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KWRDS: App Store Optimization

by Steffen Ruppel

  • Developer Tools
  • Free
View on the App Store
Rating
0.0
0 ratings
Last update
Sep 21, 2026
Version 2.1.1
Size
16 MB
Category
Developer Tools
Tracked since
Oct 24, 2026

Estimated revenue

A model estimate with a likely range, not a reported figure.

No estimate. Apps outside the grossing charts, and some countries, have no reliable rank-to-revenue curve, so we show nothing rather than a guess.

How this estimate is made

Rank: we take the app's best Top Grossing rank on the latest chart day. Category ranks are converted into an equivalent overall rank, because a #10 in Finance earns far less than a #10 overall.

Curve: that equivalent rank is read off a rank-to-revenue curve anchored on public company revenue and market-data reports for the US iPhone App Store.

Country: the curve is scaled per country (Japan is roughly a third of the US, Türkiye under 1%). Where we have no scale, such as China, there is no estimate.

Range: the likely range widens with country, category and chart depth. It is a plausible span, not a statistical confidence interval.

Not included: ads, payments outside the App Store, and any difference between gross sales and what the developer keeps. Low confidence means indirect data; medium means several independent sources agree.

Full method

Screenshots

As shown on the App Store listing.

About this app

Text from the App Store listing.

Track. Research. Optimize. Your entire App Store keyword workflow in one native Apple app.

KWRDS (KeyWoRDS) is a focused App Store Optimization (ASO) toolkit for iPhone, iPad and Mac – built for indie developers, publishers, ASO managers and agencies who want to get found in the Apple App Store without juggling spreadsheets and web dashboards.

WHAT YOU CAN DO

◆ Track your keyword rankings
See where your apps rank for the keywords that matter – across countries and storefronts. Update rankings in the app and review positions, changes, gains and risks at a glance.

◆ Discover keywords with keyword networks
Start from a single term like “fitness” and KWRDS builds a network of related terms, realistic search phrases and long-tail combinations – so you spot opportunities a flat list would hide.

◆ Analyze your competitors
Add direct and similar competitors and see the keywords you share, where you overlap, and the gaps that become your next opportunities.

◆ Plan your metadata with confidence
Draft your app name, subtitle and keyword field with Apple’s character limits shown live (30 / 30 / 100). Compare versions before you apply them yourself in App Store Connect.

◆ Cover more with cross-localization
See which keywords are already covered by other localizations in a storefront – and stop wasting characters on duplicates.

◆ Manage many apps and markets
Add as many of your own apps as you need. Organize tracking, competitors and metadata per app, country, language and storefront.

TRACKED KEYWORDS vs. METADATA KEYWORDS
Two different things: the ranking keywords you actively track can depend on your plan – but the keywords you plan in the metadata editor are always unlimited.

BUILT FOR APPLE
KWRDS is a native app for iPhone, iPad and Mac, focused entirely on the Apple App Store.

Download KWRDS and turn keyword guesswork into a clear, repeatable ASO workflow.

What’s new

Release notes for the latest version, as written by the developer.

v2.1.1

Technical improvements for better stability and various bug fixes.

Rank history

Daily position in the Israel charts. #1 is at the top.

Best rank
#10
Current rank
#14
Days charting
2 of 7

On the charts today

Small & simple score

How realistic it is for a small team to build something like this.

59/100

Not flagged as small & simple

  • Developer size40% · 25

    Developer: 3 apps on the charts

  • Audience size– · 20

    No data, weight redistributed

  • App size100% · 10

    Size: 16 MB

  • Price model80% · 15

    Price model: free with in-app purchases

  • Demand proof50% · 30

    Best rank #10, 2 countries

An indicator, not a fact. We can't see team size. Calculated Oct 25, 2026 · method i2

How the score works

Version history

  • 2.1.1LatestReleased Sep 21, 2026