Backflip · Real estate lending

Saving the product everyone wanted to cut

Analyzer was expensive and slated for removal. It was also the single strongest retention driver in the product — 60% Month 1 retention against 9% for everyone else. I kept the product and removed 90% of its cost instead.

Role

Head of Product (previously Senior Product Manager)

Scope

Analysis, executive persuasion, vendor renegotiation, scope reduction

The problem

Analyzer — the deal analysis tool real estate investors used to evaluate properties — carried a large third-party data bill. In a cost review it read as an obvious cut: high spend, no direct revenue line attached to it.

The case for cutting it was entirely about the cost side of the ledger. Nobody had put a number on what it was worth.

What I did

I ran the retention and conversion analysis for Analyzer users against everyone else. The gap wasn’t marginal:

• Month 1 retention: 60% vs 9% — 6.7×
• 85% more loan applications per user
• Conversion: 23% vs 17%

That reframed the decision. The question was no longer “is this feature worth its bill” but “are we willing to remove the strongest retention mechanism we have to save a line item” — and once framed that way, the answer was obvious to everyone in the room.

Winning the argument didn’t make the bill go away, though, and I didn’t want to defend it again in the next cost review. So I went after the cost directly. I isolated the true cost driver rather than treating the vendor bill as one undifferentiated number, and found the spend was concentrated in an automated feature with low usage — a background process pulling data most users never looked at. Eliminating it cut data volume roughly 75%. With volume down, I renegotiated the contract to usage-based pricing so the remaining spend tracked actual demand.

Two different arguments: one to prove the product was worth keeping, one to make sure nobody had to make that argument again.

Tradeoffs

Cutting the automated feature meant a small set of users lost data they hadn’t explicitly asked for but might have benefited from. The usage numbers said that risk was worth a 90% cost reduction, and preserving the feature would have preserved the whole cut argument along with it.

Usage-based pricing also trades a predictable bill for a variable one. That’s the right trade when volume has just fallen 75% and you want the savings to be real rather than averaged away in a flat rate.