Drilling Down Newsletter # 13 -
October 2001 - Software Development
Practice What You Preach: Online Advertising
Effectiveness? Tell Me About It... (Part 6)
If you have just joined us, the previous five Parts of this series can
be read on a single HTML page right here.
Last month we took a look at the influence of a custom landing page on
the quality of visitors generated by the GoTo and Google Adwords
pay-per-click / search programs:
|Search Visitor Conversion
|Visit Length (mins)
% 1 Page Visits
The custom landing page resulted in higher abandonment (bad), and
higher download, bookmark, and subscribe rates (very good, since these
stats directly correlate to future purchase of my book) when compared
with the home page. This was expected, since the very targeted
nature of the custom landing page tends to screen out everybody but
the most focused visitors, and for the same reason, drives higher
"action behavior" (bookmark, subscribe, download).
Did you notice how the stats get better and better as you read from
the left to the right of the chart? Scroll up and look at it
again. Weird, huh? Almost mystical in consistency. I
get better performance from natural search traffic than I get from
driving highly targeted (and paid for) traffic to the generic Home
Page. And "natural" Google traffic is even better than
"All Search" engine traffic.
This makes me wonder - do the different engines really deliver
traffic all that different in quality? Google is a bit of a
strange bird, because it is currently a media favorite and never got
into the "portal" business. What about all the other
Here's what the "action behavior" (behavior leading to
book purchase) stats look like on the rest of them, in order of the
percent of traffic they deliver to my site. Note: In the next
table, "Yahoo" excludes Google default web pages.
|% Visitors from Each Search Engine
Engaging in an "Action Behavior"
|% of My Site's
Note: In the next table "Lycos" excludes Hotbot
|% of My Site's
|% My Search
Hmmm. Sure are different, aren't they? There's frequently
difference of double or triple in the same metric across the
engines. But traffic also matters. FAST delivers great
stats but hardly any traffic, so I should probably look into what
is going on there.
And I will. Fortunately, you will be spared the results,
as this is the promised end of the series on analyzing web
logs. What did we learn?
Keywords, landing pages,
search links, and the search engine itself all have a tremendous
impact on the quality of your visitor traffic. Not just "an
impact," but a huge impact. All traffic is not created
and if you are not doing this kind of analysis for your site, you
are undoubtedly wasting resources chasing what you think is
working, as opposed to what you know is working. My advice - let the behavior tell the tale. Find out what works!
Questions from Fellow Drillers
Q: Recently I had the opportunity to read your book
Down - Turning Customer Data into Profits with a Spreadsheet."
It has been some time since I have come across a book of its
kind. The concept you highlight is both interesting, and elegant
in its simplicity.
A: Aw, shucks. Thanks for the kind words.
Q: I would like to know your opinion as to how this approach
could be modified suitably for implementation in a Software
Development and IT outsourcing firm like mine.
A: Generally, any transactional activity can be profiled using
the RF scoring method. It is used for everything from predicting the
likelihood of someone to commit another crime to predicting the
likelihood of someone to make a bank deposit. RF is based on
human psychology and is therefore applicable in any culture. Any
part of your business where transactions are generated - medical
transcription, attendance records, project tracking, and so on. All you have to do is think of
situations where the prediction of repeat behavior likelihood is desirable.
In some cases, frequently in service businesses, the desired
outcome is inverted - that is, it is positive if people become less likely to do
something. For example, in regards to
attendance tracking, if you want to predict the likelihood of a
person to skip or call off work, look at the Recency and
Frequency of this past behavior. If you were using RF scoring, a
falling score for the person would be positive, since they are
becoming less likely to call off again.
In transcription, for monitoring coding errors, the higher the
Recency and Frequency of past errors, the more likely they are to
be committed again. A falling RF score for a transcriber would
be positive, since they are becoming less likely to commit
another error. A rising score, they are becoming more likely
to commit an error.
I don't know if likelihood prediction is useful for the
transcribed records themselves, but it could be. For example,
predicting the likelihood of a doctor to prescribe a certain
medicine or order a certain procedure. The tracking of these
things might be useful to a client and you could offer this as an
added service to them.
As far as software development for clients, there are any number
of situations where a simple predictive model may be useful,
especially where there is transactional activity related to
purchases in B2C and B2B - reordering / replenishment for
trading hubs, for example. And of course, in CRM, there are
many, many uses for simple predictive behavior models.
Generally, one should try using the RF scheme for prediction
before any more complex modeling operations are carried out. Often, after a long and torturous data mining project is
completed, one finds Recency and Frequency to be the primary
variables predicting the behavioral outcome; much time and
effort could have been saved by using the simple RF
scoring process detailed in my book in the first place!
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 at the top and bottom.
If you would like me to teach you these concepts using your
own business model and customer data, check out my
workshops and project-oriented service: Customer
Consulting. 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 to me.
'Til next time, keep Drilling Down!
- Jim Novo
Drilling Down - Turning Customer
Data into Profits with a Spreadsheet
Customer Valuation, Retention,
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Copyright 2001, The Drilling Down Project by Jim Novo. All
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