analiza migracji klientów: jak przeprowadzić prognozę ...€¦ · analiza migracji klientów:...
TRANSCRIPT
Analiza migracji klientów: jak przeprowadzić prognozę churnu z użyciem Machine Learning?
Wiktor Zdzienicki
1. Why is churn modeling important?
2. Challenges
3. Featured solutions
4. Project stages
5. Solution example – Azure ML Studio
6. RFM & Score
7. Solution example – Power BI
Monitor your business
It’s a crucial metric for a growing number of companies in a variety of industries
Traditional consumer behavior patterns disappear, along with the decrease of customer loyalty and retention
Companies must make use of the data to analyze not only the probability of churning but also combine this with an evaluation of customer value
Customer churn – why is this important?
New customer habits
Appreciate your existing customers
Not only churning
Acquisition of a new customer is much higher than the cost of retaining current clients
Challenges
• How to use analytics to determine the probability of customer churn?
• How to combine it with scoring and estimation of customer value?
Featured solutions
Churn
Churn analysis
Identification of the reasons which made a
customer leave
The final effect is the probability of situation in
which a customer stops using our products or
services
Benefits
o Lower cost of generating campaigns
o Specifying the negative effects of potential
churn
o Tailor-made and targeted marketing actions
Project Stages
Project Stages
Project Stages
• Removing outliers• Imputing empty records• Format correction• Binning & grouping• Target creation
• Enriching with dictionaries• Extracting dates• Interactions• Aggregations• Dummy features
Project Stages
• Exploratory data analysis• Correlations• Distributions • Log transformations• ANOVA
• Manual selection• Stepwise selection• LASSO• Feature importance
Project Stages
• Dividing data for test & train sets
• Complexity & interpretability discussions
• Selection of few rival models, eg.:• Logistic
regression• Random forest• Neural net• Gradient
boosted decision trees
Project Stages
• Generating predictions on known test data• Used for
investigating accuracy
• Generating predictions in production process• Used for making
business decisions
Project Stages
• Setting desired accuracy metric
• Analysingconfusion matrix in terms of:• Optimal
threshold• Accuracy• Recall• Precision
• Selecting the best model basing on selected metrics
Project Stages
• Models with default parameters do not give optimal results
• Tweaking range of input values in order to achieve the pest performance
• Optimisingcalculation time & model robustness
• Producing final results
Solution example
Modeling in Azure ML Studio