Category Archives: Driller Q & A

Profiling Subscription / Service Customers

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.

This Driller knows his stuff, using advanced database marketing techniques on the customer acquisition side. But in a business where the bill gets paid every month and it’s about the same (electric utility), how do you get to the R and F (Recency and Frequency) to model potential customer defections? Great question, and a pithy answer.

Remember, you can use RF profiling on any behavior not just payment related activity! If you don’t know what we’re talking about with this RF stuff, see explanation here.

Q. Jim, Ordered and read your book AFTER reading EVERY page in your website. Your newsletter is outstanding and you seem to be one of the few with real life experience in database marketing with the skills to simply explain with pragmatic examples of how RFM and LTV should be used.

We are a technology based Call Center company with over 70 clients – we do a lot of the “operational” CRM stuff you refer to – Siebel, Onyx, Kana, Webline, as well as a lot of custom developed SFA solutions and data warehousing solutions we developed – mostly the premise of investing to collect enough information to do the 360 view of the customer across communication medium (email, chat, phone, fax) and reason for calling (campaign, sales, orders, info, customer service).

We have a good mix of B-B as well as B-C. We already do a lot of the demographic modeling for list acquisition (SIC codes, size, number of computers, Geo …). One thing I noticed is that we do a lot of lead generation based upon list acquisitions along with inbound marketing campaigns that seem to address one shot Sales, not recurring sales.

For example, we sell and service de-regulated energy for one client – this is sell once, then service. Since they pay every month for the service, how do you suggest the RFM model be used for service based sales since there is not really an R or an F??? We still have acquisition and retention problems, but we mainly focus on operational efficiency through technology, not strategic use of CRM data. I would really like to be able to add real value based upon the data collected.

I know this is not your forte, but I was just curious if you had any opinions using CRM data in an RFM model when the product is basically recurring service.

A: Thanks for the compliments on the site, book, and newsletter. I hope they will be helpful to you as we try to get a firmer grasp on these subjects this year!

It’s a little tough to provide you a direct answer to such a broad question without more details, but in general, R and F are highly predictive of any action-oriented behavior. In a “billing / service” business like a utility, you sometimes have to hunt a bit harder for the action you want to model as predictive.

For example, at Home Shopping Network, use of the automated ordering process (touch-tone interface to the ordering system circa 1990) was very highly correlated with Future Intent to Purchase. Not exactly a traditional RF action, to be sure, but a falling RF score on use of the interface was very highly predictive of a defecting customer.

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Subsidy Costs and Halo Effects – Measuring True Marketing Profitability

Analytics Education, Customer Experience, DataBase Marketing, Driller Q & A, 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.

Got a couple of top shelf questions this issue from the more experienced side of the customer retention biz. As I’ve said before, the Drilling Down method works whatever size your business is. The tools used are different, not the ideas driving their use. For example, a small business may be using MS Excel or Access to keep track of customers, and a large business would be using a CRM app or rules-based engine. Doesn’t matter, works for both. So, hey little guys, don’t let the experts hog the spotlight, send your questions in!

These two questions from Drillers make a nice pair; they are related features of customer buying behavior!

Q:  Jim, I still don’t get Subsidy Costs.  I get the idea of using control groups, but how can a response to a marketing campaign be bad?

A:  Most response is good, of course.  Subsidy costs refer to a hidden cost primarily in best customer programs, where customers have a high probability of making a purchase anyway.  When you promote to these people and offer discounts, you run the risk of spending money and margin you did not have to spend to generate the sale.

Think of it this way.  You have a specific purchase in mind.  The catalog / web site / shopping network mails or e-mails you pretty frequently (you’re a best customer), so you are waiting intently for a communication to arrive before you make the purchase in case there’s a discount available. Communication arrives, and low and behold, has a “20% off” coupon.

You go ahead with the planned purchase and spend 20% less than you intended.  That’s a subsidy cost.  Great for the customer, bad for business. Why care? You can measure these subsidy costs, and create promotions that financially cover the subsidy costs they create.  Or, a better alternative might be to intentionally design the promotion to minimize subsidy costs. For example, make discount good only on orders over a customer’s average purchase size, meaning customer generates more than average margin, hopefully covering the subsidy cost.

Q:  Hi Jim.  Is there any way to tell what the best length of time is to look for Halo Effects?

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Defining Behavioral Segments

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 purchased your book and have a few questions you can hopefully help me out with.

A: Thanks for that, and sure!

Q: We have 4 product lines and 2 of them are seasonal. i.e we have customers that year in year out purchase these items consistently but seasonally, for example, every spring and summer.   Then they are dormant for Fall and Winter.  Should I include these customers along with everyone else when doing an RFM segmentation?

A: Well, it kind of depends what you will using the RF(M) model for, what kinds of marketing programs will be activated by using the scores. If you know you have seasonal customers and their habit is to buy each year, AND you wish to aim retention or reactivation programs at them, I would be tempted to divide the customer base so that seasonal customers are their own segment.  Then run two RF(M)  models – one for the seasonals, and one for everyone else.

Q: If I include seasonal customers, and I run RFM say on a monthly basis, these seasonal customers will climb / fall drastically with time depending on the season, so it seems like it may complicate the scoring process.

A: Sure, and you could segment as I said above.  Or, you could run across a longer time frame, say across 2 – 3 years worth of data. This would “normalize” the two segments into one and take account of the seasonality in the scoring – perhaps be more representative of the business model.  However, the scores would become less sensitive due to the long time frame so the actions of customers less accurately predicted by the model.

Q: Can you provide me with some examples as to how segmentation is carried out?  Let’s say I being with RFM and all my customers are rated 5-5, 5-4, 4-5 etc.  What are the next steps, do we overlay with other characteristics like age, gender, etc?  Or are the 5-3 etc. our actual segments?

A: This goes back to what you want to use the RF(M) model for.  In the standard usage, each score will have roughly the same number of customers in it, those with higher scores will be more likely to respond to marketing and purchase, lower scores less likely.

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