Are product recommendations in marketing automation worth choosing?
Marketing automation is a very costly, resource-intensive system that personalizes experiences for 5%* of users, while leaving 95% of users with a loop of a few popular products.
While marketing automation “handles” personalization for only 5% of sessions, Quarticon personalizes experiences across all channels for 100% of users.
To genuinely increase e-commerce sales without increasing traffic, choose a recommendation engine and a basic email marketing tool: MailerLite, Mailchimp, Brevo, or Mautic.
100% personalization • Lower costs • Less aggressive communication • Consistent communication across all channels
*For a medium-sized online store, only around 5% of users are identified (recognized) on the site.
Marketing automation: a costly solution that personalizes only 5% of sessions
Marketing automation personalizes the store’s offer for ~5% of users. Only this share of users on store websites is recognized—that is, linked to a unique customer identifier, most often an email address. The remaining 95% are anonymous users, for whom marketing automation displays popular products.
Marketing automation: missed e-commerce opportunities
95% of users are missed
Marketing automation personalizes products for ONLY 5% of users (sic!).
The entire main user group (95%) on the site is shown the same carousels of best-selling products.
Popular products are promoted
The most commonly used mechanisms in marketing automation are heuristic statistics—popular and best-selling products.
This is not AI. These mechanisms are fast and inexpensive, and require neither complex models nor resources for complex calculations. But they deliver results that are more than three times lower.
Models are trained incorrectly
Training models on just 5% of users is subject to substantial errors.
20 times less data, 20 times longer training time.
The level of training Quarticon achieves after 1 month (which is still a fairly low level of model accuracy) takes these systems nearly 2 years to reach! This does not even account for the errors caused by training on 5% of the population.
See how Quarticon’s recommendation engine turns traffic into sales
Personalizes all traffic
No name, phone number, date of birth, or email address is needed. What is needed is user behavior, which is compared with that of hundreds of thousands of others to identify the user’s purchasing profile.
100% of users are reached in 100% of sessions.
Selects products more effectively
The first impression for an anonymous user features bestsellers. The second, however, does not. Even on this short journey (2 products), an attempt is made to select the next product that best matches the user.
The pool of recommended products differs for each user and changes with every interaction. Products are selected to drive sales quickly.
Trains models faster
Models are trained using 100% of users and all sessions. They are not skewed by a potentially specific group of newsletter subscribers who click on emails.
Training takes little time, although the models continue learning throughout their lifetime.
Did you know …
Quarticon’s recommendation engine does not require cookies
The only recommendation engine that works without cookies (cookieless), in incognito windows (used daily by 12% of users), with Firefox ETP (used by 5% of users), and with ad blockers (used by 43% of users).
While Quarticon reaches 100% of users, other systems (particularly CDP platforms) attempt to reach only 40%.
Quarticon was created even before OpenAI
Our product and content recommendation models have been developed since 2010.
That was much earlier than the creation of generative GPT models. This is because Quarticon operates in a different field, which had already been developing for much longer: predictive AI (pAI), rather than generative AI (like ChatGPT).
More about predictiveAI here.
Quarticon performs hundreds of millions of calculations every day
This is made possible by a powerful technology architecture. Processing large matrices of users and products involves distributed computing systems for model training, GPU/CPU cluster management, and data flow. The need for rapid model updates also requires caching systems. This is one reason e-commerce platforms cannot offer AI recommendations and instead provide only simple rules.
Why choose Quarticon’s product recommendation engine
Quarticon drives even greater sales growth
Based on predictive AI algorithms, Quarticon’s product recommendation engine increases conversions and clicks on product pages, sales, the number of transactions, and average order value. These results are reported in our highly accurate statistics.
Quarticon product recommendations reduce bounce rates by presenting products that engage users, even when they may have landed on the site by chance.
See how to achieve an ROI of 9000+ (sic!) with Quarticon recommendations
Quarticon works with both anonymous and registered users
Quarticon’s product recommendation engine works equally well with known and anonymous users.
If 95% of traffic can be handled, the remaining 5% can be handled too. Recommendations for known users take into account their entire purchase history and current activity.
If the last 3 transactions by a user named Marta96 were for dresses, but today Marta96 is looking for shorts for a 3-year-old, marketing automation will almost certainly insist on recommending dresses, while Quarticon will recommend children’s shorts bought by other mothers.
Quarticon’s recommendation engine handles email
Quarticon’s product recommendation engine handles the product content in emails: which products from a catalog of hundreds or thousands to show to each recipient. Instead of creating segments and separate emails for each one, a single Quarticon block can be added and the email will be automatically populated with products tailored to each user.
There are as many segments as there are records in the database. And all it takes is one product block added to an email—it does not need to be changed for each send.
Discover our clever email personalization solution and increase sales from email marketing by 60%+
Quarticon AI emails make it easy to create product blocks that can be added to any email in any sending system—even Gmail!
How to improve marketing automation efficiency: 2 simple steps
1. Reduce marketing automation costs
Since marketing automation means:
– targeting only 5% of website sessions
– requiring complex, time-consuming segmentation
– building campaigns from blocks
– often manually embedding product offers in emails
– and, ultimately, only ~10–15% of recipients opening the email, with 3% clicking,
there is only one solution.
Switch to the simplest and cheapest email sending platform. It doesn’t even need advanced segmentation! All it needs to do is send emails.
2. Try Quarticon
Try Quarticon and add personalized product frames throughout your store to increase conversions by an average of 3–3.5x compared with popular products and bestsellers.
Add autonomous product blocks to your emails and enjoy 60% higher email sales compared with irrelevant product offers in emails.
Plus, save on an expensive marketing automation system.
Plus, save time otherwise spent working with segmentation and matching offers to segments.
Who is marketing automation for?
The beautiful vision promoted by marketing automation systems—that we know 100% of users, their purchases, names, addresses, sizes, etc., and can send them personalized offers—simply does not exist in reality.
Only the largest e-commerce businesses and marketplaces come close to this, relying on apps: Zalando, Modivo, Allegro, Amazon, Shine. App users are generally not anonymous. Marketing automation works well in this case.
In other cases, there is one best solution: an email sending platform (and possibly web push notifications) + Quarticon.
Schedule a demo to find out how to save on marketing automation and get the maximum conversion from email campaigns, without increasing the resources required on your end.
Recommendation Engines vs. Marketing Automation/CDPs: Key Differences
Marketing automation / CDPs do not reach anonymous users
CDP (Customer Data Platform) and marketing automation systems have one thing in common: they were designed to serve so-called known users (email address, app ID).
A recommendation engine (e.g. Quarticon) is designed to serve, in real time, primarily unknown (anonymous) users (after all, they account for as much as 95% of website visitors), while also serving known users.
Marketing automation / CDPs require significant effort
CDP and marketing automation systems require a team—sometimes a large one—to design and run targeted campaigns. They consume significant time and resources.
A recommendation engine (e.g. Quarticon) is autonomous—no team is needed to operate it. It makes the best decisions on its own, learns from every user action, and continually adjusts to current trends.
Marketing automation / CDPs are highly closed systems (walled gardens)
High-end recommendation engines work across all channels. Quarticon’s engine has one more advantage: it not only works across all channels, but also works with any CDP and marketing automation system, without integration.
What does this mean? In advanced MA systems, it eliminates 99% of daily tasks. In simpler MA systems, Quarticon brings them up to the level of Klaviyo. There is no need to change MA providers to get AI on the website, in the app, and in emails.
Schedule a demo to find out how to save on marketing automation and get the maximum conversion from email campaigns, without increasing the resources required on your end.
Optimize the technology stack with Quarticon
Get in touch
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