Category Archives: Measuring Engagement

Analyzing Airline Customer Frequency Programs

Analytics Education, Customer Experience, DataBase Marketing, Marketing thru Operations, Measuring Engagement, ,

Jim answers questions from fellow Drillers
(More questions with answers here, Work Overview here, Index of concepts here)

Topic Overview

Hi again folks, Jim Novo here.

Recency and the RFM model are both very powerful predictive models of customer behavior, But they’re not always the BEST models to use, because the nature of some business activities do not create the kinds of behavior these models are good at predicting. For example, any business – or segment of the business – that is likely to have a rhythmic repeating behavior (event occurs every week, or every month, or every summer, or every Christmas, etc.) will not benefit much from Recency analysis. But it is ideal for Latency analysis – when to expect an event, did it happen or not? Great example of both ideas in a single business would be the airline business – you have random flyers, and you have frequent flyers. Use Recency for random flyers, Latency for frequents. You dig? Great, let’s Drill it !

Q:  Can you please direct me to specific information (in your site) regarding analyzing data in the Airline frequent flyer programs?

A:  There isn’t anything specific to airline frequent flyer programs on the site, so I’ll create something though with this reply!

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 potential defection.

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 customers”.

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.

Continue reading Analyzing Airline Customer Frequency Programs →

Second Purchase Marketing

Analytics Education, Customer Experience, DataBase Marketing, Digital Analytics, Driller Q & A, Marketing thru Operations, Measuring Engagement, ,

Jim answers questions from fellow Drillers
(More questions with answers here, Work Overview here, Index of concepts here)

Topic Overview

Hi again folks, Jim Novo here.

HIgh end hardgoods. One of the most difficult retail categories to deal with from a customer retention perspective, both offline and online. Only vehicles are tougher. In some ways, the category can be easier online, but perhaps not for a single local store due to competition. So what’s the best way to attack the repeat purchase peoblem? Focus on where you have the highest likelihood of success – 2nd purchase Latency. Ready for the Drillin’?

Q:  I loved your book, thanks.  Armed with it, I feel like I can achieve much more than most small retailers in terms of CRM.

A:  Thanks for the kind words!

Q:  I have a question though.  I sell relatively high-priced furniture and design items, and as this is our first year of business, our inventory is pretty small.  As a result, my Frequency totals range from 1 to 4.  That’s it, after a year of business.  About 75% of our customers have bought once and it “ramps up” to 4 from there.  I use “ramp” in the broadest sense of the word.

A:  Yep.  That’s the hardgoods business, especially on the web.  Don’t beat yourself up, it’s early in your game with lines like these, and don’t blame it too much on inventory either.  In the long run, it’s better to sell the *right* stuff than everything you can find, trust me.

Q:  So when I compute RF quintiles, the totals don’t cleanly fit within quintiles.  In other words, for RF scores of X1 ­ X4, customers have purchased once.  X5 customers have purchased 2, 3, 4, or 5 times.  If I raise the hurdle and only look at customers who have purchased more than once, I still can’t fit them cleanly in five quintiles.

A:  That’s one problem with RFM, it’s a bit robotic and works best with larger (usually meaning older) databases…

Q:  I read your article on durable goods purchases and avoiding the one-time-buyer problem.  I guess I’m looking for advice on how to make the “F number” significant until we’ve been in business long enough to get a broader range of frequency options.

Continue reading Second Purchase Marketing →

Using RFM Scores to Predict Profits

Analytics Education, Customer Experience, DataBase Marketing, Driller Q & A, Marketing thru Operations, Measuring Engagement, ,

Jim answers questions from fellow Drillers
(More questions with answers here, Work Overview here, Index of concepts here)

Topic Overview

Hi again folks, Jim Novo here.

Subsidy costs. You’re just starting to hear people talk about these ideas in online marketing, but they’ve been around for years offline in direct marketing. The basic idea is this: sending a discount to someone who is very highly likely to make a purchase without the discount is a waste of margin dollars best spent elsewhere. And you can measure this effect quite easily using Control Groups, another concept starting to get some recognition with online marketers.

Discussing / implementing these topics can be a bit difficult, though the Finance people will get it immediately and love it if you go in this direction. A plus for fellow Drillers out there is you can start to see some of these ideas in action BEFORE you start going deep using the RFM & Lifecycle data we’ve been talking about and using for years.

Below is a great example using RFM data from a fellow Driller. You ready to go ?

Q:  Since our last conversation few months ago, we went ahead and tested 3 different promotions using the RFM model.  

The 1st promotion was the test for RFM method itself to see what patterns emerge for response rate, incremental sales, etc.  The next 2 promotions targeted the customers from RFM cells with the highest incremental lift from the 1st test promotion.  Here is what we saw.  Since the targeted audience were our loyalty card members, they transact and spend at a fairly high level (the data below is modified but the trend is maintained).  For the response rate, we saw a sawtooth pattern:

(Jim’s note: RFM is the 3 digit score, Rate is Response Rate.  More on RFM here and here.)

A: Yes…

Continue reading Using RFM Scores to Predict Profits →