09 May 2017
4836
8 min
3.77
Ideas for Using Artificial Intelligence in Email Marketing
Ten years ago implementation of artificial intelligence (AI) in large-scale business processes seemed to be unreliable, inefficient and noncompetitive with human performance. However, today AI really astonishes: it can paint pictures and guess what you wanted to draw from a few sketches. And that is just the beginning, as neural networks and big data open new possibilities for AI. That is why today it is essential to include the potential use of AI elements in future business models, whether it is a manufacturing or capital raising services in a consulting company.
Email marketing is one of the areas where AI is not just advisable, but obligatory for implementation. A great amount of incoming and outgoing data, the real response of end users, the possibility of monitoring tendencies, making experiments, and processing huge data arrays are excellent instrumental in AI implementation and learning. You may ask: what is the use of it? Just recollect how computers have changed our life. We are sure that AI mainstreaming in business processes will have an immense economic and progressive effect.
So, let’s consider the areas of AI implementation for email marketing optimization and increase of your business efficiency.
1. Reactivation Recommendations
We often hear the question: What is the right moment for reactivation of clients or subscribers? The answer is always quite general without due regard to your business segment and your subscribers.
But in practice for the smart reactivation campaign we need clear signs showing that this or that client is being lost, as every client requires an individual approach and one client should be reactivated in a week, another one - in a month and the rest - in six months. In this case, AI makes segments of clients, determines the reactivation time frame and creates the event “it’s time to reactivate”.
2. Frequency Recommendation Engine
Let’s imagine that there is a filter for passing emails on the basis of some criteria. These criteria can be absolutely different, for example, a client has already received this email, unsubscribed or marked an email as spam. Any action which can be considered as an entry point may be used: previous activity, source (the channel of getting a subscriber), email subject and category, and emails planned for this subscriber. The task of AI is to detect these criteria.
Benefits?
Speed – no more manual strategy description.
Efficiency – no need to do segmentation by client’s activity, subscribers stay engaged in campaigns and aren’t disturbed by irrelevant triggers.
3. The Product Recommendations
Personalized product recommendations are the most popular sphere of AI application for sales increase. AI makes product recommendations on the basis of users’ behavior in online stores and in the Internet. Recommendations can be added as an additional block in any email type: regular promotional campaign, any triggered message (order confirmation, cart abandonment email, invitation to give feedback) or sent in a separate email at any time or in accordance with the scenario.
4. The Order Of Promo Blocks Suggestions
There is one more process where AI is much more required. Imagine that you have a list of products for promotional campaigns and all of them should be used in any way. All subscribers have different preferences and few people will buy a lawn mower instead of a tablet. AI is of great use here to monitor subscriber’s insights.
Along with that, there are a number of other questions: what to write in the subject line? It would be nice to list the top 3 offers in the subject line and put the other top 2 in the preheader. What is more efficient: to mention a brand name or product category in the subject line?
It is not a secret that this is a subject line that defines opening rate, however, the offers work the best when they are put at the head of the list.
5. Marketing Qualification Recommendation
Defining the most valuable contacts as potential clients can be done on the basis of criteria and features collected, organized and assigned to each contact. It isn’t just about such simple criteria as email opening. Let’s make parallels with an IT company, which requires managers, who can make the best solutions with an account of client’s needs, cooperate with a big team having high staff turnover, know English, have more than 3 years of managerial experience and know one or several programming languages. The task of AI is to find the right people matching specific criteria.
6. Sales Qualification Recommendation
This function is used in the same way as the marketing component with the only difference: the subscriber is ready to become a buyer. There are a lot of signs showing that a client is ready to make a purchase: view of similar products, reading reviews, questions in reviews, reactions to discounts and overviews of the desired product. All this is a signal to sell.
7. Customer Lifecycle Optimisation Recommendation
To promote a client in the sales process series of messages should be sent to different channels. At the same time, there are numerous important issues influencing the achievement of the desired result, such as: when the first email should be sent? Which channel to use? What is the best pushing "message" in email? How long should we wait before sending the last email?
This is really time-consuming and labor-intensive to draw a plan with all possible variations. At such volumes, no time is left for client support. It would be much more convenient to make a general pattern (stages, email variants and their parameters, possible communication channels, etc.) and the entire process of calculations and comparison will be done by AI with no involvement of a marketing specialist.
8. Build Segment by Personas
To build a segment by personas we use a template of an ideal client with a set of suitable characteristics. Further, we create a segment of clients similar by behavior and key characteristics. As a result, we get a data array about an ideal client, which is constantly updated and self-learning.
9. Find a Unique Group (Clusterization)
Segments differing from other segments are distinguished algorithmically by a set of characteristics and features (with the use of neuronet and by indistinct features). Examination of such segments allows for making special and more effective offers to groups of subscribers previously disregarded.
10. Score Email Copy and Fine Audience
If you have prepared an offer or an article, you need a method that defines subscribers potentially interested in this topic, and to whom sending this info will be the most efficient. You can indicate segment size with an account of your budget or offer's exclusivity. This method also helps to increase the chance of the audience's reaction by changing some message parts on the basis of the results received earlier.
11. Personal Content Generator
The method is similar to the one mentioned above: you also have an email and several different topics or forms of offer submission in email and you need to make focus on discount, products' popularity or characteristics in a personalized way. And this is the task of AI to define what should be focused on for each client.
12. Send Time Optimization (Day of Week and Time)
Everything is simple here: AI is used to define when, to whom and through what channels a message should be sent within the specified time frame to maximize the intended result.
All the same, even the smartest AI still requires human intervention. That is why we have created a whole department of mathematicians, who build mathematical models and estimate the chances, implement and test results.
