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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 Metrics
Metric Home
Visit Length (mins) 3.35  3.15 2.61 2.60
% 1 Page Visits 40% 44%  52% 53%
% Download 3.2% 3.4% 3.6% 6.0%
% Bookmark 3.7% 5.4% 7.4% 9.8%
% Subscribe 3.2% 3.6% 3.7% 3.8%

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 search engines?

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"
Engine MSN A.Vista Yahoo
% of My Site's
Search Traffic
35% 20% 19%
% Download 3.2% 4.8% 2.1%
% Bookmark 6.5% 8.1% 4.2%
% Subscribe 2.3% 2.8% 3.6%

 Note: In the next table "Lycos" excludes Hotbot

Engine Excite NScape Lycos
% of My Site's
Search Traffic
7% 5.7% 4.6%
% Download 1.4% 3.5% 3.2%
% Bookmark 8.6% 1.8% 5.4%
% Subscribe 2.8% 6.1% 6.5%


Engine HBot NLight Fast AOL
% My Search
3.5% 3% 1.2% 1.1%
% Download 1.4% 1.6% 8.3% 0.0%
% Bookmark 8.6% 12.7% 12.5% 4.4%
% Subscribe 2.9% 0.0% 4.1% 8.7%

Hmmm.  Sure are different, aren't they?  There's frequently a difference of double or triple in the same metric across the engines.  But traffic also matters.  FAST delivers great overall 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, paid 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 equal, 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 "Drilling 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,
Loyalty, Defection

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Copyright 2001, 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
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