Check out what I made: a heatmap of deflection rates by time of day

Hey everyone, been deep in the data again 😅. I was testing the new analytics API from one of the big support platforms and noticed their "deflection success" metric felt a bit... flat. It's just a daily average, which hides so much.

So I built a quick script to pull hourly deflection rates over the last month and plot them as a heatmap. The results are actually pretty fascinating!

Here's a snippet of the core logic using Python and the platform's API (anonymized):

```python
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Pseudo-API call structure
def get_hourly_deflections(date):
# Returns dict of hour: {interactions: x, deflected: y}
# ...
pass

# Aggregate data
data = []
for day in date_range:
hourly = get_hourly_deflections(day)
for hour, metrics in hourly.items():
rate = metrics['deflected'] / metrics['interactions'] if metrics['interactions'] > 0 else 0
data.append({'date': day, 'hour': hour, 'rate': rate})

df = pd.DataFrame(data)
pivot = df.pivot_table(index='hour', columns='date', values='rate')
sns.heatmap(pivot, cmap='YlGnBu')
plt.show()
```

**Key observations from my dataset:**
* **Post-lunch slump (2-4 PM):** Deflection rates dip noticeably. Are agents less precise with AI-suggested replies, or is customer intent different?
* **Early morning (7-9 AM):** Surprisingly high deflection. Maybe simpler, repetitive issues come in first thing?
* **Friday afternoons** are a distinct cool patch in the heatmap. Could be a training opportunity.

This feels way more actionable than a single number. You could use it to:
* Schedule training or prompt-tuning sessions for low-deflection windows.
* Test if different AI reply presets perform better at different times.
* Correlate with ticket complexity metrics (if available).

Has anyone else done deep dives on temporal patterns in your support AI metrics? I'm curious if this "trough" pattern is common, or if it's just my sample. Thinking of running the same for Claude vs. GPT-powered auto-suggest next.

--experiment