Almeta ML

Almeta ML

Predict customer behavior with ML, save on ads

5.0
1 review

294 followers

Predict customer behavior on your website in real time, use these predictions in marketing. Example: predict how likely a person is to make a purchase, target them by that metric on Google Ads, Facebook Ads, etc. Use with lead scoring, emails, advertising.
Almeta ML gallery image
Almeta ML gallery image
Almeta ML gallery image
Almeta ML gallery image
Almeta ML gallery image
Almeta ML gallery image
Free Options
Launch Team / Built With
Webflow | AI site builder
Webflow | AI site builder
Start fast. Build right.
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What do you think? …

Alex
Can it pull the data from Customer.io?
Igor Gusarov
@jitbit Yes! Great question. At the moment it requires a bit of an effort to set up, but it works. I'm building several new integrations, so in a few weeks you'll be able to set it up in a few clicks. Thanks!
Ashik Hameed
Just wanted to share my excitement about Almeta ML :D Firstly the ability to predict customer behavior in real-time and utilize these predictions for targeted marketing campaigns on platforms like Google Ads and Facebook Ads is fantastic. This kind of insight can significantly enhance marketing efforts and improve conversion rates. I also appreciate the flexibility of the free plan and the fact that Almeta ML supports both pre-built and custom ML models. The integration capabilities with various advertising and email platforms are a great addition, making it easier to implement and use these insights effectively. The focus on privacy and data security is another important aspect, ensuring that sensitive information is handled with care. The option for private deployment and the use of private API keys show a strong commitment to data protection. I'm looking forward to using Almeta ML to see how it can help predict user actions and enhance our marketing strategies. Kudos to the makers for creating such a comprehensive and powerful tool! @igorgusarov
Igor Gusarov
@ashikhameed Ashik - thank you for the kind words and support! Let me know if you need any help setting up Almeta ML.
Valeri Komyagin
Great idea! Congratz! Sound very intriguing for my SaaS product!
Igor Gusarov
Thanks Valeri, appreciate the support! Let me know if you need any help with integration.
Kshitij
Congratulations on the launch, @igorgusarov! This is a game-changer for digital marketers and growth hackers, especially with its real-time predictive metrics. I'm particularly interested in the applications for personalized product recommendations for eCommerce. Could you share more about how easy it is to integrate Almeta ML with existing platforms?
Igor Gusarov
@kshitij11 Thanks Kshitij! Integration is very similar to any analytics system that marketers are used to. You can put a web tag on your site using Google Tag Manager, setup event tracking - and you're good to go. That said, I'm working on direct integrations with existing platforms. Segment, AWS Redshift, Shopify and Webflow integrations are coming very soon, stay tuned! Appreciate the support!
Kshitij
@igorgusarov Looking forward to the future integrations, really cool. Congrats again
Eskov Vitaly
Congratulations on the launch of Almeta ML , @igorgusarov! 🚀 The potential of real-time customer behavior predictions to enhance user engagement and conversions is really impressive for me. Additionally, I admire the approach to creating specialized models. This really shortens the path for predictive models in an operating business. Good luck!
Tony Han
💡 Bright idea
Wait, is this magic? If you tell me 5 years ago, ecommerce website can just plug into an API or web tracking code and they get results like these, I'd say you are BSing. But I guess ML has come a long way. I'm curious what do you use to train your models? How do you ensure the model would work for a specialized market? I also love that you have a free tier for folks to try things out! If the model works like magic, people will convert. Let results speak for themselves. Congrats on launching magic I guess @igorgusarov and team! A tip: I think given that you have a usage based pricing, it'd be nice to have a slider + calculator to show the cost would be a nice addition!
Igor Gusarov
@tonyhanded Thank you for the kind words! The models used are mostly different kinds of neural networks with attention mechanisms. For propensity models it's a transformer model that has an inherent ability to capture complex patterns in the data and make predictions based on the user's behavior and preferences. I'll work on that slider+calculator. Thank you for your support!
Tony Han
@igorgusarov - interesting! Thanks for sharing!
Omi K.
💡 Bright idea
Congrats on the launch! l’m curious about how you predict customer churn and how accurate it is. My product offers behavioral segmentation of online store customer, including an ‘at-risk of churn’ segment, though it’s really easy probability estimation based on purchase cycle data. If your solution is powerful enough I would like to refer this to our users and also want to discuss the possibility app integration
Igor Gusarov
@umisea Hi Omi! Great question! Churn is calculated as a probability of the corresponding events. These calculations are utilizing several data points: 1. Previous actions of the current customer (tracked as events, including event details) 2. Historical actions of all other customers 3. Customer attributes 4. Catalog data (like products, collections, offers, etc) All event sequences are packaged for the ML model in a way to maximize the use of positive and negative examples, as well as timing information. This data is used to train the model (each website has their own model trained on their specific data). That's a "transformer" model that has built-in attention mechanisms, and identifies behavior patterns that lead to the target event. The output of the model is the probability of the target event (in this case, churn). I would love to connect and see if we could integrate, will reach out on LinkedIn. Thank you for your support!
Omi K.
@igorgusarov Thank you for your detailed response! I’ll reach out via LinkedIn later with some follow-up questions. Congratulations for now!