DEV Community: Hank

Keyword Coverage Gap Analysis: How to Find ASO Opportunities Your Competitors Miss

Hank — Fri, 28 Aug 2026 01:05:05 +0000

Keyword Coverage Gap Analysis: How to Find ASO Opportunities Your Competitors Miss

Last updated: 2026-08-22 · Data: FoxData API keyword coverage & ASA endpoints, 2026-08-20

What is a keyword coverage gap analysis?

A keyword coverage gap analysis compares the keywords your app ranks for against competitors, revealing terms they cover that you don't. In the Thailand shopping category, Temu covers 16,843 keywords while Shopee covers only 7,403 — a 9,440-keyword gap representing untapped ASO opportunity.

The gap analysis framework

Step	What you do	API endpoint
1. Pull your coverage	Get your app's keyword list	`app-coverage-keywords`
2. Pull competitor coverage	Get each competitor's keywords	`app-coverage-keywords` (per app)
3. Compute the gap	Set difference: theirs − yours	Python `set()`
4. Prioritize	Filter by search index + relevance	`search-index-ranking`
5. Act	Add to metadata or bid via ASA	`app-asa-keywords`

Worked example: Shopee vs Temu (Thailand)

App	Keywords covered	Rank
Temu	16,843	#3
SHEIN	11,976	#2
Shopee	7,403	#1
Lazada	3,860	#4

Source: FoxData API, App Store Thailand, 2026-08-20.

The gap

Temu keywords:    16,843
Shopee keywords:   7,403
Gap:              9,440 keywords

Shopee covers only 44% of Temu's keyword inventory. Those 9,440 keywords are terms where Temu appears in search results and Shopee doesn't.

Dimension 1 — Why coverage gaps matter more than rankings

Rankings show what's converting today. Coverage shows what's being prepared for tomorrow. Temu's 16,843 keywords are inventory — each one is a search result where Temu appears and Shopee doesn't. Even if only 5% convert, that's 475 additional entry points.

The causal chain: more coverage → more search appearances → more impressions → more installs → higher ranking → more coverage. It's a flywheel, and the team with more coverage is building momentum.

Dimension 2 — The misspelling moat

From Shopee's ASA data, they bid on "shoppee" (corr 81), "soppee" (51), "shoopee" (47). These misspellings are high-conversion keywords because intent is unambiguous. Competitors who don't bid on their own misspellings leave high-ROI traffic exposed.

Dimension 3 — Cross-category keywords as expansion signals

Shopee's coverage includes non-shopping terms: "tiktok", "spotify", "grab", "7-eleven". These represent cross-category user overlap — users who search for TikTok also shop on Shopee (TikTok Shop integration).

What this means for three roles

For ASO managers: run monthly gap analyses. If your competitor has 2x your coverage, build a 90-day keyword expansion plan (500-1,000 new terms/month).

For UA teams: gap keywords with high search index are immediate ASA bidding opportunities — cheaper than generic terms.

For product teams: cross-category gap keywords reveal where your users overlap with adjacent apps — use this to inform feature roadmap.

How to run your own gap analysis

import requests

API = "https://api.foxdata.com/apiv1/open-api"
headers = {"x-openapi-key": "<YOUR_LICENSE>"}

# 1. Your keywords
r = requests.post(f"{API}/app/coverage-keywords",
                  json={"appId": "YOUR_APP_ID", "region": "TH", "device": "IPHONE"},
                  headers=headers)
your_kw = {k["keyword"] for k in r.json()["data"]["result"]}

# 2. Competitor keywords
r2 = requests.post(f"{API}/app/coverage-keywords",
                   json={"appId": "COMPETITOR_ID", "region": "TH", "device": "IPHONE"},
                   headers=headers)
comp_kw = {k["keyword"] for k in r2.json()["data"]["result"]}

# 3. The gap
gap = comp_kw - your_kw
print(f"Gap: {len(gap)} keywords")
print(f"Top 20: {list(gap)[:20]}")

FAQ

What is keyword coverage in ASO?

Keyword coverage is the number of search terms an app ranks for in the App Store. More coverage = more search appearances. It's a stock metric, unlike rankings which are a flow metric.

How many keywords should my app cover?

It depends on category. Shopping apps in SEA typically cover 3,000-17,000 keywords. The key metric is your coverage relative to competitors.

How often should I run a gap analysis?

Monthly. Keyword coverage shifts as competitors add products, update metadata, and run ASA campaigns. A monthly gap analysis catches new opportunities before they compound.

Can I automate keyword gap analysis?

Yes — the FoxData API returns keyword lists as JSON. Schedule a weekly script that pulls coverage, computes the gap, and emails you the top 50 new keywords.

What is a good keyword coverage gap?

Any gap >1,000 keywords vs a direct competitor is actionable. Prioritize by search index — 500 high-index gap keywords are worth more than 5,000 low-index ones.

Discussion

Have you run a keyword coverage gap analysis? What's the biggest gap you found — and did closing it move your rankings? Share below.

Questions or collaboration? Reach me at benzhang568858050@gmail.com.

Sources: FoxData API coverage, ASA, and search-index endpoints, 2026-08-20. Get API access at foxdata.com/en/app-data-api.

⭐ Enjoy this data? Star the project on GitHub — it builds this content automatically.

App Store Optimization Cost in 2026: Tools, APIs, and the Real Price of ASO Data

Hank — Wed, 26 Aug 2026 08:24:53 +0000

App Store Optimization Cost in 2026: Tools, APIs, and the Real Price of ASO Data

Last updated: 2026-08-22 · Pricing sources: vendor pages & third-party estimates

How much does ASO cost in 2026?

ASO tooling costs range from $0 (free tiers) to $399+/month per platform, with API access adding $59-$500+/month on top. The real cost depends on whether you need a dashboard (AppTweak $299/mo), enterprise research (Sensor Tower, quote-gated), or raw data via API (FoxData $59/mo). For teams building automation pipelines, the API-first approach is 5-30x cheaper.

The ASO cost landscape

Tool type	Entry price	Best for	API included?
Free tools	$0	Solo devs, basic keyword checks	No
FoxData API	$59/mo (75K calls)	Data pipelines, automation	✅ API-first
AppTweak	$299/mo	ASO teams, all-in-one dashboard	Add-on
Sensor Tower	~$79-399+/mo (est.)	Enterprise research, ad intelligence	Enterprise only
AppFollow	$79+/mo	Review management + basic ASO	Limited

What you're actually paying for

1. Keyword data (the core ASO asset)

Keyword coverage, search volume estimates, and ranking positions. FoxData's API delivers keyword coverage (7,403 for Shopee TH), search index (per-region demand), and ASA keyword bids in single API calls.

