Customer Analytics in Python: Drive Business Growth
Leverage Python for powerful customer segmentation, lifetime value prediction, and behavioral analysis. Understand your customers deeply to drive targeted strategies and growth.
Course Syllabus: Customer Analytics in Python
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A Brief Marketing Introduction:
Understand the fundamentals of marketing and the role of data-driven insights in customer relationship management and strategy.
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Segmentation Data:
Learn how to collect, prepare, and understand data suitable for customer segmentation, identifying key customer attributes.
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Hierarchical Clustering:
Dive into hierarchical clustering techniques to group customers based on their similarities, revealing natural customer segments.
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K-Means Clustering:
Master K-Means clustering, a widely used algorithm for segmenting customers into distinct groups based on their characteristics.
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K-Means Clustering based on Principal Components:
Combine K-Means clustering with Principal Component Analysis (PCA) for more effective segmentation in high-dimensional datasets.
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Purchase Data:
Explore the structure and analysis of customer purchase data to identify buying patterns, product preferences, and sales trends.
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Descriptive Analyses by Segments:
Perform in-depth descriptive analysis on identified customer segments to understand their unique behaviors and profiles.
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Modeling Purchase Incidence:
Develop predictive models to forecast the probability of future purchases by customers, aiding in targeted marketing efforts.
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Modeling Brand Choice:
Learn to model customer decisions related to brand selection, understanding factors influencing brand loyalty and switching.
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Modeling Purchase Quantity:
Build models to predict the quantity of products customers are likely to purchase, optimizing inventory and sales strategies.
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Deep Learning:
Apply advanced deep learning techniques to customer analytics, unlocking deeper insights from complex behavioral data.