CUSTOMER SUPPORT

Escalation Sentiment Trend Warehouse + Weekly Brief

Nightly, scores the day's escalated tickets for sentiment and root-cause theme, writes the structured results to Snowflake.

CategoryCustomer Support
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNightly after-hours schedule
  • ActionPull day's escalated ticketsZendeskZendesk
  • ActionScore sentiment + root-cause theme as JSONOpenAI
  • ActionUpsert scored rows into SnowflakeSnowflakeSnowflake
  • LogicIf Friday, compute week-over-week delta
  • OutputPost weekly trend brief to leadershipMicrosoft Teams

What it does

Turns one-off escalation summaries into a durable dataset. Every night it enriches escalations with sentiment and theme, lands them in Snowflake, and once a week reports the trend so leaders see direction, not just today's snapshot.

When to use it

Use it when leadership asks "are escalations getting better or worse?" and you have no history to answer. Best for teams that want analytics-grade escalation data in their warehouse alongside a digestible weekly readout.

How it works

  1. 1A nightly schedule triggers after business hours.
  2. 2The flow pulls the day's escalated Zendesk tickets with their threads.
  3. 3An OpenAI step returns structured JSON per ticket: sentiment score, churn risk, and a root-cause theme tag (billing, bug, SLA, onboarding).
  4. 4An action upserts each scored row into a Snowflake escalations table for historical trend analysis.
  5. 5A logic step checks the weekday; on Fridays it queries the week's rows, computes the sentiment delta versus the prior week, and posts a trend brief with theme breakdown to the leadership Teams channel.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect ZendeskTickets, queues, knowledge base.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect SnowflakeWarehouses, queries, shares.
  4. 4
    Connect Microsoft TeamsChannels, chats, files.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

Run this workflow in your colony.

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