Competing against free

TomTom GO Navigation has never had the luxury of being the default. On most phones sold today, a free, pre-installed mapping app is already sitting on the home screen, and Google Maps alone accounts for more than 70% of the navigation market. TomTom GO Navigation built its business on the opposite premise: no ads, no data monetization, a paid subscription that has to be worth paying for from the very first session.

That premise has found an audience. The app counts more than 26 million total downloads and 400,000 daily active users navigating in over 200 countries, inside a category still projected to grow at a 10.8% CAGR through 2028. The market is expanding. So is the competition for the same free-to-paid moment TomTom has built its entire model around.

"Ads and data monetization would be the easy way out," is essentially the bet TomTom has made since the app's earliest days. Instead, the company chose to depend entirely on people deciding, screen by screen, that the app was worth a subscription. That decision shapes everything downstream. There is no ad revenue to fall back on if onboarding underperforms. No data-resale margin to cushion a weak paywall. Every euro of revenue starts with someone choosing to convert.

Against free, pre-installed alternatives, TomTom couldn't out-spend its way to growth. It had to out-learn the market instead.

→ So what happens when your monetization model leaves you no room for a weak funnel?

Every conversion has to be earned

For a subscription-only app competing against free defaults, the paywall is never just one screen. It's the sum of dozens of decisions: how onboarding introduces the product, which price point feels fair, which users get shown a truck plan versus a car plan, which message reaches someone who's been driving with the free trial for three days and hasn't converted yet.

Historically, each of those decisions carried the same cost: an engineering ticket, a release cycle, an app store review. Testing a new onboarding carousel meant the same production timeline as shipping a new feature. Testing a price point meant waiting for the next release to see whether users reacted the way the team expected.

That constraint didn't just slow down experimentation, it narrowed what the team was willing to test at all. Bold hypotheses, the kind that assume you might be wrong about your own pricing, or your own segmentation, require a fast, cheap way to find out. When every test costs a release cycle, teams learn to test less and guess more.

TomTom's product and growth teams had a long list of open questions, the kind that come from running a subscription business inside a category where the majority of users have never paid for navigation in their life: Does a longer onboarding build more trust, or more drop-off? Are truck drivers and car drivers really the same buyer, priced the same way? Would a discount work better than a free trial? None of those questions could be answered by making the best possible guess once and shipping it.

→ How do you build a testing culture inside a subscription-only app, without multiplying your engineering headcount?

Choosing a partner built for the whole funnel

TomTom partnered with Purchasely to move faster than a release-cycle-bound roadmap would ever allow. Rather than treating experimentation as a series of one- off feature requests to engineering, Purchasely gave TomTom's product and growth teams a way to build, launch and measure in-app experiences directly, from onboarding through pricing to retention.

That mattered because TomTom's open questions didn't sit in one part of the funnel. Onboarding, segmentation, pricing, upsell and retention were all connected parts of the same subscription decision, and testing them one at a time, through separate tools or separate engineering requests, would have meant losing the ability to see how they interacted. A price test only means so much if you can't also see how it plays against a specific onboarding variant, for a specific segment.

Over four quarters, that shift let TomTom run more than 90 experiments across the full in-app experience: onboarding copy, layout and video content; car-versus-truck and power-user-versus-price-sensitive segmentation; price points, trial durations and plan structures; car-to-truck, monthly-to-yearly and personal-to-family upsells; and churn-prevention and win-back flows at the retention stage.

No single test was expected to carry the business. What mattered was the ability to run all of them, across every stage of the funnel, without waiting.

→ "90 experiments in four quarters. What does a team actually learn from that many tests?"

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Turning a backlog into a testing engine

What changed first wasn't any single result, it was the production speed itself. TomTom's teams went from a small handful of tests a year, each requiring a development slot, to a pace of iteration that let them test dozens of onboarding variants alone.

Paywall and onboarding screens that once took a release cycle to design, build and ship could now go live in a fraction of the time, roughly a 10x improvement in production speed, at around 90% less cost than the previous testing and deployment process. That combination, faster and cheaper, is what allowed the team to treat experimentation as a constant activity rather than an occasional project.

The scale of that shift shows up most clearly in onboarding, where TomTom ran 35 separate tests, more than a third of all experiments across the four quarters. That concentration wasn't a coincidence. Onboarding is the first place a free-app user decides whether a paid navigation app is worth their attention, and it was the stage where the team had the most open questions and the least room for a wrong guess.

"Purchasely is a true game changer for us," says Pim Spaanderman, Managing Director Consumer at TomTom. "We are now able to do rapid iterations on our onboarding flow, improving the financials of our app performance with great pace."

That pace is what made it possible to stop treating onboarding as a single, finished screen, and start treating it as a question the team could keep asking, in dozens of small variations, until the answer became clear.

Four questions, four answers

Once the testing engine was running, the value came less from any individual experiment than from the range of assumptions it let the team challenge, at every stage of the subscription journey.

Onboarding: does more information build more trust? The team tested long-form onboarding content against a simplified carousel format, expecting more detail to build more confidence in the product before asking for a trial. The simplified version won: the control drove 2.5% more trial starts and 8% more paid conversions. TomTom users, it turned out, preferred clarity over detail, a finding that reshaped how the team approached the 34 onboarding tests that followed.

Pricing: will a higher price generate more revenue? Against a rising-price trend across the category, the team tested several price points against each other. The lowest price point, $19.99, won across every metric that mattered: trial starts, conversion to paid and ARPU. Despite a market moving toward higher prices, TomTom's own users weren't ready to follow it there.

