Predictive Analytics in Marketing and Retail Competency (Intermediate Level)

  • 24m
  • 24 questions
The Predictive Analytics in Marketing and Retail Competency (Intermediate Level) benchmark measures your ability to apply machine learning algorithms and perform predictive analytics to predict sales and customer lifetime value. You will be evaluated on your skills in predicting responses to marketing campaigns and performing market basket analysis. A learner who scores high on this benchmark demonstrates that they have experience in applying predictive analytics in the marketing and retail domain with minimal supervision.

Topics covered

  • calculate recency, frequency, and monetary (RFM) value of customers
  • classify e-commerce data using logistic regression
  • compare and contrast different regression models to predict Walmart sales
  • create a pipeline for performing classification
  • encode data for performing market basket analysis
  • explore how to perform market basket analysis
  • form shopping carts in grocery store order data
  • import and process marketing campaign data
  • perform agglomerative clustering on RFM data
  • perform cross-validation and feature selection on a CLV prediction model
  • perform DBSCAN clustering on RFM data
  • perform feature selection using recursive feature elimination (RFE), lasso regression, and support vector regression (SVR) on a CLV prediction model
  • perform market basket analysis
  • predict CLV using linear regression
  • predict Walmart sales using a regression model
  • read in and explore grocery store data
  • remove outliers from and visualize RFM data
  • select features for predicting marketing responses
  • split and encode e-commerce data for machine learning
  • use feature selection with different types of models to predict responses to marketing
  • use ridge regression, kNN, decision trees, extra tree regressors, and random forests to predict Walmart sales
  • view the results for market basket analysis
  • visualize campaign data using charts
  • visualize CLV data