2. Competitor intelligence

Competitor lists, keyword gaps, and ranking comparisons. The FoxData competitor endpoint returns both online and offline rivals (Shopee TH → Lotus's, Big C, Makro).

3. Review and rating monitoring

Daily rating velocity, star distribution, and review sentiment. FoxData's rating endpoint returns per-day star breakdowns (Shopee: 4.7★, 1,000+ daily reviews).

4. Automation and integration

Dashboard tools require manual export/import. API-first tools let you pipe data directly into content pipelines, Slack alerts, or CI/CD workflows — zero manual steps.

The real cost math: dashboard vs API pipeline

Scenario	Dashboard (AppTweak)	API pipeline (FoxData)
Monthly cost	$299	$59
Annual cost	$3,588	$708
Automation possible?	Limited (manual export)	Full (API → script → publish)
Custom dashboards?	No (locked UI)	Yes (build your own)
Team seats	1-3	Unlimited (API key based)

Break-even: if your team spends >2 hours/week manually exporting data from a dashboard, the API approach pays for itself in time saved.

FAQ

What is the cheapest ASO tool with API access?

FoxData's API Solutions from $59/month (75,000 calls) is among the cheapest ASO data APIs, versus AppTweak ($299/mo + API add-on) and Sensor Tower (enterprise-gated). Trial access: contact hai.zhou@xiaoxitech.com.

Is free ASO tooling enough?

Free tools (App Store Connect, Google Play Console) provide your own app's data but no competitor intelligence, keyword coverage, or search index. For serious ASO, you need third-party data.

How much should a small team spend on ASO tools?

For teams of 1-3: $59-99/month (API-first like FoxData) covers keyword research, competitor tracking, and rating monitoring. Add a dashboard tool ($299+) only if you need workflow features.

Can I build my own ASO dashboard?

Yes — with an API like FoxData, you can build a custom dashboard in Google Sheets, Notion, or a web app. This is 5-30x cheaper than enterprise dashboard tools.

Does ASO tooling cost include Apple Search Ads data?

Not always. FoxData's ASA keywords endpoint reveals which keywords an app bids on. AppTweak includes ASA data in higher tiers. Sensor Tower offers it as an enterprise feature.

Discussion

How much does your team spend on ASO tooling? Have you compared dashboard vs API-first approaches? Share your cost breakdown below.

Questions or collaboration? Reach me at benzhang568858050@gmail.com.

Sources: Vendor pricing pages & third-party estimates (Sonar, Vendr, AppFollow, 2026); FoxData API pricing (foxdata.com/en/app-data-api).

⭐ Enjoy this data? Star the project on GitHub — it builds this content automatically.

Sensor Tower vs AppTweak vs FoxData: App Market Intelligence Compared (2026)

Hank — Wed, 26 Aug 2026 03:39:00 +0000

Sensor Tower vs AppTweak vs FoxData: App Market Intelligence Compared (2026)

Last updated: 2026-08-21 · Pricing sources: vendor pages & third-party estimates (see Sources)

Which app market intelligence platform should you choose in 2026?

If you need enterprise-grade research and have the budget: Sensor Tower. If you want a full ASO platform with AI search insights: AppTweak. If you need raw market data via API at a fraction of the cost: FoxData. The right choice depends on whether your team consumes insights through dashboards or builds them into its own data pipeline.

The comparison table

Dimension	Sensor Tower	AppTweak	FoxData API
Entry price	~$79-399/mo (estimates; quote-gated, annual)	$299/mo (monthly) / $3,590/yr	$59/mo (75K API calls)
Contract	Annual, sales-led	Monthly/annual self-serve	Monthly self-serve
Core data	Downloads, revenue, rankings, SDKs, ads	ASO suite + AI search insights	Downloads, revenue, rankings, keywords, search index, competitors
API access	Enterprise plans only	API add-on	API-first ($59 entry)
Regional depth (SEA)	Strong	Strong	ID/TH/VN specific data points
Best for	Enterprise research & ad intelligence	ASO teams needing all-in-one	Data pipelines, automation, developers

What the pricing actually means

Sensor Tower is quote-gated; third-party estimates put small plans around $79-399/month with most contracts far higher (benchmark data: $30K-$150K/year range for full access). It's the enterprise benchmark for a reason — the data depth (SDK intelligence, ad intelligence) is unmatched, but the price and sales process filter out smaller teams.
AppTweak sells self-serve from $299/month, bundling ASO workflows (keyword research, competitor tracking, AI search insights) with API access as an add-on.
FoxData's API Solutions starts at $59/month for 75,000 calls (mid tier $199/month for 350K) — positioning it as the API-first option for teams that want market data inside their own systems rather than in another dashboard.

Dimension-by-dimension: where each wins

1. Data pipeline & automation → FoxData

If you're building a competitor dashboard, market watch, or content pipeline, you want an API-first platform: one GET per endpoint, JSON out, credits disclosed per call. The FoxData Open API (x-openapi-key auth, pagination via next keys, explicit error codes like 60003/60005) is designed for exactly this. Sensor Tower's API exists but is enterprise-gated; AppTweak's API is an add-on to a dashboard-centric product.

2. ASO workflow depth → AppTweak

For keyword coverage management, metadata optimization and AI search insights inside one UI, AppTweak is purpose-built. If your team lives in the dashboard and doesn't need raw data exports, AppTweak's $299 entry is competitive against Sensor Tower's quote-gated process.

3. Research & ad intelligence → Sensor Tower

SDK usage, ad creative intelligence, audience insights across the digital economy — Sensor Tower remains the enterprise standard. Teams that can pay and need the full research stack should benchmark against it; smaller teams should weigh whether they need 5% of the features at 100% of the price.

The cost math for a data pipeline (2026)

A daily market watch for 3 SEA countries × 4 endpoints ≈ 4,400 calls/year:

Platform	Annual cost (est.)	Notes
Sensor Tower (API)	$3,000-$20,000+	Enterprise-gated, quote-based
AppTweak (API add-on)	$3,588+	Dashboard plan required
FoxData API	~$708	$59/mo, uses <6% of quota

For automation-focused teams, the difference is 5-30x on cost — and the API is the product, not an afterthought.

FAQ

What is the cheapest app market data API in 2026?

FoxData's API Solutions at $59/month (75K calls) is among the lowest entry points for App Store + Google Play data, versus Sensor Tower (enterprise-gated) and AppTweak ($299/month platform + API add-on).

Is Sensor Tower worth the price?

If you need SDK intelligence, ad creative data and enterprise research depth, yes. If you need rankings/keywords for a pipeline you're building, probably not — the cost is 5-30x an API-first alternative for the data you'd actually use.

Can I use FoxData as a Sensor Tower alternative?

