MarketingExperiments Blog: Research-driven optimization, testing, and marketing ideas

You’ve finally set up tracking on your site and have gathered weeks of information. You are now staring at your data saying, “Now what?”

Objectively interpreting your data can be extremely overwhelming and very difficult to do correctly … but it is essential.

The only thing worse than having no insights is having incorrect insights. The latter can be extremely costly to your business.

Use these six simple steps to help you effectively and correctly interpret your data.

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Some marketers simply drop two landing pages into a split test tool, click a button, and then push live the page “winner” with the larger results number.

If you really want to benefit from split testing, you need to do a little more. You need a basic understanding of what you’re really doing when you’re testing. That includes validity, which is, at its most basic level, an assurance that the results from your tests actually reflect what is going on in the real world.

Let me show you an experiment with a MECLABS Research Partner in which an understanding of validity helped the team find a conversion lift that would have otherwise been missed.

Background: Consumer company that offers online brokerage services

Goal: To increase the volume of accounts created online

Primary research question: Which page design will generate the highest rate of conversion?

Test Design: A/B/C/D multi-factor split test

CONTROL

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What do you get when you divide Jacksonville Beach, Fla. by Arden Hills, MN? I’m sure there’s a punch line in there somewhere. However, if you were tracking your customers’ ZIP codes in a database you would have 32250/55112, or 0.585.

Never mind that it doesn’t make any sense to you and me to divide one ZIP code by another, but a statistical software package is happy to do exactly that for us. Most software just isn’t smart enough to realize that each ZIP code holds a discrete meaning from the next. It sees them as numbers: values which can be sorted in order and used in any type of calculation.

That is why researchers and statistical software packages classify variables into four main types: Nominal, Ordinal, Interval and Ratio.

In this post, I’m going to describe each type of variable to help you understand how they should be used, let you know how this can help improve your data collection … and, while we’re at it, help you sound sharp the next time you’re chatting with your data analyst at the water cooler.

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As I prepare to wade through hundreds of submissions for the MarketingSherpa 2012 Marketing Wisdom Report, (sponsored by HubSpot) I was compelled to take a final glance at the 2011 edition.

While combing through the pages, many of last year’s submissions evoked some forward-thinking thoughts for 2012. Here are just a few of the standouts…

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Optimizing your webpages and marketing campaigns is a daunting task for any marketer. We need all the help we can get. Free help is even better (as long as it’s actually, well, helpful). So to assist our audience of marketers with daily optimization tasks, our researchers have created 11 free marketing tools over the years, and I compiled the below list.

You can think of this as our idea of Halloween candy.

Please try them out and let us know how they work for you in the comments. Read more…

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In my job as a Senior Research Manager at MarketingExperiments, I talk to a lot of people who are new to online testing. Before I started, I used to think the most intimidating factor for newbies would be coming up with page designs and development. But it’s not. Dealing with metrics, by far, is the most intimidating.

Maybe it’s because, when they reached the fork in the road, they took the marketing path because they simply didn’t like math.

Or maybe metrics are intimidating simply because there are so many of them!

• Which metrics should I care most about?
• How do I interpret them?
• Which metrics should I install for my test?

I have some good news: You can eliminate all the confusion and anxiety you have if you organize your metrics into four major groups. And guess what? Specific groups (like Source and Nature) are far more important to planning great tests than others (like Amount and Result). Let me explain. Read more…