Business Intelligence Using SAS Final Presentation
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The group presented models to predict customer subscription to term deposits for a Portuguese bank. They described the customer data, built decision tree, regression, and neural network models with and without customer profiles. The decision tree model performed best with a profit of $15,768 and savings of $10,356. Rules from the decision tree could save $1200 by not contacting some customers or generate $10,000 revenue from others. The project aimed to boost sales, loyalty, and recapture customers through targeted marketing.
Business Intelligence Using SAS Final Presentation
- 1. Bank Marketing Project Group 7: Zhaodi Liu Preete Dixit Nandini Naik Rashmi Nadubeedi Ramesh Pravin Kumar Prem Kumar
- 2. Agenda • Project Motivation • Data Description • Our BI Models • Experimental Results • Association Rule mining • Managerial Implications • Challenges • Conclusion
- 3. Project Motivation • Direct marketing targets customers directly with a personalized message as opposed to Mass marketing • The primary benefit to businesses: – Increased lead generation – Increase sales volume – Increased customer base – Minimize losses • Focus on generating more "qualified" leads
- 4. Impact of Data Mining • Can be very effective for direct marketing • Use of sophisticated algorithms generate rules, determine the most useful attributes and predict future outcome • Our goal is to predict the probability of a client subscribing to the term deposit • In the interest: – To boost sales to existing customers – Increase customer loyalty – Recapture old customers and generate new business
- 5. Data Description • The data is related with direct marketing campaigns (phone calls) of a Portuguese banking institution Data Set Characteristics: Multivariate Number of Instances: 45211 Area: Business Attribute Characteristics: Real Number of Attributes: 17 Date Donated 2012-02-14 BANK CLIENT DATA Serial No Name Description Data Type 1. age Client’s age numeric 2. job type of job categorical 3. marital marital status categorical 4. education level of education categorical 5. default has credit in default? binary 6. balance average yearly balance in euros numeric 7. housing has housing loan? binary 8. loan has personal loan? binary
- 6. DATA RELATED WITH THE LAST CONTACT OF THE CURRENT CAMPAIGN Serial No Name Description Data Type 9. contact contact communication type categorical 10. day last contact day of the month numeric 11. month last contact month of year categorical 12. duration last contact duration, in seconds numeric 13. campaign number of contacts performed during this campaign and for this client numeric 14. pdays Number of days that passed by after the client was last contacted from a previous campaign numeric 15. previous number of contacts performed before this campaign and for this client numeric 16. poutcome outcome of the previous marketing campaign categorical OUTPUT VARIABLE (DESIRED TARGET) Serial No Name Description Data Type 17. y has the client subscribed a term deposit binary
- 7. BI Model - With Target Profile
- 12. Regression
- 13. Regression Node: To convert categorical values to interval value using dummy variables concept. To group logically related categories in order to reduce the number of independent variables in regression equation.
- 15. Neural Network
- 16. The model converged after 70 iterations and contains 124 weights.
- 17. Neural Network with Input Selection
- 18. • Reducing the number of modelling inputs reduces the number of modelling weights as well as computational costs and possibly improves the model performance. • The useful inputs are selected by connecting the Neural Network Node to the Regression Node.
- 19. The model converged after 42 iterations and contains 46 weights.
- 20. BI Model - Without Target Profile
- 21. Decision Tree
- 22. Regression
- 23. Neural Network The model converged after 70 iterations and contains 124 weights.
- 24. Neural Network with Input Selection The model converged after 76 iterations and contains 85 weights.
- 25. Model Assessment and Scoring results with Target Profile Model – The performance of the four models are compared based on the average profit using the model comparison node.
- 26. Fit Statistics
- 27. ROC Plots
- 28. Confusion Matrix
- 29. Scoring – Scoring is used to implement the model deemed best by the model comparison node for predicted the outcome for a new case/observation for which the outcome is unknown. Replaced Variables: – Job – management – Education – Secondary – Contact – Cellular Rejected Variables: – Poutcome – Target y
- 30. Scoring
- 31. Scoring Actual Data Scores: – Percentage No = 88.476% – Percentage Yes = 11.524% There is a slight difference of 1.725% in the prediction model outcome and the actual outcome.
