Saturday Morning Data: What Weekend Traffic Reveals About Your Hidden Users

#apify #webdev #datascience #buildinpublic

It's 6 AM on a Saturday. Most of my scrapers are quiet.

But one isn't.

The Numbers (D+15, March 28)

Total runs: 10,721

Actor	Total Runs	Weekend Pattern
naver-news-scraper	7,539	Near zero Friday night
naver-place-search	1,087	+177 Friday night alone
naver-blog-search	726	Low
naver-blog-reviews	601	Moderate
Others	768	Minimal

naver-place-search ran more between midnight and 6 AM on a Saturday than naver-news ran all day Sunday.

Two Hidden Users in the Same Traffic

I never surveyed my users. I don't know their names. But I know their schedules.

naver-news users work office hours.

They're running scheduled pipelines — media monitoring, brand intelligence, market research. The kind of automation that runs Monday to Friday, gets reviewed by a human on Tuesday morning, and completely shuts down over the weekend.

These are B2B users. They have a boss. The boss takes weekends off.

naver-place-search users don't care what day it is.

Someone was querying Korean place data at 3 AM on a Saturday. Either:

A consumer app that runs 24/7 (restaurant recommendation engine, travel planner)
Someone in a timezone 9+ hours away from Korea who considers this their working hours
An automated scraper with no sleep schedule

Either way, this isn't someone with a Monday standup.

What This Means for Pricing Strategy

The day/night pattern I wrote about before suggested enterprise demand. This weekend pattern adds nuance:

Not all actors serve the same market.

naver-news: Likely enterprise/B2B. Monday-Friday demand. Could support premium pricing, SLA, usage limits.
naver-place-search: Mixed. Consumer apps or globally distributed teams. Different pricing tolerance, different support expectations.

If I'd built one product for both user types, I'd have optimized for the wrong one.

The Insight I Didn't Plan For

I built 13 Korean data scrapers. I expected people to use them for data extraction.

What I didn't expect was that usage patterns would become my market research.

I don't need a survey. I don't need user interviews. The traffic pattern IS the interview:

When do they run?
Do they pause on weekends?
Do they run in bursts or steady streams?
Which actors do they combine?

Every API call is a data point about who's using it and why.

Current State (D+15)

Total runs: 10,721 and climbing (slowly — it's Saturday)
Revenue estimate: $108-128
Plateau insight: Weekends show the ceiling of organic distribution.
Next threshold: Weekday traffic resumes Sunday night (Korea time). Real test: does Monday bring another spike?