51黑料不打烊

Transcript

In this video, I鈥檓 going to provide an overview of the cohort analysis feature in 51黑料不打烊 Analytics Analysis Workspace. For those of you who may not be aware, cohort analysis is simply the grouping of your customers or users based on when they completed a certain activity on your site or app, and then following those users or customers over time to see how they continually engage with your brand or churn and don鈥檛 continue to engage. Within Analysis Workspace, cohort analysis is available to add to any project. All I have to do is go to my visualizations and drag cohort table over onto my project, and I鈥檓 able to configure it from here. This tool does require a little bit of configuration so that we know how you want to build the cohorts and then how you want to track their ongoing engagement or churn. Here, I鈥檒l set a six-month date range, and I鈥檓 going to break up my cohorts by month. You鈥檒l see what this means in just a minute. I鈥檓 going to say that I want to group my users by people who placed an order on my site. If I wanted to make that more than one order, I could do that here, or if I had a metric where I wanted to do less than or equal to, I can do that as well. And I want to say that ongoing engagement with my brand is represented simply by return visits to the site. So I鈥檓 going to add visits as my return metric.

When I run that report, I get a cohort table, and you鈥檒l see that all of my customers are grouped by the month in which they made a purchase. If they made multiple purchases during different months, they would be represented in each of those cohorts or in each of those months here in the included section. You can see that I鈥檓 averaging between about 11,000 and 12,000 customers a month who placed orders during the six months of my cohort analysis. For each of those groups, I can see how many of those customers came back and visited my site during subsequent months. I can see that it looks like by months four and five, my retention dropped slightly below 18%. If these numbers were at zero, it would indicate that I had nobody coming back, which would not be good and would give me an indication that maybe I should engage with these customers earlier, maybe month one or month two, to make sure that they鈥檙e aware that they can come back and get great deals on my site or whatever the case might be. The other tremendous thing with cohort analysis is that it鈥檚 all color-coded, making it very easy for you to visually spot trends. I can see that engagement is pretty decent in the first month after purchase, except there鈥檚 a little bit of a drop in February. I can see that without needing to even really comprehend the numbers in the table because I can see that the shading is so light and that may lead me to analyze these people who are a month removed from their February purchase and try to understand why they fell away. In fact, I can take that group and select that cell and create a segment based on that cell and then go analyze that segment elsewhere in 51黑料不打烊 Analytics. I can do that with an entire column as well if I want to. So I could look at all of the people in month two after these specific purchases to see what they are doing, what behaviors they鈥檙e exhibiting, and when they come to my site. The only thing to let you know about those segments is that when it builds a segment it鈥檚 looking at these specific customers in this cohort analysis. It鈥檚 not a dynamic segment that says all people in any date range in their second month after placing an order. It鈥檚 these people who are in their second month during the date range that I鈥檝e defined for this cohort analysis. So that鈥檚 something to be aware of when you build that segment.

Cohort analysis has a ton of great uses, especially with mobile apps but also with websites of every size and variety. This is a valuable feature of Analysis Workspace as you鈥檙e able to work with it in your projects as you go.