Practitioner pattern · Thomas Petit (Sells consulting services and runs a paid signal-engineering workshop at App Growth Annual; works with Voyantis)
Report low-value users as worth less and high-value users as worth more
Pushes delivery toward top users but hides true ROAS; has backfired when overdone
What they did
What happened
In their words
Signal Engineering: Strategic Data Filtering for Better Ad Performance — Thomas Petit, Independent Consultant
Thomas Petit… these users, and you're trying to get as close as reality as you can get. And one thing that is actually hard like is I've got this with a team right now, where we're monitoring the value we're sending the platforms against the real value that's happening, and we've got like this curve that are following, and as soon as we see that there's a gap in there, we're like, we know something is going wrong. As dynamic and sophisticated as the model is,
Thomas Petitit never follows the exact reality, so we're looking at closing the gap always and always. This developer works on being as close as possible as the real value, but then you can engineer further. And I had this other conversation with, you can fake it, and typically, something that I do is I like to amplify it to make the networks job even easier. So, let's say, let's simplify the situation, I've got users worth $5, and I've got users worth $50.
Thomas PetitWhat I'm going to tell the platform is that these $5 are actually three, but that this $50 is actually 100, like, and I'm going to force it, like I'm going to make it a little bit more extreme. The trick here is that it makes that I can't really read the value anymore in the platform. I can't read my ROAS on Facebook, because it's all faked. I'm going to look at it on my side, but it's okay. I'm going to try to force the platform to gear towards the users that are most valuable to me by forcing it a little bit.
Thomas PetitAnd I had this fun conversation with someone from gaming, who said, "Oh, I do the opposite." "What? So, when you have a high-value user, you tell the platform that he's a medium-value user?" He's like, "Yes, because we've got whales, and then if I really declare a big whale of 500 or 1,000, Meta is going to say, 'That's it, my job is done for the day, I bring that one user, and I'm done.'" So, they actually tap it. The thing is that it it's a lot …
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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
- 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
- Treat users who rush a 30-screen onboarding in under a minute as low valueHeld for one app; another app found its fastest finishers were its best users
- 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: meta, applovin, signal-engineering, value-optimization, value-amplification