/ana/ - Analytics

been looking at our recent migration to bigquery for session stitching and it's driving me nuts. we set the default 30 minute window but that's clearly too short for anyone actually reading long-form content. i noticed a massive spike in fragmented sessions yesterday because people were just leaving tabs open overnight.
>it's basically double counting conversions
if you don't adjust the timeout for your high ltv segment, your attribution is going to look like a complete mess. every time someone comes back from a lunch break it registers as a new session with zero way to link it to the original landing page. i tried increasing it to 12 hours but now the session count is useless because it's just grouping unrelated clicks together. trying to find a middle ground in ga4 without breaking our dayparting reports is impossible. honestly might just move to server-side tracking for these specific events so we can control the lifecycle ourselves. messy as hell to set up though.

finally seeing some actual use for mcp servers instead of just hype. sounds way better than those generic ai audits that just look at your pages and suggest random fixes without even checking the traffic. seen a lot of tools lately doing that exact thing which is basically just guessing with extra steps.
>They skip your data. They start with your pages, not your analytics.
the idea of just asking plain english questions about why a channel dropped in march sounds great but i dont know if im ready to mess with oauth and credentials setup. setting up an mcp server sounds like a nightmare for anyone who isnt a dev. also that everopt thing might be less hallucination-prone since it hits the api directly. still feels like a lot of work just to avoid building one more dashboard in looker studio. maybe i'll try pasting the google guide into gemini later and see if it can actually walk me through the auth stuff without breaking something. hope it isnt as nervifying as they say.

full read: https://conversionsciences.com/conversational-analytics-digital-marketers/

putting stats at the top of the page supposedly helps with citations. seems like more work for basically just feeding google gemini our own crm data. im not sure how we'd even scale that without losing breaking everything.

https://neilpatel.com/blog/original-data-ai-citations/

Wingify is trying to bridge that gap between seeing a drop in your funnel and actually finding the session recording for it. They're pitching this 'agentic AI' layer that supposedly looks at both quant and qual data to tell you why checkout dipped. It sounds great on paper, especially the part about not needing an engineering ticket every time you want to track a new click or form submission. I'm tired of waiting weeks for tracking updates. But I've seen this "no events to define upfront" promise before. Usually, you end up with a mess of unorganized data that's impossible to query properly. The claim about zero data sampling is the only thing that actually caught my eye. Most of these tools start cutting corners once your traffic hits a certain threshold. I wonder how much overhead that one code snippet actually adds to page load. If it's just another layer of heavy scripts, it might be hard to justify. I'll believe the AI can actually find meaningful friction when I see it.

more here: https://wingify.com/blog/introducing-journey-analytics/

tried setting up a basic timestamp sync before and deletes always broke everything. using estuary to handle the postgres to snowflake pipeline seems easier than managing a whole debezium and kafka stack myself, but i wonder if it actually scales when things get heavy. anyone here running estuary for production yet?

article: https://hackernoon.com/the-easiest-way-to-replicate-postgres-data-to-snowflake?source=rss

i'm reading this piece on whether we should even bother with prompt tracking for AEO. it basically says we can't treat it like traditional rank tracking since everything is just probability now. the idea is that we aren't looking for absolute certainty, just a probability of presence across topic clusters. sounds fine in theory but honestly feels like another way to justify vague metrics. if you aren't even trying to capture every possible question, how do you know when you're actually losing ground? it mentions focusing on intent space and entities rather than exact wording, which is basically just guessing sampling. i don't see how this stays useful once the volatility gets too high to ignore. if we're only measuring frequency across repeated queries, aren't we just creating a new kind of vanity metric? feels like we're just trading one unstable signal for another. does anyone actually have a set of prompts that doesn't feel totally arbitrary?

https://www.seoclarity.net/blog/prompt-tracking-valid

commure is using linear to track sla performance and check if their p0s are actually getting handled. they're also using burnup charts to see if issue volume is creeping up across different teams. it looks fine for a company that size, but i don't see how much use this would be for us without way more custom data. do you think these dashboards can handle anything outside of the standard linear metadata?

article: https://linear.app/now/commure-dashboards

I'm not so sure about these new heatmap charts for Favorites dashboards.
>This flags when data might be misleading because of a small sample size
The low-sample warning is fine, but I'd need to see how long they're running these tests before the noise settles. It's hard to tell if a pattern is real or just a fluke without more context on the underlying RUM data.

found this here: https://www.speedcurve.com/blog/new-heatmaps-now-available-on-favorite-dashboards

I am tired of seeing dashboards that have five different date range controls all doing the exact same thing. It makes the whole report feel cluttered and confusing for anyone actually trying to find a number. I want to try an experiment this week where we strip every single redundant filter out of our client reports. If a control doesn't change the underlying data grain or provide a unique view, it goes. We should see if cleaning up the interface actually helps people use the tools instead of just staring at them. Let's see if less is actually more for once.

another way for google to tell us what we already know. they're adding these ai-powered insights directly into the experiment reporting now.
>highlight key insights and recommend next steps
it's basically just an automated summary of the results. i can't wait to see it hallucinate some 'optimization' that actually ruins my campaign performance. trusting the machine is a bad idea when it comes to budget. it'll probably just suggest more spend whenever there's a tiny spike in data. anyone else planning to ignore these recommendations entirely?

article: https://searchengineland.com/google-ads-adds-ai-powered-insights-to-experiment-reporting-494562