Understanding A/B Testing

Understanding A/B Testing

A/B testing is the process of testing two elements to determine which performs more effectively.

Use correctly, it can help your organization identify value-added activities with data you already have.

Allowing you to capitalize on opportunities quickly.

With customer data being more readily(便捷的) attainable than ever,businesses are in a position to make smarter, more informed strategic decisions.

Being able to quantify that data means you're dealing with specific accurate and verifiable information.

A/B testing, also known as split testing or bucket testing, is valuable technique for measuring the data your organization captures, and turning it into strategic decision-making.

Let's say your business has a web banner with a click throught rate of 2%. if you want a rate of 4%, you need a better banner.

During A/B Testing, analysts can compare two or more versions against each other, and determine which of the two is better at generating page views. The user interface and user experience design for any product is intended to attain specific goals.

Designers plan clear interaction and navigation systems for effective and pleasing user experiences.
But what worked well last year may not have the same effect this year.
Business goals change over a product's lifetime, so the design may benefit from some improvements.

To that end, The A in an A/B test represents the current user experience and acts as the control. While B represents a deliberate variation on the current version that's intended to improve on it,or in some cases, to prove or disaprove its effectiveness.

The adjustment could be as simple as a change to a banner, or as complex as a total product redesign. In our web banner example,traffic to the page is split equally between versions A and B, with each online user interacting with one or the other version, but never both.

Users collective interactions with the respective versions are captured in an analytics database and a data analyst evaluates the results using his statistical engine.

In this way, the company can assess whether the modified version of the web page outperforms the standard or baseline version.if it does,the company can make the relevant changes to optimize the outcome.

while all data is measurable, the true accuracy of an A/B test largely depends on the number of users included in the experiment.

the bigger the sample size the more reliable the insights gained from the test.If, say a social media provider with hundreds of thousands of subscribers were to choose to sample ten suers in its A/B test,and four of the ten prefer option B over version A. This trial of group would be too small to statistically be considered a realistic reflection of the general public's viewing preferences.

In contrast, if the company were to test on a few thousand active users daily,it would be better positioned to reap(获得) the rewards of the insight's gained through A/B testing.

Companies can collect significant amounts of data,allowing them to implement one change at a time, and make the necessary adjustments quickly, to optimize the user experience.

Testing one change at a time helps a company pinpoint which changes had an effect on user behavior.over time the company can combine the effects of multiple changes from experiments to demonstrate the measurable improvement of the new experience over the old one.this allows verified statistical data to demonstrate the impact of new features or changes to a user experience.

A/B testing takes the guesswork out of the process for determining the best design for a product or service. it enables data informed decisions,shifting the conversation from we think to we know. By measuring the impact of changes,businesses can ensure that every change will produce positive results.

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