I've been trying out the new cohort analysis module in Ideogram for the past week, and I have to agree with the sentiment in the thread title. It feels like a promising feature that was released a bit too early.
The setup was straightforward, which I appreciated. I could segment my users by their sign-up date and track a core metric, like feature adoption, over their first 30 days. That part works. But the moment I wanted to go deeper, I hit walls. For instance, I wanted to compare cohorts based on their referral source (e.g., organic search vs. a specific campaign). The interface lets you define the initial cohort, but layering in a secondary behavioral filter for the analysis itself seems impossible. You have to create a whole separate, static cohort first.
This makes the analysis feel very rigid. In my event management work, understanding how different *types* of users (like those from a webinar sign-up vs. a paid ad) engage over time is the whole point. Right now, I'm basically just seeing if all users from week X are better or worse than all users from week Y, which isn't that actionable.
Also, the visualization options are limited. You get the standard grid, but exporting the data to manipulate elsewhere is clunky. I was hoping to use these insights to tailor my email marketing sequences in our CRM, but the data output isn't clean enough to easily map cohorts to different campaign workflows.
I really want to like this module because cohort analysis is so crucial for lead generation and campaign management. But in its current state, it's more of a high-level glimpse than a true analysis tool. Has anyone found a workaround for the segmentation issue, or are we all waiting for an update?
The rigidity you mention with secondary filters is a classic design flaw in early-stage analytics features. I've seen this pattern before where the initial cohort definition API is tightly coupled to the visualization engine, making runtime segmentation impossible.
You could try a workaround by exporting the raw cohort data via their events API (if available) and joining it with your source data in a separate tool like Metabase. It's a clunky pipeline, but it bypasses the UI limitation until they expose a proper filter DSL.
The lack of visualization options, especially for export, points to a backend that hasn't abstracted the data layer from the presentation layer. They probably built the grid as a one-off component.
benchmark or bust
The secondary filter problem is just a symptom. Your real issue is that you're locked into their data model.
If you can't slice cohorts by source during analysis, the feature is a dead-end report, not a tool. They baked a specific business question (how do *all* week X users behave?) into the architecture.
Building a dozen separate static cohorts is a maintenance trap. Until they expose a proper query layer, you're stuck.
Simplicity is the ultimate sophistication
Oh that's a really good way to put it - a dead-end report, not a tool. I haven't used it enough to feel completely locked in yet, but I can see how building static cohorts would become a mess fast.
When you say "query layer," do you mean like a place where you could write a simple filter statement? That's what I'd need to compare, say, users from a product hunt launch versus a newsletter signup within the same week.
It's a shame because the initial setup *feels* flexible.