- 32. Model Assessment and Scoring results without Target Profile Model – The performance of the four models are compared based on the misclassification rate using the model comparison node.
- 33. Fit Statistics
- 34. ROC Plots
- 35. Confusion Matrix
- 36. Scoring
- 37. Scoring
- 38. Scoring Actual Data Scores: – Percentage No = 88.476% – Percentage Yes = 11.524% There is a slight difference of 1.725% in the prediction model outcome and the actual outcome.
- 41. Data Pre-processing • Default : D(Yes), D(No) • Housing : H(Yes) , H(No) • Personnel Loan : PL(Yes),PL(No) • Age : 20- 40, 40-60, 60-90, and 90-100
- 43. Managerial Implications if pdays < 19.5 or MISSING AND month IS ONE OF: MAY, JUN, JUL, AUG, NOV, JAN or MISSING AND duration < 348.5 or MISSING AND age < 60.5 or MISSING then Predicted: y=YES = 0.02 Predicted: y=NO = 0.98 A total of 100 customers who and the cost of calling a customer is $12 then there will be a saving $1200 just by not contacting these set of customers. if pdays < 19.5 or MISSING AND month IS ONE OF: FEB AND housing IS ONE OF: NO AND duration < 466.5 or MISSING AND day < 20.5 AND day >= 9.5 AND age < 60.5 or MISSING then Predicted: y=YES = 0.75 Predicted: y=NO= 0.25 A total of 100 customers and cost of calling a customer is $12 and if the profit is $100 then the Ban could generate revenue of $10,000.
- 44. Decision Tree Model – Our best-fit model for maximizing profits Decision Tree Predicted Positive Negative Actual Positive 1314 1330 Negative 863 19098 Predicted Positive Negative Actual Positive $15,768 $0 Negative -$10,356 $0
- 45. Regression Model • If the pdays increase by 1-unit then it has absolutely no impact on the odds of not subscribing to the term deposit. ��.���𝟖𝟗� ≈ �
- 46. Challenges • To implement the Profit/Loss matrix • Absence of ROC plot in the result of Model Assessment • Non-convergence of Neural Network • Scoring
- 47. Conclusion • Successfully implemented 2 predictive analysis models to predict the outcome of term deposit subscription • Decision Tree best fit-model based on Profit/Loss • Decision Tree best-fit model based on Misclassification Rate • Using the Decision rules – Results in a saving of $1200 – Generates a revenue of $10,000 • Using Profit/Loss Matrix – Profit of $15,768 – Savings of $10,356
- 48. Q&A
Editor's Notes
- #9: The Replacement Node was used to replace the unknown and other values by “Missing” values in order to improve the performance of the predictive models. The Impute Node was used to replace the missing values (unknown values replaced by missing in the above step) for class variables by the most frequently occurring value. The Data Partition node was used to split the dataset into 50% Training Dataset and 50% Validation Dataset.
- #11: the optimal tree has 34 leaves whereas, the maximal tree has 57 leaves.
- #12: the optimal tree has 34 leaves whereas, the maximal tree has 57 leaves.
- #17: The model converged after 70 iterations and contains 124 weights. The iteration plot is displayed below. As per the plot, the model obtained after 47 iterations has the highest average profit.
- #20: the model obtained after 11 iterations has the highest average profit.
- #22: Maximum Branch - 3 and above average square error and misclassification rate increases Maximum Depth – 9 and above the average square error and misclassification rate is constant where as below 9 it’s increasing Leaf Size – lease average square error and misclassification rate is obtained with value 2 the optimal tree has 34 leaves whereas, the maximal tree has 57 leaves. Train: misclassification rate: 0.1006 Valid: misclassification rate: 0.102 Train: misclassification rate: 0.0941 Valid: misclassification rate: 0.097014
- #23: the misclassification rate for the training data is minimum for the model trained in Step 12. Hence model trained in step 12 is selected. Interation plot: minmum valid misclassification rate=0.105042
- #24: The model converged after 70 iterations and contains 124 weights. The model converged after 76 iterations and contains 85 weights. , the model obtained after 44 iterations has the minimum valid 0.100863 misclassification rate
- #25: the model obtained after 44 iterations has the minimum misclassification rate.