Marketing should be treated like a science.
If you are serious about making money on
the Internet, it is absolutely crucial
that you spend some time testing your results
and refining your approach.

Let me drive this point home. Say for example
you have a website that gives you a visit to
sale ratio of about 200 to 1. Not really bad
as far as web sites go. Most do far worse.

Now, assume that you get 5,000 visits per month.
Do the math. That’s 25 sales per month.
If your profit from each sale is $50, you are
making $1250 per month profit from your website.
Now, what would happen, if by changing one
small thing on your website, you could improve
that ratio to say 150 to 1.

It may not seem significant, but let’s see
how it works on paper. Now, instead of 25 sales
per month, you are doing 33. Multiply that by
$50 and you are now making $1650 per month.
That’s a difference of $400.

It’s not like you’re having to work any harder
for that additional $400. Your website is always
there.

Now, the example I have shown you here is quite
mild to some of the drastic improvements I have
seen as a result of market testing.
All my life I’ve heard people say
"don’t work hard - work smart!"
This is excellent advice. Working smart means
getting more from less effort.

Well, testing is a systematic way of helping you
to work smart. It’s just like the scientific
method, really. You may remember it from school.

There are many versions of the scientific method
for different disciplines
and many scientists will argue about which
approach is best. For our purposes, let’s
take a very bare-boned and simple version.
It’s really all we need:

a. Generate a hypothesis.
b. Test your hypothesis by performing an
experiment and recording the results.
c. Perform calculations and draw conclusions.
d. Confirm or refine your original hypothesis.

For example, let’s say in the above example,
we hypothesize that by adding a money back
guarantee, we will get a better visit to sale
ratio. This is our hypothesis. To test this,
we make the change on our websites
and record our results. That’s our experiment.
After a few weeks (or any period of time we
determine to be sufficient) we conclude the
experiment and perform calculation on our
collected data.

We then discover that the visit to sale ratio
did, indeed, improve during the course of
our test run. We can then draw a conclusion
that our original hypothesis is correct.

Seems pretty simple, right?
Well, actually no -

* Controls and Statistical Significance*

In the preceding example, we did a pretty
straightforward and simple test.
However, the results we have drawn may
not be valid. For example, what if the
majority of visitors we received before the
test run came as the result of a banner ad
campaign we were running?

Then, without us knowing it, sometime during
the campaign a company favorably reviews our
product and drives people to our site.
It could be the case that people who read
that review were more likely to buy the
product. It could also be the case that they
were much more likely to buy and that adding
the guarantee lessened the effectiveness
of the site. Or, it could even be the case
that the guarantee did increase the
effectiveness of the site, but not as much
as we thought.

The problem is, we just don’t know.
To address this issue and to improve the
reliability of our tests, we need to
establish "controls".
A control is a measure that allows us to
isolate the factors which are causing
the effects we record.

For example, a better way to run this test
would have been to run two websites at the
same time.
Both of them identical in every way
(promoted the same way, designed the same
way - everything)
except for the one element we think will
make a difference. The site without the
change is called the control site.
The site with the change is the test subject.

This same principle can be applied to any
other type of marketing. If you want to
test the effectiveness of a banner ad, run
two banner ads on the same site for the
same period of time. If you run one banner
ad on one site and the other on yet a
different site, you can’t be sure that your
results are valid.
Using strict control techniques improves
the chances that your findings will be valid.

* Statistical Significance*

Even if we use good controls, we can still
never be sure of our results.
There may be something affecting the test
we haven’t thought of. That’s why we
also need to resolve ourselves to the fact
that the results of one single test should not
be accepted as conclusive.
Rather, we need to perform a great many tests
and look at the over all trends.
Only after we have compiled a considerable
amount of information can we safely draw any
conclusions.

Just how much testing is enough is up to you.
You should test until you are satisfied, but
be honest with yourself. Don’t just stop
testing out of laziness!

* Turning tests into profits*

The idea here is to test as many different
methods as possible, discover the most
effective method, and crank up the volume on
your best method. When you get into serious
volume and your business is really doing well,
even minor differences in results will have
a major impact on the amount of money you
take home.

And sometimes very subtle and minor changes
to a website will make huge differences
in your results.

If you could find out which of your advertising
campaigns was bringing in the most money for
you, wouldn’t it make sense to put more effort
into that campaign? Or do more of that type
of campaign? Wouldn’t you be crazy not to?

In the following chapters...

... we will bring you exactly how to track
and monitor the results of all of your Internet
marketing efforts.
After we show you the logic behind each method
of testing, we’ll show you the physical tools
and techniques you need to employ to track your
success.

UPDATE: A FREE Tracking system is now available,
Courtesy of ROIbot & Get your Link Rated FREE
by Websmostclickedon.com

Tracking is just recording the results of one
campaign or another.
Testing is the systematic monitoring of these
campaigns to discover what is working and what
is not using the scientific method.

Copyright 1999/2000 Aesop.Com

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