How To Set Up A/B Testing for Success

February 24, 2016
Setting Up Optimization Experiments

Optimization is a process characterized by continuous iteration and driven by constant testing. For years, the only tools for A/B and multivariate testing required technical skills to run even the simplest of tests. But in early 2010, a new breed of optimization tool called Optimizely was introduced that made it easy for businesses to conceive and run experiments that helped them make better data-driven decisions. Here are a few tips on how to set up Optimizely projects for success and integrate your A/B and multivariate tests with a powerful project management tool called MonkeyWorks.

Step 1: Creating and Distributing Your A/B Testing Plan

The first step in setting up successful A/B testing is to create a test plan that answers the 5 W’s:

Who are you targeting? What audience segment will be bucketed into the test?
What specific changes are you testing? New form layout? Landing page redesign?
When is the test projected to run?
Where is the test running?
Why are you testing this and what are your hypotheses?

These parameters will be used to drive your experiments and clearly defining them up front will help impose a structure moving forward.

Once you’ve created your test plan, the next step is to distribute it throughout your organization. Traditionally, only select members of a company would have access to the tests being conducted, but by distributing your plan through MonkeyWorks your entire organization can gain visibility into what tests your running and how they’re performing.

Why is this important? Let’s say you’re a designer or copywriter and you’ve created a few different sets of banners for a client. Once you’ve integrated Optimizely with MonkeyWorks, you have your own portal that provides visibility on the tests being conducted and how your work is performing. Is your product-centric approach more effective in getting users to convert than the benefits-driven approach? Is a certain visual layout driving more conversions than another?

By gaining access to this type of data, job functions that typically do not fall in the bucket of being data-driven can be better informed of what’s effective. From there, those job functions can gain a continuously more robust understanding of user behavior.

Step 2: Integrating A/B Testing Tools with Your Analytics Platform

As more and more tools in the digital marketing space use API’s to communicate with one another, the net effect and power of your A/B or multivariate testing tools are growing exponentially. Integrating Optimizely with MonkeyWorks connects the dots between how testing affects traffic beyond your KPI’s. For example, if you’re testing ecommerce sales and notice that blog engagement is also rising, you get a “cross-pollination” effect where you gain insight into effective strategies for things that weren’t even on the radar.

Step 3: Building a Testing Culture at your Company

The most important step in setting up Optimizely projects for success is to build a culture of A/B and multivariate testing into the fabric of your organization. On first thought this step may seem frivolous, but if you take a step back and examine the far-reaching effect of how this optimization culture can create a well-oiled optimization machine, the results speak for themselves.

Developing an optimization culture allows you to get buy-in from other key stakeholders in your organization and consequently reveals the importance testing plays in increasing the quality of work produced. Additionally, by sourcing ideas from all corners of your organization you can solve friction points that impact other areas of your company and also lets you draw test ideas that impact the full customers experience.

Lastly, it’s always good to keep in mind that optimization testing is an ongoing, iterative process and that no experiments can truly fail. Even if an experiment provides data that goes against your hypothesis, any data gained that can inform your efforts is a success.

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