New RFM: Web Site & E-mail Metrics
Drilling Down
Newsletter
# 39: 11/2003
Drilling Down - Turning Customer
Data into Profits with a Spreadsheet
*************************
Customer Valuation, Retention,
Loyalty, Defection
Get the Drilling Down Book!
http://www.booklocker.com/jimnovo
Prior Newsletters:
http://www.jimnovo.com/newsletters.htm
========================
In This Issue:
# Topics Overview
# Best of the Best Customer Marketing Links
# Question - Web Analytics Vendors
# Question - Scoring E-mail Campaigns
# New RFM Metrics: Take 10 on Retention
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Topics Overview
Hi again folks, Jim Novo here.
We have a couple of provocative customer marketing articles followed by two questions from fellow Drillers,
one on comparing web site analytics vendors and the other on using simple
scoring models for e-mail campaigns.
These folks may be a bit ahead of where you are in your quest to
Turn Customer Data into Profits, but I absolutely believe understanding how **all** kinds of businesses
look
at these metrics will help you increase your ROI. Every
biz situation is different, and by exposing you to a lot of varied
approaches, I'm hoping to trigger your personal "Ah-Ha!!"
moment.
Good To Go? Let's do some Drillin'!
Best Customer Retention Articles
====================
Note to web
site visitors: These links may
have expired by the time you read
this. You
can get these "must read" links e-mailed to
you
every 2 weeks before they expire by subscribing to the newsletter.
CRM is a Four-Letter Word
October 1, 2003 Direct Magazine
Yes, here we are - again. The author does not reveal which word he was
thinking of, but hits on a problem we've
discussed before with the way many CRM systems are designed - they depend on
the customer to initiate contact. That's insane; the real ROI is in proactive
Customer Retention. But many of
these systems can't handle the batch operations so critical to High ROI
Retention Marketing. Oh...
Basic
RFM Analysis Yields Binding Results
October 22, 2003 DM News
Some of the smartest web marketers out there are using offline postcards to
retain or reactivate customers. This article isn't specifically an online
/ offline story, but it does show you the power of
RFM - a 600% jump in response rate for a $3,500 product is not something you
can easily ignore.
-------------------
If you are in SEO and the client isn't converting the additional
visitors you generate, you can help them make it happen - click here.
-------------------
Questions from Fellow Drillers
=====================
If you don't know what RFM is or how it can be used to drive customer profitability in just about any business,
click
here.
New RFM: Traffic Analysis Technology
Q: I own your Drilling
Down book...
A: Thanks for that!
Q: ...and then recently (in your newsletter) you
pointed me to look at your presentation at WebTrends...
A: The one on customer
retention, I take it...
Q: I think WebTrends products are something we
should use (over what we are using now) which is Gotoast.com
A: Well, GoToast is a good product for what it does.
If all you really want to do is connect spending or source of customer
to sales, seems to me it is pretty good at that, and makes it easy to
report on these metrics. For many people, that's all they ever
want to know. Truthfully, I wish more people used it, or used
ClickTracks, which also does a fine job. What is scary is the
number of people who use no tracking at all!
Q: I also was wondering if you have compared NetIQ's
WebTrends against Omniture's service/product:
http://www.omniture.com/metrics.html
A: I've never used Omniture, and I get nervous when a
company doesn't provide sample reports or even pricing on their web
site for such a web-centric tool. I know the guys at WebTrends
don't think a lot of the product, but they're biased. It's hard
for me to believe it is that much better / worse than most other
tag-based reporting.
The truth is, all these products have upsides and downsides, and
these depend largely on the kind of technology you are using on your
web site, what it is you want to know, and what you will do once you
know.
Q: I actually think that WebTrends is superior, but am
I missing something here?
A: Well, I'm sort of biased there too, I have to
admit. And the reasons I like WebTrends may not be important to
you. Here's my position, for what it's worth.
For me, it is important that I can analyze any kind of site, using
any site technology, and get any type of information I need from the
reporting. Also, if a client changes their site technology, I
don't want to have to ditch
all the history, I want to keep everything going along and not miss a
beat.
