Customer Behavior

Segmentation Analysis

Cornell Course

Improvements in marketing practices in recent decades have had a significant impact on productivity, driven by the increasing efficiency and relevance of customer data collection. Leveraging that data effectively, however, requires that marketing efforts are directed only toward those who are likely to respond, rather than casting as wide a net as possible. Yet to target the right customers, you have to know who they are, which requires that customer data is leveraged such that relevant behaviors can be identified, differentiated, and understood at a granular level. Only then can the value they provide be classified and segmented, allowing for productive management of customers based on those characteristics.

In this course, you will explore RFM (recency, frequency, and monetary value) analysis as a means to classify customer purchase behavior characteristics indicative of a likely response to marketing efforts. You will go from identifying the implicit purpose and value of RFM metrics and workflow to developing and assessing the performance of response models with respect to profitability. Along the way, you will evaluate the pros and cons of RFM analysis in the real world.

The following courses are required to be completed before taking this course:

• Leveraging Customers for Growth
• A/B Testing and Analytics

Note: While familiarity with R/RStudio is not a prerequisite for this course, you will be asked to use R/RStudio for a variety of analytical activities. Solution code provided for practice opportunities can be copied, pasted, and adjusted for graded assessments, but some manipulation of that code will still be required.