Analyzing Airline Passenger Data
# 61: 10/2005
Drilling Down - Turning Customer
Data into Profits with a Spreadsheet
Customer Valuation, Retention,
Get the Drilling Down Book!
In This Issue:
# Topics Overview
# Best Customer Retention Articles
# Analyzing Airline Passenger Data
Hi again folks, Jim Novo here.
Sometimes the "standard" RFM, Recency, or Latency models
probably won't provide actionable answers to your customer behavior
questions. Such is the case with Frequent Flyer program
analysis; sometimes you just have to break the models down into
components and "build your own" out of the parts.
We'll do that in this newsletter.
We also have a couple of great customer marketing article links.
The first provides some high level background on why you should be
looking at customer value models. The second is a customer
segmentation case from the banking world that is jam-packed with the
"how to's" and all the numbers. A 45% decrease in
defection rate in a most valuable segment? Dude, that's some
Let's do the Drillin'!
Best Customer Marketing Articles
**** Customer Value Models
7, 2005 DM Review
A great overview of the reasoning behind building customer value
models, suitable to send "upstairs". These models
don't have to be complex, at least in the beginning; use a simple Current
Value / Potential Value matrix.
***** Slice of Life
October 24, 2005 CMO Magazine
Inside the guts of the customer segmentation practice at RBC Royal
Bank. This is the latest segmentation success story making the
rounds for good reason - it's easy to understand and chock full of
numbers, including lift and ROI calculations. Nice work, RBC!
Questions from Fellow Drillers
Analyzing Airline Passenger Data
Q: Can you please direct me to specific information (in
your site) regarding analyzing data in the Airline frequent flyer
A: There isn't anything specific to airline frequent
flyer programs on the site, so I'll create something though with this
Q: Are there any "success methods" that
proved to be the right way to define one flyer over the other?
A: Not sure what you mean by "define"... the
triggers I have seen used in these kinds of programs usually have to
do with changes in rate balanced against the value of the flyer.
So, you look for slow-downs in frequency, for example, people who used
to fly 3 times a week that now fly only 1 time a week. Their
"fly rate" has dropped significantly and could be a flag for
Q: I'm familiar with the RFM method, and wonder how to
implement an RFM score considering that you have a:
* Flyer that is true loyal and doesn't have any interest in flying
to other parts of the globe (expensive long mileage ones) and yet,
* due to the method of RFM you will not find him at the "top
Well, I'm not sure RFM would be the right model for this kind of
program. You want to look more at a "rate of change"
kind of model since there are many levels of activity and Recency
isn't always a controlling factor.
So for example, you could create "Frequency buckets"
based on deciles - divide customers into Top 10%, second 10%, third
10%, 4th 10% down to the bottom 10% based on their annual Frequency.
Then track people based on how they are moving between the buckets.
Somebody who was in the Top 10% that falls down to the third 10%, then
falls lower in their annual rate would be a likely defector.
Q: Do you suggest modifying the basic RFM model, for
example, R+F+M+(factor) instead of only R+F+M? (factor) could be
anything that will enhance the information regarding the
average-target destination-flyer behavior.
A: No, I don't think a "factor" will do
anything for you because RFM is not the basic model you want to be
dealing with in this situation. Now, there are probably some
**segments** where RFM would work - if you could identify them first.
Affluent leisure travelers are such a group, because they have
"free will" and the program is probably not very important
to them in terms of deciding when / where they are going to fly.
RFM is best used in a "frictionless" situation where the
population's behavior is not influenced or controlled by external
factors (such as would be the case with business travelers). There are too many behavioral cross-currents in a Frequent flyer
airline program for RFM (in the classic sense) to
be of much help.
So what you need to do is break the elements of RFM down and use
them as appropriate to the business model. The
"buckets" example above would be one way to do this, relying
only on Frequency. You could make the buckets model more
"sensitive" by adding a Recency or Latency component to the
buckets to provide more predictive power.
For example, use the "average trip Latency" to provide
another trigger. If on average, someone flies at least once
every 30 days, and 45 days go by with no trip, that's a trigger.
At that point, you could then check the "bucket model" to
see if Frequency is still in the same 10% range as it has been.
If Frequency has started falling, then you know you have a potential
defection and you could test what kinds of actions might influence the
future behavior of the traveler.
Once you determine the behavioral segments, then you can look for
any other "factors" you may have available to further flesh
out the model (destinations, seasonality, average price, demographics,
surveys) but you always want to do the behavioral segmentation work
*first* so that your segments are actionable.
With that, now I'll have something on my site about analyzing
Frequent Flyer program data. Thanks for the idea!
If you are a consultant, agency, or software developer with clients
needing action-oriented customer intelligence or High ROI Customer
Marketing program designs, click
That's it for this month's edition of the Drilling Down newsletter.
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'Til next time, keep Drilling Down!
- Jim Novo
Copyright 2005, The Drilling Down Project by Jim Novo. All
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