Event-Driven Models in B2B
# 54: 2/2005
Drilling Down - Turning Customer
Data into Profits with a Spreadsheet
Customer Valuation, Retention,
Get the Drilling Down Book!
In This Issue:
# Topics Overview
# Best Customer Retention Articles
# Event-Driven Models in B2B
# Dealing with Extended Latencies
Hi again folks, Jim Novo here.
Man, February went by very quickly. So I'm late with the
newsletter, my apologies. Some of the blame goes to the newly
Analytics Association. I'm Co-Chair of the Education
Committee and, well, there's a ton of work to do! From the site:
"With your help, we can develop and offer training and
certification to advance the professionalism of our trade. We
can encourage institutions of higher learning to add web analytics to
their curricula". I'm supposed to do that. Want to
help? Become a member
and then please volunteer!
Speaking of web analytics, I did a seminar with Brent Hieggelke
of WebTrends for the AMA (American Marketing Association) on web
segmentation and visitor / customer retention. It's free to
check out, though they want some personal info from you. See:
This month, we're looking at how to "roll your own" model
of customer behavior in a B2B lead generation context. Also, we
cover one of the challenges of defining a customer defection using the
We also have a couple of great customer marketing article links.
In the first, some hot shots comment on the big customer marketing
ideas for 2005. In the second, more help on defining customer
OK, let's do some Drillin'...
Best Customer Marketing Articles
February 7, 2005 Target Marketing
Wisdom from the "gurus" on what will be hot this year in
marketing, as direct and database marketing continue to bleed into and
influence all marketing strategies and programs. ROI based on
customer value, the fall of mass marketing, the rise of analytics, and
February 21, 2004 Target Marketing
A nice primer on one of my favorite topics - defining a customer
defection. You can't increase profits by retaining customers if
you don't define what a defection is first! The article also
supports a frequent suggestion of mine - quit paralyzing yourself by
trying to nail it down 100%. Put a stake in the ground, start
somewhere. Then it's a matter of using
the right metrics, testing, and timing.
Questions from Fellow Drillers
Event-Driven Models in B2B
Q: My company is really interested in pre-qualifying
leads driven through the internet channel based on perceived
interest e.g. downloading a particular white paper, or
returning, responding to an offer, what have you. We haven't
implemented this, but this scoring mechanism is sort of happening
behind the scenes and is collected in a database for future use.
I know about your RFM and I think it's very appropriate here based on
activity, but my sense is that I could also use significant events to
help me pre-qualify you.
How feasible is this sort of additional event scoring and what do you
think you'd discover at the end of the day when you spoke to your
marketer who ultimately tried to contact these leads based on this
scoring? i.e, guy comes into the car dealership, but the dealer
already knows he can pre-qualify you for a $40,000 car. Smart
harvest marketing or just a waste of IT resources?
A: This kind of thing is done all the time in B2B lead
gen, and many of the "lead management" software packages
have this capability built in, more or less. They're usually not
RFM is really not going to be the ticket here, because you have too
many discrete events, and the definition of "Frequency" and
"Monetary" in this environment is elusive. In other
words, the RFM definition of Frequency is repeated occurrences of
the same event, not a series of different events. So in B2B
lead gen, RFM is usually not optimal. However, Recency and first
cousin Latency are still very valid, and in the beginning, the early
discovery, Latency can
help you organize the data.
The easiest way to start with something like this is similar to the
way Pay-Per-Click is managed - you look at the end result and track
back to the beginning, for example, I am buying all these search
terms. At the end of the month I sort by sales volume and can
see which phrases generated the highest sales, and
further, which ended making / losing money.
So what you do is focus on the "end game", whatever that
is, and compare success to failure. When comparing, what was the
source of the lead / what actions were taken / in what order were they
taken / what was the timing of these actions for successful outcomes?
And for failures, the same? Compare and contrast success and
failure, and you are on your way to building your own model.
This real world info provides you a "base" from which you
can set up "tests" that can prove / disprove what effects
source of the lead / what actions were taken / in what order were they
taken / what was the timing of these actions have on outcome.
The trick to all this is tracking all the events to the lead so you
have historical info.
Further, if you can collect the data, and there are a lot of significant
behavioral events (a page view probably is not, a newsletter sign-up
probably is), and you think the data is "clean", you can use
machine intelligence to help run some of this data. Search for
the models CART and CHAID for more info. You get into some
tricky business here, and you can NEVER let the machine over-rule
common sense, but these models can be helpful in the
"sifting" process as you look for predictive variables.
Free / cheap software is available to run these so-called
"Decision Tree" models, see here.
