Showcase: Visualizing channel crossover effect with simple network graphs.

Good catch on the missing bracket, that'll definitely break the script.

On pairwise vs permutations, I'd add that `pairwise` also handles the sequence order naturally for building directed edges. With permutations, you have to manually filter out the reversed pairs later, which adds unnecessary code.

I use pyvis for exactly that reason - initial exploration. It's great for spotting obvious clusters or outliers before you lock down a static layout.

I love this idea of mapping connections instead of just assigning credit. That snippet is a great starting point.

I have a question about the core logic, though. You're using `permutations` from the itertools library. Doesn't that create edges for every possible order between any two channels in a path, even if they didn't happen sequentially? For a path like Social -> Email -> Direct, wouldn't permutations also create a weak Social -> Direct edge, skipping the Email step in between? Should we only be connecting channels that actually appeared next to each other in the sequence?

Yes, that's correct. The original post recommended using `pairwise` instead, which only connects adjacent steps. Using `permutations` creates noise by connecting non-sequential steps, inflating edge weights with misleading signals. Your Social -> Direct edge example is spot-on; it shouldn't exist in the graph.

Stick with `pairwise` for sequence analysis. The extra edges from `permutations` just obscure the actual flow patterns.

A scheduled notebook in Datadog is a slick workaround, I'll give you that. But you're just moving the hard part upstream. If your log query logic for sessionization and path building is wrong, you're feeding perfectly scheduled, perfectly formatted garbage into the graph.

The real risk is that a clean-looking automated output lends false credibility. Everyone nods at the pretty graph, forgetting it's built on a dozen untested assumptions hidden in a SQL query.

Interesting idea. The snippet seems cut off though, right after 'Social', 'Organic', 'Direct']. I don't see the actual graph creation code.

Assuming the rest builds the graph, I'm new to this. Could you explain how you calculate the edge weight? Is it just a count of paths where both channels appear, or do you consider the order? I think order would matter for influence.

That's a really crucial point about normalizing against the base rate. The lift metric suggestion makes a lot of sense to filter out that noise.

But when calculating lift for an edge like Paid Search -> Direct, what denominator do you use for the expected co-occurrence? Would you use the overall conversion count for the source channel, or do you need to consider the independent probabilities of each channel appearing in any position? I'm worried about building another flawed assumption into the calculation.

Great point about the procurement angle. That data portability clause is something teams often overlook until it's too late. On identity gaps, you're right that fragmentation is a major headache. One approach I've seen work is using a probabilistic stitch based on time windows and device fingerprints as a fallback. It's not perfect, but it can reconnect some of those broken paths that otherwise create phantom channels in the graph.

That probabilistic stitch is a solid fallback, and you're right about fixing phantom channels. The catch is the tuning time for the match thresholds. If you set it too loose, you start stitching unrelated sessions and create misleadingly strong edges in the graph.

We used a similar approach but had to validate it with manual sampling for a few weeks. It's a bit of a time sink, but it beats having half your paths cut off because of a single missing ID.

Manual sampling for validation makes a lot of sense. That tuning phase must be difficult to balance across different user segments, though. For example, a threshold that works for high-engagement return visitors might be far too loose for new, anonymous traffic.

How did you structure that manual review process? Did you set up a separate dashboard to flag stitched sessions for human verification, or was it more of a random daily audit?

Your snippet is cut off mid-data, but the use of `permutations` is a problem. It'll create edges between non-adjacent channels, like connecting Social to Direct when Email is in between.

Use `pairwise` from `itertools` instead. It only connects consecutive steps. Also, weight edges by the raw count of those adjacent occurrences, not just co-presence in a path. That visualizes the actual flow.

Absolutely, the `pairwise` vs `permutations` distinction is critical for clean data. I'd add that the problem with `permutations` goes beyond just creating noisy edges - it can also artificially inflate the importance of channels that act as common "hubs" in a path.

A channel like 'Email' might appear in many conversion paths, and with `permutations`, it would create a direct edge to every other channel in that sequence, making it look like a super-connector when, in reality, its actual sequential influence might be limited to the step right after it.

Sticking with `pairwise` gives you a much more honest view of the handoffs. Good catch!

Totally agree on pyvis for exploration. I use it to get that first interactive feel, then export to a static plot with matplotlib or networkx for the final report. The force-directed layout helps spot patterns you might miss in a fixed drawing.

> pairwise also handles the sequence order naturally

That's a key point. It lets you focus on the actual flow logic instead of cleaning up data. I've seen scripts balloon because someone started with permutations and then had to add a ton of logic to dedupe and filter direction. Pairwise just gives you the right edges from the start.

Exactly, the force-directed layout is key for exploration. I've found pyvis especially useful for spotting clusters of weak edges that hint at a common third channel acting as a bridge, which a static diagram might just present as a hairball.

One caveat with that interactive export to static workflow - make sure you're capturing the node positions from pyvis. Exporting just the graph structure to networkx and letting it re-layout can sometimes scramble those emergent patterns you wanted to preserve. I usually serialize the positions dictionary.

Yes! The flow of actual user journeys is what matters, not just every possible combination. Pairwise respects that timeline.

I love that pyvis workflow for spotting those early patterns. One thing I've started doing is using a light gray for weaker edges in the interactive view, just to keep the visual noise down while I'm exploring. Helps the real connections pop.

That's a neat idea. But honestly, you're just building another attribution model. The edges are just a weighted version of path frequency.

If you want to see actual influence between channels, you need a proper experiment. This graph will just show you what channels are in the same paths, not whether one causes the other. Feels like a prettier correlation matrix.