I'm [App]Lovin' it

Meta's newest challenger

Mark Zuckerberg, Meta’s hydro-foiling, UFC-fighting, and occasionally CEO-ing maestro, recently took a jab at Apple CEO Tim Cook, claiming, “Apple hasn’t innovated since the iPhone 20 years ago.” Bold words from a guy whose social media empire relies on practically one revenue stream (advertising) to carry the profits. One could argue that the only ‘innovation’ contributing to their bottom line comes from the brands and advertisers renting Meta’s real estate.

If you’ve spent even a week running ads, you’ll know Meta’s platform certainly doesn’t feel like it was engineered by NASA, nor is it particularly outspoken when it comes to true incrementality. But with its social media monopoly, Meta theoretically holds an iron grip on TOF (top-of-funnel) customer acquisition. While YouTube has been quietly fortifying its ad engine through Google, a new challenger has stepped into the ring to take on Team Zuck: AppLovin.

AppLovin CEO Adam Foroughi

---

AppLovin

So how does it work? AppLovin is a mobile ad network that helps brands advertise through in-app ads, primarily in mobile games. It uses programmatic advertising with its AXON AI platform and AppDiscovery to drive user acquisition. E-commerce brands can leverage AppLovin for in-app video ads, interactive experiences, and retargeting, especially if they have a mobile app.

AppLovin’s acquisition of MoPub from Twitter (another L for Twitter) further expanded its reach in the mobile ad ecosystem, giving it access to more premium inventory and strengthening its position against competitors. By integrating MoPub’s supply-side platform into its own ad network, AppLovin gained greater control over mobile ad monetization by managing both supply and demand-side bidding.

---

What is ‘incrementality’

If you’ve ever been on a call with a founder or e-commerce operator discussing marketing, you’ll start noticing a few quirks. One of them is the excessive use of the word incremental, dropped into every other sentence like a verbal tic. It’s similar to how, after running a business or spending too much time around VCs, you start working net into casual conversation: “Taking the bike instead of the subway saves me net 10 minutes” or “I can’t really afford to put that on the personal P&L.”

For marketers and advertisers, incremental is the magic word. Overused? Definitely. But also critical. With so many touch-points leading to a purchase, figuring out which ad channel actually moves the needle, especially for brands with long consideration cycles and high AOV, is getting murkier. A buying journey can stretch for weeks, even months, well beyond typical 1, 7, or 30-day attribution windows.

Google is great at harvesting conversions, while display channels like Meta and YouTube are better at pulling in completely cold top-of-funnel users. Google can still capture cold traffic, but as a search engine, most users already have intent before they convert. For example, if I search “baby stroller,” I’m already in the market for one, I just haven’t decided on a brand or style yet. The advantage of Google is the data it provides, giving advertisers a clearer picture of how far their bidding strategy can take them in terms of impressions and clicks on specific search terms.

Display networks like Meta and AppLovin, on the other hand, are heavily creative-dependent. They’ll burn through your budget and tell you, “Your creative needs to do the work.” That’s why, for any advertiser serious about scaling, the real game is acquiring cold traffic as profitably as possible, while knowing which platform takes the credit.

---

Why AppLovin seems to be ‘incremental’

With Meta’s monopoly on social media, and TikTok’s future uncertain, media buyers and brands are frustrated with rising costs and limited display options. So how’s AppLovin solving some of this issue?

Like any display network, AppLovin isn’t just about getting users to click and buy, they’ve already shown that works. The real test is whether their formula can do this efficiently for e-commerce brands at scale. Meta, despite its costs, has proven that even at high spend, audience targeting remains relatively effective within the 7-day click attribution model. This means a user actually clicked (not just viewed) an ad within seven days and then purchased, often the gold standard for comparison. Click attribution is almost always more reliable than view attribution, making it a key metric when measuring incrementality.

In the Common Thread Podcast, CEO Taylor Holiday breaks down what he’s seeing with brands trying to scale on AppLovin, and the results are mixed but promising. A case study by Haus, a growing platform for lift testing, sampled four brands - 50% saw improvements in their targets, while 50% saw a decline. Taylor argues that within a 7-day attribution window, AppLovin has proven itself as a legitimate top-of-funnel driver, though it still lacks widespread validation in the e-commerce space. One major barrier is accessibility, as the platform is estimated to require a minimum spend of around $3-5K/day.

Interestingly, many media buyers claim AppLovin provides greater incrementality than TikTok for first-touch attribution. Studies also suggest that TikTok users scroll through content at a much faster pace than on other platforms, limiting engagement time per ad. With mobile games generally offering a more immersive user experience, AppLovin aims to leverage this behavior to prove itself as a viable performance channel for e-commerce businesses.

---

Why ‘lift testing’ is the only way to know the truth

What is a lift test? Let’s ask our good friend ChatGPT:

A ‘lift test’ in e-commerce measures the true incremental impact of a marketing channel by comparing a group exposed to ads with a control group that isn’t. The goal is to see how much additional revenue or conversions the channel actually drives, beyond what would have happened organically.

While seeking new channels is one thing, understanding how to best utilize your channels in an effective media mix (how much you spend on one platform vs. another) is the bigger question. All brands, whether spending $500/day or $100K/day, will eventually hit their efficiency thresholds, where scaling past a certain point, with all things held constant, leads to acquiring users at a premium (a venture-backed favorite).

After speaking to directors at Haus, Recast, and other MMM (media mix modeling) platforms that measure the incrementality of ad channels, I’ve realized that understanding true sales drivers is much more powerful than simply relying on in-platform data. The challenge is, of course, volume. The greater the spend and revenue, the easier it becomes to identify trends in platform incrementality. This is why platforms like Haus and Recast have hefty minimums, as larger businesses are willing to pay big bucks for even a few percentage points of efficiency improvement, because the impact on the P&L would be far greater relative to the software retainer.

I hope, as time goes on, we’ll find cheaper solutions that democratize the ability to run lift tests, beyond just manually ramping up and dropping spend across platforms, for small e-commerce businesses. As I’ve written before, all channels tend to claim conversions, artificially inflating their impact and encouraging founders to spend more. Until a cost-effective, proven solution exists for all e-commerce brands, true incrementality will keep founders chasing their tails.