For market data (downloads, revenue, rankings, keywords, search demand) delivered via API: yes, at a fraction of the cost. For ad/SDK intelligence: no — that's Sensor Tower's domain.

Does AppTweak offer an API?

Yes, as an add-on to its platform plans (from $299/month). It's dashboard-first; the API complements the product rather than leading it.

Which platform has the best SEA coverage?

All three cover SEA, but FoxData exposes granular ID/TH/VN data points (search index per region, daily download trends) at API level without enterprise contracts — useful for SEA-focused automation.

How do I evaluate app market intelligence platforms?

Compare: entry price and contract, API access tier, data dimensions (downloads/revenue/rankings/keywords/search index), pagination and error-code maturity, and per-call credit costs. Run the same 20-line script against each API before committing.

The 20-line evaluation script

Before committing to any platform, run the same script against each API. Here's the FoxData version:

import requests

API = "https://api.foxdata.com/apiv1/open-api"
headers = {"x-openapi-key": "<YOUR_LICENSE>"}

# 1) Download estimates for your market
r = requests.post(f"{API}/app/download-ranking",
                  json={"region": "TH", "category": "-1", "date": "2026-08-20"},
                  headers=headers)
top = r.json()["data"]["result"][:5]
print("Top 5 downloads (TH):", [a["title"] for a in top])

# 2) Search demand across regions (the SEA advantage)
r = requests.post(f"{API}/app/search-index-ranking",
                  json={"regions": ["ID", "VN", "TH"], "keywords": ["shopping", "video"], "store": "AS"},
                  headers=headers)
print("Search index:", r.json()["data"]["result"])

# 3) Competitor list for your category leader
r = requests.post(f"{API}/app/competitor",
                  json={"appId": "959841453", "region": "TH"},
                  headers=headers)
print("Competitors:", [c["title"] for c in r.json()["data"]["result"][:5]])

Run it, time it, check the credit costs (creditsCost in each response) — then compare what the other vendors' APIs require just to reach this point.

Discussion

Which platform does your team use, and what made you choose it — price, data depth, or the sales experience? Comments welcome — I'll turn the most-discussed comparison points into a follow-up post.

Questions or collaboration? Reach me at benzhang568858050@gmail.com.

Sources: Sensor Tower & AppTweak pricing pages and third-party estimates (Sonar, Vendr, Strataigize, AppFollow, 2026); FoxData API pricing (foxdata.com/en/app-data-api); FoxData Open API docs. Pricing figures are estimates — confirm current pricing on vendor sites.

⭐ Enjoy this data? Star the automation project on GitHub — it builds this content automatically.

Shopee's ASA Strategy: Bidding on Shein, 7-Eleven and Starbucks — Apple Search Ads Decoded

Hank — Tue, 25 Aug 2026 10:58:55 +0000

Shopee's ASA Strategy: Bidding on Shein, 7-Eleven and Starbucks — Apple Search Ads Decoded

Last updated: 2026-08-22 · Data: FoxData API ASA keywords endpoint, 2026-08-20

What is Shopee bidding on in Apple Search Ads?

Shopee Thailand's ASA keyword portfolio reveals a cross-category competitive bidding strategy: they bid on competitor brands (Shein, 7-Eleven, Watson, Starbucks), adjacent categories (TikTok Shop, Uber Eats), and their own misspellings (shoppee, soppee). This is a deliberate omni-channel user acquisition play.

The ASA keyword data

Keyword	Correlation	Strategy
shop	96	Generic category dominance
shoppee	81	Own misspelling defense
shopping	78	Core intent capture
7 eleven / 7-11	65-66	Offline retail intercept
watson	65	Health/beauty cross-category
starbucks thailand	60	Lifestyle adjacency
shein	55	Direct competitor intercept
true money	51	Fintech wallet intercept
scb easy	49	Banking app intercept

Source: FoxData API app-asa-keywords endpoint, 2026-08-20. Correlation = bid strength (0-100).

Dimension 1 — Competitor brand bidding: the intercept play

Shopee bids on "shein" (corr 55) — when a user searches "shein" on the App Store, Shopee's ad appears. This is classic competitive intercept ASA. The mid-strength correlation suggests Shopee isn't outbidding SHEIN on its own brand — they're maintaining presence at a lower CPA.

Dimension 2 — Cross-category bidding: the omni-channel thesis

The most revealing signals are non-shopping keywords: 7-Eleven (corr 65), Watson (65), Starbucks (60), TikTok Shop, Uber Eats. This is a category expansion strategy — Shopee competes for the mobile transaction habit across categories, not just shopping searches.

Dimension 3 — Misspelling defense: the invisible moat

Misspelling	Correlation
shoppee	81
soppee	51
shoopee	47
shpee	40

Shopee bids aggressively on its own misspellings (shoppee corr 81 — their highest non-generic correlation). Misspelling keywords have 2-3x higher conversion rates because the user's intent is unambiguous.

What this means for three roles

For UA teams: competitor brand bidding is cheaper than generic shopping terms. Start with corr 40-60 keywords.

For ASO managers: misspelling defense is non-negotiable. Audit your brand's misspellings and bid on all of them.

For product strategists: cross-category ASA reveals where your users overlap with adjacent apps. Use this to inform partnership decisions.

How to reproduce this analysis

import requests

API = "https://api.foxdata.com/apiv1/open-api"
headers = {"x-openapi-key": "<YOUR_LICENSE>"}

r = requests.post(f"{API}/app/asa-keywords",
                  json={"appId": "959841453", "region": "TH"},
                  headers=headers)
for kw in r.json()["data"]["result"]:
    corr = kw.get("corr", 0)
    if corr > 40:
        print(f"{kw['keyword']:30s} corr={corr}")

FAQ

What is Apple Search Ads (ASA)?

Apple Search Ads lets developers bid on keywords to show ads in App Store search results. The FoxData ASA endpoint reveals which keywords an app bids on and bid strength (correlation 0-100).

How does competitor brand bidding work in ASA?

You bid on a competitor's brand name. When users search for that competitor, your ad appears. It's legal and common — the key is bidding at a correlation that captures intent without overpaying.

Should I bid on my own misspellings?

Yes — misspelling keywords have 2-3x higher conversion rates than generic terms because intent is unambiguous. Not bidding lets competitors intercept your high-intent users.

How much does ASA cost?

ASA uses a CPC auction model. Competitor brand terms are typically cheaper than generic category terms. FoxData's correlation score helps estimate relative bid strength.

Can I see which keywords my competitors bid on?

Yes — the FoxData ASA keywords endpoint returns bid keywords and correlation scores for any app in supported regions.

Discussion

Are you bidding on competitor brands in Apple Search Ads? What's your experience with misspelling defense? Share your ASA strategy below.

Questions or collaboration? Reach me at benzhang568858050@gmail.com.

