The Ai4 roster exercise proved a sequence. Take a population, gate it on a hard ICP test, enrich it, then work it. That population arrived pre-qualified, because everyone on it had chosen to attend an AI conference. This time I had to build the population myself, from a database, for a North America ABM program, which I built on my own for the sales team to work. What follows is the full log: the saved searches, an honest read of what each signal proves and misses, and the six rounds of testing that moved net-new precision, the hit rate on accounts the team has not already qualified, from roughly 2 percent to 31.
Ask for technology instead of industry labels
An industry field is somebody's description of a company. A technology install is the
company's own behavior. Ask Apollo for the “computer games” industry and you get
whoever chose that label. Ask for companies currently running AppsFlyer or Unity and
you get companies that pay real money to measure app installs or build in a game
engine. So I filtered on currently_using_any_of_technology_uids, US and
Canada, and let the tech stack testify. All counts below are Apollo company-search
totals for the exact filters described, pulled 2026-08-03.
The saved searches and what each one proves
The mobile attribution stack (AppsFlyer, Adjust, Branch, Kochava, Singular) returned 2,179 companies and is the best single filter, because attribution exists only to measure app installs. Game engines (Unity, Unreal) returned 2,607. An engine proves games or real-time 3D, without distinguishing mobile from console and PC, and Unity now shows up in industrial simulation, architecture and automotive. On its own that search is not a mobile-game list.
Mobile ad monetization returned 203, the narrowest signal of the set. I got this one wrong at first. Of the seven ad networks I listed, Apollo recognized just AppLovin and ironSource, so the 203 covers two of them.
The combined ABM-sized search, a union of those signals with sub-11-employee shells
removed, returned 3,163. The individual searches overlap heavily, which is why 2,179
+ 2,607 + 203 lands nowhere near 3,163. I dropped one search. Mobile dev frameworks
(React Native, Flutter, Swift, Kotlin and friends) returned 81,119, because Apollo
reads swift as the SWIFT banking network, which contaminates that filter
beyond use.
The exclusivity test sorts every signal
A signal only works as a filter if a company cannot have it without shipping an app. Product analytics (Amplitude, Mixpanel, Heap, Pendo), engagement (Braze, Airship, Iterable, CleverTap), experimentation and CDPs are the strongest commercial signals we have at ThinkingAI, which sells a mobile-first product analytics platform to other businesses, with agentic AI on top of the analytics, since a company paying for them has already bought a piece of the loop we sell whole. But web-only SaaS companies are heavy users of all of them, so filtering on them pollutes the list, and they belong downstream as tags within an already-exclusive set.
Apollo also has a quiet tell. A technology it recognizes comes back with a proper
display name (appsflyer returns “AppsFlyer”), while an unrecognized one
is echoed back raw (revenuecat returns “revenuecat”) and silently
matches nothing. A zero only means Apollo does not track the tool. I ranked the signals on exclusivity and
recognition. ASO and store intelligence came back at 1,220 and it is clean, since you
only buy store intelligence if you are in a store. Mobile build and release (Bitrise,
TestFlight) scored 583 on the same logic, since you do not wire up mobile CI and a
TestFlight distribution flow without a mobile app to ship. I never sampled this one,
and after what attribution did I would not assume it holds. Mobile crash tooling returned only 8 companies, too few to use. And subscription and IAP infrastructure (RevenueCat,
Adapty, Qonversion), the highest-exclusivity signal, has zero recognized vendors,
which is a blind spot for subscription apps. The 13 signals that passed both tests
union to 2,671 at 11+ employees. Hold that number loosely; it comes back up.
Ad monetization cuts both ways
The ad-network signal looks precise and is a trap. AdMob sits in news, weather, utility and content apps, close to noise. AppLovin spans games and apps. Only Chartboost, Tapjoy, Unity Ads and ironSource skew hard toward games. Worse, the signal misses the games we most want. Mid-core and hardcore titles (RPG, strategy, simulation) monetize through in-app purchase and often carry no ad SDK at all. Those studios have the economy and LTV problems our product fits best. Filtering on ad tech would screen out our strongest ICP.
So I infer the IAP studios, because “mobile game monetized by IAP” is not a technology tag. Game backend and live-ops stacks are the best proxy, since a company running PlayFab, Photon, Nakama or Xsolla operates a live game with an economy, ads or no ads. Engine plus attribution (Unity or Unreal alongside AppsFlyer or Adjust) is a strong mobile-game signal, because a console-only studio has no reason to buy mobile attribution. Attribution and engine with no ad SDK is the tell for a pure IAP title. Apollo's technology filter is OR within a single query, with no way to express “Unity AND AppsFlyer” in one call. So I pull broad and compute the intersections locally.
Six rounds of testing reversed the conclusion
Everything above was written before the filters met real output. The six rounds ran across about two weeks, in between other work. When I sampled 100 companies at a time and hand-checked them, the technology filters produced lists that were 70 to 98 percent junk, and the “2,671 verified universe” turned out to be a count nobody should quote. Apollo detects technographics far more loosely than its clean display names suggest. A recognized display name only proves Apollo knows the word. Attribution tags fire on any company that advertises, and Apollo attributes any app in a large enterprise's portfolio to the entire enterprise, which is how $10B+ conglomerates and a federal government department ended up on a “mobile app” list.
I measured each round on a deep page for net-new precision. v1, technology plus 11+ employees, returned 2,671 companies at roughly 2 percent, with 75 of the 100 sampled being $10B+ enterprises. v2 added a revenue cap and hit roughly 30 percent, on a pool of 421 that was too small to run a program on. v3 kept headcount only and returned 746 companies at 15 percent, since dropping the revenue cap let heavy industry back in. v4 gated on NAICS 51321 and scored 24 percent on just 98 companies, the wrong gate because “Software Publishers” admits all B2B SaaS and excludes game studios. v6 dropped technology for keyword tags and returned 11,572 companies at 11 percent. v7 trimmed the keywords and excluded services SIC codes, which produced 1,013 companies at 31 percent. I adopted that one, 2.8x better than v6 on the number that matters.
The cost math before any export
Counting companies is cheap. Apollo charges per person. Covering the 3,163-account combined search at two or three decision-makers each runs to roughly 6,000 to 9,000 enrichment credits, against a balance in the low thousands, so the full universe is unaffordable in one pass. That is why I classify first and enrich second. I tier the accounts on free company data, then spend credits only on the top tiers. The Ai4 run is the precedent, where roughly half of attempted matches returned a usable contact, so I budget for a share of spend returning nothing.
Where I would spend the next hour
Stop optimizing the filter. Another round is unlikely to beat 31, because the remaining error is Apollo's data quality. Pull broad with the adopted filter (1,390 companies pulled and kept locally), then have a cheap model label every row: mobile game, PC/console game, mobile app, vendor, agency, out of ICP. Sorting a thousand rows costs cents, and a model reading a company name and domain judges better than any keyword tag. And remember where the good list came from. Ai4 worked because the population qualified itself. For the next list I would start from a source that does the same, an app-store publisher ranking, and use the database only to fill gaps. Nothing has gone out to the 1,013 yet. It is a pool, and the program is the next step.