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Methodology

The AppSniper Methodology

AppSniper applies the same discipline a careful investor uses before committing capital — evidence collection, a structured counter-case, and a tracked record — to the decision of whether a native app idea is worth building. Every verdict is evidence-based, challenged before it's issued, and tracked over time.

Evidence Hierarchy

Four tiers of evidence. Not all signals are equal.

Every finding AppSniper produces is tagged with the tier it was measured at, not just presented as a flat fact. Tiers are weighted by observability (can we directly measure it?) and how far removed the finding is from raw App Store data.

TIER 1 Raw App Store ranking positions, keyword search results, review counts, star ratings, screenshot and IAP capture straight off the storefront. Directly observed, not modelled. Highest Weight
TIER 2 Derived Fact Rank velocity, review-growth rate, modernization decay, market-neighborhood role assignment. Computed deterministically from Tier 1 inputs — the calculation is fixed and repeatable. High Weight
TIER 3 Inferred Signal Organic-versus-paid confidence, complaint-cluster severity, monetization willingness-to-pay. Pattern-matched from indirect signals rather than a single deterministic formula. Medium Weight
TIER 4 Interpretation Market-structure classification, primary blocker, primary supporting claim. The synthesized read that ties every lower-tier finding together into the verdict. Judgment

Every claim in a Decision Memo carries its own state, independent of tier: Confirmed, Partial, Inferred, Proxy Only, Missing, Stale, Contradicted, or Unsupported. A claim tagged Stale or Proxy Only is never allowed to quietly read the same as one tagged Confirmed.

Market Neighborhood Engine

One competitor is not a market map.

Most tools compare your idea to a couple of obvious competitors. AppSniper builds the full neighborhood: it unions chart-ranked incumbents with everything that actually surfaces in App Store search across your top keywords, then classifies every app in that neighborhood by the role it plays.

R1

Trigger App

The app that made the opportunity visible in the first place — usually a chart mover or a keyword front-runner.

R2

Direct Incumbent

A live, healthy competitor solving the same job. The core of the competitive set your thesis has to beat.

R3

Copycat

A low-traction clone that surfaced on the same keywords. A cluster of these is a warning sign, not a validation signal.

R4

Behemoth

A category-dominant app backed by a multi-app developer or a rating count that puts genuine displacement out of reach in the near term.

R5

Stale Incumbent

A ranked competitor that has stopped shipping. Modernization decay and update recency flag exactly how vulnerable it is.

R6

Small Entrant / Adjacent Substitute

A recent, low-rating-count entrant, or an app solving an adjacent job rather than the same one — both context, not direct competition.

The role distribution rolls up into one market-structure verdict: Open Niche, Wedgeable but Crowded, Behemoth Fortress, Copycat Swamp, High Demand / Low Supply, Low Supply / Unproven Demand, High Demand / High Supply, Semantic Drift, or Insufficient Evidence. That verdict, not a single competitor's star rating, is what a BUILD decision actually rests on.

Decision Criteria

Six criteria. One verdict.

Each evaluation scores the opportunity across six weighted criteria. The weighted composite becomes the Opportunity Score, which drives the BUILD / WATCH / KILL verdict.

C1

Search Accessibility

Can new entrants rank for high-intent keywords without extraordinary ASO investment? We score keyword difficulty, brand lock-in, and non-branded term volume.

C2

Monetization Signal

Does the category support sustainable subscription or IAP revenue? We evaluate ARPU proxies, paywall patterns, and willingness-to-pay signals from review sentiment.

C3

Incumbent Defensibility

How entrenched are the top-3 players? We analyze review moat depth, brand term ownership, update cadence, and switching cost indicators.

C4

Category Saturation

How many credible competitors exist at each price point? We map the competitive density and identify structural gaps a new entrant could occupy.

C5

Trend Trajectory

Is category demand growing, stable, or declining? We assess keyword volume trends, install velocity curves, and seasonal patterns over 12–18 months.

C6

Execution Fit

Does your current distribution, team expertise, and existing portfolio create an asymmetric advantage in this specific category?

Adversary Review

Every thesis gets challenged.

The Adversary Review is a mandatory step where we build the strongest possible case against the opportunity. This is counter-diligence, not devil's advocacy.

Every Decision Memo includes an Adversary section that must be addressed before a BUILD verdict can be issued. A WATCH verdict means adversary risks are real but not disqualifying. A KILL verdict means adversary evidence is decisive.

What adversary review covers
Platform policy risk: App Store rule changes that could eliminate the category
Incumbent counter-move probability: how likely they are to respond effectively to a new entrant
Market timing risk: whether the entry window is already closing
Execution assumptions: where the thesis requires above-average execution to hold
Data quality caveats: where evidence is too thin to support high confidence
Decision Readiness

BUILD is a verdict. Lockable is a different question.

A BUILD verdict tells you the evidence supports the thesis. It does not by itself tell you whether the data behind that verdict is fresh enough to act on today. AppSniper keeps those two judgments separate instead of collapsing them into one badge.

Every memo carries a lockability status alongside the verdict: Build Lockable (evidence is fresh and sufficient — commit), Review Required (evidence supports BUILD but a spot check is needed first), Watch Only (real opportunity, not a commit decision yet), Kill Lockable (the case against is settled), or Not Lockable (underlying data is too stale to trust any verdict right now, regardless of what it says).

Every memo also names
Primary supporting claim — the single strongest piece of evidence the verdict rests on
Primary blocker — the single gate that would have to clear for the verdict to change
Completeness state — whether the underlying evidence is complete enough, partial but reviewable, insufficient for a build call, or stale
Calibration Loop

Decisions are tracked. The model gets better.

AppSniper maintains a calibration log. When outcomes are known, we record the result against the original verdict: which BUILDs launched, which KILLs were skipped. This is the learning loop most teams never build.

What gets tracked

Verdict Accuracy Rate

What % of BUILD decisions led to successful launches? What % of KILL decisions were later validated by market events?

Evidence Tier Reliability

Which evidence tiers most reliably predicted outcomes? Which signals were noisy? The model is retrained on actuals.

False Positive Rate

When we issued BUILD and the team passed on it: what did they find after launching anyway? These cases are the most valuable for calibration.

Known Limitations

What AppSniper cannot tell you.

AppSniper is evidence-based. The verdict is a thesis rating, not a forecast or guarantee.

We evaluate thesis quality, not team execution quality
App Store revenue and download figures are estimates, not exact data
Platform policy changes (App Store rules, algorithm updates) cannot be predicted
A BUILD verdict is a thesis rating, not a financial forecast or guarantee of success
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If you build native apps and want a clear answer before committing a sprint, AppSniper is built for that problem. Join the waitlist and we will reach out if you are a fit.