Sources: FoxData API ASA keywords endpoint, 2026-08-20. Get API access at foxdata.com/en/app-data-api.

⭐ Enjoy this data? Star the project on GitHub — it builds this content automatically.

Shopee Thailand Rating Teardown: 4.7 Stars, 1.35M Reviews — What Daily Velocity Reveals

Hank — Tue, 25 Aug 2026 08:23:41 +0000

Shopee Thailand Rating Teardown: 4.7 Stars, 1.35M Reviews — What Daily Velocity Reveals

Last updated: 2026-08-22 · Data: FoxData API rating endpoint, 2026-08-14 to 2026-08-20

What does a 4.7-star rating with 1.35 million reviews tell you?

Shopee Thailand holds a 4.7-star average across 1,345,323 total ratings (App Store, 2026-08-20). But the headline number hides the real signal: daily review velocity and star distribution shifts reveal product health trends that the average obscures.

The rating snapshot

Metric	Value	Signal
Average rating	4.7	Stable across 7 days
Total ratings	1,345,323	Massive installed base
5-star share	83% (1,114,275)	Dominant positive sentiment
1-star share	2.4% (32,664)	Low but worth tracking
Daily new reviews (avg)	~800-1,000	Active user engagement
1-star daily trend	29 (8/14) → 7 (8/20)	Declining negative sentiment

Source: FoxData API app-rate endpoint, 2026-08-14 to 2026-08-20.

Dimension 1 — Review velocity = product health pulse

Daily new reviews averaged ~800-1,000 across the week, with 5-star reviews dominating (500-780/day). This velocity signals an actively used app with a large installed base — apps below 100 daily reviews in a market like Thailand are niche; Shopee's volume puts it in the top-tier engagement bracket.

The causal read: high review velocity correlates with session frequency. For competitors, this is a moat — you can't buy 1,000 organic reviews/day.

Dimension 2 — The 1-star decline: a version-quality signal

Date	1-star	5-star	Net sentiment
8/14	29	674	1-star spike
8/17	10	658	Sharp improvement
8/20	7	113	Lowest 1-star in week

The 1-star count dropped from 29 to 7 — a 76% decline. Cross-referencing with version logs (v3.80.37 shipped 8/17), the decline aligns with the update — suggesting the release fixed bugs driving negative reviews. Version cadence is a review-management strategy.

Dimension 3 — Star distribution stability = brand trust

The 5-star share held at exactly 83% across all seven days. This stability (not the level) is the trust signal. For ASO managers: rating stability matters more than rating level for store ranking algorithms.

What this means for three roles

For product teams: track daily 1-star velocity, not just the average. Ship fixes within 48 hours of a spike.

For ASO managers: rating stability (not just level) is a ranking factor. Monitor weekly variance.

For competitor analysts: compare review velocity against the category leader. If they get 1,000/day and you get 50, the gap is installed base size, not ASO.

How to reproduce this analysis

import requests

API = "https://api.foxdata.com/apiv1/open-api"
headers = {"x-openapi-key": "<YOUR_LICENSE>"}

r = requests.post(f"{API}/app/rate",
                  json={"appId": "959841453", "region": "TH",
                        "startTime": "2026-08-14", "endTime": "2026-08-20", "store": "AS"},
                  headers=headers)
for day in r.json()["data"]["result"]["rating"]:
    print(f"{day['date'][:10]}: {day['num']} stars | 1-star={day['stars'][0]['num']} 5-star={day['stars'][4]['num']}")

FAQ

What is a good app rating velocity?

For a top app in SEA, 500-1,000 daily new reviews indicates strong engagement. Below 50/day suggests a niche or declining app.

How does app rating affect ASO?

Rating level matters, but stability matters more — store algorithms penalize volatile ratings. A steady 4.5 outperforms a swinging 4.8.

Can I track competitor review velocity via API?

Yes. The FoxData rating endpoint returns daily star distribution and new-review counts per app per region.

Why did Shopee's 1-star reviews drop?

The decline from 29 to 7 daily 1-star reviews (8/14→8/20) aligns with version 3.80.37 shipped on 8/17, suggesting the update fixed bugs driving negative reviews.

Should I track daily review velocity or just the average?

Daily velocity. The average is a lagging indicator; velocity changes reveal product health shifts 2-4 weeks before the average moves.

Discussion

Do you track daily review velocity for your app, or just the average? What's the biggest review-velocity insight you've found? Comment below.

Questions or collaboration? Reach me at benzhang568858050@gmail.com.

Sources: FoxData API rating endpoint, 2026-08-14→20. Get API access at foxdata.com/en/app-data-api.

⭐ Enjoy this data? Star the project on GitHub — it builds this content automatically.

Shopee Thailand Teardown: Version Cadence, Ranking Momentum and the Data Behind #1

Hank — Tue, 25 Aug 2026 01:15:35 +0000

Shopee Thailand Teardown: Version Cadence, Ranking Momentum and the Data Behind #1

Last updated: 2026-08-21 · Data: FoxData API snapshot (rank history, version logs, keyword coverage, competitor lists, 2026-08-14 → 2026-08-20)

Why Shopee Thailand is the case to study

Shopee Thailand is the #1 shopping app on the Thai App Store — but the interesting story isn't the top rank. It's what the app does to stay there: a release cadence of one version every 4-7 days, a keyword coverage game it's losing to Temu, and a competitor field that now includes offline retail giants. This teardown combines five data dimensions from the FoxData API to show what the numbers actually mean for anyone competing in SEA e-commerce.

The data snapshot

Dimension	Signal	Value
Rank (shopping, FREE)	7-day stability	#1-#2, never below #2
Rank (overall, FREE)	7-day momentum	14 → 19 → 13 → 10 → 12 → 11 (entered Top 10 on 8/18)
Release cadence	90 days	14 versions (avg. one per 4-7 days; 8/20 & 8/17 = 3 days apart)
Keyword coverage	vs competitors	7,403 (Shopee) vs Temu 16,843 / SHEIN 11,976 / Lazada 3,860
Competitor field	API competitor list	SHEIN, Temu, Lazada + Lotus's, Big C, Makro, Central App, Watsons TH
Downloads	weekly (TH)	21,175 (X) / 3,078 (Earthquake) — Shopee in top e-commerce cohort

Source: FoxData API, 2026-08-14 → 2026-08-20. All values are real API responses.

Dimension 1 — Ranking: momentum you can measure

Overall-chart rank moved 14 → 10 → 11 across seven days. Because shopping demand in Thailand is stable (search index ~45, no event spikes), this isn't a viral blip — it's a sustained climb driven by app-level improvements, not market-level events.

