Internet Marketing Digital_marketing_for_dummies | Page 332

Getting Ready to Test
After you determine what pages to test and select the appropriate variants, you’re well on
your way to implementing your test. You still have several other elements to keep in mind
before you start your test, however. Pay attention to these components, described in the
following sections, to create a strong split test.
Developing an optimization hypothesis
Your test needs a hypothesis. For your test to truly be meaningful and actionable, you need
to come up with a plan, and you need to document statistics. Testing for the sake of testing
or for a particular hunch only wastes your business’s time and resources. A clear
hypothesis puts a stop to ad hoc testing.
Create a hypothesis based on this format:
Because we observed [A] and feedback [B], we believe that changing [C] for visitors
[D] will make [E] happen. We’ll know this when we see [F] and obtain [G].
Following a basic hypothesis format like the preceding one sets your test’s scope, the
segment, and the success criteria. Without a hypothesis, you’re guessing, and you don’t
want to base a campaign’s success or failure on a guess.
Choosing the metrics to track
After you choose a page to split test and the variations you will be testing on the page, you
need to determine the key performance indicators (KPIs) that you will use to evaluate your
split test. KPIs are metrics that gauge crucial factors and help you to determine the success
of a test. For instance, if you run a test that looks only at top funnel metrics, such as clicks,
you don’t get a full understanding of the actual impact. For this reason, you need to select
your KPIs and know how they impact your business goals.
To help define your KPIs, make sure to have page-level goals as well as campaign-level
goals for all your tests. Your split test goals might look like this:
Page goal: Leads generated
Campaign goal: Specific product purchased
Page and campaign goals give you the short view, that is, what happened on the page; and
the long view, that is, how what happened on the page impacted your overall campaign. It
is possible to see an improvement in the performance at the page level while experiencing
a decrease in performance at the campaign level. In our preceding example, we may run a
test that generates more leads at the page level but actually decreases the number of
products purchased at the campaign level.