At the higher end, check to see if there is a copy of SAS or SPSS
around the company, they are usually equipped with these models or
"suite" add-ons are $3000 - $5000.
If you are a consultant, agency, or software developer with clients
needing action-oriented customer intelligence or High ROI Customer
Marketing program designs, click
Dealing with Extended Latencies
Q: Is latency, as a metric, out of the question when
the spread of the number of days in a latency period is so wide that
to average them out and call the resultant figure "Acceptable
days to date of predicted purchase" would seem meaningless?
I am thinking about the disparity in latency between customers who are
Heavy, Moderate and Low users.
A: I'm not sure I have enough context to understand the
question (what are you trying to accomplish by using the metric?) but
Latency is what it is. In other words, you take your clue from
the existing behavior itself. If the average Latency for a
certain segment is 2 years, well, it is, and that's not too long or
too short, it just is. Whether you can act on that information
is another story; it depends on what you are trying to accomplish.
For example, average Latency on major home appliances, depending on
brand, is anywhere from 5 to 10 years. Is that too long of a
"spread" to make the metric useful? No. It just
is what it is, and you deal with it.
Now, it could be what you are really getting at has more to do with
failing to identify the defection. In other words, you are
trying to average customer Latencies that are "open-ended"
or infinite and you're coming up with a useless number. If
that's what you mean by "latency period is so wide that to
average them out would be meaningless" it sounds to me like you
need to call the defection so there is some endpoint you can use to
For example, let's say you have 10 years of data and it includes
people who bought only one time 10 years ago. Clearly, those
people are no longer customers. To include them in a Latency
analysis would create a meaningless number, both arithmetically and
behaviorally. You have to put a stake in the ground and declare
"no activity for over 2 years, they are a defected customer and
will not be included in the analysis". This takes the
"infinite Latency" problem out of your hands.
Another approach (and I'm just guessing you are talking about
retail) is to look at 2nd purchase Latency - average number of days
between 1st and 2nd purchase. Let's say it's 90 days. So
from now on, any new buyer who doesn't make a 2nd purchase within 90
days of the first is considered a defected customer and excluded from
any analysis, because literally, they are likely to have infinite
Latency and the "spread" is meaningless.
A third approach taken by people who have been in this kind of
business a long time and trust these metrics is to set a threshold for
being a customer at all. At HSN (after many years of testing),
we came to the point where we didn't even consider you a
"customer" until you made a 2nd purchase. We kept
track of the number of 1x buyers, but that's about it. We
discovered there was no profitable way to create a
"relationship" with these buyers, so they were not
considered customers from a marketing investment perspective.
Q: In this case (Latency period is so wide), is
Recency the way to go?
A: Well, again, I lack enough context to answer that
question specifically because I don't know what you are trying to
accomplish and with what kind of business. But in general,
Recency is a better bet when the behavior is not predictable, and Latency is a better bet when the behavior tends to be cyclical or
repeat at regular intervals. So for general retailing, Recency
is a better predictor of likelihood to purchase. If you are
looking at the oil change business, Latency would be a better
predictor of likelihood to get another oil change at a certain point
Latency and Recency can be used at the same time on different
segments, it's not an "either or" situation. For
example, you could use Latency for Heavy and Moderate, Recency for
Low, if that makes sense in your situation.
Let's say that in your case "Low" means there really
isn't enough transactional activity to determine Latency. So you
use Recency for that segment. This approach is often used
in remote retail, I call it a "one and done".
After first purchase, you allow Recency to expand, so that
multi-buyers "tip their hand" and you don't end up making
offers you did not have to make to get the 2nd and 3rd purchase.
At some point (typically 45 - 90 days out), the majority of
multi-buyers have shown themselves, and you make one offer to the
remaining 1-time buyers. Those that respond become multi's, the
majority of the rest will never buy a second time and it's a waste of
money to promote to them - this is what I mean by "one and
In other words, you give them some time to prove whether they are
customers or not, then you provide them one last shot by giving them a
nudge. Anybody who does not respond is now a defected customer
and that's it. If they come back and prove themselves to be a
customer, fine, we'll include them in all the customer marketing.
But if they don't, it's relationship game over.
Q: My purchase of your book has generated a second
purchase! By a colleague of mine.
A: Thanks for that! If you'd like to elaborate
on the specifics of what you are trying to accomplish, perhaps I can
be more helpful!
That's it for this month's edition of the Drilling Down newsletter.
If you like the newsletter, please forward it to a friend! Subscription instructions are top and bottom of this page.
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
'Til next time, keep Drilling Down!
- Jim Novo
Copyright 2005, 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
credits, including live web site link and e-mail link. Please
tell me where the material will appear.