WebTrends is the only product that allows you to do this, the only
one that uses the same database for log-based analysis, tag-based
analysis, and SDC, the SmartSource Data Collector, which is
essentially a tag-based analysis that you host yourself - it's not an
ASP. The SDC approach is favored by banks and insurance
companies who for legal and privacy reasons can't have the data
hosted outside their environment, but want to take advantage of the
benefits of tag analysis.
It is important to me to be able to serve any client, regardless of
their needs, and do that using the same interface no matter what the
technology is. So you really have to decide what it is you want,
and then of course, there is price. Here are some rules of
thumb:
Publishers and people who rely on page views for revenue should
always use a tag-based system. It's the only way to actually see
all your page views. Many tag-based systems have poor path
analysis, it tends to be From / To for each page as opposed to a
linear "path." So when page view counts are not really
that important and pathing is (retail is a good example), then use log
analysis or a tag-based product that offers linear pathing.
There are exceptions. Companies with multiple servers in
different time zones should probably be using tag analysis, as should
sites with gigantic traffic or lots of traffic they don't care to
analyze but a certain portion they do. These are usually
IT decisions, as in, it simply costs too much to aggregate / store all
that info and a tag-based log file can be 1/10th or 1/20 the size of
log data.
Also, a tag-based system is by definition forward looking - no data
unless the tags are installed. You can get history from the log
files, and so many times logs are where an analysis project starts -
for the history.
In general, since I'm a slicer-dicer, I have trouble getting to
**exactly** what I want to see with most tag-based systems. By
definition, they are somewhat restricted in their ability to customize
due to the "one size fits all" ASP model. This is not
true of the WebTrends Reporting Service ASP, though there are some
"governors" on how many custom reports you can have, for
example. In the SDC model, you have total control and can track
stuff most of these other providers have never heard of before.
The other good thing about WT Reporting Service is you can start
with "basic" and work that, then if you want to get into
some more advanced stuff, they just flip a switch and you have it (at
a higher price, of course) all the way up to Enterprise, which
includes visitor history tracking. This capability is what is
needed to produce the key metrics from the retention presentation -
Recency, Frequency, Latency, and LifeTime Value - at the individual
visitor level. It also captures initial (first visit) and most
recent referrer, search engine, search phrase, campaign, and
more. Hopefully, in the future these visitor history tables will
be exportable and also capable of ODBC connections to the outside
world - Excel, Access, analytical databases, CRM, etc.
And that will be when things will really take off in web site
analysis. It's the missing link to really driving profitability
on the web. GoToast / ClickTracks / Omniture / HitBox can all
tell you if your clicks are paying out today. But that's not the
real question, it's did a click 6 months ago pay out today, and then
the ability to predict **in advance** which ones will pay out in
6 months. The only way to do that is with individual visitor
history, and the only way you used to be able to get that is with a
very high end package or a custom-built solution.
Hope that helps. If you have any questions, feel free to ask
- you're a customer now!
Jim
-------------------------------
If you are a consultant, agency, or software developer with clients
needing action-oriented customer intelligence or High ROI Customer
Marketing program designs, click
here
-------------------------------
New RFM: Scoring E-Mail Segments
Q: I recently purchase your book "Drilling
Down." Really enjoying reading it!
A: Well, thanks for the kind words.
Q: I had a question about the implementation of the
RFM model against email campaigns. Say we have a client that has done
this:
- Has sent out 2 emails to entire database (in June and July)
- Has sent out 3 targeted emails to a specific segment of the database
(June, July and Aug)
From my CTR and Open Rates I know that the targeted segment
performance is better. For my scoring I am using the following:
- Recency, last email responded to, and
- Frequency, number of emails where an action (a click-thru) was taken
So the question is when trying to apply an Recency / Frequency (RF)
score to the entire database, do you / can you use all 5 email
programs? Would Recency include the email to the specific
segment in August? Would frequency include the segment that
received the email in August?
