How to Test What Works Better

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Bob's trying to generate more leads for his catering company.

  1. Bob: "Hey, let's test if this direct mail piece performs better than the one we mailed last month!"
  2. Dikembe: "Bob, you're a moron."

Why Is Bob a "Moron"?

What occurred last month probably won't occur this month.

For instance, more graduation-dinners/weddings/conferences will probably happen this month than last month -- rendering Bob's test super flawed.

To test well, testing conditions must be as identical as freakishly possible.

How to Test Effectively

  1. Run tests simultaneously.
  2. See what works better.
  3. Win.

Restrictions:

  • Population (e.g. target markets) should be identical.
  • Subjects (e.g. people) should be randomly chosen.

Hooray!

Are results really reliable?

You want statistically significant results -- not results that happen by pure chance.

For instance:

  1. You interview three ugly people.
  2. Two want to jump off a cliff.

Does that mean 66% of all ugly people in America want to jump off cliffs?

NO WAY JOSE! HIGH-FIVE!

To Test for Significance...

Use this free split-testing calculator to see if your results are really significant.

By the way:

  • Goals: Mean the ^ of desired actions taken (e.g. newsletter sign-ups)
  • Visitors: The number of people tested in each group

(There, we just saved you from solving complex statistical mathematics.)

If you see your results as being at least 90% statistically significant, take it.

Remember, you want to make consistently good decisions that over time, will pull you ahead -- regardless if there's a slight chance that your results might be incorrect.

Example Up

Bob identifies his testing parameters:

  • Target market: 1000 California business owners between 30-35 years old.
  • Testing collateral: Two different ads pitching catering services.
  • Mail date for both: Tomorrow.

Flash Forward a Few Weeks...

Bob gets back his results (so far):

  • Ad A: 500 targets, 25 leads called
  • Ad B: 500 targets, 10 leads called

He plugs his results into the nifty split-testing calculator, and sees that Ad A has a 98% chance of kicking Ad B's ass if Bob sent his ad to all of California's 30-35 year-old business owners.

(Note: anything above 90% chance is good. Take it.)

So, Bob concludes Ad A is the winner! YAY!

Simultaneous tests that reach statistically significant results win, b!^ch.

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Posted on April 25

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