Lessons learned from 17,000 user interviews

The title's a bit of a misnomer. I didn't conduct 17,000 user interviews myself, but I built a user interview tool that did - Resubscribe.ai.

I spent several months working on Resubscribe alongside two other friends. We took the product from idea to five paying customers before shutting it down in late 2024 due to lack of product-market fit.

Even though we shut it down, I wanted to share a bit about my experience and learnings from Resubscribe.

The product

Resubscribe was an AI user feedback tool that helped B2C founders gather continuous qualitative feedback at scale.

You can check out our Product Hunt launch video to learn a bit more.

As a previous app founder myself, I was solving a pain point I had experienced. When working on my previous app, Umi, we spent a lot of energy increasing conversion. There were many times where I just wanted to directly ask some of my app's users why they didn't want to pay.

So an AI feedback tool seemed like an obvious solution.

The pain point

It's fundamentally important that B2C founders gather feedback from users, but it's extremely difficult to get users to talk to you.

Think about it from your own experiences - when was the last time you saw an email in your inbox asking for feedback and you responded?

Often B2C founders try to solve this problem by offering incentives like $100 Amazon gift cards to get users on the phone. This solution can work, but doesn't create a continuous discovery loop.

Why did the product idea fail?

Ultimately, the product failed due to a lack of product-market fit with our ICP of early-stage founders.

We had pursued a weak beachhead market:

1. Many early-stage founders don't intuitively value user discovery. Just look at how vigorously accelerators like Y Combinator try to incept the idea "talk to your users" into their portfolio companies.
2. Some early-stage founders don't have the pain point, already having direct connections with their early customers.

Unintuitive learnings

Even though we failed to create a successful product, we did learn a bit about the psychology of user feedback in a B2C context.

Alternative feedback channels were "good enough"

One trend we saw from B2C founders was that they believed that their current feedback channels were good enough. Many of them were already:

• Collecting feedback via an open text field in a form
• Collecting feedback from support channels
• Collecting feedback from review channels (app stores, etc)

We received a clear signal that they didn't value the additional channel we were providing.

Feedback value has a ceiling

After we activated a few customers and started conducting interviews of their users, we noticed an interesting pattern. The value of the collected feedback hit a ceiling quicker than we had hoped.

Collecting 20-50 user interviews provided value in every interview, but when we collected 200 user interviews for the same customer, we found that most of the feedback was duplicative.

People love to share feedback with AI

LLMs are great at affirmation and making people feel heard. In the graphic here, you can see:

• On average, 83% of users who sent one message, sent at least one other message
• The number of responses peaks at 5 messages. That's a lot in B2C where users notoriously don't engage!
  • (The big spike at the start was due to lots of garbage data from free users)