Category Archives: Driller Q & A

Loyalty Program Structure & Tracking

DataBase Marketing, Driller Q & A, Newsletters

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

Q: I’m involved in a loyalty program analytics project.  This client is a local pharmacy. All sales are done directly in store, the web site is just for communication purposes. The general problem we are trying to solve is the manager doesn’t have any detailed ideas about shoppers behavior apart from human observation.

The idea is to launch a card-based loyalty program which will track sales activity and give insight into customer behavior. The program will be points-based calculated on amount spent.  Points can be redeemed as rebates, coupons, gift certificates, or use points to buy items in loyalty program catalog.

The task is to segment customers according to their recent purchase behavior and determine the customer lifecycle. I’ve been able to do some basic analysis using the R package and MySQL database, but am unable to detect customer lifecycle.

Can you please give me guidance on this?

A: What is the Objective of detecting the LifeCycle, to create a more “active” customer retention program? Loyalty programs can be quite “passive” and often benefit from a more active overlay. But there can be many reasons to want to understand the LifeCycle…

Q: My 2nd task is to use the behavioral data with demographics to build a direct marketing strategy and provide management with insight into the customer base, for example: percent new customers, % of Gold customers who passed to Silver in last quarter.

A: Again, it would be helpful to understand how management would take action on this data. But I suppose you are in the common position of not knowing the tactical approach, and nobody will lay it out for you (a.k.a. they are clueless)…and you don’t know the right questions to ask or how to ask them.

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Lead Scoring and Nurturing

DataBase Marketing, Driller Q & A, Measuring Engagement, Newsletters

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

Q: I received this article (Norms of Reciprocity, measuring value of Social Marketing) via a friend’s Twitter account. Very interesting.

A: Glad you enjoyed it!

Q: It has made open up my ACT! database, and my Outlook databases and add the metric of Growing / Strong / Weakening / Failed to my normal Sales and Business progress metrics. If I group those categories and correlate to traditional metrics, it’s impressive how they reflect each other.

A: Yes, most people are surprised. It’s a very, very simple idea that seems to work across just about any human activity including crime, attendance, and so forth.

The more Recently someone has done something, the more likely they are to do it again. Conversely, the longer since an activity last took place, the less likely the person will do it again. Often called Recency in Psychology and studied quite a bit.

Q: Now I have to think about how I really use and apply this. : )

A: Well, if I can guess you are in Sales from your title, typically one of the best applications is in what Strategic Marketing folks might call “allocation of resources”, which probably translates into “lead nurturing” for you.

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Hacking the RFM Model

Customer Experience, DataBase Marketing, Driller Q & A, Measuring Engagement, Newsletters, Web Analytics

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

Q: First of all thank you for your help.  I have some questions I would be pleased if you answer them for me.

A: No problem!

Q: 1. RFM analysis – is it possible to use some other ranking technique rather than quintiles Using quintiles for bigger databases will cause many tied values, isn’t it a problem?

A: Sure, you can use it any way it works best for you. There is no “magic” behind quintiles, you can use deciles or whatever works best. It’s the idea of ranking by Recency, Frequency, and Value that is the key concept in the model.

I’ve seen dozens and perhaps hundreds of variations on the core RFM model, depending on how you classify a “variation”. One change that’s common is changing the scaling, as you mention above, to accommodate the size of the database. Smaller databases use quartiles or even tertiles. Larger databases, choose the ordered distribution that meets the need.

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