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Drilling Down Newsletter # 23 - August 2002 - Latency Models, RFM, LifeCycles

Drilling Down - Turning Customer
Data into Profits with a Spreadsheet
Customer Valuation, Retention, 
Loyalty, Defection

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Prior Newsletters:

In This Issue:
# Topics Overview
# Best of the Best Customer Marketing Links
# Latency: Services, Non-Profits, Distribution
# Questions: RFM and Customer LifeCycles

Topics Overview
Hi again folks, Jim Novo here.

August is the month I try to keep the newsletter short.  But I'm not going to do what the trade press does and trot out a bunch of lame content.  Nope, this issue is covers various Latency models and sets up our next topics - Recency and RFM models - with a question from a confused fellow Driller.

A special note: I'm on a panel at the upcoming Search Engine Strategies 2002 in San Jose, CA

Perfecting Paid Listings
Tuesday, August 13, 1:15pm - 2:45pm

This is an "advanced class," for people who have some experience with paid listings but are looking to really crank up the ROI on them.  Copy, tracking methods, tips and tricks, all up for grabs.  Yours truly is of course addressing metrics and tracking...more info here.

If you're around the show, please try to say Hi, I always like to put a face with the typing!

OK - Let's do some Drillin'!

Best Customer Retention Articles
This section flags "must read" articles moving into the paid trade magazine archives before the next newsletter is delivered.  But in July, the trade media realizes nobody is paying attention anyway and pulls out all the "filler stories" from the files.  I figure you've had enough "360 degree view" crap to last a lifetime at this point, so no links this month. 

Tracking the Customer LifeCycle:
Real World Examples 

Note: If you are new to our group and want to know more about the following ongoing discussion, the background theory is here.  The prior examples: Hair Salon and B2B Software.

Latency: More Real World Examples in
Services, Non-Profits, and Distribution

Some businesses look on the surface like they many not be suitable for using behavioral profiling.  Take the electric power business.  The customer bill is just about the same every month, with seasonal variations, and it's not like the customer has a choice as to whether to buy power each month.  Yet they can defect to an alternative provider, and before you know it - it's too late.  The cellular biz has similar attributes, particularly with the growth of annual contracts and bulk rate minutes.  How do you know when a telecommunications customer is about to defect?

Or take the insurance business.  A very long cycle business with very little transactional activity.  You sign up for insurance, get billed once or twice a year, and that's it.  How do you profile behavior in a business like this, where the customer may be around for 5 years and then all of a sudden, just defects?

The answer is you profile activity other than the revenue related activity, for example, calls to a phone center or visits to a web site.  When you look at "best" customers vs. "worst," are there calling or visiting patterns which stand out?  

The key is to group customers by "best" and "worst" and look for any pattern that separates the two, then use behavioral profiling methods to detect the likelihood of defection.  If you have a website or telephone "self service" interface, falling use of it might mean customers are getting ready to defect, or it might mean they are satisfied and are going to stay long term.  In situations like this, simply find "known behavior" (actual defectors and best customers) and then compare.  This exercise should allow the creation of  actionable trip wires for the business.

In a long cycle, low transaction frequency business like insurance, you may have to extend your time horizon to pick up enough meaningful transactions.  Instead of looking at behavior on an annual basis, you might have to look over a 3 year or 5 year period.

There's no way to tell in advance what these metrics will be, but the customer behavior will "speak" and tell you which data points matter. 

In the non-profit world of donors instead of customers and donations instead of purchases, there is more emotional attachment involved with the behavior.  It pays to  focus attention on Latency by average donation amount, for example.  

A Latency trip wire for a small, impulsive donor is likely to be shorter than that of a large, emotionally attached donor.  If you try to repeat a large donor too soon after a substantial donation, you risk looking a bit greedy.  The only way to discover the appropriate Latency trip wire for different types of donors is to test. 

Set up some average donation "ranges," rank donors with these ranges, and see if you can discover the best trip wire for each range.  You might also look at LifeTime donations in the same way, creating a second ranking or "score."   If you rank donors this way, you can up with a two variable scoring system that maximizes response and donation value over time.  The timing of your mailings would be triggered by some combination of average and LifeTime donation scores.  For example, a large average donation donor with low LifeTime donations could be approached more frequently than a large average donation donor with high LifeTime donations. 

Distribution or Supply Chain mechanics can benefit a great deal from Latency trip wires, because you have built-in data points.  If you think about it, re-order or stock-out points are a type of trip wire, but they are focused on the end user.  What about the supplier?  The supplier should have their own set of trip wires, tracking the average length of time between orders from end users.  When the order doesn't come though, something is going on.  Usually by the time a supplier realizes the order is late, they have lost the contract.

