Category Archives: Analytics Education

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?

Continue reading Marketing Model or Financial Model? →

Intra-Company Promotional Risks

Analytical Culture, Analytics Education, DataBase Marketing, Driller Q & A, Marketing thru Operations, ,

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.

Eating Your Own is not a great idea. Yet in many large companies, different divisions literally try to steal customers / sales from each other using the common customer database. Sure, everyone gets real excited over the plans to merge databases across the company and get the “full view” of the customer, and it makes perfect sense. Perfect sense – if you’re also institutionally prepared for wild battle over customer ownership and value stealing to come. This is particularly true (and difficult to deal with) in multi-channel retail environments.

Got your Drillin’ shows on? Let’s take a stroll through subsidy cost land …

Q:  Hi Jim,

Our company has 8 divisions and we completed the integration of all the customer databases a couple of years ago.  We have the 360 degree view of the customer, at least as far as sales transactions, across the entire company in one database.

A:  Congratulations!  However, I note not joy, but some kind of concern in you voice.  I’m just waiting for the “But…”

Q:  The database services group I am part of is under IT.  We respond to requests from the different divisions for customer analysis and the creation of promotional lists for email, direct mail, and telephone campaigns.  It’s interesting because our group is finally directly involved with increasing the profitability of the business and we have some input, which makes the job more rewarding.  I picked up your book because I thought reading it might increase our ability to contribute.

A:  Well, again, congratulations!  But I’m still getting that nagging feeling from your tone.  Still waiting for the “But…”

Q:  I’ve got a two part question for you:

1.  What I am seeing is the different divisions promote to “best customers” of the company as a whole, or even try to target best customers of another division for their campaigns.  It seems to me that contacting these same people over and over from the different perspectives of the divisions is not optimizing customer value, and might actually be irritating to the customers (I know it would be irritating to me).  

The contact frequency across all divisions to the same customer can reach 4 – 6 times a month through various media (phone, mail, e-mail).  Also, there is no customer retention effort going on that I am aware of, it appears it is all acquisition oriented but the main targets are customers of other divisions.

A:  Oh boy, the database marketing pendulum has swung the other way, from “we don’t know what to do with all this data” to “we’re really maximizing that database asset”. You are correct to be concerned about this issue.  Talk about “push marketing”… the intent of each division is “pull” because of the targeting but the result is “push” because of the volume and “noise” created at the customer level.  Too much too fast.

Continue reading Intra-Company Promotional Risks →

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 →