The Question

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Why do high-intent rental users drop off before converting on NoBroker and where is the single highest-leverage fix?

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• Out of 50,000 users, only 1,033 paid. That's 2 in 100.
• 11,000 users started signing up, left within 13 seconds, and never came back.
• Fixing one thing could bring in ₹4,76,634 more every month.

Full notebook and data on GitHub → https://github.com/rishikajonna-dev/NoBroker-Funnel-Analysis

INDEX

1. Context

NoBroker is India's largest broker-free property platform. Founded in Bangalore, its core promise is simple — connect renters directly with owners, eliminating broker fees entirely. The platform operates on a freemium model: users get 9 free owner contacts, after which they hit a paywall ranging from ₹1,099 to ₹7,499.

This case study investigates the rental funnel specifically, the journey a user takes from landing on the platform to paying for a premium plan. The analysis is scoped to Bangalore, NoBroker's home market and strongest city, using simulated event data modelled on real platform behaviour, industry benchmarks, and firsthand product observation conducted in April 2026.

This is not a theoretical exercise. Every friction point documented here was personally observed by browsing the platform as a real user.

2. Problem Statement

The core tension is this, NoBroker's brand promise is zero friction. Its product delivers maximum friction at the exact moment of highest user intent. That gap between promise and experience is where users are lost. And the data shows they never come back.

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NoBroker's rental funnel contains three distinct drop points, mid-browse signup wall, post-signup spam exposure, and free-to-paid paywall — each eroding user trust incrementally. This analysis identifies where trust breaks irreversibly and recommends one intervention with the highest recovery potential.

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3. User Flow (what actually happens)

3.1 How I Discovered Two Paths

Before touching any data, I spent time on NoBroker as a real user to understand what the rental experience actually looks like end to end. During that session I noticed the signup wall appeared at two different moments depending on what the user was doing. Sometimes it interrupted mid-scroll before finding any specific listing. Other times it only appeared after clicking contact on a listing the user had already decided they wanted. Same wall, two different trigger points, two different user states. The dataset was built to reflect both and timestamp analysis confirmed the split — 60% of users hit the wall mid-browse, 40% hit it after a contact click.