Advancements in machine learning, predictive AI and generative AI are helping marketers do more with the data they have. Many vendors had already incorporated AI and ML into their platforms to drive segmentation and predictive analytics, but the rapid growth of generative AI has opened the door to many new possibilities.
One area of particular interest is ideation and content creation for text and images. Generative AI is being integrated into workflows for creating email messaging, to suggest subject lines, body text, calls-to-action and visuals. It can also leverage customer data to create highly personalized emails in keeping with an individual’s preferences and past interactions or help with the translation of content for global campaigns.
Additionally, email vendors are using it to automate A/B testing, perform sentiment analysis and enhance deliverability.
After ChatGPT burst onto the market in late 2022, generative AI began showing up in software
of every type, and email marketing platforms were no exception. Many vendors are offering
generative AI assistants to help marketers generate winning subject lines, text content for
messages and even images. Behind the scenes, AI and machine learning assist with segmentation
as well as predicting the best messaging and send times.
Email marketing helps organizations acquire and retain customers, build businesses and make more money. Explore the platforms essential to email marketing in the latest edition of this MarTech Intelligence Report.
Here are some specific areas of email marketing where genAI is having an impact.
The process of creating the text and visual content that will go in the template for each individual message has taken a giant leap forward with the advent of generative AI. What once could have been as straightforward as cutting and pasting text and images into a template may now involve prompt-driven generation of a subject line or multiple subject line options, as well as body copy and possibly even images.
With more sophisticated systems, marketers can generate a number of different versions of the message — each tailored to segments or individuals based on the data available about them. Some platforms also offer machine-learning-driven capabilities for running A/B tests that automatically optimize for the most favorable response.
The ways email marketing platforms allow marketers to capture, analyze and act on data are some of the areas of the greatest competition and differentiation between email marketing platforms.
The most sophisticated platforms, however, go well beyond this by incorporating customer data platform (CDP) functionality. These systems gather data from recipients’ interactions with emails but can also append information from a variety of on- and off-line touchpoints, as well as third-party data sources.
More sophisticated applications for data include audience building and segmentation, including
the use of artificial intelligence and machine learning to surface insights from big data stores.
Some platforms include full-fledged customer data platforms, but even if they don’t, the customer databases associated with these systems can serve as a single source of truth across an organization. Such unification can provide businesses with a complete portrait of their customers, permitting them to leverage data for marketing, customer service and product development purposes.
The unified data trove gives marketers the opportunity to get to know their customers better and also to
identify lookalike audiences by connecting to additional data sources. Artificial intelligence and machine learning capabilities can find useful patterns that marketers may not know to look for.
Though email marketing is a well-established practice, the space isn’t standing still. As marketers seek to get to know their customers and use that knowledge to improve the relevance of their communications, data management and personalization technologies have grown in importance. Additionally, advancements in artificial intelligence and machine learning are fueling improvements across the platform, enabling more efficient creation of personalized content for AI-identified segments as well as testing and continuous improvement.
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