---
title: Understanding Your Report
description: How to read an ASO audit — the readiness score, the pillar model, the exact engine weights behind the score, and how to work from quick wins to prioritized recommendations.
---

An audit report is a single 0–100 **ASO Readiness Score** on top of a set of engine sections, each with its own score and a list of prioritized recommendations. This page explains what the score means, how it's built, and the order to read the report in so you spend your time on the findings that matter.

## Why it matters

The readiness score is one number you can track over time and compare across markets. But the number isn't the point — the point is *what to change*. The report is built so you can go from "where do I stand" (the score and pillars) to "what do I do next" (quick wins, then prioritized recommendations) without reading every section end to end. Reading it in that order is the difference between a score you glance at and a listing you actually improve.

## How to read it

Read the report top to bottom in this order.

### 1. Start with the ASO Readiness Score

The headline is your **ASO Readiness Score**, from 0 to 100. It carries a plain-language band so you know at a glance whether you're in good shape:

| Band | Score |
|------|-------|
| **Excellent** | 80–100 |
| **Good** | 60–79 |
| **Fair** | 40–59 |
| **Poor** | 0–39 |
| **Not yet scored** | no score available |

A band tells you the altitude; the pillars and sections tell you where the work is.

### 2. Read the four pillars to locate the work

Engine results are grouped into four **pillars**. Pillars are a reading aid — they tell you *where* your ASO stands, not a separate calculation (see [How the score is actually composed](#how-the-score-is-composed)):

- **Discoverability** — keyword- and search-driven visibility. Can people find you?
- **Conversion** — turning store visits into installs. Do your visuals and copy close?
- **Trust** — review sentiment and signal quality. Do users and the store trust the app?
- **AI Discoverability** — presence in LLM-powered search and recommendations (ChatGPT, Gemini, Claude, Perplexity, Google AI Overviews).

A low pillar points you at the sections underneath it. Localization is **not** a pillar — it's a scored engine that feeds the overall score directly (see below).

### 3. Take the Quick Win first

The overview surfaces a single **Quick Win** — the highest-impact, lowest-effort action across the whole report. It's the one thing to do first. Start there before you dig into individual sections.

### 4. Work the Top Recommendations by priority

Below the Quick Win, the overview collects the strongest recommendations from every engine. Each recommendation is tagged with a priority:

- **Priority 1 — Critical**
- **Priority 2 — Important**
- **Priority 3 — Polish**

Work top-down: Priority 1 first. Each recommendation card explains **Why this matters** and gives you a concrete **Try this** action. To save, dismiss, or deep-link a recommendation, see [Working with recommendations](/docs/audits/recommendations).

### 5. Open the sections that need attention

Each engine has its own section with a section score, its findings, and its own recommendations. Open the ones the pillars and top recommendations flagged. Each engine section has two affordances worth knowing:

- **Read Full Analysis** opens the full long-form narrative for that engine — the complete reasoning behind the section, not just the summary.
- **Methodology** opens an explainer for that engine: what the score means, the scoring model and signals behind it, the inputs used, the run details, the sources and citations, and the section's known limitations. Use it whenever you want to know *why* a section scored the way it did.

## How the score is composed {#how-the-score-is-composed}

This is the part most people get wrong, so it's worth being precise.

The **ASO Readiness Score is a direct weighted average of the engine scores** — not an average of the four pillars. The pillars are derived from the engine scores afterward, purely for display. Changing a pillar doesn't change the score; changing an engine's score does.

### Engine weights

Each engine contributes its own score (0–100) to the overall, at these weights:

| Engine | Weight | Why it carries this weight |
|--------|-------:|----------------------------|
| Keywords | 15% | Core discoverability driver |
| Store Text | 15% | Keyword placement + conversion copywriting |
| Screenshots | 15% | Primary visual conversion driver |
| Reviews & Sentiment | 15% | Trust signal and ranking factor |
| AI Discoverability | 12% | LLM recommendation presence and share of voice |
| Intent | 12% | Intent coverage and journey fit |
| Localization | 8% | Locale coverage and localization readiness |
| Icon | 8% | Brand recognition and search tap-through |

The four highest-weighted engines — Keywords, Store Text, Screenshots, and Reviews & Sentiment — are the discoverability, conversion, and trust drivers, and together they account for 60% of the score. The weights sum to 100%.

:::note[Which engines actually run depends on your plan]
Not every engine runs on every plan. For example, the Sentiment Engine's review lookback window and the number of LLMs the AI Discovery Engine queries scale with your plan, and some engines are gated entirely. See [Plans and entitlements](/docs/billing/plans-and-entitlements). The weights above are the full-model weights; the section below explains what happens when an engine doesn't run.
:::

### When an engine can't score

### When an engine can't score {#missing-engines}

An engine can be missing because your plan doesn't include it, because a brand-new listing doesn't have the data yet (for example, too few reviews for a full sentiment read), or because it couldn't finish (a [partial run](/docs/audits/run-an-audit#when-a-run-doesnt-fully-succeed)).

When that happens, the missing engine's weight is **redistributed proportionally** across the engines that did score. The overall stays on a 0–100 scale rather than being dragged down for data that was never available. This is why two apps can both score, say, 72 while different engines contributed to each number.

### How the pillars are derived

For display, the four pillars are composed from engine scores like this:

| Pillar | Composed from |
|--------|---------------|
| **Discoverability** | Keywords (50%) + Store Text (50%) |
| **Conversion** | Screenshots (50%) + Icon (30%) + Store Text (20%) |
| **Trust** | Reviews & Sentiment (80%) + Listing freshness (20%) |
| **AI Discoverability** | AI Discovery Engine score (pass-through) |

**Trust** is the one pillar with a state, because reviews aren't always available:

- **Scored** — the Sentiment Engine ran on enough reviews (at least 30) to be confident.
- **Provisional** — reviews are thin (fewer than 30) or the engine couldn't run, so Trust is estimated from the app's star rating plus listing freshness. Read it as directional, not precise.
- **Not scored** — no sentiment and no usable rating, so Trust isn't shown.

Listing freshness rewards recently updated listings: an update within the last 30 days scores highest, 30–90 days is middling, and older than 90 days (or unknown) scores lowest.

## The full summary view

For a top-level narrative that pulls the whole report together, open the overview's **Read Full Analysis** ("Full summary") view. It's the report's executive read — useful when you want the story of the listing before drilling into any one engine.

## How it connects to what you do

- The Quick Win and Priority 1 recommendations are your immediate to-do list — [save the ones you'll act on](/docs/audits/recommendations) so they carry into your Revision Plan.
- A recommendation that genuinely doesn't apply can be [dismissed with a reason](/docs/audits/dismissal-rules) so it stops reappearing on future audits.
- To see how the score and pillars move as you ship changes, use [Audit History](/docs/audits/history).
- To understand any single engine in depth, start from [Audit Engines](/docs/audit-engines/overview).

## Related

- [Run an Audit](/docs/audits/run-an-audit)
- [Working with recommendations](/docs/audits/recommendations)
- [Audit History](/docs/audits/history)
- [Dismissal Rules](/docs/audits/dismissal-rules)
- [Audit Engines](/docs/audit-engines/overview)
- [Plans and entitlements](/docs/billing/plans-and-entitlements)