← Article List
Nest Article · 사례

Google Sheets Started Predicting 'Buyer Response Rate'

시리시리·2026-08-19
⚙️ Nest Article #250

Google Sheets Started Predicting 'Buyer Response Rate'

We built an automation system that predicts buyer responses before email follow-ups are sent. Using Claude Code and Google Sheets, based on 4 weeks of pattern learning, we score response probability and help the team prioritize outreach more effectively.

시리

시리

시리

🪺 Bella's Nest Article

Before: Days of Waiting in Vain

We sent 50 buyer emails every week, but had no idea when replies would arrive. We wasted time tracking emails that never got responses. The team could only answer 「Will this buyer likely reply?」 based on experience and intuition.

After: The Signal the Scorecard Speaks

We collected 3 months of email metadata (subject line length, body word count, send day, buyer industry) and actual response outcomes, then trained Claude Code to learn the patterns. Now every new email automatically receives a score.

Buyer Response Prediction Score System

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

80+ points: High response likelihood within 48 hours (intensive tracking)

50~79 points: Response within 1 week (regular check-in)

Below 50: Cool-down period (resend after 2 weeks)

What Actually Changed

1. Contact timing became precise

Emails sent on weekday mornings tend to score high, while Saturday sends score low. The team now concentrates efforts on Tuesday through Wednesday mornings.

2. Non-responsive buyers identified quickly

Buyers consistently scoring in the 40s signal zero interest in our products. This silent nudge tells us to stop chasing and move to fresh prospects.

3. Team psychology shifted

Instead of applying equal effort to every buyer, the team now wonders 「Why hasn't this 75-point customer responded yet?」 and starts experimenting with better email copy.

Surprising Discoveries

Response patterns vary by industry. Some sectors show 80% reply rate on first contact, while others only respond to second emails.

Buyer names containing certain regional markers boost response probability by 10 points.

Overly long body text (300+ words) drops predicted scores by 15 points.

Next Phase

Google Sheets scoring automation works well, but still requires some manual calculation. We plan to use Claude Code to convert this into a Windows background task, so every morning at 6 AM, all yesterday's emails will be automatically scored.

The most striking thing is that the bot sees patterns before we do. Now we're learning to read its signals.