Customer segmentation is the process of dividing customers into groups based on common characteristics so companies can market to each group effectively and appropriately.
In business-to-business marketing, a company might segment customers according to a wide range of factors, including:
- Industry
- Number of employees
- Products previously purchased from the company
- Location
In business-to-consumer marketing, companies often segment customers according to demographics that include:
- Age
- Gender
- Marital status
- Location (urban, suburban, rural)
- Life stage (single, married, divorced, empty-nester, retired, etc.)
Segmentation allows marketers to better tailor their marketing efforts to various audience subsets. Those efforts can relate to both communications and product development.
The project uses the machine learning concept of clustering to segment these customers.
The algorithm used is the unsupervised learning algorithm K-Means Clustering.
- Numpy and Pandas to load and transform data.
- Matplotlib and Seaborn to visualize data.
- Scikit-Learn for finding optimal 'k' using Elbow Method and Silhouette Method and for applying the K-Means algorithm.