Continuing the discussion about customer metrics and analysis from previous posts; this post explores the relevance of the RFM (Recency, Frequency, Monetary) customer analysis model for B2B marketers and businesses.
The RFM customer analysis model has been around for over 40 years and is commonly used by Retail, Database Marketing, Direct Marketing, Non-profits and other primarily B2C businesses and marketing organizations. I have personally only encountered minimal use of RFM in B2B marketing but believe there is value in using this model in some aspects of B2B marketing depending on the specific circumstances of a business.
The premise of the RFM model is straightforward:
- Recency – when did a customer last buy? Research shows that Customers who purchased recently are more likely to respond to an offer than those who purchased some time ago.
- Frequency – how many times has a customer bought? More frequent buyers are more likely to buy again.
- Monetary value – what is the value of their lifetime actual spend? Big buyers are more likely to spend more than small buyers.
The RFM model has limitations and risks such as:
- Historical behavior does provide indicators for future behavior, but it’s not truly predictive.
- Continually targeting high-scoring customers could annoy or alienate them.
- Neglecting lower-scoring customers that should be nurtured.
- Just analyzing the numbers without relating the RFM score to specific business, product and marketing events and circumstances.
- The 125 cell (5x5x5) RFM model is too granular – rather group scores into clusters or bands to get a better picture of what the data are communicating.
The RFM model could be a valuable marketing analysis and segmentation tool to complement and qualify other analysis and segmentation tools used by B2B marketers:
- Relating customer RFM scores to lifetime customer value (LCV) can provide insights for developing and improving revenues from existing customers.
- In addition to the RFM score, the trend or migration between cells over time can provide further actionable information for marketing.
- The RFM score trend over time for major customers or segments of similar customers can provide insights into changing buying behavior and revenue performance.
- Relating RFM scores to results for various campaigns can provide insights into the effectiveness and appeal of particular campaigns for different RFM segments of customers.
- Relating RFM scores to products or product categories. For example, if a customer buys something in a product category do they usually buy more in that category or does it lead to cross-sell opportunities in other categories. Or if they buy something of low monetary value does that lead to buying something of higher monetary value or vice versa.
Copyright © 2009 The Marketing Mélange and Ingistics LLC. http://marketing.infocat.com
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I am fortunate to have worked in a number of roles in marketing and other areas of businesses. I have done, run and led marketing. I have worked in other functions that are impacted by marketing.
I'm a keen observer on how marketing is positioned and perceived within companies and what is expected in return for the marketing budget. The Internet, Web and technology innovations continue to change and
expand marketing approaches and possibilities. Hopefully this blog will in some small way contribute to making marketing more effective for all areas.
