Product Cluster Analysis: Unveiling Market Insights through Data

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PRODUCT CLUSTER ANALYSIS

Agenda
 Introduction
 Project Objectives
 Methodology
 Exploratory Data Analysis (EDA)
Model Selection and Evaluation
 Results
 Conclusion

Introduction
Description: Conducting product clustering analysis using historical sales
data to group similar products based on sales patterns.
Aim: To discern trends, optimize inventory management processes, and
tailor offerings to better align with customer preferences.

•Streamlined Inventory Management: Ensuring optimal stock
levels for each product category by clustering similar
products.
•Tailored Product Offerings: Empowering the curation of a
more targeted and diverse product portfolio.
•Informed Decision Making: Data-driven decisions regarding
purchasing, marketing, and sales strategies.
•Sustained Competitive Advantage: Maintaining a
competitive edge by understanding market trends and
consumer behavior.
Project
Objectives

 Data Collection: Historical sales data including retail sales, retail
transfers, and warehouse sales.
 Data Preprocessing:
 Handling missing values by filling or dropping.
 Detecting and handling outliers using the IQR method.
 Exploratory Data Analysis (EDA):
 Distribution Analysis
 Correlation Analysis
 Model Selection:
 K-Means Clustering Algorithm
 Feature scaling using StandardScaler
 Model Evaluation:
 Silhouette Score
Methodology

 Distribution Analysis: Histograms for retail sales, retail
transfers, and warehouse sales.
 Correlation Analysis: Heatmap to visualize correlations between
numerical features.
 Pairplot: Visualizing relationships between numerical features.
Exploratory
Data Analysis
(EDA)

 K-Means Clustering:
Number of clusters: 5
Feature scaling for better performance.
 Silhouette Score:
Measures how similar an object is to its own cluster compared
to other clusters.
Silhouette Score achieved: [Insert Silhouette Score]
Model
Selection and
Evaluation

•Clusters Visualization: Scatter plot showing clusters based on retail sales
•Cluster Report: Summary of average retail sales, retail transfers, and ware
•[Insert Cluster Report Table]
Results

Click to edit
Master title style
Conclusion
 Summary:
 Accurate grouping of similar products based on sales patterns.
 Insights to optimize inventory management and product offerings.
 Impact:
 Improved inventory management efficiency.
 Enhanced sales performance.
 Future Work:
 Further refinement of clusters.
 Integration with real-time sales data.

Thank You!

Product Cluster Analysis: Unveiling Market Insights through Data

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Product Cluster Analysis: Unveiling Market Insights through Data