You’re Probably Looking at Your Customer Segments Incorrectly. Here’s Why.
By: Jeff Sehl
Businesses use customer segmentation to share the right messaging with the right customers at the right time. Unfortunately, successful segmentation isn’t always easy to pull off. Many companies make the mistake of segmenting on incorrect or overly specific variables and are left incapable of leveraging insights. To avoid these common missteps, GALE has developed a segmentation approach called micro-clustering, which strikes the balance between granularity and actionability.
Businesses traditionally rely on four methods of customer segmentation when limited data and human capital is available to them:
1. Demographic segmentation: this is useful for small businesses without access to large amounts of data. It uses readily available data to develop messaging and promotions relevant to customers. Customers can be segmented according to age, race, religion, gender, family size, ethnicity, income, and education level. The downfall of demographic segmentation is that it assumes that the needs of customers in the same demographic groups are the same, which isn’t always true.
2. Geographic segmentation: this segments based on city, state, country, etc. Marketers can also divide the market based on population density (e.g. rural, suburban, and urban areas) or climate. This method should be used in conjunction with another segmentation method because, like demographic segmentation, it falsely assumes that all consumers in the same location have the same needs.
3. Needs-based segmentation: this divides customers based on their specific needs. Needs-based segmentation may also not reflect the actual behavior of customers when they make purchases, as segments are based on self-reported behavior.
4. Attitudinal segmentation: this is useful when determining overall branding strategies, but can be a time consuming process because multiple studies (surveys and focus groups) are necessary to capture differing attitudes. The insights you drive to through this type of data tend to expire over time, because attitudinal groups aren’t static.
The Shift to Personalization
Many businesses overcome the shortcomings of traditional segmentation by developing one-to-one segmentation approaches. Here are some examples of companies that segment successfully:
Amazon: a Data first approach
Amazon applies transactional data, A/B tests, and advanced algorithms to send offers that relate directly to unique needs and that also predict behavior. This marketing strategy has led to high customer retention, with over 60% of orders coming from repeat customers.
Disney: Creating Experiences
Customers that book trips online with Disney receive a personalized book in the mail to help plan their getaway. The book directs the customer to the Disney website where they can further personalize their “Disney Experience.” They are also instructed to download the Disney mobile app, which provides itinerary details for their trip.
While these examples come from massive brands with major resources, effective personalization can be achieved at a much smaller scale. At GALE, we employ a unique data-driven segmentation method that uses transactional and behavioral information to get to the core of customer needs.
On top of that, we produce clusters based on actual consumer behavior rather than on reported or demographic behavior. When viewed correctly, even small amounts of customer data contain valuable insights that can drive revenue for your business and increase your bottom-line.
Jeff is a senior associate on the strategy team at GALE. He works to help brands become more relevant to today’s digital consumer.