Shake – Analyze any company with online reviews

Hacker News

Hello HN! We all know how important online reviews are - I've personally had a front row seat in the industry from running both Reviewshake and Datashake for the past 4+ years. Online reviews are used to inform decisions by everyone from consumers to businesses like asset managers and consultancies to risk and reputation management firms. The challenge is that reviews are spread across the web - McDonald's for example has thousands of locations and over 100k+ review profiles across a whole host of different review sites, and analyzing this data has been incredibly difficult in the past. Shake fixes this problem by exposing data powered entirely by our technology in a visual interface, allowing anyone to get a singular view for customer experience. This works whether it's a company with 1 to 50k+ locations, software businesses, e-commerce players and more. Not only can you analyze a single company in this interface, you can compare up to 3 companies side by side. Here are some sample profiles to see just how powerful this is: 1. McDonald's vs Burger King vs Five Guys: https://shake.io/reviews/mcdonalds/vs/burger-king/vs/five-gu... 2. Twilio vs MessageBird: https://shake.io/reviews/twilio/vs/messagebird 3. ReviewTrackers vs Birdeye vs Podium: https://shake.io/reviews/reviewtrackers/vs/birdeye/vs/podium We're just getting started here! Are you looking to use online reviews in your business? Get in touch!

Visit product View on Hacker NewsDec 13, 2022

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

82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

58%58% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: visual, single · Missing: mac, agents, macos

48%48% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

38%38% 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

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · 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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