KPI Crunch – instant insights into any competitive landscape

Hacker News

We've just released the beta version of KPICrunch, a free tool giving you instant insights into any competitive landscape. The MVP offers 2 options: 1° create a list based on 2 reference domains (that's the default option) 2° paste an existing list of up to 25 (+1) domains Then let the KC engine collect & crunch data. In this first iteration, you'll get the following data points for each domain: - global web rank (we're using OPR) - # of indexed pages on Google (nice to evaluate the online footprint) - estimated organic traffic - # of KW indexed in G Top 100 + Keyword Performance (Traffic To Keywords) - automated identification of social media channels + engagement per channel You'll also get a high-level summary of the landscape, incl. a traffic distribution pie. I invite you to test the app. The MVP is the first stage of a more ambitious project: we're building a Competitive Intelligence Open Platform, a productivity tool where you'll be able to consolidate all your Competitive Intelligence research efforts, on a wide range of data points (we'll soon add business data, HR data, UX/UI changes monitoring, pricing & features monitoring, newsletter & advertising monitoring, etc.). You'll be able to create your own custom lists, add personal notes and tags to filter your lists + collaborate with other team members. The tool will also become a CI Search Engine, giving you access to the landscapes shared by the community (you can already get a taster via the search engine on the current home page). Looking forward to your feedback!

Visit product View on Hacker NewsAug 3, 2022

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, new, using · Missing: mac, agents, macos

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best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: platform, soon · Missing: plus, intuitive, reviews

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal, google · Missing: mobile apps, ios, entrepreneurs

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: just released, exist, lua · Missing: https docs, excited, open source

32%32% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: collaborate · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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