An app with a 30-screen onboarding · B2C · Thomas Petit client
Treat users who rush a 30-screen onboarding in under a minute as low value
Held for one app; another app found its fastest finishers were its best users
What they did
What happened
In their words
Signal Engineering: Strategic Data Filtering for Better Ad Performance — Thomas Petit, Independent Consultant
Thomas Petit… that is not very complicated. For me, the most interesting part is not so much the execution, which is I mean, there there are issues, it's regular, but there's nothing as a breakthrough there. It's not like It's not that you code it better, that the signal is going to be better, is really how do you define the signal that is going to make it. And all these questions around onboarding, and about segmenting users, creating buckets of value,
Thomas Petitis actually a really interesting, because it has the crossing of like I mean, there's a lot of data analysis, but there's always new stuff we discover. Like, there's so many factors, and I like to play around it across different apps. And I'll tell you one, for example, like we noticed with somebody I was working, that the completion time of the onboarding was a decisive factor in in value. And we're like, "Oh, okay, those people who are answering it in less than a minute, and there was like 30 screens,
Thomas Petitthere was nothing there." They're like, "Yeah, probably they click next, next, next, next, they start the free trial, and they cancel." And I start bringing this to other apps, and they're like, "Dude, I checked, and actually my fastest onboarding completion are actually my best users, because they already know what they want," and so on. It's not universal, necessarily, and this criteria is not a great one, but there are a couple that are usually very big. So, edge, we mentioned. The thing around goals is is very often a very big one.
Thomas PetitAnd a lot of apps have different types of users. They would have like more pure consumer users, like full B2C, but then they would have small teams, a lot of solopreneurs, people with a Shopify shop, or influencers, who are actually they're a one-person business. Like, and there's a lot of them. If you manage to identify who are like these small business, one-person business, solopreneurs, they typically have a completely different Of course, if …
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More from this episode
- Check that Meta and Google receive the same event counts as your own analyticsGaps of 30-50% are common; fix anything above 10% before touching ads
- Send trial starts, direct purchases, trial conversions and renewals as separate eventsDefault SDK mapping often lumps them all into one event; guest says fix it first
- Send ad networks only the trials likely to pay, as their own eventGuest's go-to first step when paid trial quality lags; no numbers given
- Tell Meta to value some users more or less instead of excluding themKeeps cheap under-25 reach instead of cutting it; no numbers given
- Report each user's expected value at month 13, not what they paid on day oneGuest's main advanced lever; no numbers given
- Look up each conversion's value from a server so you can change it without a releaseAvoids app releases for every price or currency change; says it makes a big difference
- Send your conversion signal within a day, waiting a few hours to see early useVoyantis showed timing matters; guest says signals after 24 hours are nearly useless
- Keep at least 10 conversion events per campaign per day before filteringUnder ~10/day optimization breaks; well above it, fewer better events win
- Report low-value users as worth less and high-value users as worth morePushes delivery toward top users but hides true ROAS; has backfired when overdone
- Send every event to every ad network, then pick a different signal for each
- Compare the value you report with real revenue by country, not just in total
- Send Google only the trials that look likely to payGoogle trials converted at ~15% vs ~40% elsewhere; the fix moved the needle
- Count a business user's yearly plan as worth 5 weekly consumer plansWorked at first; went stale after 18 months of price changes and needed fixing
- Change which events you send to ad networks, even as a young appBig uplift per the guest; details saved for a blog post
- Put all spend behind one channel and one optimization signalGrew from 0 to eight figures in two years, then had no fallback when it broke
- Let a signal specialist filter and send your ad events, and test itOne of two tests looks good, the other doesn't; not conclusive
- Tell ad networks which users reported high anxiety, since they pay far more
- Change what you report as user value when cash matters more than long-term LTV
- Sell $5-10 top-up credits on top of subscription tiers
- Send buyers to web checkout once the store fee jumps from 15% to 30%Works for them, per the guest; no numbers
- Report a $1 value for non-payers who look likely to stick around
- Report your biggest spenders as mid-value so Meta doesn't stop early
Tags: signal-engineering, onboarding-speed, value-criteria