ASOLOOP vs Spreadsheets
ASOLOOP vs spreadsheets.
The spreadsheet records what you did; ASOLOOP runs the loop and remembers what each test taught — connect, decide, and denominate in revenue.
Verdict
The spreadsheet records what you did; ASOLOOP runs the loop and remembers what each test taught — connect, decide, and denominate in revenue. If you can't fill in “our last four PPO tests taught us that ___,” the spreadsheet is still fine; if you can't but wish you could, that's the gap.
Side by side
Where ASOLOOP and Spreadsheets actually differ.
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| Capability | Spreadsheets | ASOLOOP |
|---|---|---|
| Primary category | Manual record-keeping | Operates the experiment loop + learning |
| Records past experiments | Yes | Yes — as bounded signals per app |
| Connects evidence across tests | Manual; lives in the operator's head | Per-app signal accumulation; weighted into the next ranking |
| Surfaces what to test next | No | Three-mode lifecycle scales candidate count to the evidence available |
| Runs PPO / CPP / CSL on the store | No | Automation handles all four end-to-end |
| Applies the winning variant for you | You do every step by hand | Yes — automated, reversible & logged, both stores |
| Translates CVR into revenue | Manual; usually skipped | Workspace revenue band from your AppsFlyer revenue (Teams tier); per-experiment ranges in progress |
| AI claim discipline | None — pasted ChatGPT outputs go straight in | Evidence trail + claim-safety validator on every output |
| Reversibility / audit trail | Cell-level edit history at best | 7-day signal revocation + per-experiment audit log |
| Decision traceability | None — a row of results, no lineage | Each hypothesis card cites the source experiments it's anchored to |
| Setup time | Minutes (free) | Connect store + MMP + AI key (~15 minutes); 7-day trial |
| Entry price | Free (plus the engineering hours maintaining it) | $49/app/mo Starter (3-app cap = $147/mo) · $89/app/mo Pro |
The right read of this table isn't “ASOLOOP wins more checkmarks” — spreadsheets are excellent at what they're built for, and an experiment-memory layer simply isn't on that list. These aren't failures; they're structural shortcomings. The cost of a spreadsheet is invisible: lost compounding, ungrounded next-test calls, CVR-not-revenue readouts.
See all comparisonsWhat each tool is
Different questions, in one line each.
The actual default for most teams' ASO record-keeping — free, ubiquitous, and already known. Excellent at recording what happened: one row per test. Built for storage, not for connecting evidence across tests, surfacing the next hypothesis, or translating results into revenue.
A structured experimentation system — schema-enforced consistency, per-app signal accumulation across cycles, and revenue-denominated confidence. It reads what happened, weights it into what should happen next, and rolls the result up into a workspace revenue band leadership can act on.
When to pick which.
When the spreadsheet is honestly fine
Zero or one experiment a quarter; ASO isn't yet a programmatic surface.
If your team runs occasional experiments and the program shape isn't established yet, a spreadsheet is operationally cheaper — and ASOLOOP has no experiments to compound from. The honest signal you're not yet our user: you can't fill in “our last four PPO tests taught us that ___” with anything specific. Come back when the cadence is set.
When you need ASOLOOP
You can list past tests but can't tell what they cumulatively taught.
With 8+ logged experiments and the question on the table being “what should we test next given everything we've already run?”, the spreadsheet has hit its limit — the connection between tests lives in your head, not in the data model. ASOLOOP's per-app evidence library is exactly the layer the spreadsheet was approximating; the 7-day trial onboards your historical experiments and surfaces the first signal-backed hypothesis from your own evidence.
When you need both (the migration question)
Your spreadsheet holds months or years of experiment context.
Don't throw it away. ASOLOOP accepts CSV imports during onboarding — past experiment definitions, hypothesis text, results, and the operator's annotations come in as historical evidence the system reads into the per-app library. The migration takes minutes, not weeks; your six months of spreadsheet data starts compounding from day one. Some teams keep a lightweight spreadsheet for ad-hoc notes, but the two don't stack at the program level.
Common questions
Questions buyers ask about ASOLOOP vs Spreadsheets.
Is ASOLOOP an alternative to Spreadsheets?
Yes — for the experiment-record-keeping job specifically. Spreadsheets are the actual default for most teams' ASO tracking; ASOLOOP is a structured experimentation system with schema-enforced consistency, signal accumulation across cycles, and revenue-denominated confidence. ASOLOOP replaces the spreadsheet as the system of record when the team feels the evidence-memory gap.
How does ASOLOOP's pricing compare to free spreadsheets?
Spreadsheets are free — plus the engineering hours maintaining them and the invisible cost of lost compounding. ASOLOOP is per-app subscription pricing — $49/app/mo Starter (3-app cap = $147/mo) and $89/app/mo Pro. The trade is a system of record that answers “what should we test next given everything we've already learned” instead of leaving that to the operator's working memory.
Can I use ASOLOOP and Spreadsheets together?
Most teams move off the spreadsheet as the system of record when they adopt ASOLOOP. Some keep a lightweight spreadsheet for ad-hoc notes alongside ASOLOOP's structured cycle, but the two don't stack at the program level — running both as systems of record reintroduces the drift ASOLOOP exists to remove.
What can't a spreadsheet do that ASOLOOP does?
Four structural things: it records but doesn't connect evidence across tests; it doesn't surface what to test next; it doesn't translate CVR into a revenue range that survives a leadership meeting; and it carries no AI claim discipline when ChatGPT output gets pasted in. ASOLOOP closes all four — per-app signal accumulation, a hypothesis surface, AppsFlyer-anchored revenue reporting (a workspace band today, per-experiment ranges in progress), and a claim-safety validator on every generated output.
Other comparisons
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