# Hearem US ASO Keyword Plan

Generated: 2026-06-14

Source: Appeeky MCP manual HTTP calls using the repo `.env` `APPEEKY_API_KEY`.
Codex native MCP did not load `.env`, so the calls were made directly against
`https://mcp.appeeky.com/mcp`.

## Executive Summary

Hearem's US App Store visibility is currently mostly branded. Appeeky found
Hearem ranking #1 for branded queries like `hearem`, but not for the broader
US discovery terms we care about. The best first US attack is to reposition the
listing around:

> PDF and image OCR content into read-aloud audio.

This matches the product's strongest first-session inputs, avoids generic TTS
positioning, and lines up with real App Store demand around `read aloud`,
`pdf to audio`, `text to speech`, and `image to text`.

## Appeeky Findings

### Current US Keyword Coverage

| Keyword | Hearem Rank | Volume | Difficulty | Note |
|---|---:|---:|---:|---|
| hearem | 1 | 1 | 40 | Branded only |
| hearem tts | 1 | 0 | 40 | Branded only |
| hearem tts text to speech | 1 | 0 | 40 | Branded phrase |
| tts text | 7 | 10 | 38 | Weak but non-branded-ish |

### China Keyword Coverage

| Keyword | Hearem Rank | Volume | Difficulty | Note |
|---|---:|---:|---:|---|
| tts | 22 | 52 | 14 | Real opportunity already present |
| tts reader | 41 | 31 | 22 | Weak but indexed |
| pdf voice reader | 50 | 26 | 14 | Relevant long-tail |

### US Target Keyword Metrics

| Keyword | Volume | Difficulty | Result Count | Read |
|---|---:|---:|---:|---|
| read aloud | 46 | 49 | 174 | Best primary keyword balance |
| pdf to audio | 40 | 60 | 177 | Core product fit, more competitive |
| text to speech | 30 | 59 | 185 | Necessary category term, crowded |
| image to text | 26 | 38 | 176 | Good OCR support keyword |
| ocr reader | 13 | 66 | 185 | Lower volume and hard; support term only |

### Apple Suggestions

Appeeky autocomplete suggestions surfaced:

- `pdf to audiobook`
- `pdf to audio free`
- `pdf to audio`
- `read aloud free`
- `read aloud books`
- `read aloud`
- `read aloud easy`
- `read aloud ai`
- `pdf converter - ocr reader`
- `readmydoc: ocr reader scanner`

## Competitor Set

### Read-Aloud / PDF-To-Audio Competitors

| Keyword | Top Competitors |
|---|---|
| pdf to audio | Speechify, Voice Aloud Reader, NaturalReader, PDF Docs Voice Aloud Reader, Voice Dream, ElevenReader |
| read aloud | Speechify, Voice Aloud Reader, ElevenReader, AI Reader: Read Aloud Pdf Book, NaturalReader, Voice Dream |
| text to speech | Text to Speech!, Speechify, Text to Speech: Voice Reader, NaturalReader, Speak4Me, Voice Dream |

### OCR Competitors

For `ocr reader`, the US top results are mostly scanner utilities rather than
audio tools: QR Reader, Adobe Scan, Mobile Scanner App, FineReader, Doc Scanner,
QuickScan, AI Scanner. This means OCR should be framed as an input path, not the
main category Hearem competes in.

## Recommended US Metadata Variant

This variant avoids repeating keywords across title, subtitle, and keyword
field where possible.

### Apple Title

`Hearem: PDF Read Aloud`

Characters: 22 / 30

Why: puts `PDF`, `read`, and `aloud` in the highest-weight field while keeping
the brand visible.

### Apple Subtitle

`OCR, Text to Speech Reader`

Characters: 26 / 30

Why: adds `OCR`, `text`, `speech`, and `reader` without repeating title words.

### Apple Keyword Field

`audio,listen,study,notes,image,scan,article,voice,book,webpage,document,tts,clone,elevenlabs`

Characters: 92 / 100

Why: supports `pdf to audio`, `image to text`, study use cases, document/article
inputs, and premium voice differentiation without wasting field space on words
already present in title/subtitle.

