Measuring Results - Conversion Rate Optimisation

How to measure the Results?

In this section, we will be discussing on how to measure the results and make simpler information of the difficult done on the website.

Understanding Statistics

Researchers may or may not understand statistics. But, A/B Testing tool prove to be a savoir by simplify those statistics. So a lot of estimate can be avoided. Most of the testing tools are consistent in using 95% criteria as a winning goal achievement.

That means out of 10 you are a winner by 9. Let us take an instance. Your testing tool description is as follows −

Variations Conversion rate
Control page 1.91%
1 2.39%
2 2.16%
3 3.10%

this report predicts a more or less .20% adaptation rate variance at the 95% interval. Statistically, the goal collection is between 1.76 and 2.06.

Fetching Insights

While preparation a test I keep two goals in mind. First, is boosting proceeds and the other is fetching insights about what encouraged a higher ROI.

For example, in a case studies we get divert traffic to creation page rather than category page or homepage improve conversion rates or not. We took three variations, in one we directed traffic on the homepage loaded with category and subcategories directing further to the product page. In the second, we concentrate traffic to category page adding filters. In the third, we directed it straight on the product detail page with buy button.

To my revelation, the third difference won. This is just the information necessary by a buyer about the product. This lets us learn how conversion rate lift and constant improvements can make us develop our leads.

Certainly, adding many variation and insights over tests gave us the website remodel.

Understanding Results

Let me understandable it out. NOT ALL TESTS WIN. Yes, it is painful yet true.

There are tests that provide you results in flying colours. There are others that after so many tries too would be without a result. But if you plan a test with an insight driving segmentation, you can have a new hypothesis to be tested on. Not all tests help you to develop profits.

Take an instance to recognize. There are three campaigns with changed conversion charge.

Campaign A 8.2%
Campaign B 19.1%
Campaign C 5.2%

Anyone will blindly say ‘Campaign B’ is a super entertainer. But let us dig some more.

Visits Transactions Conversion Rate
Campaign A 1820 150 8.2%
Campaign B 20 4 19.1%
Campaign C 780 41 5.2%

Observe closely, ‘Campaign B’ is too small to be statistically significant. Campaign B with 1 transaction with one visit will give 100 percent conversion rate. ‘Campaign A’ perform over ‘Campaign C’. While ultimate results, there are some factors that need to be looked at and it may differ every time. It is you who need to look at all insight and choose the results.

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