You have Klaviyo in your e-commerce and are wondering if that’s enough. Klaviyo is an advanced platform for email marketing and automation, with a product recommendation feature based on Product Feeds. In practice, the key limitation of this feature stems from its reliance on recognized (logged-in or identified) users, of which there are usually few. ⚠️
3 main problems from the e-commerce store’s perspective
Klaviyo personalizes for “known” customers. In many e-commerce stores, this is only about 5% of the traffic, these are users with a profile, so-called recognized. The rest (guests, adblock, incognito) remain anonymous.
Worse still, models based only on data from 5% of users learn more slowly and may be subject to sampling error, failing to capture the full spectrum of behaviors. Therefore, recommendations in emails may be less accurate.
For anonymous visitors directly in the store, Klaviyo often defaults to showing general products (popular, bestsellers), which reduces accuracy and potential conversion.
Professional recommendation engines work with all yellow circles (potential customers) and the red circle (past customers).
Is the professional recommendation engine an alternative or a natural complement?
Klaviyo is a platform for emails and communication automation.
The recommendation engine is a specialized tool making product decisions for each user, regardless of whether they are logged in or anonymous.
So it is not an alternative, but rather a matter of complementing. A recommendation engine that learns from full traffic (anonymous + logged-in) can improve the accuracy of suggestions on the site for those 95% anonymous users and also accuracy for those 5% logged in on the site and in emails.
What do you gain by combining Klaviyo with a dedicated recommendation engine?
First of all, greater accuracy for most of the traffic on your site – for anonymous users, better product suggestions on the site and in campaigns. It also means faster model usability, systems training on full traffic achieve efficiency faster than those limited to a small sample.
Why all this? For better conversion results. A more personalized experience for most visitors translates into increased sales. You don’t have to give up Klaviyo to add a recommendation system; you can integrate the tool with recommendations on the site.
Klaviyo is one of the few systems for e-commerce that allows full personalization for recognized customers in messages (and not just based on segments). But good personalization in emails will not compensate for the lack of personalization in the store.
Quarticon, on the other hand, is one of the few recommendation engines that allows personalization both in the store and in emails, without the need for complicated integration (but it does not send emails itself – for that task, Klaviyo is needed).
Therefore, possible usage scenarios are as follows:
– Quarticon recommendation engine – personalization on the site + Klaviyo – personalization of emails and direct channels
or
– Quarticon recommendation engine – personalization on the site and email product embedding (+ in direct channels) + Klaviyo – campaign sending / triggering.
Conclusions
If your goal is to significantly increase the accuracy of recommendations primarily for anonymous users (and thus better sales results), Klaviyo alone is insufficient as the only tool. A sensible approach is to evaluate and test a dedicated recommendation engine (e.g., Quarticon) alongside Klaviyo, as a complement to a key fragment. Ultimately, this can enhance the effectiveness of both emails and, of course, sales on the site.
Read more about product recommendations in Klaviyo and about Quarticon’s recommendation engine.
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