumption that people would want to ask their friends for financial help.

In short, the answer was a hard “No” with over 48% of people saying they had never asked for help from family.

When shown our product, 80% said they would never “share” a financial goal.

All was not lost however.

Some of the devs along with our in-house certified financial planner had been creating an AI financial advisor Slack-bot planned to live within the hyve app to help users achieve their financial goals quicker.

Based on the positive feedback it had received from investors and users we had tested it on + shifting appetite of VCs to AI startups, the decision was made to turn the AI Chatbot into THE product.

However, we still had the same target date for raising the next round…

We knew we had to learn from our mistakes so I proposed starting with testing positioning and problem awareness and problem solution fit.

I pushed out some quick tests on Maze followed by survey in Pollfish of 2,500 who matched our target demographic. We tested messaging and which problems we should look to tackle first (debt consolidation, investing or more broadly financial advice).

With the new focus on financial advice, a calmer, more trustworthy visual brand identity felt appropriate. With a shift in focus from Gen Z to Millenials, the new target audience was older, higher net worth individuals already actively seeking out financial advice. The brand needed to speak to this.

The hard work then began of our to transition our product and existing users towards the new experience. In order to utilise our existing customer base to test the efficacy of the financial plan and advice provided by the chat-bot, we decided on a phased approach:

• Phase 1: A floating chat button provided the entry point to a casual chat about finances to build a plan with the rest of the product remaining the same

• Phase 2: The dashboard with the users financial goals got merged into a financial plan with their goals listed as custom financial goals.

• Phase 3: We refunded users any balances they had in the app and the onboarding became the chat in order to build the financial plan.

This pivot came at a critical time in the upswing of interest in AI products by VCs and we’ve now managed to raise another $5M to help us on our way.