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12 tactics from Giordano Contestabile
Protecting Freemium at 100M Users AND Driving $500M Revenue – Giordano Contestabile, Life360
Watch the full episode“we started with a grow team uh but really a system and for everyone in the company to believe that growth is part of their job... ideally everyone in the company allocates part of the bandwidth to experimentation”
Turn growth into a system, not a team
A dedicated growth team caps growth at that team's headcount. Make experimentation everyone's job — finance experiments with pricing tiers, HR experiments with hiring funnels, customer service experiments with retention drivers. Align the whole company on the topline revenue metric, then let each function pick the submetric they actually influence. That converts headcount into an experimentation machine.
“we have a fellowship program where basically you can raise your hand and say for the next three months instead of doing my job I want to go and figure out how to make my team faster how to use technology to make my team faster”
Run a "builders" fellowship to surface internal growth talent
Don't try to top-down assign which people inside the company will drive AI / growth experimentation. Instead, run a 3-month fellowship: anyone raises their hand, gets pulled off their day job, and is paid to figure out how to make their team faster with new tools. The self-selectors are exactly the people you want — agency-seekers who will keep doing it after the fellowship ends.
“the only experiments that we are sad about is an experiment that is inconclusive Right then we feel we we wasted our time or we didn't set up the experiment correctly or the hypothesis was not right... If we lost then well then we just learn something”
Treat experiments as a portfolio — inconclusive is the only real failure
Score the experimentation program on velocity × win-rate × average win. Losses still ship learnings; wins ship features. The only failure mode is an inconclusive test — you spent the bandwidth and produced no decision. Track inconclusive percentage as a portfolio health metric and explicitly attack it (better hypotheses, better instrumentation, larger sample) rather than chasing higher win-rate vanity.
“it didn't work but it worked well for users that live in the suburbs and I've been into in the platform for at least a month and have a dog right”
Segment failed experiments before discarding them
When a test fails overall, don't shelve it — slice the result by tenure, geography, plan, behavior, and household composition. The headline loss often hides a tight subsegment where the feature actually crushed. Those subsegments become your next experiment (ship the feature only to them) or your next personalization rule (target the prompt to them).
“the reason number one why people tell us they don't subscribe is because the free tier is good enough... Our philosophy is that we want to do something about it But that something is not making the free tier worse Is trying to provide more value and really diversify the subscription offer”
When the free tier is "too good," diversify the paid tier — do not nerf free
When users tell you free is good enough, the lazy fix is to remove free features. The right fix is to invent paid value users actually want — new tiers, new services, new categories. Life360 protects free as the network-effect engine (parents-tell-parents is the #1 acquisition source) and instead expands what paid means. If you have network effects in free, nerfing free is taxing your own distribution.
“we did some testing in the past if you cut it down to 6 hours or even 12 hours like we double the amount of subscribers from the hook So you know it will be a sizable win Uh a lot of company will take that but but we haven't”
Refuse short-term wins that erode the free experience
Cutting location-history from 2 days to 6 hours doubled subscriber conversion in a clean test — and Life360 refused to ship it. The bar isn't "did this lift conversion?" but "is this feature still useful enough for free users to keep recommending the app?" If a paywall narrows the feature into uselessness, you've taxed your distribution engine to inflate one quarter. Compound that decision 10x and you're a worse product.
“we offer roadside assistance to our subscribers right we could not offer roadside assistance to free users because you know every time they they call it we pay for the toe... in some in other cases we think that every feature should be premium meaning that every feature... should be great in the free version”
Default features to free unless they have real marginal cost
Decision rule for new features: if it has hard variable cost per user (roadside assistance, GPS device service), it's a paid feature — users expect that. If it's software-only, build it free and great, then find net-new value to layer on top as paid. Avoid the artificial gate ("two for free, rest is paid") — that's the lazy version of freemium that customers can smell.
“there is a certain circle size three to four people that has significantly higher conversion and retention than lower and higher circle sizes... higher secret sizes is often friend groups or things like extended family or they probably less motivated to pay for safety features”
Design for the buying unit (3-4 person circle), not the individual user
Life360 found that 3-4 person circles convert and retain dramatically better than 1-2 (couples who outgrow the use case) or 5+ (friend groups with weaker pay motivation). If your product has a group dimension, find that band and design for it explicitly — onboarding, defaults, paywall framing. The unit isn't the signup; it's the household.
“I would not say as a loss leader for sure... I will say that we will always index toward trying to reach more users with the devices and bring more users in the ecosystem versus trying to maximize the the margins of the devices”
Treat hardware as ecosystem glue, not loss-leader or profit center
When you have both software and hardware, the temptation is to optimize each P&L separately. Don't. Price hardware to maximize ecosystem entry while staying break-even or modestly profitable on the device itself. Hardware buyers retain better on the SaaS, extend the usage lifecycle (Tile in a backpack before a kid has a phone; Pet GPS after the kids leave for college), and lift LTV through both channels.
“I think very few companies have success developing a second app you no matter the size of your first... Even Duolingo... they launched I think it was either chess or math as a separate app and then they had to bring it back into the main app”
Expand the app — don't spin off a second one
Resist the "let's spin out a sister app" instinct. Even Duolingo failed at it and pulled the standalone back into the main app. The filter for any new feature inside an existing app: (1) does it solve a real pain users already complain about, (2) does it overlap with your existing audience, (3) do you have permission from your brand to enter that adjacency? If yes to all three, ship inside the main app and lean on orchestration to surface it.
“we use machine learning to target platinum uh to people that we thought based on data based on previous experiments based on profile we have about 900 kind of data points that we use for that people that we thought had a higher propensity to subscribe to platinum It doubled the percentage of new users that subscribing to platinum But he did that without losing a single gold subscriber”
Use ML to find net-new premium buyers, not to cannibalize existing ones
ML-targeted upsell to a higher tier isn't about pushing existing buyers up — it's about routing the small slice of users with platinum-shaped preferences directly to platinum (instead of the safe gold default they would have settled for). Done right, you double the higher-tier mix with zero cannibalization of the lower tier. The signal is whether your ML lifts higher tiers while leaving the lower tiers flat in absolute terms.
“we wanted to replicate that in the app... nothing really really you know failure across the board And and the reason is people my age or or families or older people are not viral You know parents are not viral”
Don't engineer virality your audience can't perform
Life360 ran multiple in-app virality experiments — gift a subscription, share-button flows, refer-a-parent — and all failed. The underlying reason: parents aren't viral creatures, even though parents ARE the buyers. 40% of users discover the app from another parent, but it happens in offline group chats and school pickup lines, not via share buttons. Don't try to recreate offline distribution dynamics inside the app if the buying demographic doesn't act that way digitally.