App Review Competitive Report — Head-to-Head Comparison avatar

App Review Competitive Report — Head-to-Head Comparison

Pricing

from $16,750.00 / 1,000 competitive reports

Go to Apify Store
App Review Competitive Report — Head-to-Head Comparison

App Review Competitive Report — Head-to-Head Comparison

Pricing

from $16,750.00 / 1,000 competitive reports

Rating

0.0

(0)

Developer

NexGen Watch

NexGen Watch

Maintained by Community

Actor stats

0

Bookmarked

2

Total users

1

Monthly active users

a day ago

Last modified

Share

App Review Competitive Report — Head-to-Head Sentiment, Themes, Complaints

Stop paying $50–100+/month for a review-intelligence subscription to compare your app against the competition. This actor delivers a full head-to-head competitive read across up to 5 apps — the same deep per-app analysis plus the positioning layer — per report, on demand, with no subscription. Apps in, one competitive intelligence report out.

Typical review-intelligence SaaSApp Review Competitive Report
Pricing$50–100+/month, every month$25.00 / report (FREE tier), pay only when you run
ScopeOne seat, rate-limited comparisonsUp to 5 apps compared head-to-head, per report
CommitmentAnnual/monthly lock-inNone — per report
Break-evenRun a competitive read every week (~$100/mo at FREE, less on volume tiers) — and only in the weeks you actually need it

If a PM, ASO consultant, or agency pulls one competitive read per week, that's ~$100/month at the FREE tier (and less on BRONZE/SILVER/GOLD) — versus $50–100+/month for an always-on seat you rarely max out, per analyst. One report already covers a 5-app landscape that a single-app tool would bill you five runs for.

What you get in one report

Feed it 2 to 5 apps (each app_id + store, optional country/company_label). For every app it fetches up to 1,000 public reviews (logged-out, public data only) and runs the full ngd-lexicon-v1 deep analysis — then adds the head-to-head layer:

Per app (same depth as the single-app deep report):

  • Full sentiment breakdown — positive / neutral / negative split, average sentiment score, average star rating
  • Theme clustering — the terms and phrases reviewers actually use, ranked by mentions
  • Complaint ranking — negative-review clusters ranked by frequency, each with its share of negatives and a real example quote
  • Feature-request extraction — reviews that ask for something, clustered into the most-requested asks
  • Trend vs prior period — automatic median split, with sentiment and rating deltas

Head-to-head positioning (across all apps):

  • Relative sentiment leaderboard — apps ranked by sentiment score, positive %, negative %, and average rating
  • Shared vs unique themes — what reviewers raise across the whole category vs what's specific to one app
  • Shared vs unique complaints — the pain everyone shares vs the pain that's yours (or your competitor's) alone
  • Winner by dimension — who leads on sentiment, rating, fewest complaints, developer responsiveness, and raised demand
  • Overall composite positioning — a single ranked standing across the leaderboard dimensions
  • Rendered markdown competitive brief — the whole comparison as a readable report (a dataset field and a key-value-store record), ready to paste into a deck, battlecard, or ticket

Output

  • One competitive_summary dataset record — the full head-to-head report (leaderboard, shared/unique diffs, winners, composite ranking, roster) plus the rendered markdown brief.
  • One app_report dataset record per compared app — the full deep analysis for that app.
  • Markdown briefs are also written to the key-value store (COMPETITIVE_<id> for the comparison, REPORT_<store>_<app_id>_<id> per app).

Input

FieldRequiredDescription
targetsyesArray of 2 to 5 apps. Each: app_id (Apple numeric id or Google Play package), store (apple or google_play), optional country (default us), language (Google Play, default en), company_label (display name)
max_reviewsnoCap on public reviews fetched per app (default/max 1000)
max_themesnoMax theme and complaint clusters per app (default 12)

Method — deterministic, transparent, model-included

Analysis runs the ngd-lexicon-v1 engine: a transparent lexicon plus a rating prior — the identical engine used across the review-intelligence fleet, so scores are comparable app-to-app. It is fully deterministic — the same reviews always produce the same competitive report. No external LLM, no API key to bring, no "AI" black box. The model is included in the price.

Pricing

  • Actor start: $0.05 (one-time, platform-reserved).
  • competitive_report: $25.00 per report at the FREE tier (BRONZE $22.50 · SILVER $20.00 · GOLD $16.75). Charged exactly once, only after the full competitive report over at least 2 apps is successfully built and delivered. If fewer than 2 apps yield reviews, or analysis can't run, the run fails and you are charged nothing.

Notes

  • Public, logged-out data only. No login, no private endpoints.
  • Cost-bounded: memory/time caps, a bounded per-page request-attempt budget, and a hard per-app review/output cap.
  • An app that returns no public reviews is skipped and listed under skipped_targets; the report still runs as long as at least 2 apps have data.