ACET had a great opportunity several years ago to analyze a large dataset containing customer transaction data on purchases. The agency wanted to know more about their customer base, particularly when each customer last made a purchase and what each customer’s purchase history included. ACET received a dataset containing more than 70 million transactions where many customers made repeat purchases.
Although this project focused on customers and purchases, analyzing information from large datasets could give important information to your agency. Transactional data reveals vital clues about your marketing efforts, fundraising efforts, volunteer hours, product sales, or donor lists. Analyzing information in this way gives you a multi-dimensional view of your data and keeps you from inundating your donors, volunteers, or customers with unnecessary contacts that lower response rates.
With 70 million transactions, ACET used an analysis technique known as “RFM.”
R stands for Recency. How long ago did the customer place an order? The premise behind recency is the more recent the order, the more engaged the customer.
F stands for Frequency. How many times has a customer placed an order? Research has shown that customers who place multiple orders are more likely to order again.
M stands for Monetary Value. What is the financial worth of each customer to your organization? Customers who spend more tend to buy again.
RFM gave the agency additional information on what proportion of customers were recent frequent consumers, recent infrequent consumers, inactive frequent consumers, and inactive infrequent consumers to better understand consumer behavior. If you are interested in having ACET examine how you can gather important information from large datasets using RFM, please contact us at firstname.lastname@example.org.
 Uzunian, M. (2013). The Importance of Repeat Customers. http://blog.sumall.com/journal/the-importance-of-repeat-customers-2.html