SUMMARIZATION
Weekly Zoom Talk-Ratio Digest from BigQuery to Slack
On a weekly schedule, aggregate stored Zoom call talk-ratios and objection themes per rep from BigQuery and post a team coaching digest to Slack.
How it runs
The automated pipeline, trigger to output.
- TriggerWeekly schedule
- ActionQuery last week's call metricsBigQuery
- ActionSummarize rankings and objection themesOpenAI
- OutputPost coaching digest to SlackSlack
What it does
Rolls up a week of Zoom sales-call metrics into a single coaching digest. It computes average talk-ratio per rep, surfaces the most common objection themes, and ranks reps so managers see trends, not one-off calls.
When to use it
Use it when individual per-call alerts aren't enough and leadership wants a recurring, comparable view of conversation quality across the team. Assumes per-call recaps already land in a BigQuery table.
How it works
- 1A weekly schedule trigger fires the run.
- 2A BigQuery query pulls the past week's call records, talk-ratios, and extracted objections per rep.
- 3An LLM summarizes the aggregate into rep rankings, notable talk-ratio outliers, and recurring objection themes with example quotes.
- 4The digest is formatted with per-rep highlights and a team average.
- 5The finished digest is posted to the sales leadership Slack channel.
Set it up
What you configure once, before turning it on.
- 1Connect BigQueryDatasets, queries, schemas.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect SlackChannels, DMs, threads, mentions.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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