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Zappi Ad Predictor
p/zappi-ad-predictorPredict your digital ad's performance instantly, for free.
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Nik Hazell

Image Ad Predictor by Zappi - Predict your digital ad's performance instantly, for free

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Based on a ton of historic data, lots of features from your ad, and some groundbreaking predictive eye tracking tech; Zappi & EyeQuant help you improve your ads instantly, for free. - Upload your ad 📷 - Tell us about it 💻 - Get results & heat maps! 🔮🔥

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Nik Hazell
Maker
Thanks for your support, PH! Roughly a year ago we launched V1 of the Digital Ad Predictor, with support for video ads only. You gave us feedback, and we've acted on it - now, test images, with a great new integration from EyeQuant that predicts exactly how well you'll capture your audiences' attention. Please, let us know - is this useful? What do you want to see next? Thanks! 😸🔮
Nik Hazell
Maker
@fares_aktouf thanks, appreciated!
Nik Hazell
Maker
@sachin_shajan thanks very much!
Alex Furman
Maker
Hey PH, Alex here - we’re excited to be back! As a quick recap, our Digital Ad Predictor gives you a free performance prediction report for your digital ads BEFORE you go live with them. Simply upload your ad, choose your ad’s category and get your report within minutes. Since our V1 launch, our biggest feature request was to add support for images (in addition to videos). And that’s exactly what we’re launching today! V2 lets you run the predictor on both your static AND video ads. Static ad reports come with a free visual attention heatmap, powered by EyeQuant - it uses some awesome (and genuinely groundbreaking) A.I. to predict what your audience will actually notice and see! Here’s a quick feature summary - Get a free strength & weakness report for your ad (static & video) - Get an industry benchmark report for your ad (static & video) - Get a free visual attention heatmap for your ad (static only) This insight is designed to be actionable, and help you rapidly improve your ad BEFORE putting it in front of your audience. Here’s an explanation of how it works: The Scores: Zappi uses a gradient-boosting machine learning algorithm to rapidly deliver a free prediction, based on a huge volume of anonymised and aggregated survey and norms data. What this means in layman’s terms is that a load of tags — both manually added and assigned algorithmically — are collected, cleaned, and fed into a decision tree model that then “boost” important components, while suppressing less important ones. This is the XGboost regression model. We’re using a machine learning model that predicts key metrics based on the attributes of the tested ad, e.g. branding, celebrity/human presence, length, etc. The model is trained on ads tested on Zappi Video/Digital and the prediction falls on average within 5% of the observed survey scores. This kind of tech traditionally lives in the Enterprise space — we’re excited to be making it available to everyone! Feel free to pop any questions in the comments 🚀
Brenna Donoghue
Love this. Very excited to try it out and take some of the guesswork out of ad design.
Alex Furman
Maker
@brenna_donoghue We hope you find it useful! ⚡ Don't forget to try the "share" feature when you get your report 😎.
Nik Hazell
Maker
@brenna_donoghue @alex_furman Thanks Brenna! We're really pleased with the predictive eye-tracking in particular - the heatmap is awesome!
Nik Hazell
Maker
@karimsaif thanks Karim!
Paxshal
Wow...Great tool to get insights before ads go live... Congratulations @nik_hazell for the launch!
Nik Hazell
Maker
@pax93fcb thank you!
Daniel Baum
@nik_hazell this is awesome! With ad spending becoming so expensive, a tool like this can really help companies optimize
Alex Furman
Maker
@nik_hazell @daniel_baum Thanks Daniel! Indeed, the Predictor is designed to complement your existing stack. Run it on your ad *before* going live and gain some insight which you'd otherwise only get from testing live on your audience!
Hannah Godfrey
Looks great! How does it work?
Ismail Pelaseyed
This looks pretty cool @nik_hazell! Congrats on the launch!
Nik Hazell
Maker
@pelaseyed thanks, much appreciated!
Ash Rahman 🎮
Sounds like a cool product. Would be game changer for small Facebook based businesses trying to optimize their ads.
Nik Hazell
Maker
@ashrahman exactly! Big brands have been able to use this type of insight for decades - but it's usually delivered by consultants, so it's usually prohibitively expensive! Hopefully, we can start to help out some of the smaller players :)
Sven Radavics
Paid ads are so challenging right now. This is the sort of thing we all need. Congrats on the launch!
Nik Hazell
Maker
@sven_radavics thanks Sven! Getting a good return on your investment is SUPER hard, so you might as well get the basics right :)
Selin Cakir
Cool product! Congrats on the launch!
Nik Hazell
Maker
@selincakir thank you Selin, much appreciated!
Ghost Kitty
Comment Deleted
Nik Hazell
Maker
@mouna_selmi thanks very much, appreciated!
Kirsten
I really love this! This should be a tool that creative agencies just intuitively use so they can best understand how their media will perform. If I could add a suggestion, you could also integrate tips on best practices across channels, i.e. how to ensure ads are set up for appropriate aspect ratios per placement (and that that helps underlying algorithms push their content).
Nik Hazell
Maker
@kirstenndb Hey Kirsten, thanks for your feedback! Great ideas - yes, we should certainly be able to do that. We can check that the file-type/ratio/quality etc are optimised for your chosen platform - and give you some technical feedback, as well as broader advertising advice!
Ingrid
🆙 🚀🚀
Nik Hazell
Maker
@ingrid_azeredo thank you Ingrid!
Irete Hamdani
Can't wait to try it out!
Nik Hazell
Maker
@ireteh Thanks Irete!
Rich Watson
great product. congrats on the launch!
Nik Hazell
Maker
@richw Thank you Rich!
Naveed Rehman
I hope this product goes far, as consumer-side adoption is a missing puzzle in this space! Thanks for making and sharing this product 🙏. Thanks for sharing this product 🙏. Happy to support you during and after the launch ❤️.
Nik Hazell
Maker
@naveed_rehman thanks for you comment Naveed - appreciate the feedback!
Naveed Rehman
@nik_hazell Many startups are publishing their product analytics publicly. It helps them grab public attention and multiplied engagement. Your product seems to have similar potential. I can guide you a bit into this. Twitter/LinkedIn DM?
Nik Hazell
Maker
@naveed_rehman we're actually a small part of a larger startup - so a bit more red-tape to navigate around sharing things publically!
Alexander Sienkiewicz
How does this work for video? What exactly are you able to obtain from video analytics? Would love to know more about that, since static image ads are less relevant. Thanks!
Alex Furman
Maker
@alexsien Hey Alex! For both video and image ads you get a strength/weakness report and an industry benchmark score. The only difference is that the image report comes with a heatmap (powered by EyeQuant) whereas the video report does not. The heatmap (Attention Map) indicates which parts of the image ad are most likely to grab your viewers’ attention. Note that this uses a predictive model - there's no live eye-tracking going on. Let me know if that makes sense! You can also check out our V1 launch which focused on video prediction: https://www.producthunt.com/prod...
Vitali G
Wow! Looks great! Curious to try it out)
Nik Hazell
Maker
@vitaligisko thanks very much!
Hiro - Atsuhiro Teshima
@nik_hazell Congrats on the launch. The service looks great. Does it support showing estimation by social platforms or by targeting?
Nik Hazell
Maker
Thanks @atsuhiro_teshima ! At the moment, Zappi's full subscription can test ads in a social media environment; we position your ad in a mocked-up feed/timeline/story, show the ad to a specific demographic, and then collect structured feedback that you can explore. Unfortunately this isn't available on the free/instant tool we've just released 😅