Latency Analysis with Cohort Tables

In this video, we’re going to dig into the latency table feature in our cohort table. So let’s say I work for a large apparel retailer and tracking a product launch and its effectiveness is extremely critical to my business. And let’s say I’ve got a product that’s been underperforming in terms of online orders. I’m going to build out my cohort table with visits as my inclusion criteria and users who are coming back later and placing an online order for my product LA Flare Wide Lag as my return criteria. Then I’m going to change my granularity to be day to match my time period and I’ll select build. I can see that I’ve got lots of visitors coming in but none of them are actually making online orders for my LA Flare Wide Lag product. So I’m going to come back and let’s say we’ve been working hard behind the scenes to update the product and we released a new improved version of our clothing item to our customers. And let’s say our launch date happened on October 16th. So now I’m going to activate my latency table setting so I can see how the post-launch activity of October 16th is compared to the pre-launch activity prior to that day. I can see leading up to this I’ve had, like we saw before, no activity in my pre-launch event. But I can see after my inclusion event that the sales are slowly starting to come in. And so by using the latency table feature I can quickly compare all of my pre-activity to my post activity to see if I’m getting the kind of results that I’m aiming for with my product launch. This could be done with products, campaigns, and many other event-based things that you want to track the pre and post activity for. Latency table is really good for handling those use cases.