Predictive Analytics: Predicting Responses to Marketing Campaigns

Predictive Analytics    |    Intermediate
  • 13 videos | 1h 16m 32s
  • Includes Assessment
  • Earns a Badge
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While growth has become a primary driver of business valuations and key performance indicators for executives, a renewed emphasis on profitability has led to greater attention being placed on cost-effective, tailored strategies for customer acquisition. This makes predicting the likely response of a prospect to a marketing campaign an increasingly important skill. Discover how to predict the purchase intention of shoppers on e-commerce websites, beginning with cleaning the data and then exploring it for patterns using heatmaps and correlation analysis. Next, perform classification analysis to predict whether a shopper will purchase something. Then, explore different evaluation metrics such as the confusion matrix, recall, precision, and the F1-score. Finally, you will predict whether a specific user responded to a marketing campaign and use feature selection to optimize the number of explanatory variables used in the process.


  • Discover the key concepts covered in this course
    Import e-commerce data into a pandas data frame
    Visualize e-commerce data using charts
    View correlations and relationships in e-commerce data
    Split and encode e-commerce data for machine learning
    Classify e-commerce data using logistic regression
    Import and process marketing campaign data
  • Visualize campaign data using charts
    Create a pipeline for performing classification
    View the performance of a marketing response prediction model
    Select features for predicting marketing responses
    Use feature selection with different types of models to predict responses to marketing
    Summarize the key concepts covered in this course


  • 55s
  • 6m 14s
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    3.  Visualizing E-commerce Data
    8m 12s
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    4.  Viewing Relationships in E-commerce Data
    4m 16s
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    5.  Splitting and Encoding E-commerce Data
    6m 14s
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    6.  Classifying E-commerce Data
    8m 24s
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    7.  Importing Marketing Campaign Data
    7m 10s
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    8.  Visualizing Marketing Data
    8m 12s
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    9.  Setting up a Pipeline for Classification
    7m 22s
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    10.  Viewing the Performance of a Model
    6m 15s
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    11.  Selecting Features for Marketing Response Predictors
    6m 20s
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    12.  Predicting Marketing Response Using Different Models
    5m 22s
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    13.  Course Summary
    1m 35s


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