Improve your strategy using machine learning models that can be applied end-to-end.
Traditional customer segmentation methods rely on attributes such as income, demographics, life stage, attitude, and behavior. According to a recent report by IBM, these attributes account for only 20% of the data, while the remaining 80% comes from unstructured internet data like images, social media, news feeds, emails, journals, blogs, audio clips, and videos. Relying solely on traditional segmentation misses quality marketing opportunities.
The limited category set reduces the actionable insights that a company can use to better serve its customers.
Additionally, companies miss personalization opportunities by limiting each customer to just one segment. In reality, customers from different segments may share common attributes. Important marketing opportunities lie not in the segments themselves, but in the overlap between their attributes.
For example, one segment might include unmarried women, living in their own homes, with a university education, under 45 years old. Another segment may consist of parents with high school-aged children, married, with a middle income, and located within the district of a specific school. There may be a common trait, like shopping at the same store, for both segments. This shared attribute may also appear in a third segment, like millennials who are minorities running their own digital businesses. At first glance, these three customer segments may seem to have little in common—until you dig deeper.
This type of overlap between customer segments enables:
- Increased revenue from low-value customers
- Developing strategies to grow and maintain high-value groups
- Identifying new customers
- Designing better personalized loyalty programs
Machine learning models can reveal patterns of customer interest that would otherwise be hard to identify, allowing new offers to be pushed to multiple customer accounts.
Currently, machine learning channels are managed by data scientists who are knowledgeable about the platforms and languages needed to run machine learning algorithms. Existing BI tools do not support machine learning algorithms. This makes executive insights, dashboard views, and report generation cumbersome.
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