Spy on Optimizely customers' experiments

Hacker News

Here is a bookmarklet you can use you browse or spy on an Optimizely customer's experiments! javascript:window.jQuery && jQuery.getScript("//gist.github.com/optimizelyspytool/7a7f573ec1657fb7db97/raw"); (highlight and drag this to your bookmark bar on your browser) Since all of their experiment code is made publicly available in the javascript library, rather than server-side decisioning, it's all waiting to be browsed! I saw a site made by the guys over at http://nerdydata.com but has since been removed. Post any interesting finds using the tool!

Visit product View on Hacker NewsMay 15, 2014

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Actual performance

9points

1comments

Made the leaderboard

Launch Intel predictions

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

64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

47%47% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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 · Missing: web3, chat, crypto

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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