A common request I've encountered in support platform administration is the need to move beyond the built-in analytics dashboards to perform custom longitudinal analysis of AI deflection features. While platforms like Zendesk or Kustomer provide aggregate deflection rates, a rigorous assessment often requires exporting raw log data for time-series analysis, correlation with deployment events, or deeper cohort segmentation. This walkthrough details a methodical approach to exporting AI deflection logs—specifically, logs containing the decisioning for each support ticket—and piping them into Google Sheets for ongoing analysis. The primary advantage of Sheets here is its accessibility for collaborative analysis and its native integration with App Script for scheduled automation.
The process typically involves two phases: first, the extraction of data via the support platform's API, and second, the transformation and loading into Sheets. Most modern platforms offer APIs that expose conversation logs, including metadata tags indicating AI-handled interactions. Below is a conceptual Python script utilizing the `requests` library. This script would be scheduled via a cron job or a CI/CD pipeline (e.g., a nightly GitHub Action) to populate a timestamped log.
```python
import requests
import json
import datetime
import os
# Configuration from environment variables
API_KEY = os.environ['SUPPORT_API_KEY']
SUBDOMAIN = "your_subdomain"
SHEET_ID = os.environ['GOOGLE_SHEET_ID']
# API endpoint for search/conversations; adjust for your platform
url = f"https://{SUBDOMAIN}.zendesk.com/api/v2/search.json"
query_params = {
'query': 'type:ticket tags:ai_deflected updated>2024-01-01',
'sort_by': 'updated_at',
'sort_order': 'asc'
}
headers = {
'Authorization': f'Bearer {API_KEY}',
'Content-Type': 'application/json'
}
response = requests.get(url, headers=headers, params=query_params)
data = response.json()
# Transform for our analysis schema
rows = []
for ticket in data.get('results', []):
created = datetime.datetime.strptime(ticket['created_at'], "%Y-%m-%dT%H:%M:%SZ")
updated = datetime.datetime.strptime(ticket['updated_at'], "%Y-%m-%dT%H:%M:%SZ")
time_to_deflect = (updated - created).total_seconds() / 60 # in minutes
row = [
ticket['id'],
ticket['created_at'],
ticket['updated_at'],
time_to_deflect,
ticket.get('subject', ''),
ticket.get('priority', ''),
ticket.get('via', {}).get('channel', '')
]
rows.append(row)
# Here, you would use the Google Sheets API v4 to append `rows`
# Example using gspread library:
# import gspread
# gc = gspread.service_account(filename='credentials.json')
# sh = gc.open_by_key(SHEET_ID)
# worksheet = sh.get_worksheet(0)
# worksheet.append_rows(rows)
```
Once the data is flowing into a sheet, you can construct pivot tables to calculate weekly deflection rates, segment by ticket priority or channel, and track the mean time-to-deflection. Crucially, you can correlate this data with other system events by importing separate logs—for instance, merging with your deployment calendar from a CI/CD tool like GitLab CI. This allows you to test hypotheses such as whether deflection rates drop following a major service release, indicating potential gaps in the AI's training data for new features.
A significant methodological note: ensure your query captures both deflected and non-deflected tickets for a control group. A simple filter on `tags:ai_deflected` may suffice, but a more robust approach is to export all tickets and use a field like `satisfaction_prediction_score` to perform a more nuanced regression analysis within Sheets. The key is to treat the deflection log not as a simple success metric, but as a performance telemetry stream similar to application latency or build duration. This shift in perspective enables the same rigorous optimization techniques we apply to pipelines to be used in improving AI-supported agent efficiency.
Measure twice, cut once.