E-mail is one of the best performing media in digital marketing, alongside social media and targeted image advertising. Marketers see email as a personalized channel between the company and the public, with space for personalization, instant communication, and easy tracking.
But email marketing doesn’t work for all brands. Some common challenges include…
- Bounce rates that evaporate much of marketing spend
- Lack of public involvement
- Commitment does not translate into regret
These questions point to a strategic problem. Each step of your email marketing strategy can be optimized with the complete addition of AI.
- More audience surveys using predictive analytics
Understanding your audience has become a matter of ownership and data processing. Spreadsheets and data visualization tools are useful, but more effective tools are available, mainly due to the high availability of predictive and cloud-based analytics.
Statistical modeling to predict consumer behavior is not a new technique. It has been used in television programming and media purchases for decades. However, it has only recently become possible to make it cheap and accurate enough for use in email marketing.
WARC conducted an innovative experiment: based on the available data and the user’s purchase history, it started to create characters for virtual users. So the campaigns were tested based on this data, to make us more confident even before the launch.
The same tactic can be used with the best processing power in AI. Its use only gives confidence in campaigns with a high probability of successful conversion.
Neural networks can also predict behavior using copper behavioral information, Google Analytics data, and structured data from third parties. These predictions become more accurate with each iteration, so you can start your campaigns based on the intent of each user at each step, using a tool like Quantcast.
- Generation of natural language for a more efficient copy of e-mails
It is not difficult to find a copywriter with experience in writing emails. However, it is impossible to find a copywriter who can do this systematically and on a large scale. Organic writing has its pros and cons, but most professionals’ analytical writing processes are limited to their own experiences – they cannot perform 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 technology to process information, you can use it to generate content. News outlets like the Associated Press have already started to do this, and companies like Phrasee have calibrated their AI engine to meet email copy needs.
With the development of natural language, it is possible to create subject lines and text for email without dozens of iterations by a copywriter. And because it works on an AI engine, you have no problems with scalability or consistency.
- Test the content of the email on a larger scale
One of the most time-consuming activities of email marketing is making customized decisions based on your understanding of what form of content works. You only have a few marketing funds to spend on automated campaigns; what you upload widely should therefore be the best content.
The old technique was to perform the A / B test. Track two copies of an email and the one with the best control will be used as the critical copy. Although the A / B technique has served marketers and advertising agencies for years, it is far from being as effective for large-scale email campaigns. The longer the email, the more comprehensive your tests should be.
To perform accurate email tests on a large scale, you can use the old defective test. The name comes from “bad guys” in casinos that use different slots to maximize your chances of winning. The Bandit test does more than A / B, testing multiple copies of an email at the same time.
Shelf analysis tools can help you find out which copies work best by pulling data from your email analysis account. AI-based planning occurs when an AI engine is used to analyze historical data and perform predictive analysis on each email.
- Targeting users and predicting behavior to promote reuse campaigns
Retargeting is one of the most used tools in email marketing campaigns. For example, many marketers use reset settings in MailChimp to send automatic reminders to abandoned carts, and while research from various agencies shows that these tactics work for certain brands, they also allow a lot to go away.
The principle of remarketing depends on the ideal time to refer the customer. Email delivery platforms can help you deliver email at a specific time, but you still need to know when.
This is where AI can be of great help. A deep learning system like Appier collects all user data, from navigation to purchase, in a structured format. He can then segment the data based on behavioral trends and suggest the right time to send his email.