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ABC Financial Marketing

ABC Financial Marketing. Group 5 Andrew Jeremy Paras Rob. Agenda. Background Classification Model Prediction Model Conclusion and Results. Percent Response. Respond rate in sample data – 43.9% Predicted rate in competition set – 59.9% using only the classification model.

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ABC Financial Marketing

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  1. ABC Financial Marketing Group 5 Andrew Jeremy Paras Rob

  2. Agenda • Background • Classification Model • Prediction Model • Conclusion and Results

  3. Percent Response • Respond rate in sample data – 43.9% • Predicted rate in competition set – 59.9% using only the classification model

  4. How Accurate Are These Models? • The pure classification model provides about 77.5% accuracy

  5. Best Classification Model

  6. The Real Measure is Profit • Since we are concerned with profit, we use the profit prediction model to determine the recipients • This yields a profit of $214,028 for the sample data vs. $203,041 when mailing to the entire population

  7. Best Prediction Model

  8. Who Should We Send To? • Recommend sending to 41.2% of possible customers • 2 reasons for difference from classification solution • A respondent who trades less than $20,500 causes a net loss • A person with a small chance for response, but a high trading rate might be beneficial for amount of risk

  9. What Criteria Are Used? • Deciding attributes are: • Total balance • Trading history • Size of customer’s first fund

  10. Low balance < $27,000 High Balance between $27,000 and $78,000 Low First Fund < $6,800 High First Fund > $6,800

  11. Conclusion The key advantage to the prediction model is the ability to avoid the loss from low balance customers with an active trading history

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