In my last post, we took a look
at a framework for optimization that creates results that will inform future
programs and tactics. Using Scientific method, we created control and test groups
to prove out our hypotheses. We also looked at ways to increase our testing
flexibility using test matrices allowing us to test multiple factors simultaneously
to get the best combination of variables. Now let’s look at a real-world
example that might challenge our assumptions.
Deodorant
goes Viral!
Let’s say our good friends at Acme, a Consume Packaged Goods company, is releasing a new mens' deodorant brand. The marketing brain trust has spent plenty of time, resources and money testing and validating the brand positioning and customer value proposition. Acme has settled in on a particular brand voice that is best presented in a humorous light.
Let’s say our good friends at Acme, a Consume Packaged Goods company, is releasing a new mens' deodorant brand. The marketing brain trust has spent plenty of time, resources and money testing and validating the brand positioning and customer value proposition. Acme has settled in on a particular brand voice that is best presented in a humorous light.
Testing on
several control groups have settled on a particular series of video clips that
have shown the potential to “go viral” and spread the new brand’s message over
the Internet for very little money. The demand gen team packages and presents
the idea to brand leadership and everyone agrees the videos are engaging and hilarious
and will definitely spread like wildfire over the Internet, so the project gets
the green light. Time to light ‘em up! The Demand Gen team posts the videos on
You Tube and eagerly waits for the results.
Within hours
the video does, indeed, go viral as expected. Views are in the millions, and
rapidly increasing. Within days the videos are at the very top of the view
lists and are making the rounds via Social Media channels. All is good, right?
And now, a word from our sponsor…
Expectations
are high, but word from the distribution team is the product is staying on the
shelves in droves. Expected backfill orders are non-existent. The videos are
obviously a smash hit, spreading the new brand across the Internet at record speed.
Why no product sales as a result? Quick, somebody check their Twitter account to
see what’s going on!
Houston, we
have a problem. While everyone seems to absolutely love the humor in the video,
the reaction is not exactly what product planners have in mind.
#worldsdumbestdeodorant#dumberthandirtdeodorant#lmaodumbdeodorant#smellslikestupiddeodorant#canyousmellthatgoodandbethatstupid
Yes, the
videos are hilarious, but nobody wants to be associated with a product whose
promotional videos depict its users as complete idiots. And it’s too late for
those tens of millions of viewers to “un-see” those videos.
What should Acme have done?
This is a clear example of where spending a little time and money
on a limited pilot could have saved a lot of embarrassment later. Executing
tests in a controlled environment with limited, but representative audiences
can provide insight into those unintended consequences. These pilots should concentrate
on understanding not only what will
happen, but why it happens.
Change the conversation.
Your pilot should be conducted with the end objective in mind.
These pilots are good at bridging assumptions you may have made in causal
relationships. In this case, ACME assumed that millions of Internet views would
cause millions of product sales. Instead, those views led to brand ridicule and
corporate embarrassment.
Pilots can also be used to test specific audience segment reactions
against the whole. It is a great way to determine how message/offer variants
track in one segment vs. another. Do customized messages really improve
responses? Better to pilot and know than spend additional time and money only
to find out after the fact you generated no additional lift for your efforts.
Notes:
Pilot before committing to an
untested plan.
Pilot with the end objective in
mind.
Pilot to correct causal
assumptions.
We’re now piloting
testing and optimizing every bit of success out of every program we execute. It’s
time to tell your C-Suite about the fantastic work you are doing. Unfortunately,
Marketing historically has done a less-than-stellar job of reporting on its
success. In our next installment, we’re going to begin a series on reporting to
overcome that legacy: Create Marketing
Reports That Will Thrill Your C-Suite!
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