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| why users acted the way they did, and how that can be influenced |
Experience: why users acted the way they did, and how that can be influenced
Determining the factors that affect user experience involves testing to determine why users do what they do. Understanding why users behave in a certain way on your web site will show you how that behaviour can be influenced so as increase successful outcomes.
Testing can be performed in a number of ways:
A/B split testing
Multivariate testing
Listening labs
Single page heat maps
Determining the factors that affect user experience involves testing to determine why users do what they do. Understanding why users behave in a certain way on your web site will show you how that behaviour can be influenced so as increase successful outcomes.
Testing can be performed in a number of ways:
A/B split testing
Multivariate testing
Listening labs
Single page heat maps
A/B split testing
A/B split testing measures one variable at a time to determine its effect on an outcome.
Different versions are created for the variable you want to test. For example:
Two email subject lines for the same email to see which produces a superior open rate
Different placements of the “buy now” on a product page to see which results in increased sales
Different copy styles on PPC adverts to see which gives a higher CTR In these cases, only one variable is tested at a time, and all other elements on the web page, in the email or part of the PPC advert remain the same. You can test more than one version of the variable; it just means that you will need to test for longer.
Traffic is then randomly distributed to the different versions, and the outcomes are measured for each version of the variable. The results are then interpreted to see if there is a statistically significant difference between the variables. The version producing the best results can then be employed.
Remember studying statistics? It’s going to come in handy here. You don’t need to send huge amounts of traffic to a different version of a web page to determine success. In fact, it can be risky to do so.
A/B split testing measures one variable at a time to determine its effect on an outcome.
Different versions are created for the variable you want to test. For example:
Two email subject lines for the same email to see which produces a superior open rate
Different placements of the “buy now” on a product page to see which results in increased sales
Different copy styles on PPC adverts to see which gives a higher CTR In these cases, only one variable is tested at a time, and all other elements on the web page, in the email or part of the PPC advert remain the same. You can test more than one version of the variable; it just means that you will need to test for longer.
Traffic is then randomly distributed to the different versions, and the outcomes are measured for each version of the variable. The results are then interpreted to see if there is a statistically significant difference between the variables. The version producing the best results can then be employed.
Remember studying statistics? It’s going to come in handy here. You don’t need to send huge amounts of traffic to a different version of a web page to determine success. In fact, it can be risky to do so.
