Machine learning, for marketing, refers to a process of configuring marketing programs in ways that help to analyze customer data and generate intelligent marketing decisions. Machine learning should not be confused with marketing automation systems since this is different from rules-based automation processes that are based on the specific programming and definite instructions from marketers and other users. Big brands such as Netflix, Google, and Amazon have already adopted machine learning tools to analyze customers’ behavior to deliver them better-personalized information, content, and solutions via their email marketing campaigns. What’s even better is that the costs of machine learning systems have significantly reduced of late that these tools are now easier to afford to adopt even in small and medium businesses.
How does machine language work and how is it better than marketing automation systems?
The efficacy of machine learning and hyper-personalization systems in revenue generation has led these tools to rise to popularity that businesses of different sizes and industries are resorting to these technologies. Hyper-personalization in the machine learning tool enables e-commerce companies to send 1:1 email which can be optimized to each subscriber based on the purchase history, responses to past email campaigns and interactions carried out on the company website. Thus, machine learning technologies help e-commerce businesses to deliver a personalized shopping experience that mimics the guided experience customers receive at physical stores. Though rules-based marketing automation systems may work in identifying and creating customer segments from the email list and create optimized messages based on the customer segments, these tools have limitations in terms of recognizing ever-changing behavioral patterns of customers. On the contrary, machine learning technologies can address this issue of constantly changing behavioral patterns by consistently tracking these insights and tailoring email messages for customers based on these updated customer data, without requiring any human intervention for setting new rules and for the program.
So, how machine learning can improve email marketing optimization?
There are numerous ways businesses can benefit by adopting machine learning technology in their marketing operations to make their brands stay ahead of the competition in their respective industries and segment. A marketing automation platform aids in customer segmentation to treat a different set of customers with a different set of personalized messages, but machine learning can take you to the next step in delivering a unique personalized experience for each customer and prospect, by constantly tweaking and improving email content for each individual recipient.
Here we have rounded up 5 ways machine learning can advance your marketing operations –
1. Sending Personalized Products
First things first: machine learning can better prepare brands in product recommendation for their customers that they will be actually interested and can end up buying those. Amazon utilizes this advantage of machine learning technologies to design 1:1 personalized emails and overall e-commerce shopping experience for prospects and customers, based on a gamut of algorithms which perform thorough analysis of audiences’ behavior across different marketing channels, including links they click on emails, social media networks, search phrases they use to browse similar products, what they add to their wish list and shopping cart, their purchase history among many others. Amazon leverages these algorithms of machine learning to make personalized product recommendations and perform behavioral target marketing to its customers automatically, via its website and app. Thereby, Amazon not only sends hyper-personalized product recommendations via email directly to their inboxes.
Moreover, businesses do not require resources and budget like this e-commerce behemoth to achieve such a level of heightened personalization. With the advent of machine learning tools, marketers get a plethora of options in machine learning tools that can fit their diverse scale and marketing budget to send personalized product update emails for improved targeting and increased sales conversions. These tools not only allow delivering personalized email updates and offers but can also harness the advantages of personalization to recommend target subscribers on their website with product recommendations based on their browsing activities and search engine activities.
2. Automated creation of email copy
The subject lines of any email you send play a critical role in deciding the fate of email marketing campaigns. These lines are the texts that entice people to open the email from their inboxes and thereby, the success of email open parameter is inevitably resting on the shoulder of email subject lines – that’s where machine learning can assist marketers in creating high-impacting email subject lines.
Apart from leveraging machine learning technology to personalize emails and product updates, these tools can also aid users in the creation of different subject line variants so that they can select the top-performing subject lines for their emails. Businesses that use machine learning can take advantage of tools such as natural language generation and natural language processing for crafting the most compelling email subject lines and email copy for their campaigns. The natural language processing feature allows a machine to turn language into a code that machines can understand, whereas the Natural language generation feature allows a program to use the knowledge of language to write messages. Machine language can add various elements into copy including narratives, emotions, descriptions, word positioning, calls-to-action, and formatting. These tools listen to the unique voice of a brand and previous copies that have resonated best with the targeted audience in various categories. The more campaigns marketers will run using machine learning language tools, the more it runs tests and learns about the segment of customers it needs to target with email copies. Thus, the language system will yield more and more accurate copies over the course of time.
3. Select and schedule the timing of emails
Nothing can exasperate your audience more than when they wake to their inboxes flooded with promotional emails which brands send while they are sleeping. And the more emails their inboxes contain, the lesser and lesser odds of them finding your emails and opening them individually. Instead, they may select all the unread emails and simply mark all the selected emails as read. Unfortunately, there is no specific time when you can guarantee that your emails will be actually opened and read by your recipients because different subscribers open their emails at a different time of the day, especially if you are targeting people from different time zones, job titles, geographic location, and age. Marketers may claim that they have found the right time to send their emails to ensure the highest email opens, sadly, this cannot be true and no marketers should heavily rely on this notion to schedule their emails at that one specific time. Instead, they should take account of different scenarios when subscribers may skip or miss your emails. Setting time by using a rules-based marketing automation system for sending newsletters can be a time consuming and sometimes, a daunting task!
At that, machine learning technologies can determine the right timing for sending your newsletters as accurate as possible by predicting when subscribers are likeliest to open newsletters which may increase open rates as well as click-through rates and thereby, odds of success of an email campaign.
4. Better A/B testing with machine Learning can
Machine learning can allow marketers to perform multilevel of email tests that can conduct ongoing tests. Then, machine learning techs apply changes based on those test results and to optimize emails immediately and continuously. These tests and implementation phases work at the same time. As marketers will set up an email marketing campaign, define their marketing goals and create email variants before they start using machine learning for testing. once set up, the software automatically starts run tests and continue testing throughout the duration of the email campaign and optimize the email strategy as the campaigns go on to enable immediate email optimization in all aspects of email campaigns including images and videos in emails, email copy and also the frequency and timing of sending emails.
5. Pushing leads through buying journey
One major advantage of machine learning is that it features the capability enables users to use a large mass of data which cannot be processed by manually. Machine learning can help a vast amounts of customer tracking including what people are viewing in a store, the number of times people are visiting websites and stores, and then help you measure visitors’ intent to provide the extra push in terms of special offers to speed up their purchase decision. Marketers can use this customer insights to raise the frequency of email delivery to contacts who have expressed their interest in products or services for purchase, at the same time, decreasing the email frequency for those subscribers who have shown demonstrated any interest in purchase. Machine learning can also enable track customers by their browsing activities and to help marketers identify products that are being commonly bought after customers view other products. Equipped with these customer insights, marketers can easily predict and identify top trends in customer behavior and sales to offer data-driven and real-time product recommendations to other prospects and customers who are engaging with emails, company websites, stores or social media channels.
Are you planning to implement machine learning technologies in your email marketing and customer experience operations? Talk to our email experts to learn how efficiently you can use machine learning for email marketing optimization.