Customer Segmentation 1.0
Learn why robust segmentation is a key component of any successful marketing strategy.
This is the first post in our Customer Segmentation series, exploring the methodology and business applications behind DELVE’s data-driven approach to customer segmentation.
In today’s world, mass marketing—or reaching the whole market with a uniform offer or message—is no longer enough. Consumers crave personalization, and robust segmentation is a key component of a successful marketing strategy.
With the rise of digital marketing, consumers have come to not just appreciate but to expect personalized messaging and experiences from brands. In fact, according to a Google study, 88% of consumers have improved sentiment towards a brand after receiving personally relevant content. This is especially true of millennials, where studies have shown that 70% not only prefer personalized emails but are actively frustrated by irrelevant blast emails.
Customer Segmentation is a crucial piece of any personalization strategy. By identifying subgroups of customers who share key similarities, unique messaging and targeting can be tailored for each segment to dramatically increase marketing effectiveness and customer responsiveness.
Simply explained, the process of customer segmentation is to divide customers and prospects into smaller distinct groups of individuals. However, the more complex ultimate goal is to devise segments that are receptive to highly targeted marketing strategies.
A simple segmentation plan, for example, could be dividing individuals into age buckets (0-20 years old, 21-30, etc). This is an acceptable starting point, but it’s likely that age by itself is not enough to devise a truly effective personalization strategy.
Layering on additional variables, such as gender, geolocation, etc, may bring you closer, but determining the best way to group customers using a combination of several variables can be difficult. This brings us to two very different segmentation methodologies: Rule-Based Segmentation and Data-Driven Segmentation.
Rule-Based segments are customer segments created using rules targeting predefined attributes. These business logic-based segments are manually defined by marketing teams using analysis and domain knowledge. For example, a rule-based segment could be defined as “Females over the age of 60 who have purchased over $100 worth of our product.”
Rule-based segmentation is useful for brands who have a clear idea of the different audience segments they would like to reach. However, manually defining the business logic for each segment becomes more and more difficult as additional variables of interest are added to the mix. Additionally, relying on rule-based segmentation can serve to reinforce existing assumptions about a brand’s target audiences instead of uncovering new insights.
Data-Driven segmentation takes an algorithmic approach to customer segmentation. In Rule-Based segmentation, marketers are in charge of both selecting variables of interest and using these variables to define their own segmentation rules.
Conversely, in Data-Driven segmentation, variables are fed into a Machine Learning clustering algorithm that uncovers an underlying logic to the data and uses this logic to define customer segments without marketer input. Customers who look similar to each other (based on all available data) are grouped together. Dissimilar customers (once again, based on all available data) are grouped into different segments.
This algorithmic approach has 2 main advantages:
- Machine Learning allows for significantly more variables to be factored into segment creation
- Data-Driven segmentation often uncovers counterintuitive insights that would be overlooked by marketers who are manually creating segments
Data-Driven segmentation can be performed using any available customer data: behavioral, financial, personal interests, and so on. However, DELVE has found that demographic and psychographic (pertaining to values, opinions, interests) based segmentation leads to the most useful and robust results.