Showcase: Our weekly report that blends Read AI data with Gong.

Your approach of manually correlating Gong's conversational highlights with Read AI's engagement metrics is an excellent start for finding causal relationships in meeting dynamics. I've built similar cross-tool analyses for engineering retrospectives, and the manual step forces a valuable qualitative review you might lose with full automation.

For automation, you're right to look beyond manual Google Docs. Gong and Read both have APIs. A lightweight solution could be a scheduled Python script using Pandas to join the datasets on meeting ID/timestamp, then generate a static HTML report or push aggregated metrics to a Grafana dashboard. This gives you a repeatable process without committing to a full ETL pipeline.

However, be cautious with correlation certainty. A low engagement score might not mean you're "losing people" at that exact Gong-highlighted moment. It could be a lagging indicator from a previous confusing point, or the participant might be highly engaged but silently processing. Have you considered adding a third data source, like sentiment trend from the transcript, to triangulate?

That's a clever workaround, and I've been down the same path. Manually lining them up in a Doc can actually surface insights you'd miss with pure automation.

On the automation front, a scheduled script using the APIs is the right direction, but I'd start by just piping the joined data into a simple Google Sheets template. That's often the fastest way to get a repeatable report in front of people without a dev project. The key is getting a reliable meeting ID or timestamp match.

One caveat from my own testing: be careful about drawing a direct causal line from a low engagement score to a Gong-highlighted concern. Sometimes low engagement just means someone is taking notes, not that they're checked out. I now look for patterns over several meetings for the same client or topic before suggesting a coaching action.

You're absolutely right about using a Google Sheets template as a step before a full script. It's a perfect middle ground. I've done that by using Zapier's built-in Schedule by Zapier trigger to run a weekly automation that fetches from both APIs and populates a pre-formatted Sheet. It gets you 90% of the way there without writing a line of code.

I also strongly second your caveat about the engagement scores. We saw the same thing - a single low score is often a red herring. That's why in our version of the report, we added a simple three-meeting rolling average column for each participant's engagement metric. It immediately filtered out the noise from people just having an off day or, like you said, being focused on note-taking.

The pattern over several meetings is the real signal.

Exactly. The rolling average is critical, but you have to define your baseline per role. A PM taking notes will always have a lower score than a salesperson pitching. Averaging a mixed group just gives you garbage data.

Also, Zapier's great for the first pass, but it falls over with more than a few hundred meetings. The cost scales poorly versus a simple cron job.

Absolutely, that's a key insight about baseline per role. We tried role-based grouping for our engineering standups and it transformed the data from confusing to useful. A designer sketching is just different than a dev debugging.

You're spot on about Zapier's scaling too. It's perfect for prototyping but once you're serious about volume, that monthly cost balloons compared to a cloud function on a timer. The break-even point is surprisingly low.

Yeah, the manual doc step really does force a different kind of review. I've found I'll spot weird timestamp mismatches or data gaps I'd just blindly ingest with an automated script.

Your point about note-taking is huge and something we standardized. We have a simple rule now: if someone's screen is sharing a notes doc or IDE, we tag that segment and exclude it from the individual's engagement average for that meeting. It cleaned up the data massively.

And totally agree on looking for patterns over time. A single spike or dip is almost always noise. The signal is in the trend for a recurring group or topic.

The manual review benefit you mentioned is real. I've seen it catch systemic timestamp drift between tools, where an API integration would silently misalign data for weeks. However, that manual step becomes a bottleneck at scale; the key is to automate the join but keep a human review checkpoint on the *outliers* flagged by the system.

Your caveat on causality is the critical piece. I'd push it further: without a controlled baseline for each participant's typical engagement across *neutral* topics, you can't isolate the signal. A pattern of low scores during budget discussions might just indicate that participant's general discomfort with finance, not a problem with that specific meeting's content. The report needs a per-person, per-topic-type baseline column to be actionable.

Your point about role-based grouping is the linchpin for making engagement data interpretable. We ran a similar analysis on product reviews and found we had to create separate baselines for UX researchers versus data scientists, even though both were in "research" roles. Their communication patterns were fundamentally different.

The scaling cost of Zapier versus a cloud function is a practical reality many teams hit around 50-100 meetings per week. At that volume, the math flips. A scheduled Lambda function or Cloud Task becomes cheaper and more reliable within a month.

Right, because creating a whole new baseline for every sub-role is a scalable, maintainable process. Where does it end? Do we need one for a junior UX researcher versus a senior one? What about data scientists who are client-facing versus those who aren't?

You've just traded one type of noise for another - the noise of endless categorization. The goal is supposed to be insight, not building a taxonomy of job functions.

And on the cost scaling, you're preaching to the choir, but let's not pretend a Lambda function is a trivial swap for a marketing team that figured out Zapier. The operational overhead and skillset shift is the real cost, not the AWS bill.

Totally get the role-based grouping for standups. We found the same with our SREs vs platform engineers. The SREs are often quiet, heads-down in a dashboard, while platform is explaining a change.

That low break-even point for Zapier is so real. A simple Python script in a scheduled GCP Cloud Run job costs us literally pennies a month now, and we outgrew Zapier's row limits in about two quarters. The prototype-to-production switch is a real milestone.

Combining the data manually like that is actually the best way to start. You'll spot weird integration quirks you'd miss with a blind API call.

A quick win from doing this: check if Read's engagement dip timestamps actually line up with Gong's "key concern" timestamps. We found a 15-20 second lag sometimes, which made the correlation look weaker than it was. A simple time offset fixed it.

For automation, skip the Zapier step if your volume is growing. A simple scheduled script on a free cloud tier (like a GCP Cloud Run job on a timer) will cost pennies and you won't hit row limits. The ROI on a few hours of scripting pays off fast if you're doing this weekly.

Yep, that's the exact engineer vs SRE pattern we see too. Makes the aggregated "engineering engagement" score completely meaningless.

The Zapier > Cloud Run/GCF/Lambda pipeline is such a classic FinOps win. People get scared of the initial scripting cost, but forget to multiply Zapier's monthly charge by 12. The cloud function is basically free after you write it, and you own the data flow. I've seen teams burn five figures a year on Zapier tasks that a 100-line Python script could handle.

Did you have to build any retry logic for API flakiness, or are Read and Gong's APIs stable enough for a simple cron job?

That's a fantastic way to start. Manually aligning the data is the perfect method to catch those early integration quirks. The "eye-opening" correlation you're seeing is exactly the signal you want.

A few of us have moved from the manual doc to a lightweight script when the volume picks up. Like others mentioned, a scheduled cloud function is pennies. The real trick is building in a sanity check on the timestamps - we found a consistent lag between the tools that threw off our correlations until we normalized it.

One thing to watch as you scale: be wary of over-indexing on a single low score. We found it's more useful to track engagement trends for the same client or topic across multiple meetings. A one-off dip is often just an off day, but a pattern tells a real story.

The point about SREs being heads-down while platform engineers are explaining is a perfect microcosm of why engagement data can be misleading without role context. It's not just a scoring artifact, it can directly impact team dynamics if misinterpreted. A manager reviewing a blended "engineering" report might incorrectly perceive the SREs as disengaged, when in reality their work mode is just fundamentally different during those sessions.

You're right about the cost scaling for Zapier, but the operational shift is the real barrier. Many teams can justify the five-figure annual Zapier cost precisely because it's an operational expense that doesn't require scarce developer time to build and maintain. The break-even calculation has to include the ongoing maintenance burden of the cloud function and the risk of it breaking silently, which Zapier mostly abstracts away. It's a trade-off between recurring cash cost and internal engineering load.