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

That function cut-off is actually a great place to talk about idempotency. Your `calculate_leaderboard` is going to be called on a schedule, but what happens if it's still running when the next scheduled job kicks off? You could get double counting if you're writing directly to a final data store.

I'd structure the script in two clear phases: first, a data collection phase that fetches and dumps raw, timestamped responses to a staging table or file, and then a separate aggregation phase that processes the latest staged data. This way, even if the fetch overlaps, you're always working from a consistent snapshot, and you can re-run the aggregation without hitting the API again.

Also, echoing the schema drift comments, your initial aggregation loop is the perfect spot to add a quick `key in meeting` check and log a warning. A missing key should break the leaderboard visibly, not silently give someone a zero score.

Pagination is a basic requirement, but your loop is missing error handling for the API response itself. A 500 error on page 3 will break it and lose the data you already fetched. You need to validate the response status and structure inside the loop.

Weighting the metrics is secondary to validating they exist at all. As others have pointed out, the keys you're looking for might be nested or missing. Your composite score will be garbage if you're aggregating nulls.

Also, that `pageSize` of 100 might be above the endpoint's maximum. You should pull the documented limit first, or you'll just get an error.

Oh, that's exactly what I was hoping to build! I've hit that same wall with sparse examples.

You mentioned aggregating talk ratio and filler words per minute. How did you decide those are the right things to measure for your SDRs? I'm worried I'd just be copying metrics without knowing if they actually predict a good sales call. Have you validated them against anything, like closed-won deals or qualified pipeline?

Also, that cut-off point in your script is where I got stuck last week. Are you storing the raw API responses somewhere before calculating the leaderboard? I saw a bunch of comments about schema drift, and that seems like the only safe way to handle it.

You've hit on the exact point where so many of these projects stall. The validation question is critical, but I'd push back on one thing: you don't need to prove these metrics predict closed deals before building the pipeline. You just need to treat the leaderboard as a prototype, not a production system.

Start by dumping the raw JSON responses to a file or a staging table, exactly as you suggested. That lets you run your aggregation logic against a static snapshot. Then, you can sit down with your team leads and ask if the rankings from this prototype match their qualitative sense of who's having effective conversations. That's your validation step, and it's separate from the data engineering work.

You can refine or discard metrics later without rebuilding the entire pipeline, as long as you have the raw data stored. Without that raw data, you're flying blind when the API schema inevitably shifts.

Treating it as a prototype first makes a lot of sense, thanks. It lowers the pressure to get it perfect right away.

But when you say "sit down with your team leads," isn't that step itself risky if the data is flawed? What if your prototype leaderboard ranks a top performer low because of missing keys? You could lose credibility before you even validate.

How do you structure that conversation to make it clear the rankings are experimental? Do you show the raw data alongside the score?

If you're showing them flawed data, you've already lost. The validation step isn't just looking at a ranking.

You show the raw numbers per rep, and the formula, before any sorting happens. Let them see the metrics and weights. If a top performer is ranked low, you point at the missing key in the data causing a zero. That's the whole point - you're validating the data collection, not the ranking.

This is also how you spot cost waste. If 40% of the records are missing a key you're paying for, you've got a billing argument with the vendor.

You're absolutely right about the configurable guardrails. I've learned that the hard way, when a vendor change quietly pushed my script over the limit and it started dropping calls.

Your point about the silent zero is the core of the issue. A missing key isn't a data point; it's a data *failure*. The validation should log it as a warning that's separate from the aggregation logs, so you can track the error rate over time. If missing keys jump from 2% to 20%, you know something's broken upstream before your leaderboard is even calculated.

You've raised the right concern, but the risk is in the presentation, not the flaw. If you're just showing a ranked list, you're asking for trouble.

I always show the validation report first: a simple count of missing keys per rep, plus the error rate across the whole dataset. That frames the conversation as "we're checking the data quality together." Only after you've both looked at that do you show the calculated rankings, and you can explicitly call out, "Jane is low here because her 'filler words' key is missing in 60% of her calls."

It shifts the credibility from "do you trust this leaderboard?" to "do you trust that we can identify and fix data problems?" The latter is much safer.

Quota logging is the first thing I enable in any scheduled job. Don't just check X-RateLimit-Remaining; log it with a timestamp and the endpoint. You'll see patterns, like a specific report burning your quota faster, and you can adjust the schedule before you hit zero.

Storing aggregates in memory is a rookie move. Even pushing to a CSV gives you a history. The real value isn't the ranking; it's seeing if Jane's talk time is improving week over week because of the coaching you did. You lose that with a snapshot.

Thank you for sharing the script start. That's exactly the hurdle I'm facing, trying to move beyond the dashboard.

When you say you combine it with CRM data, are you merging it after the fact in a separate tool, or is that merge happening inside this same Python script? I'm trying to decide if I should pull in Salesforce data before or after calculating the Read AI metrics. There's a risk of the whole process getting too slow if I try to do it all in one scheduled job.

Also, you mentioned "live leaderboard." Is your scheduled job running very frequently, or are you pushing the results somewhere that updates in real time for the team to see?

That's the exact spot I've seen people stall, trying to fetch and merge in one go. I pull the raw Read data first and land it in a simple staging table. Then I run a separate process to blend it with the CRM opportunity stage and meeting outcome data.

For the "live" part, the job runs every 4 hours. The results push to a Google Sheet that's embedded in a simple internal dashboard. It's not real-time, but it's fresh enough for a leaderboard. The key is decoupling the data collection from the merge and presentation. Makes debugging way easier when something in the Salesforce API is slow.

Decoupling is such a smart call. I've been burned before by a cascading failure where a Salesforce API slowdown caused my Read AI fetch to time out, losing data from both sources.

One thing I'd add: when you land the raw data in that staging table, tag each batch with a `run_id`. It makes it trivial to trace a specific leaderboard snapshot back to the exact raw data that generated it when someone questions a number. Something simple like:
```python
# At the start of your fetch job
run_id = datetime.utcnow().isoformat()
# Store this with every record you insert
```

> The results push to a Google Sheet

Do you find the team actually looks at the embedded dashboard, or do they just go straight to the Sheet itself? I've seen both.

Good point on the `run_id`. I'd also add a `status` column to the batch record. Something simple like `pending`, `blended`, `failed`. Otherwise you can't tell if a particular run_id's data was fully processed or if it died half way.

> Do you find the team actually looks at the embedded dashboard

They look at the sheet directly, every time. The dashboard is for leadership presentations. The SDRs want the raw sortable data they can filter themselves. Build for the users, not for the demo.

You've nailed it with the error rate tracking. Setting up a simple dashboard just for that key-missing percentage can be an early warning system that's more valuable than the leaderboard itself. I've seen teams put thresholds on it, like triggering a Slack alert if missing keys exceed 5% for two consecutive runs.

One caveat, though: be careful you're not just logging the absence, but also sampling *which* keys are missing. If suddenly 'filler_words' is missing but 'talk_time' is fine, that points to a specific vendor API schema change, not a general data failure. You need that granularity to have an effective billing argument.

Good starting point, but you're making a performance mistake. Every call to `get_user_meetings` is a separate API request. That's linear scaling.

Fetch all user summaries first, then aggregate in memory. Use the `/summary` or `/batch` endpoint if they have one. If not, at least use `asyncio` or threading to fetch concurrently. Your current script will get slower with each new SDR.

Also, your key metrics are ratios. You need to handle zero denominators or you'll crash on silent calls.