Dynamic AI recommendations - presenting products tailored to the user's current preferences based on our proprietary pAI models
Using similar products, linked in paths, purchases, and visual similarities to increase sales of complementary items.
Delivering personalized offers not only on the website, but also in the mobile app, emails, SMS, and web push notifications
The only recommendations that work without cookies (cookieless), in incognito windows, with Firefox ETP, and with ad blockers.
What difference does it make? Up to 3.5 times higher conversions compared to simple rules from competitors or store engines.
Don’t be fooled by random product selection and presentation of best sellers under the guise of “Selected for you.”
Our recommendations allow for the implementation of various scenarios in different industries. It is neither a black box nor a single strategy without configuration options. It consists of dozens of strategies based on various pAI models and configurable to the needs of our clients.
Our own CDN allows for faster loading of customer pages (reducing the load on our own resources) and faster delivery of content to users. Our systems prepare and deliver images in dozens of different sizes – the most suitable for the user interface.
We say that this is probably the most accurate analytics in the world. We do not engage in “Analytics Fraud,” which involves attributing all transactions and the value of the entire shopping cart to ourselves.
Nor do we rely on Google Analytics, which facilitates such practices. We provide a true picture of the effectiveness of our product recommendations.
Our solution is cookieless. Resources are not blocked by most adblockers or browsers themselves.
This allows for higher conversions. In incognito and ETP mode, we still deliver recommendations while others are left with empty spaces.
For even higher conversions and faster content delivery, we have launched a globally unique semi-API implementation. As simple as from a script, without involving the IT department, but it works like an API. Conversions? Up to twice as high as with traditional scripts.
Of course, the semi-API is not the only implementation method available. Our products can also be implemented via API or, in the traditional way, via a single, simple JS script that is not blocked by ad blockers and does not require third-party cookies.
Yes, we don’t need templates. With each implementation, we recreate the appearance of elements from the client’s website. Our recommendation frames do not differ in appearance from other elements on the website.
For faster model training, the collected data can be supplemented with historical data, which will increase the effectiveness of recommendations from day one. Similarly, we supplement information about registered users, increasing the effectiveness of our recommendations.
The only recommendations that work without cookies (cookieless), in incognito windows (12% of users use them daily), with Firefox ETP (5% of users), and with ad blockers (43% of users). While Quarticon reaches 100% of users, others only manage to reach 40% of your users.
We have been developing our product and content recommendation models since 2010, which is significantly earlier than the creation of GPT generative models. This is because we operate in a different sector that developed much earlier, namely predictive AI (pAI) rather than generative AI (such as ChatGPT).
And behind it all is a powerful technological architecture. Processing large arrays of users and products involves distributed computing systems for training models, managing GPU/CPU clusters, and data flow. The need for rapid model updates also requires caching systems. This is one of the reasons why e-commerce platforms cannot offer AI recommendations, only simple rules.
Select one of several available algorithms based on popularity heuristics to target very first impressions of a new or anonymous visitor.
With the first viewed product we are trying to match the new visitor with customer journeys of all past users. It encourages to deeper product discovery and to purchase.
Post purchase recommendations (from any surface: web site, application, marketplace, brick & mortar stores) targeting with post-purchase cross-selling items .
Autonomous bundles trained on historic cart data do not require manual management, matching, linking or marking product series.
Our AI product recommendation based on pAI algorithms increase conversions and clicks on product pages, sales, number of transactions, and order value. We report this in our super-accurate statistics.
Quarticon’s Product Recommendations reduce bounce rates by presenting engaging products to users, even if they arrived at the site by accident.
We help reduce the number of abandoned shopping carts. In this way, we also influence the overall user experience and their satisfaction with using the store.
Our product recommendations affect the costs of search engine campaigns. A lower bounce rate means greater search engine user satisfaction, which reduces the effective costs allocated to the search engine.
Good product recommendations improve eCPC. How is this possible? In affiliate programs, they increase conversions, publishers achieve higher sales, effective eCPC grows, and as a result, interest and effectiveness of the program grow without raising CPS commissions!
By taking on some of the tasks ourselves, we increase the efficiency of e-commerce stores, solving their key challenges and optimizing important elements on the website.
Quarticon’s product recommendations are not just onstite product frames. They are also emails (so-called AI-mails), push notifications, and text messages. They can be used in any digital channel, which means consistent suggestions across all channels for users recognized between channels. This makes our tools truly Omnichannel (360°).
Recommendations are not limited to product frames. They can be presented in any way. For example, a list of products in an email, graphics with a discount in a push notification, an overlay with a product suggestion and additional social proof recommendations, or an in-app story. The possibilities are endless.
Personalized AI recommendations in mobile apps for new and returning users, for those who buy regularly and for those before their first purchase.
CDP (Customer Data Platform) and marketing automation systems have one thing in common—they are designed to serve so-called known users (email, application ID).
Quarticon is designed to handle anonymous users first (because there are many more of them) in real time, as well as known users.
CDP and marketing automation systems require a team (sometimes a large one) to design and implement targeted campaigns. They are resource-intensive and time-consuming.
Quarticon is autonomous—it does not require a team to operate. It makes the best decisions on its own, learns from every user action, and automatically adjusts to current trends.
Quarticon has another advantage. Not only does it work across all channels, but it also works with any CDP and marketing automation system, without the need for integration.
What does this mean? In advanced MA systems, it eliminates 99% of daily tasks. In basic MA systems, Quarticon makes them “the world’s most advanced.” That’s why you don’t have to cha
nge your MA provider to have AI on your website, in your app, and in your emails.
When it comes to e-commerce and product recommendations, no one knows it better than we do. Neither e-commerce platforms, nor marketing automation systems, nor CDP systems. We have been building recommendation models (pAI) since 2010.
With our tools, you can offer your users a much higher level of experience.
Fill out the form and see how we can take care of conversions in your store.
Order Product Recommendations Start on our website. See the price list for all available options.
The primary goals of implementing AI-based product recommendations are to sell, sell and sell again.
With every movement of the user, we know better and better which product he/she will be interested in and which he/she will put in his basket. We even know this earlier than the user himself.
Recommendations improve also overall shopping experience by consistently providing personalised suggestions for the most relevant products.
Absolutely! You can customize personalised product recommendations to suit your preferences. This level of customization ensures that the tool reflect a your specific interests and needs, enhancing the overall shopping experience.
Personalised product recommendations are created using a range of Big Data, including behavioral data (such as anonymous purchases, views, and clicks, browsed products, time spent) and content data (like item metadata). The system can also tailor product suggestions based on a user’s browsing history.
By offering relevant product suggestions, personalised product recommendations can boost user engagement and enhance the browsing experience, leading to higher user retention and lower bounce rate. Since engagement and user experience are crucial factors in SEO rankings, these recommendations may indirectly contribute to improving your website’s SEO.
Many entities provide product recommendations, but this does not mean that they are valuable recommendations based on machine learning. Finding connections between products requires significant resources and computing power. Most providers, such as shopping platforms, marketing automation systems, and CDP systems, simply provide simple rules – best sellers or recently viewed items. These are not real recommendations! Even random product displays achieve better responsiveness than many simple rules.
No. ChatGPT, or generative AI (gAI), and AI-based predictive models (pAI – more about predictiveAI here) are two different branches of artificial intelligence. Contrary to popular belief, generative AI will not diagnose diseases, adjust drug doses based on patient genomics, analyze epidemic trends, predict the development of infectious diseases, detect cardiac problems, detect behavior patterns between products and users, or recommend relevant products. This requires pAI, and in the latter application, Quarticon.
There are several cases when it is not worth using Quarticon’s product recommendations: