Walkthrough: Using the API to build a leaderboard for my SDRs.

Your starting point is solid, but the endpoint you're using likely isn't the most efficient. The `/meetings` endpoint with a `userId` filter will give you full meeting objects, which is often overkill for aggregation and will hit rate limits faster.

You should check if there's a dedicated `/analytics/summary` endpoint. Many of these services provide a summary endpoint that returns pre-aggregated metrics per user over a time window in a single call. That would let you fetch data for all your SDRs in one or two requests instead of N.

Also, on the metric calculation, I'd suggest weighting them. A raw filler word count per minute can be misleading on very short calls. Consider normalizing it against the total word count for that speaker, or applying a minimum call duration threshold before including it in the leaderboard calculation.

Your approach of aggregating talk ratio, questions, and filler words per minute is a logical starting point for coaching, but I'd caution that these metrics alone can create perverse incentives. An SDR can game a high talk ratio by monologuing, and filler word count can penalize natural conversation styles without necessarily correlating to poor outcomes.

A more sustainable model integrates these API-derived metrics with a conversion-focused outcome from your CRM, like meeting acceptance to next-stage rate. This creates a composite score that balances behavior with results. The long-term value is in identifying which specific conversational behaviors, as measured by Read AI, actually predict successful pipeline movement.

Be mindful of your vendor lock-in with these calculated fields. If you build your entire coaching system on "filler words per minute" and then switch vendors, that metric may not translate. Consider abstracting your key calculations to be based on more generic concepts you could source elsewhere.

Absolutely on point about perverse incentives, and vendor lock-in is the critical footnote everyone skips. We built a composite score last quarter but made the mistake of baking the Read AI metric weights directly into the logic. When we piloted a competitor's API for a month, half our formulas broke because their "interaction score" was on a 0-10 scale, not 0-1.

The real power move is that conversion-focused outcome you mentioned, but you need to be brutal about the correlation lag. We found that "questions per minute" this week might correlate with "meeting acceptance" next week, not the same call. That means your blending script needs to join CRM data from a future date, which feels weird but stops you from optimizing for last week's noise.

Abstraction is painful but worth it. We defined internal generic metrics like "conversational balance" and "engagement signals," then wrote adapters to map different vendor fields into those buckets. It's more code, but now the business logic stays put if we switch.

That's a very clean starting point for the data extraction layer. I'm working on a similar integration for our Tableau dashboards, and I've found the structure of the API response itself can become an issue for reliable aggregation.

Specifically, what's your strategy for handling missing or null fields in the `meetings` JSON array? In our tests, the `questions` field was sometimes an integer and sometimes absent entirely for meetings under a minute, which caused our initial aggregation logic to fail silently. We had to add a layer of validation on each meeting object before the aggregation step.

I'm also curious, in your `calculate_leaderboard` function, are you weighting each metric equally for a final score, or are you keeping them as separate dimensions for the SDRs to review individually? The latter approach seems more useful for targeted coaching, in my view.

Exactly. The perverse incentive is why we ended up deprecating our composite score after six months. It became a target, not a measure. SDRs would have two monitors: the dashboard and a notepad with the exact formula. They'd hit the numbers while the conversational quality, anecdotally, degraded.

Your point about vendor lock-in is crucial and often a second-order effect. It's not just switching vendors; it's when your primary vendor changes their calculation. We built on "average talk speed" from one provider, and after a 'model improvement' rollout, the baseline shifted by 15%. Our historical comparisons were instantly invalid. The abstraction layer we eventually built treated all third-party metrics as 'signals' with a confidence interval and a version tag, mapped to our own internal domain logic (e.g., 'conversational_dominance', 'disfluency_rate'). It was painful, but now we can plug in a new source or version with a remapping step, not a logic rewrite.

The real gold is in that correlation lag between behavior and outcome. We found that a high talk ratio in discovery calls *negatively* correlated with pipeline movement, but only after we aligned the meeting date with the opportunity stage date two weeks later. Most leaderboards just align on calendar week, which is statistically noisy. You need to blend on opportunity creation date, not meeting date, which requires a more complex join but surfaces the actual leading indicators.

