Emails that are valuable to the user are more likely to be seen, opened and read! But you can also go a step further, for example Gmail (among other providers) tries to categorize incoming emails, distinguishing between “primary”, “social” and “promotional” emails, and intelligence can also come into play here, and then artificial learning from the behavior of individual users! Anyone who wants to compete in the age of AI should not be afraid of these tools, fearing that they might take away from the end user. Thanks to using intelligent solutions, you will be able to spend all your time on generating valuable content, which is the real key to building emails that your customers can read and appreciate.
Smart Composition Staying in the field of email AI, the topic chinese overseas europe phone number list of smart composition (such as the one integrated into Gmail) should be mentioned – albeit at a fast pace. Today, the tool not only suggests the right words when you write an email, but is also able to replicate the tone of the email, suggest the most polite signature that fits the context, and remember to insert a “how are you?” or a “thank you” to the recipient. Given these premises, how will the way we communicate via email change? How will the way customers communicate with businesses change at this time? Current limitations of AI in email marketing Does AI solve everything? Obviously not, to take advantage of it, you need to know how to use it, which also means understanding its limitations and challenges.
When does AI in the field of email marketing give partially or completely invalid answers? If the available data is insufficient or of low quality For example, if my goal is to determine the best time to send an email, I will have to start with historical data of sufficient quantity and good quality. If I am in the early stages of my business, for example, I have just opened my website, the monthly traffic is low and there are few subscribers to my newsletter, I will not be able to apply such tools right away. Even if we want to train a generative AI tool like ChatGPT in-house, the lack of data can be a problem.