Email marketing is still a key tool for many businesses. It remains one of the most effective ways to establish and maintain relationships with customers. Few companies can afford their own mobile app, so for most, email contact is often the only solution.
Is Email Marketing Still Effective in the AI Era?
Analyzing statistics on cold emails (sales emails sent to random companies), email open rates in the last 6 months (10’25-03’26) have dropped by as much as 40%! Inboxes are overflowing with unwanted messages that are not tailored to the recipient. AI-generated content generates no engagement.
You might say this doesn’t concern me because I don’t send unwanted messages. My database consists of subscribers who signed up for the newsletter themselves because we offered a 5% discount on the first purchase. Well, not quite.
First, an inbox filled with GPT-generated emails for your subscriber means less attention to other emails. They get lost in the mass of mail and are almost automatically deleted along with other messages.
Second, a weak email from a company I subscribed to ends up in the same group as a very weak AI-generated email. Weak means not responding to needs, not responding to interests. In other words – non-personalized.
How to Stand Out in a Sea of Weak AI-Generated Emails?
Personalization and tailored content are now the most important methods to stand out in a crowded inbox. However, personalization should be distinguished from personalization. Personalization is not greeting the user with their name in the vocative case. This part can basically be skipped. Personalization in almost the third decade of the 21st century is individually tailoring the offer for each recipient, automatically. Traditional segmentation (demographics, purchase history, RFM) is increasingly unable to keep up with rapidly changing customer preferences.
So how to personalize? The key is audience segmentation. Unfortunately, the bad news is that the segmentation we know from marketing automation systems is already insufficient. Customer preferences in an e-commerce store change faster than the segmentation capabilities in the tool itself, and certainly faster than the ability to tailor content to these segments.
Marketing departments equipped with marketing automation tools cannot keep up with the strategy of “reach faster with individually tailored content.” Building and managing several segments plus maintaining communication is already a big challenge. Slicing the recipient list into nano-segments is a gargantuan task, requiring a huge team and a huge budget. And thus, marketing automation tools end up as tools for sending emails with a few gimmicks that no one uses.
To succeed in email marketing, prioritizing personalization (and actually nano-personalization) is necessary for email campaigns to resonate with customers and deliver results.
External Layer of Personalization in Emails? What Is It?
At first glance, this may seem difficult to implement. Theoretically, you would need to set up webhooks/streaming events, set up a recommendation API, implement some solution for handling dynamic blocks and feature flags. A simpler solution is to set up an external layer of personalization (microservice) that receives signals and decides which modules to insert — facilitating iterations independently of the MA tool’s UI.
The external layer of personalization is an independent service outside the marketing automation (MA) tool that:
- collects user signals in real-time,
- calculates attributes and personalization decisions (propensity, recommendations, module selection),
- returns the result in the form of instructions (e.g., which content blocks to insert) for the sending system or email template API.
The advantage of this solution is separating personalization logic from the MA interface, allowing for faster iterations and implementations. Such a tool is also a central place for predictive models, rules, and fallbacks. It allows for versioning of rules (and not only rules) and A/B tests independently of MA.
How to Implement Nano-Segmentation in Email Marketing?
Such a microservice, although much simpler for the user, is provided by Quarticon – AI-mails. AI-mails are precisely the external layer of personalization in email marketing.
In our solution, we went even further – the system returns results not in the form of instructions that need to be integrated with the marketing automation system, but in the form of ready-made product blocks (images), which are very easy to embed in the email content. This way, maximum simplicity of implementation is achieved.
How does the Quarticon microservice for nano-personalization of emails generally work? You want to have a product block with 6 products tailored for each user – you add the code of 6 images (and links) to your email and send it. You don’t do any segmentation, no product selection. You want to send a spring offer in a month – you change “Winter Offer” to “Spring Offer” in the email and send it. You don’t touch the image part at all, you don’t define products.
Behind this solution is the entire decision-making layer – ML models, event storage, feature calculation, content mapping (not just products), or experiment management. All of this is provided “in a package.” Without complex infrastructure, you can achieve a level of offer personalization in emails that the biggest players haven’t achieved with their systems.
The Future of Email Marketing
The future of email marketing in a highly competitive landscape relies on personalized content (and even nano-personalized) to stand out in crowded inboxes. In e-commerce, this is crucial and easily measurable – it increases open rates, click-through rates, conversions.
It also applies to other industries – publishing (paid content), media, classifieds services, VOD services. Data and analysis are key in optimizing email marketing strategies, but they are not there to be pored over. Data from a month ago in e-commerce is already history. Everything happens here and now, in real-time.
Do AI-mails generate content of the e-mails?
No, Quarticon AI-mails do not generate content and do not use Generative Pre-trained Transformers (GPT) at all. They use AI to analyse users’ behavior and recommend right products or content you already have (e.g. products in e-commerce, movies in VOD services or blog posts / articles) to the right audience in the right time. It’s another field of AI, called predictive AI.