Your core script highlights the exact moment where monthly bills start to creep. That linear API call pattern? It's a direct line-item to your CFO.

You're making `N` serial calls for `N` SDRs. That's fine at 5 users, but at 50 you're looking at noticeable runtime and hitting rate limits, which either breaks the job or forces you to add delays, making the "live" leaderboard a joke. The cost isn't just the compute for the script; it's the engineering time you'll burn in three months when this thing times out and you have to rebuild it with proper batching or concurrency.

And those metrics? Calculating ratios on the fly for every meeting, every run, is wasted cycles. These analytics platforms are built for batch queries. There's almost certainly an `/analytics/summary` endpoint that will give you aggregated stats per user over a date range in one or two calls. You're paying for the data, might as well get it in the most efficient shape.

> talk ratio, number of questions, and filler word count per minute
These are fun, but they're vanity metrics unless you're actively correlating them to pipeline movement from your CRM. Otherwise you're just optimizing for a number, not for sales. I've seen teams spend six figures on infra to track stuff that had zero correlation with won deals. Build the abstraction layer now, before the vendor changes their model and your historical comparisons evaporate.

You're right to question the vendor's definitions. I ran a similar validation last year and found their "interruption count" included any speaker change within 500 milliseconds, flagging natural conversation flow as an interruption. That metric was useless for coaching.

Your point on rhetorical questions is key. We saw the API count "How are you doing today?" as a question, which inflated scores without indicating genuine discovery. It's a data quality issue that directly impacts your cloud spend - you're paying to process and store metrics that don't align with your process.

Your example, while pragmatic, glosses over a critical technical dependency: the API's underlying data consistency. You're fetching `meetings` with a `userId` filter, but have you verified the data pipeline's latency? If their system processes meetings asynchronously, your `days=7` window might not be complete, and your leaderboard becomes a random sample of processed calls rather than the actual last week.

your aggregation runs on each execution. Without caching the raw meeting objects, you're recalculating historical data repeatedly, which is inefficient and risks introducing variance if their API's metric calculations are updated retroactively. A proper implementation should separate the raw data ingestion from the scoring logic, storing immutable snapshots to allow for reproducible benchmarking over time.

> Here's the core of the Python script I run as a scheduled job

That's your first mistake. Scheduled jobs using a linear, per-user API call pattern won't scale. You're going to hit rate limits and your CFO is going to ask about the bill when you have 30 SDRs, not 5. Look for a bulk analytics endpoint or implement proper concurrency now.

Also, aggregating filler words per minute from raw meetings is a waste of cycles. If the platform doesn't offer a pre-aggregated summary, you're paying to redo their computation. Validate the data pipeline latency too, or your 'last 7 days' will be missing unprocessed calls.

Straight to the point, I like it. But you're going to hate the next chapter. That per-user loop is a performance cliff waiting for you, and you've already got everyone here talking about data quality.

Before you even get to caching or scoring logic, run one basic check: fetch the same user's last two days of meetings twice, an hour apart. See if the metrics change. If they do, your "live" board is just a snapshot of their processing queue, not reality. Then watch the bill when you scale that linear call pattern.

Also, tallying filler words per minute yourself is paying twice - once for the raw data, once for your own compute to redo their math. See if there's a summary endpoint, even if it's not documented. Sometimes they exist but aren't in the official guides.

Yep, the retroactive updates are the silent killer. We built a scoring layer once and had six months of "improving" scores because the vendor was tuning their speech-to-text model in the background.

Your point about caching immutable snapshots is spot on. We started storing raw API responses with a timestamp and API version tag in S3. That way you can recalc the leaderboard with the logic from any point in time. It feels like overkill until your top SDR from Q3 suddenly looks average in Q4 because the metrics shifted.

The S3 snapshot approach is smart, but now you're your own data vendor. Have you built the compliance controls for it yet? You're storing call transcripts with PII, so you better have a retention policy, audit trail, and immutable deletion process for that S3 bucket. That "overkill" feeling just became a whole new project.