Take control of Klaviyo with artificial intelligence and double the effectiveness of your email marketing automation

The principle is simple: our QuanticFlow algorithms directly drive your marketing automation in an ultra-personalized way (customer by customer), taking orders from Klaviyo.

We call this "one-to-one" marketing, or PREL (for Programme Relationnel).

Here's a performance scale based on the probability that one of your customers will make a purchase:

In other words, a Flow is 8 times more effective at generating results, and a PREL is 16 times more effective than a newsletter!

When we say "our algorithms take control of Klaviyo", we mean that you don't have to do a thing. Not only do you avoid the hassle of setting up dozens of flows, but you also boost performance considerably.

Let's put aside the newsletter and its performance: it plays its "mass-market" role as a communication tool to express your values, announce product launches, and warm the chestnuts (Mother's Day, Easter, Christmas...).

Let's talk instead about e-mail marketing, whose primary role is to generate sales, largely embodied by your automation.

Your Kalviyo flows are based on a trigger (an event) and a delay (recency of the event). In this way, all your customers who performed this event a certain time ago will receive the same e-mail. For example, "abandonniste D+1". Assuming - theoretically - that you think you can cover predefined behavioral scenarios ("just signed up", "just gave up", "just bought"), this could indeed remain manageable with classic Klaviyo flows.

But then, no such luck... In real life, a customer never follows the theoretical sequence we're taught in books, such as: sign up > click > add to cart > buy. Customer behavior is erratic, random and unpredictable.

The role of AI is precisely to adjust to the precise behavior of each of your customers. In other words, to an infinite number of possible behavior combinations.

When we say that "the principle of automation is to adjust to the behavior of each of your customers on an individual basis", what we mean in concrete terms is: "increase sales interactions with a customer in proportion to his or her intention to buy". This is the key.

And it's best understood: the more e-mails I send, the more I sell :)

On the other hand, it's easy to imagine that: the more e-mails I send to people who aren't in the buying phase, the more credibility my brand loses.

This is why a "one-to-one" e-mail is based on a score that corresponds to a probability of purchase. The higher the probability, the closer the individual is to making a purchase, and the greater the commercial pressure by e-mail.

But it's also conceivable that: the more e-mails I send to my customers with products that don't suit them, the more credibility my brand will lose, even if they are in the buying phase.

Which leads to the next point: if you want to increase the pressure on people in the buying phase - to increase sales - you have to be relevant in the content and choice of products you offer them.

This is where the product recommendation engine comes into play: thanks to machine learning, it alone can rank your entire product catalog in order of relevance for each of your customers every day. And thus tell each of your customers, individually, a story that speaks to them.