Target Marketing For Donation Soliciting By: Sai Praneeth

Project Methodology1. Problem Definition2. Data Cleansing 3. Data Visualization4. Model Building/Training5. Model Comparison/ Validation 6. Model Scoring

Problem Definition Current State

• A veterans organization seeks to better target its solicitation for donation.

• So that less money is spent on solicitation efforts and more money is available for charity.

Future State

• Develop a predictive model to identify potential donors.

• Reduce cost and increase donations for the organizations.

Note: Soliciting involves sending greeting cards, emails, phone calls etc. along with a request for donation

Data Overview

Target Description

1 Donated

0 No donation

Response Variable

Predictors DescriptionDem Age Age

Dem Gender Gender

Dem Home owner

Home owner

Dem Income Median Income

Gift Avg 36 Gift Amount Average 36 months

Gift Avg all Gift Amount Average all months

Gift time last Time since last gift

PromCnt12 Promotion Count card 12 months

StatusCat96NK

Status Category 96nk

Predictors

Data CleaningChecking for Missing ValuesRare event over Sampling Check for data entry errorsCheck for DuplicatesCheck for Outliers

Data Visualization

Model Building As our model has binary response there are 2 possible predictive models.

1. Decision Trees

2. Logistic Regression

Decision TreesStep 1: Data Partition

o Training (50%)

Variable Numeric Value

Frequency Count

Percent

Target_B 0 2422 50.01Target_B 1 2421 49.98o Validation (50%)

Variable Numeric Value

Frequency Count

Percent

Target_B 0 2421 49.98Target_B 1 2422 50.01

Step 2: Model Construction

Maximal Decision Tree

Step 3: Model Assessment

Note: Cleary there is a problem of overfitting.

Step 4: Model Optimization

• The ASE is for the validation data set is least at 5 leaves, which is the optimal tree.

Step 5: Optimal Decision tree

Decision Rule: People who have made donations 3 or more times in the past 36 months & the donated amount was less then $ 7.5 , have 66% chance of making a donation now.

Logistic RegressionStep 1: Input Selection

o Forward Selection

o Backward Selection

o Stepwise Selection In this case stepwise selection with P-Value of 0.05 was chosen.

Step 2: Model Assessment

• From the graph it is evident that the Misclassification rate for the validation data set is least at step 3, which is the optimal model.

Step 3: Optimal Model

Based on the P-Value the significant variables are:

o Dem Median Home Value

o Giftcnt36

o GiftTimeLast

Odds Ratio Estimates:

Effect Point EstimateDemMedHomeValue

1.00

Giftcnt36 1.121GiftTimeLast 0.965

Step 4: Model Interpretation

o For Giftcnt36 odds ratio is 1.121; this means that for each additional donation in the 36 months, the odds of donation increases by 12.1%.

o For GiftTimeLast the odds ratio is 0.965. That is if the time since last gift increases by 1 month, the odds of donation decrease by 3.5%.

o For DemMedianHomeValue the odds ratio is 1. which indicates that median home value does not significantly impact donations.The conclusion obtained from logistic regression model is similar to that obtained from decision tree.

Model ComparisonSelectedModel

Model Type

ValidationMisclassification

Validation ASE

Y Decision Tree

0.42804 0.2433

N Regression 0.4391 0.2442

Adjustment for oversampling

SelectedModel

Model Type

ValidationMisclassification

Validation ASE

N Regression 0.5001 0.2442

Y Decision Tree

0.5001 0.2432

Adjusted Fit-Statistics

ROC Chart

Lift Plot

Model Scoringo Once the best model is selected, implement the model on a

scoring data set.

o Then export the scored data set to a .csv or SAS file for implementation.

Miscellaneouso Check for Missing Values

o Check for Outliers

o Over Sampling

o Data entry errors

Thank You

Any questions?