Marketers have used segmentation as a technique to target customers for communications, products, and services since the introduction of customer relationship management (i.e., CRM) and database marketing. Within the context of segmentation, there are a variety of applications, ranging from consumer demographics, geography, behavior, psychographics, events and cultural backgrounds. Over time, segmentation has proven its value, and brands continue to use this strategy across every stage of the customer journey:
- Acquisition
- Upsell/cross-sell
- Retention
- Winback
Let's provide a proper definition for this marketing technique. As my SAS peer and friend Randy Collica stated in his influential book on this subject:
"Segmentation is in essence the process by which items or subjects are categorized or classified into groups that share similar characteristics. These techniques can be beneficial in classifying customer groups. Typical marketing activities seek to improve their relationships with prospective and current customers. The better you know about your customer's needs, desires, and their purchasing behaviors, the better you can construct marketing programs designed to fit their needs, desires, and behaviors."
Moving beyond the academic interpretation, in today's integrated marketing ecosystem, SAS Global Customer Intelligence director Wilson Raj provides a modern viewpoint:
"In an era of big data, hyperconnected digital customers and hyper-personalization, segmentation is the cornerstone of customer insight and understanding across the modern digital business. The question is: Is your segmentation approach antiquated or advanced?"
This provides a nice transition to review the types of segmentation methods I observe with clients. It ultimately boils down to two categories:
- Business rules for segmentation (i.e., non-quantitative)
- Analytical segmentation (i.e., quantitative)
Let's dive deeper into each of these...
Business Rules For Segmentation
This technique centers on a qualitative, or non-quantitative, approach leveraging various customer attributes conceptualized through conversations with business stakeholders and customer focus groups to gather pointed data. This information represents consumer experiential behavior, and analysts will assign subjective segments for targeted campaign treatments. Although directionally useful, in this day and age of data-driven marketing, it is my opinion that this approach will have suboptimal results.
Analytical Segmentation
Within this category, there are two approaches marketing analysts can select from:
- Supervised (i.e., classification)
- Unsupervised (i.e., clustering)
Supervised segmentation is typically referred to as a family of pattern analysis approaches. Supporters of this method stress that the actionable deliverable from the analysis classifies homogeneous segments that can be profiled, and informs targeting strategies across the customer lifecycle. The use of the term supervised refers to specific data mining (or data science) techniques, such as decision trees, random forests, gradient boosting or neural networks. One key difference in supervised approaches is that the analysis requires a dependent (or target) variable, whereas no dependent variable is designated in unsupervised models. The dependent variable is usually a 1-0 (or yes/no) flag-type variable that matches the objective of the segmentation. Examples of this include:
- Product purchase to identify segments with higher probabilities to convert on what you offer.
- Upsell/cross-sell to identify segments who are likely to deepen their relationship with your brand.
- Retention to identify segments most likely to unsubscribe, attrite, or defect.
- Click behavior to identify segments of anonymous web traffic likely to click on your served display media.
After applying these techniques, analysts can deliver a visual representation of the segments to help explain the results to nontechnical stakeholders. Here is a video demonstration example of SAS Visual Analytics within the context of supervised segmentation being applied to a brand's digital traffic through the use of analytical decision trees:
Critics of this approach argue that the resulting model is actually a predictive model rather than a segmentation model because of the probability prediction output. The distinction lies in the use of the model. Segmentation is classifying customer bases into distinct groups based on multidimensional data and is used to suggest an actionable roadmap to design relevant marketing, product and customer service strategies to drive desired business outcomes. As long as we stay focused on this premise, there is nothing to debate.
On the other hand, unsupervised approaches, such as clustering, association/apriori, principal components or factor analysis point to a subset of multivariate segmentation techniques that group consumers based on similar characteristics. The goal is to explore the data to find intrinsic structures. K-means cluster analysis is the most popular technique I view with clients for interdependent segmentation, in which all applicable data attributes are simultaneously considered, and there is no splitting of dependent (or target) and independent (or predictor) variables. Here is a video demonstration example of SAS Visual Statistics for unsupervised segmentation being applied to a brand's digital traffic (including inferred attributes sourced from a digital data management platform) through the use of K-means clustering:
Keep in mind that unsupervised applications are not provided training examples (i.e., there isn't a 1-0 or yes/no flag type variable to bias the formation of the segments). Subsequently, it is fair to make the interpretation that the results of a K-means clustering analysis is more data driven, hence more natural and better suited to the underlying structure of the data. This advantage is also its major drawback: it can be difficult to judge the quality of clustering results in a conclusive way without running live campaigns.
Naturally, the question is which technique is better to use in practice – supervised or unsupervised approaches for segmentation? In my opinion, the answer is both (assuming you have access to data that can be used as the dependent or target variable). When you think about it, I can use an unsupervised technique to find natural segments in my marketable universe, and then use a supervised technique (or more than one via champion-challenger applications) to build unique models on how to treat each cluster segment based on goals defined by internal business stakeholders.
Now, let me pose a question I have been receiving more frequently from clients over the past couple of years.
"Our desired segmentation strategies are outpacing our ability to build supporting analytic models. How can we overcome this?"
Does this sound familiar? For many of my clients, this is a painful reality limiting their potential. That's why I'm personally excited about new SAS technology to address this challenge. SAS Factory Miner allows marketers to dream bigger when it comes to analytical segmentation. It fosters an interactive, approachable environment to support working relationships between strategic visionaries and analysts/data scientists. The benefit for the marketer campaign manager is the ability to expand your segmentation strategies from 5 or 10 segments to 100's or 1000's, while remaining actionable within the demands of today's modern marketing ecosystem. The advantage for the supporting analyst team is the ability to be more efficient, and exploit modern analytical methods and processing power, without the need for incremental resources.
Here is a video demonstration example of SAS Factory Miner for supersizing your data-driven segmentation capabilities:
I'll end this posting by revisiting a question we shared in the beginning:
Is your segmentation approach antiquated or advanced?
Dream bigger my friends. The possibilities are inspiring!
If you enjoyed this article, be sure to check out my other work here. Lastly, if you would like to connect on social media, link with me on Twitter or LinkedIn.
