RFM Segmentation and Analysis - Part 1
RFM modeling is a powerful tool for marketers. It is a concept taken from direct mail that has been adapted to online, and as we're going to look at today, email. RFM analysis and segmentation takes the sum of past behavior to predict future behavior and adjusts messaging to target that future behavior.
The methodology has, previously, applied only to past customers; today, thanks to the ease of tracking email behavior, it also can be applied to any behavior through a tool I call progressive profiling. During the analysis, we take a look at each piece of the "RFM" puzzle separately:
R = Recency
When did the customer last buy from you? When did the customer or subscriber last open/click? Take the entire range of data, from most recent (usually day of or previous day) through a pre-determined cutoff point based on your buying cycle. Let's say that's 3 buying cycles back -- everyone that purchased/opened/clicked prior to that get lumped into an inactive file.
Then, based on your buying cycle, determine the number and ranges of segments for recency of behavior. It could look something like this:
1: 0-30 days: 5,100 customers/subscribers
2: 31-60 days: 12,300
3: 61-90 days: 32,800
4: 91-180 days: 75,000
5: 181-365 days: 123,400
As we will continue to explore, your "best" segment should always have the fewest number of customers/subscribers and your "worst" should always have the most. They are the best and worst for a reason, so don't modify the parameters to try to shoehorn data to even out the segments. It should look like an inverted pyramid. In rare instances, your middle segments are the largest, but this is rare.
F = Frequency
This stands for the frequency with which a customer buys or subscriber opens/clicks. Amazon, for example, can and probably does, have a very high frequency reorder rate. The best RFM segments for a company like Amazon are probably very quick turnaround times, very frequent and very high dollar values.
Much like recency, we'll want to take a look at the entire range from 0-1 through X number of purchases/behaviors. Then, we'll want to take both the median and the average to determine the ranges for each segment. Again, remember not to sell yourself short when building the modeling to ensure you have "statistically significant" segments. It's more important to have your contacts in the appropriate bucket.
The data also doesn't have to be in even amount differences. If there are peaks and valleys in buying behavior, the segments should reflect that. It could look like this:
5: 0-1 purchases/behaviors
4: 2-4
3: 5-11
2: 11-15
1: 16+
M = Lifetime Monetary Value
The "M" stands for lifetime monetary value. Over their lifetime, how much is each subscriber/customer worth to you? For non-monetary analysis, you can score each behavior and assign a value to each.
As before, take a look at the ranges of the data, the median and the average to build your segment ranges.
5: $0-200
4: $201-350
3: $351-600
2: $601-1000
1: $1001+
What's important to remember is to take a holistic approach to combining these segments as someone that's a very high "M" could have potentially only purchased from you once 7 months ago, in which case would be considered a 5R-5F-1M, which could indicate dissatisfaction with your products, service or another unknown.
When conducting this analysis you can look at just email subscribers or you can open it up to your entire database for a more holistic view on what impact your marketing channels have on purchases, which produce the most valuable customers, how to utilize the various channels to generate desired responses and so forth.
From here, we will want to group each of the individual ranges into their RFM combined segments to determine the best messaging for each. I will be covering messaging in the next post. Any thoughts on the analysis above?
Kelly Lorenz
Email Marketing Strategist at Bronto
@KNLorenz
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About the Author
With a background in Account Management and Client Services at the top agencies in the Southeast, Kelly brings both hospitality and retail experience. In her role at Bronto, Kelly guides clients to strategically optimize their email marketing campaigns and drive ROI. Kelly’s specialty in the online retail space is evidenced in her work with clients listed on Internet Retailer’s Top 500 List. Kelly has been has been featured in Marketing Profs Get to the Point Email Marketing, Retail Email Blog, Email Marketing Reports and Internet Retailer. In her spare time, Kelly can be found training for her next marathon.
Hi Kelly, Good post and
Hi Kelly,
Good post and thanks for explaining the methodology.
One caveat to RFM analysis is that it seems to focus on past behavior, not future behavior, and doesn't give you much detail about what the customers actually want.
RFM is useful in direct marketing where the costs are high of sending mail, but in the world of email, it is virtually free to send emails to everyone.
I am working with a client who uses RFM analysis with their email campaigns and at the end of the day they only use it as a reporting tool. In other words, they don't treat their most frequent or infrequent, or recent or least recent customers any different. They still blast them all with similar e-mails - maybe giving the least recent a slightly better discount to get them to come back, but virtually the same with no targeting.
I believe a better way to segment your customers would be based on true behaviorally defined segments, which leads to a more actionable outcome. What clusters emerge in your transaction data based on what they have bought. Call it pet food. Are their groups of people who buy only high end dog food? Are their low end cat food buyers? Are there groups who are more price sensitive than others where a discount would be better used than on someone who would have bought anyway. With this kind of information you can start to treat customers differently and deliver relevant emails.
At the end of the day, I don't see how you can create targeted and relevant treatments with RFM analysis - please correct me if I am wrong. Maybe thats your next post.
Thanks!
Max
Max, Thanks for stopping
Max,
Thanks for stopping by and sharing your thoughts. I respectfully disagree that RFM analysis doesn't tell you what people want or their future behaviors or that you can't use it to develop messaging strategy.
I have written follow ups to this post around messaging strategy, so keep an eye out for those coming soon and feel free to disagree and share your thoughts on those.
RFM analysis is great from a data perspective, but if you aren't utilizing that data to modify future campaigns, I'm not sure what the use is, and quite frankly, you're wasting your time. In order to utilize the data, you have to send different messages to different groups based on their RFM affinity group.
You do get this idea as what you call behaviorally-defined segments ARE RFM segments. Those that only buy high-end dog food would likely be in the 1R-1F-1M segment under the dog food category and should be messaged semi-exclusively about high-end dog food.
I may be missing your point, but I don't see how you CAN'T create targeted and relevant treatments via RFM analysis. That's the whole point.
Please do let me know whether I am off-base reading into your response. Thank you -
Kelly@Bronto
Hi Kelly, Thanks for the
Hi Kelly,
Thanks for the follow up. Healthy discussion! I really look forward to your next posts on messaging.
I think you hit it on the head when you said that "Those that only buy high-end dog food would likely be in the 1R-1F-1M segment under the dog food category and should be messaged semi-exclusively about high-end dog food"
I believe that "under the dog food category" is the operative piece. This implies that you have created a product based behavior segmentation to find the dog food buyers in the first place. Finding the groups of customers who are under the dog food category is no small feat (unless of course you have asked them, but that's a luxury) - and this is what I believe is the useful piece. Applying RFM to those under the dog food category may give you a proxy for high end dog food but why not just create a "high end dog food" category in the first place?
I look forward to your next post but so far, RFM doesn't tell me what products someone likes, their price points, what discounts they respond to, or the timing that best works for them.
I'm not trying to discount your post at all, I'm just trying to push it so I learn. I really appreciate your response and look forward to the next post.
Best Regards
Max
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