Segmentation analytics is a customer analysis technique that involves dividing a customer base into smaller groups (segments) based on common characteristics or behaviors. The goal of customer segmentation is to identify and understand the unique needs, preferences, and behaviors of each customer segment, and tailor products, services, and marketing efforts to each segment.
Customer segmentation can provide valuable insights for businesses, including a deeper understanding of customer needs and preferences, identification of high-value customer segments, and improved targeting of marketing and sales efforts. The use of customer segmentation can also lead to increased customer satisfaction, loyalty, and revenue.
The steps involved in customer segmentation include:
- Data collection: Collecting customer data from various sources such as surveys, transaction data, and demographic information.
- Data preparation: Cleaning and transforming the customer data into a format suitable for analysis.
- Segmentation technique selection: Selecting the appropriate segmentation technique, such as clustering, decision trees, or decision rules, based on the customer data and the segmentation goals.
- Segmentation analysis: Applying the chosen segmentation technique to the customer data to identify customer segments.
- Segment characterization: Describing the characteristics of each segment, including demographics, behavior, needs, and preferences.
- Segment targeting: Using the insights from the customer segmentation analysis to inform targeted marketing and sales efforts, product development, and customer experience improvement.
There are several theories and approaches used in segmentation analytics, including:
- Demographic Segmentation: Dividing the customer base into segments based on characteristics such as age, gender, income, education, and geographic location.
- Behavioral Segmentation: Dividing the customer base into segments based on behaviors such as purchase behavior, usage rate, and brand loyalty.
- Psychographic Segmentation: Dividing the customer base into segments based on personality, values, attitudes, and lifestyle.
- Benefit Segmentation: Dividing the customer base into segments based on the benefits customers seek from a product or service.
- Attitudinal Segmentation: Dividing the customer base into segments based on their attitudes towards a product, brand, or company.
- RFM Segmentation: Dividing the customer base into segments based on Recency (when was the last purchase made), Frequency (how often purchases are made), and Monetary Value (the amount spent).
- Personas: Creating detailed, fictional profiles of representative customers, used to inform product development and marketing efforts.
There are several groups of users of segmentation analytics, including:
- Marketing departments: Using segmentation analytics to better understand customer needs and preferences, and to target marketing efforts more effectively.
- Sales departments: Using segmentation analytics to prioritize sales efforts and to better understand the needs of specific customer segments.
- Product development teams: Using segmentation analytics to inform product design and development, based on the needs and preferences of specific customer segments.
- Customer service departments: Using segmentation analytics to improve customer support and experience, by better understanding the needs of specific customer segments.
- Executive teams: Using segmentation analytics to inform strategic decisions, such as resource allocation, product development, and marketing efforts.
- Data Scientists and Analysts: Using segmentation analytics to analyze customer data and identify customer segments, and to support other groups in the organization in their use of segmentation analytics.