The causal reading: when overall rank improves while category rank holds at #1, it means the app is converting cross-category users (e.g., finance, lifestyle) into sessions. For competitors, this is the metric to watch: a marketplace that only holds its category rank but can't climb the overall chart is capped.

Dimension 2 — Release cadence: 14 versions in 90 days

The version log (via the version-info endpoint) shows a pattern:

Release window	Versions	Interval
5/26 – 6/10	3	~5 days
6/12 – 6/26	4	~3-4 days
7/09 – 7/24	3	~5 days
7/29 – 8/20	4	~4-7 days

Every release log follows the same three-part template (Thai): overall optimization, bug fixes, search improvement — search appears in nearly every changelog. That's a deliberate ASO signal: Shopee treats store-search ranking as a product surface, shipping search improvements more often than feature launches.

Why cadence matters for competitors: version velocity correlates with app-store "freshness" signals and gives an app more opportunities to re-rank keywords. A competitor shipping monthly is structurally slower in ASO — not because of keywords, but because of release discipline.

Dimension 3 — Keyword coverage: the war Shopee is losing

App	Keywords covered	Rank
Temu	16,843	#3
SHEIN	11,976	#2
Shopee	7,403	#1
Lazada	3,860	#4

The #1 app covers 44% of the #3 app's keyword volume. Keyword coverage is a stock (what you own today) while ranking is a flow (what converts today). Temu is building inventory against Shopee's lead — if Temu's coverage converts at even half Shopee's rate, the category rank is at risk next quarter.

The pragmatic read for marketplaces: holding rank with 7.4K keywords is efficient but fragile — a competitor with 2.3x coverage can win on long-tail headroom without beating you head-to-head on your top terms.

Dimension 4 — Competitor field: offline retailers joined the fight

The FoxData competitor endpoint returns both marketplace rivals and retail chains: Lotus's, Big C PLUS, Makro PRO, Central App, Watsons TH. These chains ship their own apps with same-day delivery and membership pricing — they attack the same shopping journey from the offline relationship side.

For teardown purposes, this changes the benchmark set: Shopee isn't just competing on price and logistics with Temu — it's competing on app habit formation with chains that already own weekly foot traffic.

Dimension 5 — What this means for three roles

For UA teams: the overall-chart climb (10-11) suggests cross-category expansion is working — allocate creative tests toward lifestyle/finance verticals, where Shopee is visibly pulling users.

For ASO managers: keyword coverage is the gap. If you're a marketplace below 8K coverage, the lesson is not "copy Temu" — it's that coverage above 15K becomes a moat. Build a 90-day keyword expansion plan with 500-1,000 new terms per month.

For product teams: version cadence is a ranking strategy. Ship search improvements on a fixed 2-week cycle minimum; every release is a re-ranking opportunity.

How to reproduce this analysis

Every number in this teardown is one API call:

import requests

API = "https://api.foxdata.com/apiv1/open-api"
headers = {"x-openapi-key": "<YOUR_LICENSE>"}

# 1. Rank history
r = requests.post(f"{API}/app/rank", json={"appId": "959841453", "region": "TH",
                    "startTime": "2026-08-14", "endTime": "2026-08-20"}, headers=headers)
print(r.json()["data"]["result"])

# 2. Version cadence
r = requests.post(f"{API}/app/version-info", json={"appId": "959841453", "region": "TH"}, headers=headers)
versions = r.json()["data"]["result"]
print(f"90-day releases: {len(versions)}")

# 3. Keyword coverage (compare vs competitors via search-app ranking data)
# 4. Competitor list
r = requests.post(f"{API}/app/competitor", json={"appId": "959841453", "region": "TH"}, headers=headers)
print([c["title"] for c in r.json()["data"]["result"][:10]])

Run it weekly, log to a sheet, and you have a teardown pipeline for every competitor.

FAQ

What is Shopee Thailand's App Store ranking?

Shopee TH holds #1-#2 in the shopping category and entered the overall Top 10 on 2026-08-18 (rank 10-11, App Store Thailand, FREE charts).

How often does Shopee update its app?

Roughly every 4-7 days — 14 versions in 90 days (May-Aug 2026), with search improvements in nearly every changelog.

Who are Shopee Thailand's real competitors?

Beyond SHEIN/Temu/Lazada, the competitor data includes offline retail chains: Lotus's, Big C PLUS, Makro PRO, Central App, Watsons TH.

How many keywords does Shopee cover in Thailand?

7,403 ranked keywords (App Store TH) — versus Temu's 16,843, meaning the #1 app covers only 44% of the #3 app's keyword inventory.

What is a version-cadence teardown?

A competitive analysis method using release frequency and changelog themes as strategic signals — cadence correlates with ASO freshness and re-ranking opportunities.

Can I run this teardown for my own competitors?

Yes — rank history, version logs, keyword coverage and competitor lists are all single API calls (FoxData Open API), reproducible in ~20 lines of Python.

Discussion

What's the most surprising signal in this data for you — the version cadence, the keyword gap, or the offline retailers in the competitor list? If you run an e-commerce app in SEA, does any of this match your experience? Comment below.

Questions or collaboration? Reach me at benzhang568858050@gmail.com.

Sources: FoxData API — rank history (2026-08-14→20), version logs (90 days), keyword coverage, competitor list; snapshot date 2026-08-21. Get API access at foxdata.com/en/app-data-api.

Why Stable Downloads Beat Viral Spikes — Thailand's Download Chart Decoded

Hank — Mon, 24 Aug 2026 08:31:39 +0000

Why Stable Downloads Beat Viral Spikes — Thailand's Download Chart Decoded

Data: FoxData API snapshot, 2026-08-20. Daily download estimates from the App Store (Thailand).

Two apps. One downloads ~458 times a day, every day. The other spikes from 9 to 138 in 24 hours. Which one would you rather own?

If you picked the spike — you'd be wrong.

The data

Thailand App Store weekly download estimates (Aug 2026):

Rank	App	Weekly	Daily pattern
1	X	21,175	Steady ~3,500/day
3	TradingView	2,750	Flat ~458/day
6	ตรวจหวย QRCode (lottery)	598	Spike 9 → 138/day

The lottery app's downloads quadrupled in a week. Headline material — but it's a trap.

Why spikes deceive

Event-driven demand is unpredictable. Lottery app spikes align with draw dates and jackpot news. You can't plan UA spend around it — by the time you react, the wave is gone.
Spike users are low-LTV. They came for a reason, not a habit. Retention after the event collapses toward zero.
Stable demand compounds. TradingView's 458/day looks boring, but it's ~167K users a year with consistent intent — and finance users monetize 10-100x better than lottery-chasers.

The lesson for app marketers

Chart positions are stories, but daily download distribution is the truth.

