How AI is set to transform email marketing
Artificial intelligence (AI) is increasingly used in email marketing, learn how machine learning enhances personalised email campaigns.
The phrase “artificial intelligence is a marketer’s best friend” is getting a little tired. Not only is this because artificial intelligence (AI) is so frequently overhyped, but it’s also because marketers’ relationship with AI has only just begun.
This is especially true for one of the most prolific marketing techniques: email marketing. While, according to Forrester, 88% of marketers have adopted, or are in the early stages of adopting, AI in their process, Econsultancy has found that just 17% of email marketers are planning to use AI to enhance personalisation.
Just 17% of email marketers are planning to use AI to enhance personalisation.
Yet, email marketing is one of the processes that could stand to benefit the most from AI, exactly because of its ability to take personalising email messaging to the next level by optimising things like send time and the actual marketing content for each customer. Personalisation is one of the most important trends in email marketing and that’s not about to change, especially with 52% of customers looking elsewhere if an email isn’t personalised, according to Active Trail.
Let’s take a closer look at how AI and machine learning can transform how email marketers work out what to say, who to say it to and how to say it.
Finding the right sending time for each customer
You’ve probably heard some version of the adage that Tuesday mornings are the time for marketing communications. And if you’ve not, just check your inbox next Tuesday and see the avalanche for yourself. Many marketers rely on the logic that at certain times of the day the majority of their email audience are going to be more receptive.
The problem with this approach is that it assumes that the best way to approach email communications is by broad time blocks. This works for the small amount of people who align with the ‘average’ habits, but it fails to consider the different schedules and personalities of individual subscribers. Considering all the variables is something AI can do, very effectively, however.
It would take a person countless hours to work out the exact right time to send to each person, and even longer to schedule. But, AI can determine individual send-out times for each customer based on previous engagement. For example, it could use the customer’s interactions to predict whether they are about to make a purchase, and send them personalised offers. Using machine learning-enabled send time optimisation (STO) results in maximised engagement and customers.
There are already a few AI email marketing tools out there that do this – Mailchimp has a popular AI tool – but machine learning-enabled STO is a practice that’s still in the early stages of implementation in email marketing.
Understanding customer content preferences at scale
And it’s not just figuring out when people want to receive their emails where AI understanding customer interactions comes in handy, it also helps to understand each customer’s content preferences, at scale.
If a marketer has ten customers, they might be able to understand from their engagement what each individual wants from the company. As the customer base grows, however, this understanding becomes much foggier, with humans much less able to understand the huge amounts of data they hold on each individual in a database of thousands, even millions. This is exactly why up to 73% of valuable customer data goes unused for analytics, according to Forrester.
AI can process all this information in moments, however. It can then use the data gathered from customer’s interactions to gain powerful insights into what kinds of content they want to see in email marketing.
For example, AI can recognise the text and images in a newsletter to understand that the articles a customer has interacted with are related to healthy lifestyle. It could then suggest that this person receives messaging focused on this specific interest in the future.
Most importantly, AI could do all this on a mass scale, completely transforming email marketing.
Optimising email content
Are you guilty of spending hours perfecting data-driven delivery systems, but find you often resort to guesswork when actually crafting the content you’re sending out? You’re not alone, and this exact problem is what can lead to lengthy A/B testing and marketer burnout.
Fortunately, machine learning is well positioned to take this problem off your hands. While machines are typically associated with crunching spreadsheets rather than being creative, AI research has focused heavily on getting machines to understand complex data, such as images or written language. AI can break down text to understand the underlying concepts behind the writing, as well as analysing the features that make up an image, and can then utilise this data to understand customer preferences from their interactions with that content. Using this, AI in email marketing can help us to predict how much a piece of content will resonate with an audience.
The technology even has the potential to anticipate what content is going to be the most effective for each individual. This is something that marketing leaders predict will be one of AI’s most significant uses: According to Salesforce, 61% of them expect it to have a substantial or transformational impact on personalisation of content in the next five years.
DataSine’s AI platform, Pomegranate, works on this exact basis. It has been trained on millions of posts and engagement data and uses insights rooted in psychology and computer vision to help marketers pick the highest-converting images. This can lead to as much as an 80% boost in engagement – try it for free now.
AI and personalisation
These are just a few examples of the ways AI is likely to change email marketing, but it’s evident that personalisation is going to be the core theme of the next few years. Unless marketers adopt AI in email marketing – and soon – they will struggle to remain competitive against those who have experimented with the technology early, and are now using it to offer the truly personalised service subscribers demand.
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