Predictive Analytics: Customer Segmentation & Market Basket Analysis

Predictive Analytics    |    Intermediate
  • 16 videos | 1h 52m 32s
  • Includes Assessment
  • Earns a Badge
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Two central goals of marketing are reducing customer acquisition costs and increasing customer lifetime value. Customer segmentation is an important step towards both of these goals - by learning more about present and prospective customers, marketing practitioners can focus on tailoring strategies to acquire and retain these different types of customers more effectively. Explore the regency, frequency, and monetary value (RFM) framework of customer interactions by performing K-means clustering and using the silhouette score to pick the optimal number of clusters. Next, switch to two alternative clustering techniques, known as agglomerative clustering and DBScan. Finally, perform market basket analysis, also known as affinity analysis, to predict what items that customers will purchase together, such as bread and jam. Use the a priori algorithm for computing frequent itemsets, and the calculation and implications of metrics such as support, confidence, lift, and conviction.


  • Discover the key concepts covered in this course
    Remove nulls and duplicates in market orders data
    Calculate recency, frequency, and monetary (rfm) value of customers
    Remove outliers from and visualize rfm data
    Standardize and normalize rfm data for clustering
    Perform k-means clustering on rfm data
    Perform clustering on rfm data and view the results
    Perform agglomerative clustering on rfm data
  • Perform dbscan clustering on rfm data
    Read in and explore grocery store data
    Form shopping carts in grocery store order data
    Encode data for performing market basket analysis
    Perform market basket analysis
    View the results for market basket analysis
    Explore how to perform market basket analysis
    Summarize the key concepts covered in this course


  • 1m 25s
  • 7m 37s
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    3.  Calculating Recency, Frequency, and Monetary Value
    7m 54s
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    4.  Removing Outliers from RFM Data
    9m 20s
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    5.  Standardizing and Normalizing RFM Data
    6m 18s
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    6.  Performing k-Means Clustering
    6m 43s
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    7.  Visualizing the Results of Clustering RFM Data
    6m 9s
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    8.  Performing Agglomerative Clustering on RFM Data
    10m 53s
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    9.  Performing DBSCAN Clustering on RFM Data
    11m 15s
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    10.  Importing Grocery Store Data
    6m 40s
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    11.  Forming Baskets in Grocery Data
    6m 44s
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    12.  Setting up Data for Market Basket Analysis
    7m 29s
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    13.  Performing Market Basket Analysis
    7m 34s
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    14.  Viewing Results of Market Basket Analysis
    5m 29s
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    15.  Processing Results of Market Basket Analysis
    6m 50s
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    16.  Course Summary
    4m 14s


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