Engagement: do in-app messages actually nurture subscriptions? The team tested a targeted in-app message encouraging car-plan subscribers to upgrade to a truck plan. The message alone generated a 14.09% engagement rate and roughly £33K in additional revenue, evidence that a well-timed nudge could unlock upgrade revenue that a static pricing page never would have surfaced on its own.

Choice and copy: do more options and different framing change conversion? The team tested offering a discounted yearly plan alongside the standard free trial, paired with more value-focused copy, instead of promoting the free trial alone. The result was a 56.8% increase in ARPU among truck-plan users and a 23.7% lift in yearly conversions. Giving users a clear, value-driven alternative to "try it free" did more for long-term value than the free trial had done by itself.

Individually, each of these experiments answered one question. Together, they started to describe something more useful: a picture of what TomTom's users actually wanted from a paid navigation app, built from evidence rather than assumption, one test at a time.

From campaigns to a culture of testing

"Many confirm that you need to run many tests on onboarding to achieve significant improvement and results," say Rick Vlaar, Head of E-Commerce Growth, and Kees Oostinjen, Senior Manager of Mobile Growth at TomTom. "So far we ran 35 tests on onboarding using Purchasely."

That line captures something important about how the team's approach to experimentation changed. Thirty-five tests on a single part of the funnel isn't the result of thirty-five separate, hard-won decisions to greenlight a test. It's what happens once testing stops being a special request and becomes the default way a team makes a decision.

Across the four quarters, that shift showed up in the sheer production volume: 112 screens deployed, a 5x increase in A/B test frequency compared to the previous approach. Each of those screens represents a moment where a team could have shipped its best guess and moved on, and chose instead to test two or three variants and let the data decide.

That pace also changed what the team was willing to test in the first place. A pricing change, a segmentation split, a new upsell message, these are the kind of decisions that used to require confidence before you started. With a fast, inexpensive way to find out, the team could afford to be wrong more often, which in practice meant it could afford to try more ambitious ideas.

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What 90 experiments add up to

Ninety-plus individual tests don't automatically add up to a transformed business. What made the difference for TomTom was that each one fed the next: a winning onboarding format informed how new segmentation tests were framed, a pricing result shaped how upsell offers were positioned, a successful in-app nudge became the template for the next retention experiment.

Over four quarters, that compounding effect showed up in the topline: TomTom GO Navigation's monthly recurring revenue grew 85%. The team's ability to personalize by segment, test pricing without waiting on a release cycle, and nurture upgrades with timely in-app messages, run in combination, moved the business further than any single change could have on its own.

The economics tell the same story from a different angle. A testing and deployment process that once ran through engineering now runs 10x faster and at roughly 90% less cost, while the pace of experimentation itself increased 5x. Put together, the team's own tracking attributes more than $400K in incremental ARR directly to A/B tests run through Purchasely, at a rate of experimentation roughly 500% faster than before, saving an estimated $100K a year in maintenance and development that would otherwise have gone into building and running these tests internally.

"Purchasely is a true game changer for us," Pim Spaanderman says. "We are now able to do rapid iterations on our onboarding flow, improving the financials of our app performance with great pace."

The team that learns fastest, wins

TomTom GO Navigation didn't need a bigger budget to compete with free, pre-installed alternatives. It needed a faster way to find out what actually convinced its users to pay. Ninety-plus experiments in four quarters didn't hand the team a single winning formula, they handed it a much clearer picture of who its subscribers were: users who wanted clarity over detail in onboarding, who weren't chasing premium pricing, who responded to a well-timed nudge more than a static sales pitch, and who valued a clear alternative to a free trial more than the free trial itself.

In a category where the default option is free, the team that learns the fastest what makes people choose to pay anyway holds the real advantage. For TomTom, that advantage now shows up every quarter, in a testing engine that keeps running long after any single experiment has closed.

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Key takeaways

● Competing against free, pre-installed alternatives means every conversion has to be earned. There's no ad revenue or data-resale margin to fall back on if the funnel underperforms.

● Removing the release-cycle bottleneck let TomTom treat experimentation as constant infrastructure rather than an occasional initiative, running 90+ tests across onboarding, pricing, segmentation, upsell and retention in four quarters.

● The clearest lessons cut against assumption: simpler onboarding beat longer-form content, the lowest price point outperformed higher ones, and a paid yearly alternative outperformed a free trial alone.

● Faster, cheaper testing changed not just results but appetite: teams could afford to test more ambitious ideas once being wrong stopped being costly.

● The compounding effect of many small, fast experiments, not one big redesign, is what moved MRR by 85% in four quarters.

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Key numbers  

• +85% MRR growth over 4 quarters
• 10x faster paywall and onboarding screen production
• 90% cost saved on testing and deployment
• 5x increase in A/B test frequency
• 90+ experiments run (35 on onboarding alone)
• 112 screens deployed
• +$400K incremental ARR from A/B tests

Key quotes

"Purchasely is a true game changer for us. We are now able to do rapid iterations on our onboarding flow, improving the financials of our app performance with great pace." — Pim Spaanderman, Managing Director Consumer, TomTom

"Many confirm that you need to run many tests on onboarding to achieve significant improvement and results. So far we ran 35 tests on onboarding using Purchasely." — Rick Vlaar, Head of E-Commerce Growth & Kees Oostinjen, Sr. Manager of Mobile Growth, TomTom

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