4 Ways to Improve Email Marketing With AI

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By 2021, 80% of emerging technologies will have an AI foundation, Gartner predicts. That will mean a gamut of technology platforms becoming available for digital marketing—especially email marketing platforms.

Email is one of the better-performing mediums in digital marketing, along with social media and targeted display ads. Marketers consider email a personalized channel between company and audience, with space for customization, direct communication, and easy tracking.

But email marketing does not perform uniformly well for all brands. Some common challenges are…

  • Bounce rates that evaporate a large proportion of marketing spend
  • Lack of engagement from the audience
  • Engagement that does not translate into conversion

Such issues indicate a strategy problem. Every stage of your email marketing strategy can be optimized with the comprehensive addition of AI.


1. Augmented Audience Research Using Predictive Analytics

Understanding your audience has become a matter of possessing and processing data. Spreadsheets and data visualization tools are useful, but more effective tools are available, especially due to the wide availability of cloud-based and predictive analytics.

Statistical modeling to predict consumer behavior is not a new technique. It has been used in television programming and media buying for decades. However, making it economical and precise enough for use in email marketing has only recently become possible.

WARC ran an innovative experiment: Based on publicly available data and user-purchase history, it started creating virtual user personas. Campaigns were then tested on that data so that they would have stronger confidence scores before they even launched.

The same tactic can be used with AI’s better processing power. Its use gives a vote of confidence only to the campaigns with a high probability of conversion success.

Moreover, using buyer-behavior information, Google Analytics data, and structured data available via third parties, neural networks can now forecast behavior. Such forecasts get more accurate with each iteration, so you can initiate your campaigns to match user intent at each stage using a tool such as Quantcast.


2. Natural Language Generation for More Effective Email Copy

It’s not difficult to find a copywriter who is experienced in composing emails. However, finding a copywriter who can do it systematically and at scale is impossible. Organic writing has its advantages and drawbacks, but most professionals’ analytical copywriting processes are limited to their own experiences: They cannot run scenario analysis at the scale of an AI-powered engine.

Natural language generation sits at the other end of natural language processing spectrum. Instead of using the technology to process information, you can use it to generate content. News agencies such as the Associated Press have already started doing so, and companies such as Phrasee have calibrated their AI engine to meet email copy needs.

Natural language generation enables you to create email subject lines and body copy without going through a copywriter’s dozen iterations. And since it runs on an AI engine, you won’t have issues with scalability or consistency.

3. Testing Email Content at a Larger Scale

One of the most time-consuming tasks in email marketing is making scaling decisions based on your understanding of which content form is working. You have only so many marketing dollars you can spend on automated campaigns; hence, what you send out on a massive scale should be your best-performing content.

The old-school technique was to conduct A/B testing. You track two copies of an email, and the one that gets better traction is used as the decisive copy. Though the A/B technique has served marketers and ad agencies for years, it is not as efficient for large-scale email campaigns. The longer the email, the more extensive your tests have to be.

To perform accurate email tests at a scale, you can use bandit testing. Its name is derived from “bandits” in casinos who use several slot machines to maximize their probability of winning. Bandit testing does more than A/B by testing several copies of an email at once.

Off-the-shelf analytics tools can help you understand which copies work best by pulling the data from your email analytics account. AI-based scaling comes in when you use an AI engine to analyze historical data and perform predictive analytics on each email.

4. User-Segmentation and Behavioral Forecasting to Drive Retargeting Campaigns

Retargeting is one of the most underutilized tools in email marketing campaigns. Many marketers use retargeting settings in MailChimp, for example, to send automated reminders for abandoned carts, and though research published by several agencies shows that such tactics work for certain brands, they also leave a lot of work undone.

The principle of remarketing depends on the optimal time to retarget your customer. Email distribution platforms can help you deliver an email at a specific time, but it’s still on you to figure out when.

That’s where AI can be of tremendous help. A deep-learning system such as Appier aggregates all of your user data—from browsing to purchases—into a structured format. Then it can segment the data based on behavioral tendencies and suggest the right time to send out your emails.

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Although email personalization and creative campaigning are effective ways to build a brand, systemic steps are required to ensure that you get the most out of marketing spend.

Plugging in the right AI tools for audience research, copywriting, campaign analysis, and retargeting will automate a lot of work that might otherwise be manual labor. That not only allows you to focus on the strategies and ideas that are producing more value but also lends you a new lens through which to view the data you already possess.

AI deployment, therefore, is much more than just automation: It unravels granular insights and patterns that the human eye naturally misses.



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