About the Customer
Industry: Retail
The client is the owner of a popular retail store, selling multiple things. Being a retail business, they have a widespread customer base that purchases different products at different times.
Since they have a large customer base, they were interested in knowing about customer behavior, preferences, and interests from their large data sets.
Key Challenges
There were some key challenges that needed focus:
- Customer segmentation based on huge datasets of information based on
- Demographics (age, gender)
- Geography (local, city, state, regional)
- Behavioristic (customer’s buying habits/behavior/patterns)
- Analysis of customer preferences and purchase history
They wanted a software solution that could overcome these challenges and implement a clustering analysis solution that could help them with customer segmentation.
Our Solution
Understanding the challenges of the client, SPEC INDIA facilitated the client by designing and developing a clustering algorithm and K-means algorithm for customer segmentation.
Solution process involved:
- Analysis of data set with an exploratory data analysis approach
- Identification of most important features based on statistical tests
- Application of machine learning clustering
- Evaluation of clustering with different methods:
- Partitioning methods
- Hierarchical clustering
- Fuzzy clustering
- Density-based clustering
- Model-based clustering
- Finding the most important customer clusters and making business plan recommendations
Tools & Technologies
Business Benefits
- Enhances customer base and work on target areas
- Avails customer segmentation based on purchase history, interests, or activity monitoring
- Targets specific clusters of customers for specific campaigns
- Targets a specific customer segment for promoting their brands and products
- Since algorithms are applicable for web, mobile and stores (kiosks), the solution can be extensively leveraged