By 2021, 80% of emerging technologies will be AI-based, Gartner predicts. This means that a number of digital marketing technology platforms are becoming available, particularly email marketing platforms.
In addition to social media and targeted display ads, email is one of the most powerful media in digital marketing. Marketers see email as a personalized channel between business and audience, with room for customization, direct communication, and easy follow-up.
However, email marketing doesn’t work consistently for all brands. Some common challenges are …
- Bounce rates that make a large part of the marketing spend evaporate
- Lack of audience engagement
- Commitment that doesn’t translate into conversion
Such problems indicate a strategy problem. Every phase of your email marketing strategy can be optimized with the extensive addition of AI.
1. Augmented audience research with predictive analytics
Understanding your audience has become a matter of owning and processing data. Spreadsheets and data visualization tools are useful, but more effective tools are available, especially due to the widespread 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 in media purchase for decades. However, it has only recently become possible to make it economical and precise enough for use in email marketing.
WARC carried out an innovative experiment: virtual user personalities were created based on publicly available data and the purchase history of users. The campaigns were then tested on the basis of this data so that they had higher confidence values before they even started.
The same tactic can be used with the better processing power of AI. Its use only gives a vote of confidence to the campaigns with a high probability of a successful conversion.
Additionally, neural networks can now predict behavior using buyer behavior information, Google Analytics data, and structured data available through third parties. Such predictions become more accurate with each iteration, so using a tool like Quantcast you can start your campaigns to match user intent at each stage.
2. Natural language generation for a more effective e-mail copy
It’s not difficult to find a copywriter who has experience writing emails. However, it is impossible to find a copywriter who can do this systematically and to scale. Organic writing has its advantages and disadvantages, but most professionals’ analytical copywriting processes are limited to their own experience: they cannot do scenario analysis on the scale of an AI-powered engine.
Natural language generation is at the other end of the spectrum of natural language processing. Instead of using the technology to process information, you can use it to generate content. News outlets like Associated Press have already started, and companies like Phrasee have calibrated their AI engine to meet email copy requirements.
With natural language generation, you can create email subject lines and copies of text without going through the dozen iterations of a text. And because it runs on an AI engine, there are no issues with scalability or consistency.
3. Test email content on a larger scale
One of the most time consuming tasks in email marketing is making scaling decisions based on your understanding of how the content form works. You only have so many marketing dollars to spend on automated campaigns. Hence, what you send out in bulk should be your top performing content.
The old school technique was to do A / B testing. You keep track of two copies of an email, and the one that gets better traction is used as the crucial copy. Although A / B technology has served marketers and advertising agencies for years, it isn’t as efficient for large email campaigns. The longer the email, the more extensive your tests must be.
To conduct accurate, large-scale email tests, you can use bandit tests. Its name is derived from “bandits” in casinos that use multiple slot machines to maximize their chance of winning. Bandit tests do more than A / B by testing multiple copies of an email at the same time.
Using standard analytics tools, you can better understand which copies work best by pulling the data from your analytics email account. AI-based scaling occurs when you use an AI engine to analyze historical data and do predictive analytics on every email.
4. User segmentation and behavior prediction for retargeting campaigns
Retargeting is one of the least used tools in email marketing campaigns. Many marketers use retargeting settings in MailChimp to send automated reminders for abandoned shopping carts, for example. While research published by several agencies shows that such tactics work for certain brands, they also leave a lot of work untouched.
The principle of remarketing depends on the optimal time to realign your customer. Email distribution platforms can help you deliver an email at a specific time, but you still need to find out when.
This is where AI can be of enormous help. A deep learning system like Appier summarizes all of your user data – from browsing to purchasing – in a structured format. The data can then be segmented based on behavioral tendencies and the right time for sending your emails suggested.
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While email personalization and creative campaigns are effective ways to build a brand, systemic steps are required to ensure you get the most out of your marketing spend.
Plugging in the right AI tools for audience research, copywriting, campaign analysis, and retargeting automates a lot of work that might otherwise be manual work. Not only can you focus on the strategies and ideas that add value, but it also gives you a new lens through which to view the data you already have.
So providing AI is much more than just automation: it reveals detailed insights and patterns that the human eye naturally misses.