Components of Growth Experiments (Part-1)
Growth marketing is based on experimenting creative ideas based on research and testing. Growth experiments consist of research, analysis, execution and testing. In this article we will be discussing components of growth experiments. Peep Laja, CXL’s founder has explained the process of growth experiments. He has explained the ideal research process and testing strategy you should use to effectively run a growth experiment. He has also discussed the process of conversion research which can help you understand conversion optimization research process. As Conversion optimization is 80% and 20% experimentation. Peep has provided a lot of information on how you can do conversion research and find the best solution of the problem which is affecting your website conversions. Conversion optimization is very important because it directly affects your lead generation. Finding the best solution of low conversion rate takes a lot of research and A/B testing. Instructor Ton Wesseling, a well-known conversion rate optimizer has highlighted some key points regarding A/B testing. He has explained the process of A/B testing in detail. Ben Labay has showed us how we can use statistics to understand the data we receive from A/B testing campaigns. He explained all the concepts of statistics which are necessary for a growth marketer and conversion optimizer.
Research and Testing
Research XL is a research process which includes six types of data sources to help you identify opportunities for conversion improvement. The first step in the research process is to check your website for any technical issues. Collect data from your website and compare it for different browser and check if there are any issues in conversion on different browser or devices. You can actually collect a lot of bugs and issues of UI/UX on different landing pages of your website with this exercise. You can also check for all technical issues of your website like slow page load speed, CTA button not working and forms submission issues. These issues can have a huge impact on conversions from website and fixing them is the first step in Research XL.
Heuristic analysis is the second step in this process in which we evaluate our website on the basis of relevancy, clarity, motivation and fiction. We check our landing pages and their relevancy as per the product or service we are offering, how clearly we are communicating our product or service to our website visitors, how we are motivating them to buy that product or service from us and we try to get rid of as much friction as we can to encourage our users to connect with our service or product with minimum possible effort.
Checking digital analytics is the third step in which explore our analytics account and try to find any leaks in our funnel, segment based analysis of user journey on the website, user’s actions on our website, the co-relation of those actions with higher or lower conversion rate.
Mouse tracking and form tracking is the fourth step in this process, in this step we try to review heat maps, actions taken by users on our website, how far down they are scrolling, difference of user interaction between devices and session replays to check user behaviour after landing on our webpage.
The fifth step is about qualitative research. In this step we try to collect data from our customers, their reviews, what they think about our website, how easy it was to make a purchase on our website and what is the one thing which nearly stopped you from buying from us. All these questions will provide us a lot of qualitative data to work with.
Last step in this process is user testing. In this process, we recruit users and ask them to perform key tasks on our websites. We need to understand how users will use our website and how we can improve the user experience.
Prioritization Framework consist of six steps. It includes research process which helps you identify all the problems your website has. Hypothesis process which helps you to understand the problem and categorize all the problems. Creating treatment process helps us to create solutions for the problems we identified. In fourth step we need to test our solutions which we developed to solve the problem and see if they are actually solving the problem or not. Then we need to analyse the results of the tests which we did to solve our problems and check them if they resolved it or how much they have resolved it. Follow up experiments are a series of experiments which we will be conducting throughout the conversion optimization exercise. We will run many experiments in order to improve our website’s conversion rate.
History of A/B Testing
A/B testing throughout the years have evolved a lot. In 1995 there was not really A/B testing available, but instead people were collecting behavioural data from log files and by the year 2000 there was meta tag redirects. These redirects were not good enough because there was no cookie collecting back then and it was very difficult to collect data for A/B testing. In 2003 tools were developed for A/B testing, which were using cookies to track user behaviour. Google optimizer in 2006 changed the face of client side A/B testing and it helped people in running A/B tests. In 2010 A/B testing tools with drag and drop functionality came to the marketplace and attracted new people in A/B testing. In 2013 the rapid development in the client side framework domain caused problems in running A/B testing campaigns. With new frameworks like React and Angular websites moved to single page applications which made it difficult to test client side. In order to run A/B tests we started embedding better testing solution inside the frameworks to collect data effectively. Artificial intelligence based solutions hit the optimization marketplace in 2016 and made it really simple to run A/B tests and get personalized and segmented data. In 2020 we are facing data quality issues with client side testing using cookies along with ITP and ETPs to track users across different experiments. To solve this issue we are now embedding, testing solution at server level in companies.
