Case Study: Automating High-Frequency TikTok Discovery at Glinteco

Our clients often ask for more than just raw data; they need algorithmic momentum. Finding a TikToker with followers is easy, but finding a creator who is currently being favored by the "For You Page" (FYP) requires looking at posting frequency.

This case study breaks down a sophisticated, two-step automation we built using Python and the Apify SDK to find "Multi-Daily" posters, the high-output creators who drive the most consistent brand ROI.

The Challenge: Finding the "1%" of Creators

For a recent campaign, a client needed a list of TikTok creators who weren't just "famous" but were hyper-active. The target criteria included a minimum of 100,000 followers and a posting velocity with an average of 1.5+ posts per day (Multi-Daily). Manually checking a creator's profile to calculate their "average posts per active day" takes minutes per person. Scaling this to thousands of profiles is impossible without automation.

The Solution: A Two-Step Scraping Pipeline

We utilized the clockworks/tiktok-scraper on Apify to build a Python pipeline that searches for content, filters by followers, and then performs a deep dive into posting frequency.

Step 1: Niche Discovery & Follower Vetting

First, we scan specific niches (e.g., #Tech, #Lifestyle) to find creators who already meet the 100k follower floor. By passing niche-specific queries into the script, we generate a set of candidate usernames.

# Part of the Glinteco pipeline: Searching for candidates step_1_input = { "searchQueries": ["gadget reviews", "tech hacks"], "resultsPerPage": 10, "searchSection": "/video", }

run1 = client.actor("clockworks/tiktok-scraper").call(run_input=step_1_input) dataset1 = client.dataset(run1.get("defaultDatasetId")).list_items().items

# Filter: Only keep creators with > 100,000 fans candidates = {item['authorMeta']['name'] for item in dataset1 if item['authorMeta']['fans'] > 100000}

Step 2: The "Multi-Daily" Velocity Check

Once we have our candidates, we perform a second scrape to pull their recent video history. We then apply a custom Python function to calculate their Average Posts Per Active Day. The Glinteco Logic doesn't just count total posts; we group posts by date to see how many videos they drop on the days they are actually active. If they average 1.5 videos/day, they are a high-momentum lead.

def is_multi_daily_poster(videos):

    daily_counts = defaultdict(int)

    for v in videos:

        date_str = v.get("createTimeISO").split("T")[0]

        daily_counts[date_str] += 1

    

    total_posts = sum(daily_counts.values())

    total_active_days = len(daily_counts)

    

    # Calculate Frequency: Must be 1.5 or higher

    return (total_posts / total_active_days) >= 1.5

The Results: Data-Backed Outreach

By moving from manual scouting to this automated Glinteco workflow, the results were transformative. We achieved 100% data verification while increasing profiling speed from 5 minutes per creator to roughly 2 seconds. This produced a clean, automated CSV ready for immediate outreach.

Why "Multi-Daily" Matters

The TikTok algorithm rewards volume. By identifying creators who post multiple times a day, we ensure our clients are partnering with accounts that have the highest "shots on goal" for going viral. This data-driven approach removes the guesswork from influencer marketing.