The Accidental Discovery
Last week, while organizing thousands of buyer communication records in Google Sheets on a Windows device, something unexpected happened. We had 10,000 buyer contacts with email and message timestamps, but using only "replied" and "not replied" categories felt wasteful.
So I asked Claude Code:
Can you calculate response rates by time of day (morning/afternoon/evening/night)?
The Surprising Pattern
The code ran in 5 minutes, and the results were striking.
2-4 PM: 67% response rate
9-11 AM: 52% response rate
6-8 PM: 38% response rate
After 10 PM: 12% response rate
My first thought was "our buyers are busy in the afternoon," but thinking deeper, the meaning was different. It wasn't just that messages sent in the afternoon got higher responses. It suggested our questions at that time might be clearer, or buyer work patterns peak during those hours.
How We Did It
The process was straightforward.
Step 1: Copy timestamp column from Google Sheets
Step 2: Upload to Claude Code
Step 3: Request "calculate response rates by time of day"
Step 4: Results auto-populate into new sheet
What Claude Code did was extract the time from each message, sort into 4 time buckets, and count how many were tagged "replied" in each group. Manually, this would have taken a full day.
How We Used It
Now we knew something important.
Send critical questions between 2-3 PM. Schedule time-sensitive requests during peak response hours. The 12% late-night response rate tells us buyers are exhausted then, no point sending urgent asks at that hour.
A Non-Developer's Insight
I used to think "data analysis" meant memorizing Excel functions or learning statistics. Using Claude Code changed that. I realized: if I ask clearly what I want, the AI finds the answer.
This isn't programming. It's conversation.
If you have data piling up, hire a bot to ask it questions. The answer might change your next decision.