How Snabbit Cut Customer Acquisition Costs by 35% While Scaling Instant Home Services on iOS

Summary

• iOS made up roughly half of Snabbit’s users, yet Apple’s SKAdNetwork’s delayed, low-granularity postbacks left the channel with limited visibility.
• Incomplete signals meant unstable campaign learning phases, unreliable CAC figures, and limited view of where users dropped off between install and purchase.
• The Snabbit team worked with AppsFlyer to rebuild iOS measurement around richer signals, cutting CAC by 35% and restoring confidence in every budget decision.

Background

Founded in 2024, Snabbit is one of India’s fastest-growing on-demand home services platforms. The company connects households with trained professionals for cleaning, cooking, laundry, and other home help, promising service at the door within 10 minutes. Snabbit is live across Mumbai, Pune, Bengaluru, and the National Capital Region, and continues to expand into new cities.

The Snabbit team manages paid acquisition across a user base split almost evenly between Android and iOS, and needed every rupee of ad spend to work as hard on iOS as it did on Android.

Challenge

Snabbit operates in India’s instant home services category, a fast-growing, high-frequency segment where speed and trust matter as much as price. The company runs on a hyperlocal model, positioning trained experts across dense micro-markets so a booking made on the app can be met within Snabbit’s signature 10-minute promise. That model only works if the marketing team can tell, in near real time, whether a spike in bookings in a given micro-market came from a specific campaign or from households discovering the category on their own. Get that wrong, and the budget either chases demand that was already there, or misses the campaigns actually worth scaling.

iOS represented roughly half of Snabbit’s users, but SKAdNetwork’s delayed, low-granularity postbacks left that side of the business with limited visibility. The team could not reliably trace the path from install to a customer’s first booking, so campaigns kept optimizing on incomplete signals, learning phases stayed unstable, and CAC came out inflated and inconsistent. 

That mattered because in a high-frequency, low-margin category like instant home services, profitability depends on getting the cost of acquiring each customer right, not simply on how many installs a campaign generates. Without a complete read on true CAC and payback periods, the team could not tell whether scaling budgets into new cities was genuinely profitable or just growth bought at an unsustainable cost, at the exact moment the category itself was getting more competitive by the month.

Solution

The AppsFlyer team worked with the Snabbit team to close the exact gap driving the ambiguity between paid and organic demand: an iOS attribution layer provided the team with more visibility into the gap while respecting Apple’s privacy restrictions.

Using AppsFlyer’s Measurement Suite, Snabbit combined SKAN data with modeled, aggregate-level signals, so a booking spike in a given micro-market could finally be traced back to the campaign that drove it, rather than written off as organic pickup or, worse, credited to the wrong channel entirely.

With that visibility restored, the team shifted what campaigns were optimizing toward, and just as importantly, gained one consistent view across both platforms instead of two disconnected ones. Instead of judging performance on installs, a metric that says nothing about whether someone actually became a paying customer, campaigns began optimizing on deeper events like activation and a customer’s first booking, measured the same way regardless of whether a user arrived on Android or iOS. 

That distinction matters in a category where installs are cheap to generate but a completed booking is the only event that reflects real demand for trained experts in a given micro-market. With iOS now held to the same measurement standard as Android, the team could compare performance across the full acquisition funnel on equal footing, rather than reading iOS as a separate, murkier problem. Early indicators from the first one to three days of a campaign’s life became reliable enough to act on across both platforms, letting the team catch underperforming spend and redirect it before a new city launch had already burned through its budget on the unclear signal.

Results

The clearest outcome was a business the Snabbit team could finally trust to scale. CAC efficiency improved by 35%, giving the team confidence that the budget pushed into a new micro-market was buying genuine, high-intent demand rather than paying for installs that were never going to convert into a booking. Attribution visibility improved by up to 40%, closing much of the gap between what the ad platforms reported and what Snabbit’s own backend showed actually happened after install.

That shift showed up directly in the numbers that matter for a high-frequency, density-driven business. Install-to-new-user conversion rose by 12% as spend followed activation and first bookings instead of installs alone, and the time needed to exit each campaign’s learning phase dropped by 30%, letting new cities and micro-markets reach stable, scalable campaigns faster. Together, these gains meant Snabbit could expand its instant home services footprint into new cities with a trustworthy read on unit economics, rather than discovering weeks later whether that growth had actually been profitable.

“Speed is our whole business, so our marketing decisions need to move just as fast. Getting reliable signals on iOS means we can act on day one or two performances instead of waiting weeks to know if a campaign is working.”

Senior Marketing Manager