Despite the current love affair with predictive models as the only customer segmentation strategy, direct marketing's three-variable elixir, Recency-Frequency-Monetary Value (RFM), still has a place in modern database marketing. RFM is not a replacement for inferential statistics, whose models are far superior in predicting response, sales from a campaign or long term value and retention. But in the real world, RFM can still be useful when models are not practical. RFM also provides an easy to understand management summary of customer behavior based on purchases. RFM even plays a role in policing the black-box results of predictive models to ensure quality before a campaign is implemented.
Segmentation of customers
What comes to mind when you read these descriptions of customer segments?
- Advocates
- Repeat buyers
- Gift givers
- Too good to be true
- Habitual returners
- Fraud
- Trial buyers
- Once was enough
- Dormant
- Defected
- About to Defect
- Revolving Door
- Cry in your pillow ("please come back")
Each of these customer segments are rooted in purchase behavior. Purchase behavior is the best predictor of repeat purchasing and loyalty. While it is measured in different ways, depending on industry and customer lifecycle, all database marketers covet its empirical facts on how often a buyer renews their subscription/membership, visits your site, shops at your store. Further, purchase behavior is about how much customers spend, the products/services they buy, in what combination or sequence. Purchase behavior codifies both the tenure- as well as recency your relationship with your customers.
The good news for today's marketers is that the most important purchase behavior is already on your customer database. What can you do with these data? You can:
- Identify groups of customers
- Target them for campaigns
- Promote repeat purchase and loyalty
- Defend against attrition/defection
- Acquire customers who resemble the best ones
A Modern Approach to RFM Segmentation
Segmentation gives direct marketers a quantifiable way to distinguish between the best and worst customers on file. Purchase behavior is the most powerful way to segment your customers by historic value.
You can also gain insight with purchase behavior from outsides sources of information such as co-op databases, which are a collection of hundreds of direct marketing lists pooled together; individual response lists and any merge/purge processing itself (with its resulting intra- and inter- matches). These data provide contextual dimensions, and will help you realize there is more going on in your customer's lives than just their relationship with you.
Other kinds of segmentation bring you closer to why your customer buys from you, particularly from primary research which adds their attitudes and experiences. This information, along with demographics, explains their motivations and brings customer segments to life. This area of research is the most interesting and strategic, and has impact on benefits customers seek, media consumption habits and advertising strategy. It is critical input to decisions around what to say and how to say it.
Recency - Frequency - Monetary Value (RFM)
Recency Frequency Monetary Value (RFM) is a quick, descriptive way to segment a marketing database on purchasing behavior that direct marketers have used with success since the 1930s.
What is RFM? How do you use it? Is it superior to inferential statistics such as predictive models and decision trees? There is no contest against statistical modeling, RFM will lose every time. While this whitepaper may have just come to a screeching halt, the balance will argue that RFM still has an important place in today's modern direct marketing as a complement to predictive models, particularly for non-statisticians (see Figure 1).
A Modern Approach to RFM Segmentation
To be fair, predictive models have critical advantages over RFM. First, RFM by definition only utilizes 3 predictor variables, whereas predictive models use hundreds. While many of these independent variables are collinear, the fact remains that more independent variables provide exponentially more power in predicting future purchase behavior.
RFM as a construct does not predict anything, that is, it has no dependent variable.
Predictive models by nature are built to predict a customer's response to a campaign, the money they will spend on it (or over a period of time) and whether they will continue to purchase or leave the brand (i.e., attrit, defect, churn).
