A cohort can be defined as a group consisting of people sharing the same or common characteristics in a certain time period. For instance, People born on a day or in a specific period as in 1948 means they are making a birth cohort. This example clearly explains that within a defined period, people are sharing the similar characteristics. The point to be noted here is that cohort is particularly based on a date.
As the activity dates are not approachable in the segmentation or the reports that is why doing the cohort analysis with the Google analytics is very difficult. That is not the only reason, another issue is that the segmentation is not based on users but on visits and if we have the date dimension for creating a segment then the segment will be representing the visits matching the criteria only and won’t be returning the data for the users from all the visits matching the criteria.
It is extremely important to inject a date for doing the cohort analysis in the Google analytics, particularly the date as the conversion takes place in to Google Analytics and connect it with the user. The most famous dates which need to be collected are initial purchase date, the most current purchase date, signing up of the user for membership for the very first time and the date on which the membership was upgraded by the user.
1-Correct solution can only be found if you know the right question. So it is better to find out the question you need to answer. As the whole point revolves around getting the correct information so that the product, business, turnover and the user experience can be improved.
2-Need to identify the metric which will be helpful in answering the question. It is the requirement of a proper cohort analysis to have the identification of the event’s peculiar properties. This type of event may keep the checkout record of the user whereas if we consider the most advanced metrics then we come to know that they do not only have the checkout record but also have the record of the user’s payment.
3-In this stage, there is a need to define the particular cohorts which are pertinent. All the genuine users need to be analyzed for creating a cohort and target them so that the relevant differences can be found so that their behavior can be explained as a specific cohort.
4-Cohort Analysis can be done through the visual graphs which will help them determining the factor of why the revenues are decreasing as the reason is that system was not being used by the high paid advanced users because of the longer period of lag time. To cop up with this issue, the companies are now improving the lag time and have started handling and entertaining the more advanced users.