Objective: reduce a telecommunication company’s churn rate (originally 14.49%) through developing predictive models with past customers’ churning data and identifying churners’ characteristics using machine learning techniques Tools used: >RStudio (ggplot2, dplyr) >Weka (for model prototyping) >MS Excel, MS Powerpoint Techniques used: >Decision tree >Naive Bayes >Random Forest >K-means clustering analysis >Recommender system

1. Colleen Bobbie, Stephen De Medicis,
Iris Huang, Krean Naidoo, Calvin Reid
CIND 119 CLASS PROJECT
Group Project Analyzing
Churn Rate

2. Project Description
CHURN

3. Data Preparation

4. Data Preparation
 Derived attributes
 Categorize attributes
 Summary statistics
 Distribution

5. Data Preparation
 Correlation plot
 Simple logistic regression (attributes most
linked to churn)
Significant imbalance in churn attribute
 Derived values: NA -> 0
 Area code: discretized

6. Data Preparation
 Correlation plot
 Simple logistic regression (attributes most
linked to churn)
Significant imbalance in churn attribute
 Derived values: NA -> 0
 Area code: discretized

7. Predictive Analysis

8. SMOTE and Cross-Validation
SMOTE

9. Predictive Modeling: Classification
Our predictive modeling covers three different
classification algorithms:
 Decision Tree
 Naïve Bayes and
 Random Forest

10. Predictive Modeling Interpretation

11. Post-Predictive Analysis

12. K-Means Clustering our ‘Churners’
“Cathy Complainers”
Total charges
Day charges
Day price per call
Price per call
Customer service calls
Voicemail messages
“Danny Daytimes”
Voicemail messages
Customer service calls
Total charges
Day charges
Day Price per call
Total Price per minute
“Irene Internationals”
Customer service calls
Voicemail messages
International charges
International price per
call
*Comparisons amongst ‘churners’ only

13. Recommendations
1. Tailored Phone Plans
2. Implementation Tool
3. Data Collection Improvements

14. 1. Retention Recommendations
“Cathy Complainers”
 Lower cost retention
plan with free
voicemail
 Offer to high volume
callers to call centre
“Danny Daytimes”
 Retention plan with
discount/additional
daytime/evening
minutes
 Offer at early contact
or proactively offer
“Irene Internationals”
 Retention plan with
discount/additional
international minutes
or better international
plan
 Offer at early contact
or proactively offer

15. 2. Implementation Tools

16. 3. Data Collection Improvements

17. Thank You!
Any Questions?

18. Accuracy
Unsmoted Smoted
Smoted
Pruned
Unsmoted
Pruned
Decision
Tree
66%/33%
Split
97.4404 81.182 93.831 97.7052
3 Fold 97.4197 81.5154 93.9031 97.5998
10 Fold 97.5398 81.4273 94.2379 97.5098
Naïve Bayes
66%/33%
Split
84.8191 81.0783 72.1617 85.7899
3 Fold 84.9385 81.7621 72.3348 85.9886
10 Fold 84.3684 82.5198 72.3348 86.1386
Random
Forest
66%/33%
Split
86.496 97.0969 93.9347 97.3522
3 Fold 88.8689 95.9648 93.4449 97.4497
10 Fold 89.679 96.1938 94.6079 97.5998

19. False Positive (where FP rate of Class 'False')
Unsmoted Smoted
Smoted
Pruned
Unsmoted
Pruned
Decision
Tree
66%/33%
Split
0.14 0.368 0.078 0.124
3 Fold 0.164 0.364 0.081 0.149
10 Fold 0.155 0.366 0.068 0.157
Naïve Bayes
66%/33%
Split
0.446 0.13 0.309 0.5
3 Fold 0.482 0.117 0.303 0.516
10 Fold 0.487 0.104 0.303 0.511
Random
Forest
66%/33%
Split
0.823 0.036 0.109 0.161
3 Fold 0.768 0.033 0.113 0.174
10 Fold 0.712 0.032 0.094 0.164

20. Random Forest