Cohort analysis, one of the latest features offered by Google Analytics, has been creating quite a bit of confusion among the analysts. And, the confusion stems from the nature of this new feature.
For one, cohort analysis is not like page or session analysis, i.e., it's dynamic. So, it doesn't merely sum up your page or session activities over a certain timeframe, but rather the user groups' behavior over time.
Secondly, producing actionable insights from cohort analysis isn't that easy. For instance, the visitors are coming back to your site in the last few weeks, but how to use that information with other data drawn from analyses?
Let's discuss cohort analysis in depth today and clear out all that confusion.
What Exactly Is Cohort Analysis?
Bruno Estrella, who currently leads growth at Webflow, defined cohort analysis as one analytical technique that helps you analyze the behavior of a defined group of people during a specific period of time. As such, the aim is to uncover insights about customer experiences and figure out ways to improve those experiences. Let's understand this definition better through an example.
Suppose a customer named Tom came to your website three months ago when there was a fifty percent discount going on and brought a trial set of your products. You started using carefully placed cookies to track the behavior of people like Tom. You would like to know if they come back to buy stuff and how often they do that.
Now, when you sit down to analyze your cookies, you would want to figure out the number of users like Tom who came to your website and purchased the same trial set. You found out that about seventy percent of the buyers of the trial set didn't come back. It's time to think about ways to remedy the situation.
Firstly, you might consider that the buyers have forgotten you in all the information that they are inundated with on a daily basis. In such a scenario, running retargeted ads at the end of their use of the product might prompt them to buy more.
Secondly, the visitors might be stopping and dropping off from the 'shipping' page. The problem might be with your high shipping cost, and the solution lies in offers free shipping or discounts at that point.
So, the analysis gave you two clear ideas to improve your conversion rates for all such groups now and in the future. You need to take the right actions and note the improvements made in your conversion and retention rates.
And that's how you do cohort analysis for your website!
Three Major Aspects to Figure out Cohort Analysis
To better understand the practice of cohort analysis, you need to know about the three aspects that constitute the analysis. So, cohort analysis entails three highly specific features:
1. A Specific Period in The Past
Cohort analysis is strictly bound by time as it is about defining the group that entered your store at a particular time. Thus, you have to start with deciding the time period that has to be analyzed.
For instance, if you are planning to analyze customer behavior during a particular promotional event, as mentioned above, your cohort analysis would cover the entire period of the event. Added filters can be included in this analysis, such as whether you want to know how many people visited via Instagram or Facebook.
2. The Lagging Period of the Analysis
The lagging period refers to the time for which the analysis is run. So, if you are planning to analyze how the users behave for a month after their first visit, one month is your lagging period.
The number varies based on your business needs and the ongoing conditions of your company.
3. Termination Time of the Analysis
After both the cohort and the lagging period gets pointed out, the termination time of the analysis is dealt with.
So, if you're tracking your cohort's behavior between April 1st and 7th and have a month as the lagging period, the termination time is May 7th. This date signifies the final signal of the lagging period for the last person in a cohort.
Using Cohort Analysis Effectively In the Business
There is no denying that it's difficult to get business value from a single cohort analysis better than the other methods of analytics. Of course, your reactions can't solely be based on the cohort data.
Suppose you are going for the funnel analysis and note the rapid dropping off of some users from a certain part of your funnel. A retargeting campaign gets launched immediately, and you work towards patching up all that's wrong with the funnel. But the feedback cycle isn't that short with cohort analysis.
For instance, you can run a cohort analysis with a month-long lagging period and implement the improvements based on the user experience of a month. But it will take a month for you to actually see the result of your steps when the journey of the present cohort gets completed.
If you make further changes, it will take one more month to see the results. Now that is pretty slow in this fast-paced world of digital marketing. Making a single set of improvements and waiting for a month to see its effectiveness might not seem viable.
But, at the same time, it's true that you get a complete look at the journey of the users through the cohort analysis. It might be slow, but it's helpful in designing campaigns that showcase results immediately. You should also leverage the data from cohort analysis to create long-term value for the company.
Cohort analysis gets you the perfect combination between time-based campaign retrospection and continuous customer experience benchmarking.
Thus, the information derived from the analysis has a long-term impact on shaping the future campaigns and policies of the business. It also tells you whether or not to continue with your current campaign or launch something in a similar vein in the next quarter.
And that's all cohort analysis is about!
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