Tag Archives: Customer State

Marketing Model or Financial Model?

Analytics Education, 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.

Where does all this kind of thinking on customer retention and value over time eventually lead you? Well, often right to Finance. Because you see, the more you can do to convince Finance the activities you are in engaging in are increasing profits for the company – and Finance truly believes this because they participated in the creation of the systems, models, and validation – the more likely your budgets supporting these activities will increase.

Simple as that. You’re not afraid of Finance, are you? Good! On to the Drillin’ …

Q:  Been reading through your site a bit.  I run the CRM and online marketing at (large airline) – a business with roughly XX million customers and X.X million members in our loyalty program.  Interested in your thoughts about RFM algorithms as well as aggregated scoring.  My predecessors set up ranked scoring along spend – essentially taking paid purchases and ranking people from high to low in R, F, and M  and then built programs around this.

My issue with this approach is that we find very different behavior  in our top 20%, 10%, 5%, and even 1% (e.g standard deviation of  population is large).  Additionally, rank ordering often grouped  individuals with the same underlying behavior in different categories  because of the arbitrary nature of where the snap lines fell.  So I altered our scoring as follows…(long description of new model)

A:  Did you by chance see this article?

Latency may be a better way to go for an overall approach to airline behavior in the business class; Recency in the tourism class.

It sounds to me what you have done is a similar idea – recognized the generic RFM model is broken for your needs, extracted the essence of the RFM idea, and rebuilt it into a model that works for you.  Nice job!

Q. But,if someone spends $400 on a flight that is 400 miles vs. 1000, the revenue has differing implications – both in terms of customer and non-customer driven fixed and variable costs.  If someone spends $400 on a flight that sells out – we are  potentially spilling revenue (not holding inventory for a bigger spender) – and thus the opportunity value is greater than the  collected revenue.  But if the  flight doesn’t sell out…this may not  be true?

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Discovering Customer LifeCycles

Analytical Culture, 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.

Today, we’re asked for a simple definition of retention. Problem is, the data / biz model really creates the definition. Meaning, you gotta match the creation of metrics with the actual actions.  So I call for segmentation first so we can put some “actionable” stuff in the mix.

Make sense? Let’s do the “simple” (easy? maybe not) Drillin’ …

Q:  For an online retailer, what is the best way to gauge retention in its most basic and simplest form?  % of orders that are from repeat buyers?  % of orders in month 2 who are repeaters that first bought in month 1?

A: I would take direction on this from the actual results of campaigns.  Basically, at the point a customer no longer responds, they have defected.  Perhaps this averages 3 months or 6 months after 1st purchase, and there will be category or price segments within these “time” segments.  Retention is really measured by the defection.

Now, that’s not to say that % orders from repeats or the other one you mentioned are not valid, but I suggest you think about the specific  question you want answered by the metric you choose.  % orders from repeats, for example, is a common metric in mail order but is often biased by campaigns, e.g. if you ratchet up customer acquisition during a single month you poison your own metrics.

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Modeling Customer Behavior with Small Databases

Analytics Education, 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.

We’re about to take a trip into the world of small scale databases. In particular, how does a not-for-profit with a small database of donors go about using predictive models? Answer: Keep it simple. Try to avoid using a lot of variables; look for the most powerful and stick with those until you are able to uncover additonal info and grow the database. Ready?

Q: I am new in the NFP (Jim’s note – Not For Profit) sector and would like some advice re:  segmentation models to optimize campaign results – both response and value (Short Term  and Long Term).  Do you know or is there any knowledgebase of how the various techniques – behavioural, RFM, demographic, geographic – generally rate against each other?

A: Not other than my web site / book, which generally covers all the simple models. There is plenty of info around on the web though.

Assuming the end Objective is a donation, the behavioral stuff is going to be much more productive than the geo / demographics are. It’s like a pyramid.  My friend Avinash “stole” (with my permission) a slide from my presentation on this topic and put it on the web, you can see it here.  You’re looking for an “action” (donation), so actions (behavior) will be the most useful segmentation, at least as a primary cut.  Then you can get into geo /  demo stuff if it improves the model.

Q: As my database is small I don’t have the luxury of testing multiple techniques and causal factors.  I will probably run tests in series but would like a general idea of which ones to test first to cut down the time.

A:  Not sure what you mean by “small”, but in general, the more complex a behavioral segmentation approach is the larger the database it needs to be useful.  So for example, with classic RFM (125 segment scores), the bare minimum for it to make any sense is probably 5000 records, and you should really have at least 10,000.

Continue reading Modeling Customer Behavior with Small Databases →