BitCrunk IT Solution is a web marketing agency that offers SEO services, PPC services, social media marketing services, web design services, web development services and a host of other online marketing services.


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A/B Testing Services in india

A/B testing, sometime also called as split testing is the process which simultaneously compares the two versions of the web page or the content elements such as- Call-to-Actions, Headlines and Banners to see which one works well. In the process, the page layout which boasts better conversion always wins.
Since every business website wants its visitors to convert into business, performance measurement of the variation or A/B means the exact rate at which it converts the visitors into the goal achievers. At BitCrunk, we can potentially do a throughout A/B testing for your website or business.

BitCrunk test execution approach for A/B Testing

At BitCrunk, we follow a standard A/B Testing Methodology, which is based on a continuous improvement philosophy, and start with identifying the test goals, audience research and segmentation, and audit of site taxonomy and information architecture. The key steps in our A/B testing process include:
1) Identifying high potential and important pages for testing.
2) Tag placement and tool configuration.
3) Content creation and hosting.
4) Setting up control and alternative experiences.
5) Run experiments and results monitoring.

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 behavior.

A/B Testing

The following is an A/B testing framework you can use to start running tests:

A/B Testing Process

Collect Data

Your analytics will often provide insight into where you can begin optimizing. It helps to begin with high traffic areas of your site or app, as that will allow you to gather data faster. Look for pages with low conversion rates or high drop-off rates that can be improved.

Identify Goals

Your conversion goals are the metrics that you are using to determine whether or not the variation is more successful than the original version. Goals can be anything from clicking a button or link to product purchases and e-mail signups.

Generate Hypothesis

Once you've identified a goal you can begin generating A/B testing ideas and hypotheses for why you think they will be better than the current version. Once you have a list of ideas, prioritize them in terms of expected impact and difficulty of implementation.

Create Variations

Using your A/B testing software (like Optimizely), make the desired changes to an element of your website or mobile app experience. This might be changing the color of a button, swapping the order of elements on the page, hiding navigation elements, or something entirely custom. Many leading A/B testing tools have a visual editor that will make these changes easy. Make sure to QA your experiment to make sure it works as expected.

Run Experiment

Kick off your experiment and wait for visitors to participate! At this point, visitors to your site or app will be randomly assigned to either the control or variation of your experience. Their interaction with each experience is measured, counted, and compared to determine how each performs.

Analyze Results

Once your experiment is complete, it's time to analyze the results. Your A/B testing software will present the data from the experiment and show you the difference between how the two versions of your page performed, and whether there is a statistically significant difference.