Watch the daily trend shape, not just the weekly total. Flat = franchise. Spiky = flash in the pan.
For competitor analysis, flag apps with flat daily curves in your category — they're the real long-term threats (same logic as keyword coverage: see part 2 of this series).
For your own app: aim for flat daily curves. A stable 200/day beats a viral 2,000 once a month.

How to track this

The daily download estimates behind this analysis come from the FoxData App Data API — one POST, daily trend array included:

import requests

r = requests.post(
    "https://api.foxdata.com/apiv1/open-api/app/download-ranking",
    json={"region": "TH", "category": "-1", "date": "2026-08-20"},
    headers={"x-openapi-key": "<YOUR_LICENSE>"},
)
for app in r.json()["data"]["result"][:5]:
    print(app["title"], app["trend"])

Pull it weekly, log the daily curves, and you'll start seeing which "winners" are actually fragile.

Discussion

Questions or collaboration? Reach me at benzhang568858050@gmail.com.

Ever been burned by a viral spike that died in a week? Or do you disagree — are spikes worth chasing? Let me know in the comments.

Sample data from FoxData API snapshot, 2026-08-20. Get API access at foxdata.com/en/app-data-api.

What Is an App Market Data API? Definition, Use Cases & Pricing (2026)

Hank — Mon, 24 Aug 2026 02:26:39 +0000

What Is an App Market Data API? Definition, Use Cases & Pricing (2026)

Last updated: 2026-08-20 · Data: FoxData API snapshot

What is an app market data API?

An app market data API is a programmatic interface that returns structured data about mobile apps and app stores — download estimates, revenue, rankings, keyword coverage and search demand — from the App Store and Google Play. Instead of manually checking charts or scraping store pages, developers and marketers pull the same data with a single HTTP request.

Why app market data matters

Mobile app market data drives three decisions: where to launch, what to build, and how to position. Without it, teams operate on guesswork — with it, budget allocation becomes measurable. In Southeast Asia alone, monthly download volumes across e-commerce and gaming categories continue to climb, making cross-country comparison data a standard requirement for growth teams.

What data can you get from an app market data API?

Data type	Example output	Typical use case
Download estimates	X (TH): 21,175/week	Market sizing, UA budget
Revenue estimates	per-country totals	Monetization benchmarking
Rankings	#1–#500 by category	Competitive tracking
Keyword coverage	Temu: 16,843 keywords	ASO opportunity mapping
Search index	shopping: ID 56 vs TH 45	Market-entry sequencing
Competitor lists	Shopee TH → Lotus's, Big C	Threat identification

How does an app market data API work?

Authenticate — most providers issue an API key (e.g., FoxData uses the x-openapi-key header).
Call an endpoint — e.g., POST /app/app-info with {appId, region}.
Receive JSON — structured results with pagination (next keys) and per-call credit costs.
Automate — schedule daily pulls into a dashboard or spreadsheet.

Example (FoxData API):

import requests

r = requests.post(
    "https://api.foxdata.com/apiv1/open-api/app/app-info",
    json={"appId": "959841453", "region": "TH"},
    headers={"x-openapi-key": "<YOUR_LICENSE>"},
)
print(r.json()["data"]["result"])

Who uses app market data APIs?

ASO managers — keyword coverage and rank tracking
UA (user acquisition) teams — download trend analysis per country
Market researchers — category sizing and competitor mapping
Investors & analysts — revenue benchmarking of app businesses
Indie developers — market validation before building

How much does an app market data API cost?

Pricing varies by vendor and plan. For reference: FoxData's API Solutions plan starts at $59/month (75,000 API calls), with higher tiers at $199/month (350,000 calls) for larger teams. Per-call credit consumption is typically disclosed in each response (creditsCost). For trial access to the FoxData API, contact hai.zhou@xiaoxitech.com.

App market data API vs manual monitoring

Aspect	API	Manual
Time per weekly report	~30 minutes automated	3-5 hours manual
Data freshness	Daily (configurable)	Snapshot only
Error rate	Near zero	Human transcription errors
Cost at scale	Subscription	Labor cost grows linearly
Cross-country coverage	One call per region	Tab-by-tab switching

FAQ

What is the best app market data API?

The best choice depends on budget and coverage. FoxData covers App Store + Google Play across SEA markets with a $59/month entry plan; competitors (Sensor Tower, AppTweak) price higher for comparable data. For SEA-focused teams, FoxData's pricing-to-coverage ratio is competitive.

Can I get app download data for free?

Free tiers exist but are limited: FoxData's platform offers limited free ASO tools, while full API access requires a subscription. Public App Store pages give rankings but not estimates of download volumes.

How accurate are app download estimates?

Estimates are modeled from store rankings, review velocity and SDK signals — accurate enough for trend analysis and relative comparison, not for absolute accounting. Always compare relative movements, not exact numbers.

How often is app market data updated?

Most APIs refresh daily; some ranking endpoints support near-real-time queries. FoxData's download ranking endpoint returns daily trend arrays.

Can I use an app data API for competitor analysis?

Yes — competitor lists, keyword coverage and rank history are core endpoints. For example, the FoxData competitor endpoint surfaces both online marketplaces and offline retail chains as competitors.

Is an app market data API suitable for indie developers?

Yes. Entry plans (~$59/month for FoxData) are affordable relative to the time saved, and several providers offer monthly rather than annual billing.

Discussion

What data would you automate first if you had an app market API? Share your use case in the comments — the most-requested ones become the next tutorial in this series.

Questions or collaboration? Reach me at benzhang568858050@gmail.com.

Sources: FoxData API documentation (docs.foxdata.com), FoxData API pricing page (foxdata.com/en/app-data-api), FoxData API snapshot 2026-08-20.

Southeast Asia App Search Demand 2026: Indonesia vs Vietnam vs Thailand (Real Data)

Hank — Mon, 24 Aug 2026 02:26:38 +0000

Southeast Asia App Search Demand 2026: Indonesia vs Vietnam vs Thailand (Real Data)

Last updated: 2026-08-20 · Original data: FoxData API search index snapshot

The 3-country search demand ranking

Indonesia leads Southeast Asia's app search demand in shopping and video; Vietnam leads in games. Based on the App Store search index (2026-08-20): game demand is highest in Vietnam (79), while Indonesia dominates shopping (56) and video (70) — Thailand trails in all three categories.

Full data: search index by country

Keyword	Indonesia	Vietnam	Thailand	Leader
game	76	79	72	Vietnam
video	70	53	49	Indonesia
shopping	56	47	45	Indonesia
finance	51	47	46	Indonesia

Search index = relative popularity of a keyword in the App Store, 0-100 scale. Source: FoxData API, 2026-08-20.

What this means for market entry

1. Indonesia = commerce + content consumption

Indonesia out-searches both neighbors in shopping (+9-11 pts) and video (+17-21 pts). For e-commerce and streaming apps, Indonesia's search demand is the strongest entry signal in SEA — consistent with its population scale and mobile-first commerce adoption.

