Direct mail marketers have used RFM (recency, frequency, monetary) analysis for over 50 years. It is a tried and true method that has a proven track record of identifying high-value customers and improving overall response rates.RFM stands for:Recency: Date of last purchaseFrequency: Total number of customer's purchases Monetary Value: Monetary value of the purchases.These three elements not only provide a way to idenify high-value customers, they also help predict when a customer will respond to a particular promotion. Recency is generally the strongest predictor of response, while monetary value is the weakest.To begin the process, you sort your customers by most recent purchase date into quintiles. The most 20 percent of customers who purchased most recently are placed in quintile 5, while the 20 percent who purchased furthest in the past are placed in quintile 1. The same process is carried out for frequency and monetary value. In the end, you are left with 125 cells coded from 111 to 555 (th best being those customers with very recent purchases, who purchase very frequently, and a who spend the most money.)If a marketer were using direct mail, he would then send a test mailing to a select number of individuals in each of the cells. For example, if there are 200,000 customers in the database, he may send out mail to 40,000 of them to see which cells "break even" (when the profits from purchases by the customers equals the total cost of mailing the offer to them.) Around 10% of cells will "break even." It's simple after that: you mail the offer to all customers in the break-even cells, and do not mail to the cells that fail to break-even. Marketers have used this technique to increase profits and response rates for years with great success.At the same time, given the increasing popularity of email (which reduces the cost of sending an offer to nearly zero), can RFM analysis still provide value?The answer is yes. Digital marketers can adapt the framework of RFM analysis by replacing traditional criteria (last purchase date, frequency of purchase, amount of purchase) with criteria that are more applicable to the digital landscape (date of last conversion, number of clicks/number of visits to site, and amount of sale.) This method, augmented with additional demographic and behavior-based characteristics (abandoning a shopping cart, clicking a particular product page, allotted time spent on the site etc.) will allow marketers to build targeted campaigns with specific messaging around subsets of individuals with common traits. I the digital world, the goal is not so much to minimize costs (since incremental costs of another e-mail are close to zero), but to mazimize the benefit - through better targeting, the same number of e-mails can generate a far greater return.In sum, RFM analysis is a low-cost, effective tool to identify high-value customers and those most likely to respond to marketing communications in the future. However, when augmented with sophisticated segmentation and modeling strategies, it becomes a very impressive predictor of response and a valuable tool for today's integrated digital marketer. Cheers, TonyTony CorettoCo-CEOPNT Marketing Services Inc. Customer Intelligence. Marketing Results.Twitter: @PNTTonyCategory: Customer Intelligence Marketing Database
