Email marketing is in for a complete overhaul. With AI, email marketing isn’t limited to rule-based triggers but has evolved into a means of more in-the-moment personalization. AI has filled in the missing gap between traditional shopping and online shopping with 1:1 personalization. Here are four of the ways AI has changed email marketing for better.
1. Best time to send to increase open rates
You have three seconds to seize the shopper’s attention. You can’t risk that chance by sending the right message to the right person but at the wrong time. Sending a blanket email to all customers at the exact same time is like throwing spaghetti at the wall and hoping it will stick. With AI-powered machine learning algorithms, smart retailers are sending the messages when users are more likely to open them based on each customer’s past behavior. For example, you might get a message in the morning when you check your emails, but I will get the same message in the evening, based on my email activity. Even a small personalization like this will affect the success rate of your email campaigns by improving your email opens and increasing conversions.
2. Product recommendations to boost email conversions
McKinsey & Company estimated that Amazon generated 35 percent of its revenue from its on-site and email product recommendations.
Personalized product recommendations in email are a proven way to increase customer engagement, click-through rates (CTRs), and ultimately sales. But to hand-pick products specific to each customer is neither scalable nor humanly possible. However, AI algorithms are trained on huge sets of data, including a customer’s past behavior, activity, and purchase history, to create personalized recommendations for every customer. AI makes it possible to send highly targeted campaigns integrating dynamic product recommendations in real time, resulting in a lift in CTR and sales. This type of personalization offers a lot of value to the customer. Personalized product recommendations can not just result in increased CTR and sales, but also magnify the order value if done right. As retail analyst Emily Bezzant told Fortune, “Brands such as The North Face and 1-800-Flowers.com are already using AI to provide personalized recommendations.”
3. Segmentation for hyperpersonalization
Email marketing has come a long way from sending batch and blast emails. Sending segmented mailers is no longer the “it” thing – it’s the standard. Marketers know that there’s no personalization without segmentation. Segmented emails have been proven to get more opens, CTRs, and conversions, besides driving customer engagement. But with the abundance of customer data, the success of your segments depends on their underlying rules and conditions. While you can go as granular as you want with segment rules, the key is to identify the hidden secrets in your data.
Machine learning has made it easier to segment, with algorithms that can automatically identify the most prominent segments in your data that also hold the most promise. For example, if you are an apparel company selling to all age groups, machine learning might identify the age group that’s giving you the most conversion. Even a simple segmentation like this can help you boost your conversion overnight.
RFM (recency, frequency, monetary) segmentation is another example of machine learning segmentation models. Machine-learning segments aren’t just meaningful, but will also save you time and resources you can apply to marketing campaigns. For example, you can craft a personalized mailer for high-value/loyal customers identified through RFM.
4. Customer insights for lifecycle marketing
AI and predictive analytics have changed the way marketers generate insights about customers. By leveraging transactional, behavioral, and intent data, predictive analytics enables marketers to send highly contextual emails throughout the customer lifecycle. With key metrics like CLTV, time to first purchase, time between purchases, and so on, marketers can create more powerful campaigns to engage their customers more effectively and in real time. For example, for cart abandoners, predictive analytics can help you create highly personalized recovery emails with a cost-effective discount offer needed to motivate the abandoner to complete their purchase, thus driving a better ROI.
AI is the single biggest change to email marketing that will drive personalization like never before. These methods mentioned above are testament to how AI can make email marketing even more powerful.
Nandini Rathi is the CMO of Betaout, a marketing automation and customer intelligence platform for ecommerce companies.