Data segmentation
Applying the selected segmentation criteria: Dividing the collected data into groups based on demographic, behavioral, geographic or psychographic characteristics.
Data Clustering: Using algorithms to find patterns among customers, such as buying habits or frequency of interaction with a brand.
Identifying Insights
Identifying the Most Profitable Segments: Studying which groups are generating the most profit and which ones have the most potential.
Identifying weak points: For example, those with high conversion kenya email list rates but low customer retention, or those that show low response to marketing campaigns.
Comparison of segments
Cost to Serve Assessment: Studying which segments are more expensive for a business in terms of marketing or service.
Customer profiling: Create “personas” for each group to help you target offers and campaigns more accurately.Examples of data collection and analysis for different types of business
For online store
Segmentation by shopping habits: Defining “single shoppers” and “recurring shoppers”.
Identifying Marketing Effectiveness: Analyzing which channels (e.g. social media, email newsletters) are driving the highest conversions.