Knowing what to sell, when, and to whom

Area of Science:

• Marketing Analytics
• Behavioral Economics
• Econometrics

Background:

• Customer relationship management (CRM) systems often fail to accurately predict customer purchasing behavior, with success rates around 55%.
• This inaccuracy leads businesses to abandon predictive analytics, resorting to broad marketing campaigns that may be ineffective.
• Existing predictive models are limited by the mathematical methods used for data interpretation, not by data availability or past behavior relevance.

Purpose of the Study:

• To introduce a novel methodology for predicting individual customer behavior.
• To demonstrate the effectiveness of this new approach in improving purchase prediction accuracy.
• To show how enhanced prediction accuracy can positively impact marketing return on investment (ROI) and sales revenue.

Main Methods:

• Developed a new predictive modeling technique based on the economic theories of Nobel laureate Daniel McFadden.
• Applied advanced mathematical methods to interpret customer behavior data.
• Validated the methodology's performance against traditional prediction models.

Main Results:

• The new methodology significantly increases the accuracy of predicting a specific customer's purchase at a specific time to approximately 85%.
• This improvement has a substantial positive impact on marketing ROI.
• Companies can achieve higher revenues and reduce customer contact frequency, suggesting over-communication may be detrimental.

Conclusions:

• The primary limitation in predicting customer behavior lies in the mathematical methods employed, not in CRM systems or historical data.
• The developed methodology offers a powerful tool for businesses to enhance marketing effectiveness and profitability.
• Optimized customer engagement through accurate prediction can lead to increased sales and reduced marketing costs.