## Appeeky Suggested Metadata

Appeeky also suggested this Apple variant:

- **Title:** `Hearem TTS: Read Aloud`
- **Subtitle:** `Pdf To Audio & Text To Speech`
- **Keywords:** `read,aloud,pdf,audio,image,ocr,reader`

I do not recommend applying it exactly because the keyword field repeats words
already present in title/subtitle. The direction is useful, though: Appeeky is
also pointing us toward `read aloud`, `pdf to audio`, and `text to speech`.

## Screenshot Caption Set

Use the first five screenshot slots to tell one capture-to-listen workflow.

1. `Scan a page. Hear it back.`
2. `Turn PDFs into audio review.`
3. `Clean up long text before listening.`
4. `Follow every word as it plays.`
5. `Keep listening on the lock screen.`

Optional alternate captions:

- `Your reading list, now playable.`
- `Listen to notes, PDFs, and scans.`
- `Review study material on a walk.`
- `From image to audio in seconds.`
- `Natural voices for serious reading.`

## Description Rewrite Direction

The first three lines should stop saying "text-to-speech assistant for
everything" and lead with the concrete job:

```text
Turn PDFs, notes, articles, and scanned pages into audio you can listen to.

Hearem helps students, researchers, and busy readers convert real reading
material into natural read-aloud audio with OCR, document import, synced text,
and background playback.
```

Then keep the feature list, but reorder it around the first-session workflow:

1. PDF and document import
2. Image OCR
3. Read aloud / text to speech
4. Synced playback text
5. Background and lock-screen listening
6. Premium voices and voice controls
7. Translation, summary, and smart split
8. History and resume

## Keyword Tracking List

Track these in Appeeky for US once keyword tracking is available:

- `read aloud`
- `pdf to audio`
- `pdf to audiobook`
- `text to speech`
- `image to text`
- `ocr reader`
- `pdf reader audio`
- `voice aloud reader`
- `text reader`
- `study audio`
- `audio reader`
- `article reader`

Track these for China separately:

- `tts`
- `tts reader`
- `pdf voice reader`
- `PDF朗读`
- `图片转语音`
- `文字转语音`
- `听书`
- `朗读器`

Attempted 2026-06-14: `my_apps_keywords_add` returned
`tracked_keyword_limit_exceeded: plan=free limit=0`, so keyword tracking was
not activated in Appeeky. `my_apps_add` also returned a `store_app_id` not-null
constraint error for app ID `6742120811`.

## Experiment Plan

### Hypothesis

If the US listing leads with PDF/image OCR into read-aloud audio, Hearem should
earn more relevant search impressions and higher product-page conversion from
students, researchers, and screen-fatigued readers.

### Apply First

1. Update US title, subtitle, and keyword field to the recommended variant.
2. Replace the first five screenshots with the workflow captions above.
3. Rewrite the first three description lines around PDFs, scans, notes, and
   articles.
4. Start tracking the US keyword list in Appeeky.

### Measure Weekly

1. Rank movement for `read aloud`, `pdf to audio`, `text to speech`,
   `image to text`, and `ocr reader`.
2. US product page conversion rate once Appeeky/ASC metrics are synced.
3. First-session activation: user creates audio from PDF or image OCR.
4. Trial/subscription conversion for users whose first input is PDF or OCR.

## Caveats

- Appeeky public download and revenue estimates are low-confidence heuristics,
  not owned App Store Connect truth.
- Appeeky owned ASC metrics currently return no synced app rows for this
  account, so conversion/source metrics still need the Appeeky ASC connection
  fixed before we can measure the full funnel there.
- Appeeky account keyword tracking is currently unavailable on the active plan
  (`tracked_keyword_limit_exceeded: plan=free limit=0`), so keyword movement
  must be checked manually or after upgrading/enabling tracking.
- Codex native MCP does not load repo `.env`; manual Appeeky HTTP calls worked
  after loading `.env` inside the command.