With machine learning, it becomes feasible to personalize content and outreach strategies to each individual contact, which helps make marketing more relevant. In fact, personalization is one of the most important uses for artificial intelligence in marketing.
The time of spammy “spray and pray” mass-marketing is over. Today’s customers require brands—and expect marketing outreach—to be more relevant in both time and content.
The time of spammy “spray and pray” mass-marketing is over.
Relevancy is becoming more important than reach; get the right content, to the right person, at the right time.
Marketers tried to become more relevant by inventing the concept of segmentation. Groups of customers or potential customers are placed into distinct sub-groups, thus making a rough classification by similarity or common needs.
Segmentation made it possible for marketers to be at least a bit more relevant, compared to sending the same marketing emails or offers to everyone.
I know a clothing retail chain that did this rather poorly, sending marketing emails pitching women’s clothes to men and vice versa. They sent exactly the same email to everyone. Another retail chain sent direct marketing information on gardening products to people living in an apartment, thus not having a garden.
These are significant blunders in today’s era, and brands just have to do better.
When you send irrelevant marketing content to your leads, you train them to avoid reading it in the future, or even make them dislike you. That in turn can harm your email sender score, thus damaging the delivery rate of future emails.
Even the most trivial segmentation—like sending different product offers to men or women—can make a major difference. Still, men are not a homogenous group, and they have different needs and interests, as do women.
A more granular segmentation would thus result in even more relevance.
With AI and machine learning, it is possible to segment the audience automatically. If this is done with sufficient granularity, we get micro-segmentation. Done correctly, this is much better than no segmentation, or rudimentary segmentation.
With AI and machine learning, it is possible to segment the audience automatically
AI-based automatic segmentation can be implemented using clustering algorithms, and can identify the attributes of ideal buyers, those most likely to convert or defect, and more.
In effect, you can ask the system to provide you with buckets of people that are similar or share characteristics in some way, even if you don’t know what you are looking for. Thus, machine learning tools can help improve segmentation, and many do.