A: The fact you are asking this question tells me you
understand the methods better than you think you do. The correct
answer is yes, and no, depending on the objective of the scoring.
As long as you **understand** that there is the potential for the
marketing to the target segment to skew the scoring of the overall
group, then you are thinking about the problem correctly.
Whether you decide to do the scoring as "everybody" or you
score the targeted segment and "everybody else" separately
really depends on what you are trying to accomplish or the objective.
Q: Wouldn't the folks in the targetd segment
potentially have a higher RF score?
A: Absolutely. But this is not bad, I mean, a
bunch of them responded, so they "deserve" a higher RF
score, yes? Isn't response good, and so they should have higher
scores?
If you look at the case for scoring the entire database, it
generally tells you who is most likely to respond to **any** campaign.
If you know a lot about your customers, you probably will not send
them all the same campaign, but create different campaigns for
different customer segments and hope to generate sales (or sign-ups,
or downloads, or whatever). What you are really getting from
scoring everybody together is identifying specific individuals or
groups who:
1. are most likely to respond, and
2. appear to be defecting so you can be proactive and go
after them with a specialized campaign addressing the potential
defection.
You can either decide to attack certain groups or not spend the
money because they are "already gone" and there will be no
ROI. This doesn't have anything really to do with any specific
campaign, it is the more about the aggregate, overall decision to
spend on any specific customer or group of customers. The fact
you did a campaign to a certain segment has no bearing on this,
because if you are successful with your campaign, those targets will
have higher RF scores - and quite frankly, that is what you want,
right? The higher the RF score, the more likely they are to
respond and the less likely they are to defect.
Now, that said, the business of database marketing is about
creating test programs, looking at the results, and realizing that
certain segments respond better than others to certain campaigns.
A classic example is the "discount
ladder," where you set the discount by RF score in order to
maximize response and ROI. It is certainly OK and desirable to
break the customer base into sub-segments and score these segments
against themselves as well as the overall population.
For example, score everybody who bought a lamp as their first
purchase by themselves and everybody who bought a chair as their first
purchase by themselves, or everybody who came from Google as a group
and everybody who came from MSN as a group.
What you get from this approach is new insight and uncovery of
new, profitable segments. So, for example, you find a
customer segment with an average RF score of 35 (average) in the overall
scoring of the entire customer base, but it has a score of 55
(highest) when just scoring people who came from MSN. Though
these people are not a "best customer" segment overall, they
are "best" within the MSN segment and through testing
you find they are generally responsive. They are best customer
segments in terms of all segments from MSN, and as such, are probably
worth targeting.
Another common use of multiple scoring groups comes into play for
1x buyers (see page 98 in the book). One time buyers by
definition all have a Frequency of "1," and online the
percentage of 1x buyers in the database tends to be huge. So if
you score the whole database together you get a very warped view of
the world - if 80% of the database is 1x buyers, some of these poor
quality customers will get fairly high scores.
A better approach is to split the database in one-time and
multi-buyers. and score each group individually. This way, you
have created segments which have very similar members, and a relative
ranking score like RF becomes much more meaningful. When scoring
the one-time buyers, you dump the frequency variable since it is the
same for all, and use just Recency or perhaps Recency and
Monetary. Then you test and see which approach is the best
predictor of response and defection.
Your ability to formulate this question means you are on the right
track. It's not an either / or situation, it's more like a
"both" depending on what you are trying to do.
Jim
New RFM Metrics: Take 10 on Retention
====================
If you would like to know more about how to use the new RFM metrics to improve your profitability on the web, check out the free "Take 10 on Retention"
package I wrote. It includes a 10 minute presentation on the strategy and
reporting behind increasing web customer ROI using simple predictive
models.
Here's the idea in a nutshell: when you make investments, you
expect the value of them to rise in the future. You have web
investment choices - media buys, ad designs, building out content,
etc. Retention metrics tell you which of these investments are
the most likely to generate increased profits in the future.
Click here for the Take 10 on Retention
-------------------------------
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
Defection here.
'Til next time, keep Drilling Down!
- Jim Novo
Copyright 2003, 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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