Testing

 Introduction:

•        Testing is the technique used to know the actual intent of the customers who are visiting a website

•        Though a website has a major goal like, content production or e-commence or engagement. There are micro elements that are also part of this. Ex: Looking for careers link in Amazon web site.

•        So it’s important to know how each group of users are responding to different group of people.

•        We can use testing to know the known- unknown elements

Types of tests

•        There are many types of testing techniques that are used. Most popular are,

                a. A/B testing or Split testing

                b. MVT testing or Multivariate testing or bucking testing

•        Any testing technique requires having the hypothesis ready and they have a clear definition of test elements.

•        By testing, you can’t derive new learning’s other than the elements that we have hypothesis for.

A/B Testing

1. A/B Testing, also known as split testing, is a method of website optimization in which the conversion rates of two versions of a page version A and version B are compared to one another using live traffic.

2. Site visitors are bucketed into one version or the other. By tracking the way visitors interact

With the page they are shown the videos they watch, the buttons they click, or whether or not they sign up for a newsletter you can determine which version of the page is most   effective.

You design two versions of a web page (A & B), divide the traffic between the two, and choose the one that gives you the maximum conversions.

How A/B Testing Works

In an A/B test, you take a webpage or app screen and modify it to create a second version of the same page. This change can be as simple as a single headline or button, or be a complete redesign of the page. Then, half of your traffic is shown the original version of the page (known as the control) and half are shown the modified version of the page (the variation).

As visitors are served either the control or variation, their engagement with each experience is measured and collected in an analytics dashboard and analyzed through a statistical engine. You can then determine whether changing the experience had a positive, negative, or no effect on visitor behaviour.

5 Simple Steps to Start A/B Testing Today

1.    Determine your goal

Your goal will vary based on your business.

For instance, a Business-to-Business company might be focused on generating more leads for their sales staff. On the other hand, an e-commerce Business-to-Consumer website might want to increase sales.

2.Decide what to test

Now that you know your goal and the page you’re going to test, it’s time to decide what element you’ll test.

Here are some options:

·         Your headline.

·         Your offer text.

·         Your button text.

·         Your form fields.

·         The colour of your form button

I could go on and on. There are so many things to test.

Note that some sites will see a dramatic improvement in their conversions just by changing the colour of their opt-in button. Other sites, however, will see little improvement from a button colour change. They’ll have to test bigger elements like their headline, offer, and USP (unique selling proposition).

Why? Well, one reason could be, if your website visitors don’t understand how you’ll help them, they won’t opt-in no matter what color the button is.

Quick Tip: If you’re having trouble deciding what to test, choose something anything. Let’s just get a test up and running. You can always test other elements later.

3. Create your test

There are two parts to this step.

• The Creative Part

The first is what you might call “the creative” or making another version of the element you’re testing.

For instance, maybe your headline doesn’t immediately tell your visitor “What’s in it for me?” Write another version of your headline, this time focusing more on your visitor.

• The Tech Part

The second part of creating your test is the technology you use to deliver each variation to your visitors.

If you’re intimidated by this step, fear not. Modern technology makes this super easy and fast.

4. Wait.

I find this step the most challenging of the five steps, mainly because waiting isn’t my strong suit. However, to get accurate results, we must wait.

Note: Go ahead, log in and check out your results during your test just do not (I repeat, DO NOT) stop, pause, or edit your test until it’s complete.

5. Determine your winner

When your test is over, it’s time to calculate your results or determine if your tests (and the results) are “statistically significant.”

Although it sounds complicated, we’re simply making sure our results will perform the same over the long haul. In other words, how sure are you that your test results are accurate?

A/B Testing Process

Example:

•        The current version of a company's home page might have in-text calls to action, while the new version might eliminate most text, but include a new top bar advertising the latest product. After enough visitors have been funneled to both pages, the number of clicks on each page's version of the call to action can be compared.

•        It's important to note that even though many design elements are changed in this kind of A/B test, only the impact of the design as a whole on each page's business goal is tracked, not individual elements.

•        A/B testing is also useful as an optimization option for pages where only one element is up for debate.

MULTIVARIATE TESTING

Multivariate testing uses the same core mechanism as A/B testing, but compares a higher number of variables, and reveals more information about how these variables interact with one another. As in an A/B test, traffic to a page is split between different versions of the design. The purpose of a multivariate test, then, is to measure the effectiveness each design combination has on the ultimate goal.

Once a site has received enough traffic to run the test, the data from each variation is compared to find not only the most successful design, but also to potentially reveal which elements have the greatest positive or negative impact on a visitor's interaction.

Getting Started With Multivariate Testing 

To create your first multivariate test, first choose a tool or framework that supports multivariate testing. You can use one of the tools listed in the section “Tools” in the end of this article. Please note that not all A/B testing tools support multivariate testing, so make sure your tool of choice allows it.

The following parts of a page (listed in order of importance) are typically included in a multivariate test:

·         Headline and heading,

·         Call-to-action buttons (colour, text, size, placement),

·         Text copy (content, length, size),

·         Image (type, placement, size),

·         Form length.

What to measure: defining test objectives

Before you start formulating a test hypothesis, or begin running tests, the first and most important step is to ensure that there are defined objectives for the website. You'll want to examine your marketing goals in order to determine the appropriate success factors that all of your organization's stakeholders can agree upon. Here are some typical measurable website goals:

Make money: sell product, generate leads, and advertising or promotional click-throughs.

Save money: enable users to adopt self-service features and/or answer product and service questions on their own (such as through online FAQs and documentation).

Examples of Multivariate Testing

 Common examples of multivariate tests include:

1. Testing text and visual elements on a webpage together

2. Testing the text and colour of a CTA button together

3. Testing the number of form fields and CTA text together

Using multivariate testing as a method of website optimization is a powerful method of gathering visitor and user data that gives detailed insights into complex customer behaviour. The data uncovered in multivariate testing removes doubt and uncertainty from website optimization. Continuously testing, implementing winning variations and building off of testing insights can lead to significant conversion gains.