Category Archives: Analytics Education

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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Do NPS / CES Feedback Metrics Predict Retention? Depends…

Analytics Education, Customer Experience, DataBase Marketing, Marketing Research, Marketing thru Operations
Survey Says?

Several questions came in on the ability of surveys to predict actual behavior, covered in the post Measuring the $$ Value of Customer Experience (see 2. Data with Surveys). My advice is this: if you are interested in taking action on survey results, make sure to survey specific visitors / people with known behavior if possible, then track subjects over time to see if there is a linkage between survey response and actual behavior.  You should do this at least the first time out for any new type of survey you launch.

Why?  Many times, you will find segments don’t behave as they say they will.  In fact, I have seen quite a few cases where people do the opposite of what was implied from the survey.  This happens particularly frequently with best customers – the specific people you most want to please with modifications to product or process.   So this is important stuff.

You’ve Got Data!

Turns out there’s a new academic (meaning no ax to grind) research study out addressing this area, and it’s especially interesting because the topic of study is ability of customer feedback metrics to predict customer retention.  You know, Net Promoter Score, Customer Effort Score and so forth, as well as standard customer satisfaction efforts like top-2-box.

The authors find the ability of any of one of these metrics to predict customer retention varies dramatically by industry.  In other words, you might want to verify the approach / metric you are using by tying survey response to actual retention behavior over time.

Continue reading Do NPS / CES Feedback Metrics Predict Retention? Depends… →