This same idea applies to "soft distribution" methods such as agent relationships.  Let's say you originate insurance or financial products that are sold by independent agents.  These agents also sell the products of other originators.  Do you have trip wires on these agents, so that an agent who is writing five whole life policies a month that suddenly drops down to one a month is identified and communicated with?  Seems like it would be a good idea to me, and not very difficult to do.

So that's it for the our Real World Examples using Latency.  Latency is a valuable tool but it can be somewhat of a fuddy-duddy; generally a more reactive metric than a predictive one.  Next month we'll move on Recency - the most powerful of the single-variable predictive metrics and the doorway to RFM - the Swiss Army knife of simple behavioral models. -----------------------------------------
I can teach you and your staff the basics of high ROI customer marketing using your business model and customer data, and without using a lot of fancy software.  Not ready for the expense and resource drain of CRM?  Get CRM benefits using existing resources by scheduling a workshop

Questions from Fellow Drillers
Jim's note: If you still don't know what RFM is and how it can be used to drive increased profitability in almost any business, read this.

Q:  I have a small sampling of the RFM scores that correspond to the various lifecycle stages.  For instance, 111 & 112 correspond to the acquisition stage, 333 & 443 to the growth stage, etc.  However, I'm looking for a complete listing of all 125 possible RFM scores and their corresponding lifecycle stages.

Can you please send this my way?

A: Wow, I certainly hope you didn't get this idea from me; if you did, I have done a terrible job of explaining something somewhere.  I would be very interested in the source of this idea, that a LifeCycle stage can correspond to a single RFM code or score.

An RFM code or score is the ranking of a single customer against all other customers for likelihood to respond and future value.  High scores equal high future value; low scores equal low future value.

A single RFM score represents this ranking at a fixed point in time - the day the scores were created.  There is no "cycle," which implies "over time," inherent in an RFM code.  Only if you knew the previous code or sequence of codes could you imply a "LifeCycle stage."  This is, of course, what my book is about - using a modified version of RFM to track and profitably act on customer LifeCycle behavior.  If you know the LifeCycle, you can predict behavior.  If you can predict behavior, you can dramatically improve marketing ROI.

If a customer is a 333, you don't know if they are falling or growing into it.  They could be coming from above it - falling in value, or coming from below it - rising in value.  For example, most new customers start at a 51x - they have to, because by definition, they are "new" (R = 5) but have bought once (F = 1).  But this same customer 3 months from now might be a 555 or a 222 - either ramping up or sliding into oblivion.  If you don't know what their score used to be, you can't imply anything about a "cycle" or any "stage" in the relationship with the customer.

That said, customers in the 111 and 112 are typically old, defected customers - not new or "acquisition stage" customers as you said.  All customers start in the high numbers and work their way down into the low numbers throughout their lifecycle.  The question is how long will it take to get from high to low, and can you do anything to slow this process or stop it.  The scores tell you if what you are doing is working, and how to drive profitability following the two fundamental rules of High ROI Customer Marketing

1.  Don't spend until you have to
2.  When you spend, spend at the point of
     maximum impact

If you are looking for some generalized system, I wouldn't worry about the detail of 125 RFM codes, there is really no meaning there unless you have millions of customers.  The most important variable, from a LifeCycle perspective, is usually Recency, so you could roughly categorize the LifeCycle of customer into 5 blocks using the R score.  The second two variables, F and M, have nothing to do with the lifecycle of the customer, but the value of the customer.  These are two different issues.  Any customer with a low R value but high "FM" value is a very valuable customer that isn't a customer anymore.  In terms of Lifecycle, they are at the end.  In terms of value, they are at the top.

If you are a consultant, agency, or software developer with clients needing action-oriented customer modeling or High ROI Customer Marketing program designs, click here.  If you are in SEO and the client isn't converting the additional visitors you generate, click here.

That's it for this month's edition of the Drilling Down Newsletter. If you like the newsletter, please forward it to a friend - why don't you do this now while you are thinking of it? Subscription instructions are at the top and bottom of the newsletter for their convenience when subscribing.

Any comments on the newsletter (it's too long, too short, topic suggestions, etc.) please send them right along to me, along with any other questions on customer Valuation, Retention, Loyalty, and Defection right here.

'Til next time, keep Drilling Down!

- Jim Novo

Copyright 2002, The Drilling Down Project by Jim Novo.  All rights reserved.  You are free to use material from this newsletter in whole or in part as long as you include complete attribution, including live web site link and/or e-mail link.  Please tell me where & when the material will appear. 


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