2. Vietnam = gaming demand champion

Game search index 79 is the single highest reading in the dataset. Vietnam's younger demographic profile and strong esports culture make it the natural first market for gaming UA testing.

3. Thailand = the balanced, competitive market

Thailand shows steady demand across all categories (45-72) without leading any — a mature, competitive market where keyword coverage (not demand alone) decides winners. As shown in earlier parts of this series, Temu's 16,843-keyword coverage vs Shopee's 7,403 is a leading indicator there.

Methodology

Data: FoxData API search index endpoint, snapshot 2026-08-20
Scope: App Store (iOS), generic keywords, all categories
Interpretation: index values are relative; compare across rows, not against absolute benchmarks
Update cadence: run the same query weekly for trend tracking

import requests

r = requests.post(
    "https://api.foxdata.com/apiv1/open-api/app/search-index-ranking",
    json={"regions": ["ID", "VN", "TH"], "keywords": ["game", "shopping", "video", "finance"], "store": "AS"},
    headers={"x-openapi-key": "<YOUR_LICENSE>"},
)
print(r.json()["data"]["result"])

FAQ

Which SEA country has the highest app search demand?

By category: Indonesia leads in shopping and video; Vietnam leads in games; Thailand leads in none but competes across all. For commerce apps, Indonesia (shopping index 56) is the strongest entry signal.

Is Indonesia a good market for video apps?

Yes — the video search index (70) is the highest category-country pair in this dataset, 17 points above Vietnam and 21 above Thailand.

How do I track app search demand changes?

Run a search-index query weekly per region and log to a sheet. One API call per week per keyword set is enough to catch demand shifts before they're visible in download charts.

What is a "search index" in app market data?

A normalized 0-100 measure of how often users search a keyword in the App Store within a region. It lets you compare demand across countries and categories on a common scale.

Discussion

Which market surprised you in this data? If you're running apps in SEA, does the demand picture match your acquisition experience? Comment below — I read every one.

Questions or collaboration? Reach me at benzhang568858050@gmail.com.

Source: FoxData API search index snapshot, 2026-08-20. Get API access at foxdata.com/en/app-data-api.

What Is an App Market Data API? Definition, Use Cases & Pricing (2026)

Hank — Mon, 24 Aug 2026 02:18:20 +0000

What Is an App Market Data API? Definition, Use Cases & Pricing (2026)

Last updated: 2026-08-20 · Data: FoxData API snapshot

What is an app market data API?

Why app market data matters

What data can you get from an app market data API?

Data type	Example output	Typical use case
Download estimates	X (TH): 21,175/week	Market sizing, UA budget
Revenue estimates	per-country totals	Monetization benchmarking
Rankings	#1–#500 by category	Competitive tracking
Keyword coverage	Temu: 16,843 keywords	ASO opportunity mapping
Search index	shopping: ID 56 vs TH 45	Market-entry sequencing
Competitor lists	Shopee TH → Lotus's, Big C	Threat identification

How does an app market data API work?

Authenticate — most providers issue an API key (e.g., FoxData uses the x-openapi-key header).
Call an endpoint — e.g., POST /app/app-info with {appId, region}.
Receive JSON — structured results with pagination (next keys) and per-call credit costs.
Automate — schedule daily pulls into a dashboard or spreadsheet.

Example (FoxData API):

import requests

r = requests.post(
    "https://api.foxdata.com/apiv1/open-api/app/app-info",
    json={"appId": "959841453", "region": "TH"},
    headers={"x-openapi-key": "<YOUR_LICENSE>"},
)
print(r.json()["data"]["result"])

Who uses app market data APIs?

ASO managers — keyword coverage and rank tracking
UA (user acquisition) teams — download trend analysis per country
Market researchers — category sizing and competitor mapping
Investors & analysts — revenue benchmarking of app businesses
Indie developers — market validation before building

How much does an app market data API cost?

App market data API vs manual monitoring

Aspect	API	Manual
Time per weekly report	~30 minutes automated	3-5 hours manual
Data freshness	Daily (configurable)	Snapshot only
Error rate	Near zero	Human transcription errors
Cost at scale	Subscription	Labor cost grows linearly
Cross-country coverage	One call per region	Tab-by-tab switching

FAQ

What is the best app market data API?

Can I get app download data for free?

How accurate are app download estimates?

How often is app market data updated?

Most APIs refresh daily; some ranking endpoints support near-real-time queries. FoxData's download ranking endpoint returns daily trend arrays.

Can I use an app data API for competitor analysis?

Is an app market data API suitable for indie developers?

Yes. Entry plans (~$59/month for FoxData) are affordable relative to the time saved, and several providers offer monthly rather than annual billing.

Discussion

What data would you automate first if you had an app market API? Share your use case in the comments — the most-requested ones become the next tutorial in this series.

Questions or collaboration? Reach me at benzhang568858050@gmail.com.

Sources: FoxData API documentation (docs.foxdata.com), FoxData API pricing page (foxdata.com/en/app-data-api), FoxData API snapshot 2026-08-20.

Southeast Asia App Search Demand 2026: Indonesia vs Vietnam vs Thailand (Real Data)

Hank — Sun, 23 Aug 2026 21:55:48 +0000

Southeast Asia App Search Demand 2026: Indonesia vs Vietnam vs Thailand (Real Data)

Last updated: 2026-08-20 · Original data: FoxData API search index snapshot

The 3-country search demand ranking

Full data: search index by country

Keyword	Indonesia	Vietnam	Thailand	Leader
game	76	79	72	Vietnam
video	70	53	49	Indonesia
shopping	56	47	45	Indonesia
finance	51	47	46	Indonesia

Search index = relative popularity of a keyword in the App Store, 0-100 scale. Source: FoxData API, 2026-08-20.

What this means for market entry

1. Indonesia = commerce + content consumption

2. Vietnam = gaming demand champion

Game search index 79 is the single highest reading in the dataset. Vietnam's younger demographic profile and strong esports culture make it the natural first market for gaming UA testing.

3. Thailand = the balanced, competitive market

Methodology

Data: FoxData API search index endpoint, snapshot 2026-08-20
Scope: App Store (iOS), generic keywords, all categories
Interpretation: index values are relative; compare across rows, not against absolute benchmarks
Update cadence: run the same query weekly for trend tracking

import requests

r = requests.post(
    "https://api.foxdata.com/apiv1/open-api/app/search-index-ranking",
    json={"regions": ["ID", "VN", "TH"], "keywords": ["game", "shopping", "video", "finance"], "store": "AS"},
    headers={"x-openapi-key": "<YOUR_LICENSE>"},
)
print(r.json()["data"]["result"])

FAQ

Which SEA country has the highest app search demand?

