How to use AI to improve customer segmentation
THE significance of accurate customer segmentation to ensure a successful digital marketing campaign cannot be understated.
When messages are customized, it becomes more relevant to consumers, and thus greater response could be expected, as opposed to non-personalized content.
While there are numerous segmentations that brands often use, the groupings still tend to be generally large and somewhat generic, instead of smaller and more nuanced.
Segmentation based on age, geographic location, first time or repeat customers are the common ones and more often than not, the level of segmentation is limited to due to how much insights are there on customers as well as resources available to craft different marketing campaign for the different segments.
However, with the help of artificial intelligence, customer data can be pored over and analyzed more thoroughly to generate more detailed, targeted segments, as well as automate the process of personalizing campaigns for each section.
Deploying AI to generate the customer segments will yield far superior results, compared to when its done manually, as AI-powered solutions offer numerous advantages that humans can’t match, such as;
- Elimination of human bias and stereotyping
- Identify hidden patterns and trends (that goes against prevailing notion or theories)
- Auto-update of segments that is accurately reflective of the change in the marketplace
- Virtually unlimited number and size of segments
- Increased level of personalization
- Operates with minimal human intervention
- Greatly scalable
Highly segmented campaign yield better ROI and improve the effectiveness of campaigns in general.
One study found that segmented marketing campaigns yield 760 percent uptick in revenue, compared to non-segmented campaigns.
Optimize campaigns using AI
Wile having increased levels of segmentation and targeting smaller more unique groups of customers may result in better conversions, but as the segments get increasingly specific, the task of marketing to them also becomes more complicated.
This complex task, however, can be tackled using machine learning technology to tweak and adjust numerous variables specific to the targeted segments.
These variables include things like delivery time, colors, images, and subject line.
Marketers have to only pick the initial set of variables, for example; a few options for images, set of headlines and process can be automated from there onwards, where ML algorithms experiments with multiple combinations of these variables.
Then, customers’ behavior could be monitored via specific metrics such as heat maps and click-throughs, and the personalized content can be tweaked for the next recipient accordingly in real-time, effectively optimizing the campaign for the individual segments with minimal human intervention.
In conclusion, data would enable businesses to have meaningful interactions with their customer, but AI-powered solution will enable more accurate customer segmentation, so that the engagements could be more personalized to enhance the customer’s experience further.
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