Loops is a no-code platform that empowers product and data managers to maximize conversion, retention and other KPIs. Instead of more dashboards, it leverages causal AI models to proactively identify insights hidden across their data.
LoopsMaker
📌Hi Hunters!!!! 😻
I'm thrilled to announce our Loops debut within the ProductHunt community. Let me provide you with some quick background on what we have achieved in the past 4 years.
We co-founded Loops in 2020 - we were sick of product analytics “data theater” - endless dashboards but little actionable insight. After running data and analytics at Google EMEA, I advised dozens of companies on analytics and saw this problem first-hand. We knew that data should truly be a force multiplier in the company's growth.
Loops empowers analytics and product teams to identify the biggest opportunities to maximize their growth and revenue. We leverage causal inference AI models, which show insights based on causation, not just correlation. Insights are automatically pushed to your Slack/MS teams/Email in natural language, leveraging our GEN-AI models.
With Loops, you get insights like:
-What’s your aha and habit moment?
-What journeys and features causally drive retention?
-Why KPIs are going down?
-What’s the impact of my recent product launch? (even without A/B tests).
With Loops, data analysts can truly make an impact on growth, and product managers focus their efforts where it matters.
Our fast-growing customer base includes large and high-growth companies looking for an edge, like Monday.com, Postman, Clickup, Scribd, and many others. ❤️ Often, our customers find an all-time top insight during a free Loops trial. Check out our case studies!
If you’re looking for a better way to drive growth, take a look at Loops.
Try our demo environment, a quick walkthrough will set you up to experience the power of Loops for yourself. Or Try Loops on your OWN data, who knows what you’ll uncover in our 14-day free trial!
I’m looking forward to meeting you in the comments. Please let me know what you think! And help make our Product Hunt debut a success!
love 🧡
-Tom
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@tom_laufer1
Congratulations on the debut of Loops in the ProductHunt community. I think many companies are interested in learning what paths and features are the reason for retaining growth and revenue. Great product!📈
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LoopsMaker
@sinikiwe_phahlane thanks a lot!
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LoopsMaker
@ansar thanks, we work hard on a full demo environment, so people can see the product first-hand!
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LoopsMaker
@sinikiwe_phahlane Thanks, check out our new demo environment!
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@tom_laufer1 Hats off to the team! Wishing you a bright and successful journey ahead. What has been the biggest surprise during development?
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Looks amazing, maybe too good ;)
Tom, How do you know that your insights actually work? my number one issue with data is trust... Thanks :)
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LoopsMaker
@ariel_kedem2 Hi. DS lead here. Great question. While there's never complete certainty in analytics, we at Loops have the privilege of having access to data from dozens of different companies running hundreds of A/B tests. We thus able to compare the results of our causal models with those obtained from A/B tests that were initiated by those causal analyses. You can read more about how we validate our release impact algorithm here:
https://www.getloops.ai/blog/the...
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@iyarlin thanks for the reply!
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LoopsMaker
@ariel_kedem2 Numbers-wise: 80% of the experiments triggered by Loops insights are successful!
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@ariel_kedem2 Definitely check out the Loops blog and the article shared by @iyarlin. Thank you so much for supporting our Hunt today!🤛
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🔌 Plugged in
A long-time Loops enthusiast here!
I've been using Loops daily for the past year, and it has significantly enhanced various aspects of my work as a product analytics group leader. Loops makes it easy to track the health of our main KPIs, alerting us to any anomalies with detailed explanations of the drivers behind each one. It validates and analyzes our AB tests enabling us to harness the power of Bayesian AB testing. We also rely on Loops for product research questions, seeking causality in existing features, and understanding feature performance across different customer segments.
The list goes on and on, keep on building a great product and good luck!
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LoopsMaker
@shnork Thanks Amir! You contributed a lot to our product design. Appreciate it!
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LoopsMaker
@aengel Thank you for being an amazing partner of Loops from the early days. We're truly lucky to have you.
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daily.devLong time user of Loops and it's just amazing! It speeds up our ability to bring valuable insights to table and eventually iterate at a speed of light. Keep up the good work looking forward for new features to unlock our growth 🤩
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LoopsMaker
@idoshamun thanks so much for supporting us from the early days!
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LoopsMaker
@idoshamun Thanks so much!
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LoopsMaker
@idoshamun Thank you!
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@idoshamun 🙌
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NebblWow, this is so cool! Will definitely try it out for our upcoming product! Congrats on the launch and kudos to the team, upvoted!
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LoopsMaker
@pavel_bocharov Thanks a lot, looking forward to your feedback :)
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@pavel_bocharov Awesome! Thanks for the support! Enjoy playing around in our demo environment and remember to let us know if you prefer to try it out with your own data!
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Sounds incredible, and you're doing a great job.
One question, though, can you elaborate on the integration, please?
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LoopsMaker
@yan_yanko Sure. It's a no-code integration. We connect to your analytics solution (like Amplitude, Mixpanel, Amplitude,etc) or data warehouse and map out the data automatically. Then you define your KPIs. On average, the integration takes a few hours. And we run in your environment: We don't store the data, and you don't send us the data, which makes it easier from a privacy/security perspective.
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@yan_yanko Thanks for the question! @tom_laufer1 has you covered on how straightforward and fast it is to connect your data to Loops.Thank you for asking and for your interest in Loops🙏
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mgmateCongrats on the launch.
Could it identify statistically nonsignificant data? From my experience product managers could push analytics for dashboards when there are no useful data, and try to deduce insight from it, even though it's impossible.
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LoopsMaker
@hr6134 If there is not enough data, we'll surface that there are non-significant results. We do use different methods to minimize the amount of data needed to produce these insights, but you're right that there is not enough data sometimes and it's just noise, and we mark that.
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LoopsMaker
@hr6134 This is a great point, I think as humans we see connections even when the data doesn't support it - this is why it's important to us to always add a statistical test to any analysis in the platform, protect from small sample sizes and in general help you translate your business question into an analysis that will actually answer the question (so many times even with enough data you can get the wrong conclusion by just phrasing the question in the wrong way).
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