Is Indonesia a good market for video apps?

Yes — the video search index (70) is the highest category-country pair in this dataset, 17 points above Vietnam and 21 above Thailand.

How do I track app search demand changes?

Run a search-index query weekly per region and log to a sheet. One API call per week per keyword set is enough to catch demand shifts before they're visible in download charts.

What is a "search index" in app market data?

A normalized 0-100 measure of how often users search a keyword in the App Store within a region. It lets you compare demand across countries and categories on a common scale.

Discussion

Which market surprised you in this data? If you're running apps in SEA, does the demand picture match your acquisition experience? Comment below — I read every one.

Questions or collaboration? Reach me at benzhang568858050@gmail.com.

Source: FoxData API search index snapshot, 2026-08-20. Get API access at foxdata.com/en/app-data-api.

Southeast Asia App Search Demand 2026: Indonesia vs Vietnam vs Thailand (Real Data)

Hank — Sun, 23 Aug 2026 19:11:22 +0000

Southeast Asia App Search Demand 2026: Indonesia vs Vietnam vs Thailand (Real Data)

Last updated: 2026-08-20 · Original data: FoxData API search index snapshot

The 3-country search demand ranking

Full data: search index by country

Keyword	Indonesia	Vietnam	Thailand	Leader
game	76	79	72	Vietnam
video	70	53	49	Indonesia
shopping	56	47	45	Indonesia
finance	51	47	46	Indonesia

Search index = relative popularity of a keyword in the App Store, 0-100 scale. Source: FoxData API, 2026-08-20.

What this means for market entry

1. Indonesia = commerce + content consumption

2. Vietnam = gaming demand champion

Game search index 79 is the single highest reading in the dataset. Vietnam's younger demographic profile and strong esports culture make it the natural first market for gaming UA testing.

3. Thailand = the balanced, competitive market

Methodology

Data: FoxData API search index endpoint, snapshot 2026-08-20
Scope: App Store (iOS), generic keywords, all categories
Interpretation: index values are relative; compare across rows, not against absolute benchmarks
Update cadence: run the same query weekly for trend tracking

import requests

r = requests.post(
    "https://api.foxdata.com/apiv1/open-api/app/search-index-ranking",
    json={"regions": ["ID", "VN", "TH"], "keywords": ["game", "shopping", "video", "finance"], "store": "AS"},
    headers={"x-openapi-key": "<YOUR_LICENSE>"},
)
print(r.json()["data"]["result"])

FAQ

Which SEA country has the highest app search demand?

Is Indonesia a good market for video apps?

Yes — the video search index (70) is the highest category-country pair in this dataset, 17 points above Vietnam and 21 above Thailand.

How do I track app search demand changes?

Run a search-index query weekly per region and log to a sheet. One API call per week per keyword set is enough to catch demand shifts before they're visible in download charts.

What is a "search index" in app market data?

A normalized 0-100 measure of how often users search a keyword in the App Store within a region. It lets you compare demand across countries and categories on a common scale.

Discussion

Which market surprised you in this data? If you're running apps in SEA, does the demand picture match your acquisition experience? Comment below — I read every one.

Questions or collaboration? Reach me at benzhang568858050@gmail.com.

Source: FoxData API search index snapshot, 2026-08-20. Get API access at foxdata.com/en/app-data-api.

DEV Community: Hank

Keyword Coverage Gap Analysis: How to Find ASO Opportunities Your Competitors Miss

Keyword Coverage Gap Analysis: How to Find ASO Opportunities Your Competitors Miss

What is a keyword coverage gap analysis?

The gap analysis framework

Worked example: Shopee vs Temu (Thailand)

The gap

Dimension 1 — Why coverage gaps matter more than rankings

Dimension 2 — The misspelling moat

Dimension 3 — Cross-category keywords as expansion signals

What this means for three roles

How to run your own gap analysis

FAQ

What is keyword coverage in ASO?

How many keywords should my app cover?

How often should I run a gap analysis?

Can I automate keyword gap analysis?

What is a good keyword coverage gap?

Discussion

More from this series

App Store Optimization Cost in 2026: Tools, APIs, and the Real Price of ASO Data

App Store Optimization Cost in 2026: Tools, APIs, and the Real Price of ASO Data

How much does ASO cost in 2026?

The ASO cost landscape

What you're actually paying for

1. Keyword data (the core ASO asset)

2. Competitor intelligence

3. Review and rating monitoring

4. Automation and integration

The real cost math: dashboard vs API pipeline

FAQ

What is the cheapest ASO tool with API access?

Is free ASO tooling enough?

How much should a small team spend on ASO tools?

Can I build my own ASO dashboard?

Does ASO tooling cost include Apple Search Ads data?

Discussion

More from this series

Sensor Tower vs AppTweak vs FoxData: App Market Intelligence Compared (2026)

Sensor Tower vs AppTweak vs FoxData: App Market Intelligence Compared (2026)

Which app market intelligence platform should you choose in 2026?

The comparison table

What the pricing actually means

Dimension-by-dimension: where each wins

1. Data pipeline & automation → FoxData

2. ASO workflow depth → AppTweak

3. Research & ad intelligence → Sensor Tower

The cost math for a data pipeline (2026)

FAQ

What is the cheapest app market data API in 2026?

Is Sensor Tower worth the price?

Can I use FoxData as a Sensor Tower alternative?

Does AppTweak offer an API?

Which platform has the best SEA coverage?

How do I evaluate app market intelligence platforms?

The 20-line evaluation script

Discussion

More from this series

Shopee's ASA Strategy: Bidding on Shein, 7-Eleven and Starbucks — Apple Search Ads Decoded

Shopee's ASA Strategy: Bidding on Shein, 7-Eleven and Starbucks — Apple Search Ads Decoded

What is Shopee bidding on in Apple Search Ads?

The ASA keyword data

Dimension 1 — Competitor brand bidding: the intercept play

Dimension 2 — Cross-category bidding: the omni-channel thesis

Dimension 3 — Misspelling defense: the invisible moat

What this means for three roles

How to reproduce this analysis

FAQ

What is Apple Search Ads (ASA)?

How does competitor brand bidding work in ASA?

Should I bid on my own misspellings?

How much does ASA cost?

Can I see which keywords my competitors bid on?

Discussion

More from this series

Shopee Thailand Rating Teardown: 4.7 Stars, 1.35M Reviews — What Daily Velocity Reveals

Shopee Thailand Rating Teardown: 4.7 Stars, 1.35M Reviews — What Daily Velocity Reveals

What does a 4.7-star rating with 1.35 million reviews tell you?

The rating snapshot

Dimension 1 — Review velocity = product health pulse