Social Influence Evaluation: Building Influencer Rankings and Commerce Scores via TikTok & Douyin API

✅ Introduction: Choosing the Right Influencer Is the First Step to Improving ROI

In the era of short-form video commerce, influencer marketing has become a critical strategy for brand exposure and sales conversion. However, many common misconceptions persist:

• More followers ≠ Stronger sales capability

• More videos ≠ Wider reach

• Lack of data analysis = Blind investments

So how can we use a data-driven approach to scientifically evaluate an influencer's "commerce power"? This article presents a reusable scoring system for influencers, powered by the TikTok and Douyin API provided by LuckData.

Section 1: Deconstructing Commerce Power — Three Key Dimensions

We define influencer commerce power as: Influence × Conversion Potential, composed of three primary metrics:

These dimensions provide a holistic view of an influencer's effectiveness beyond vanity metrics like follower count.

Section 2: API Data Collection — Using Python to Access LuckData Endpoints

To support this evaluation system, we use several APIs to collect real influencer content data. Below are three key use cases:

1. Get TikTok Influencer Video List

import requests
url = "https://luckdata.io/api/tiktok-api/user-post-videos"
params = {
"user_id": "example_user_id"
}
headers = {"apikey": "YOUR_API_KEY"}
res = requests.get(url, params=params, headers=headers)
videos = res.json()

Fields to extract for analysis:
play_count, like_count, comment_count, description, has_product_tag

2. Get Douyin Trending Influencers (For Initial Screening)

url = "https://luckdata.io/api/douyin-API/get_xv5p"
params = {
"city": "110000",
"type": "rise_heat",
"start_date": "20250518",
"end_date": "20250519"
}
res = requests.get(url, params=params, headers=headers)
hot_users = res.json()

This API helps identify rising influencers within specific cities and timeframes.

3. Get Douyin Video Details (To Detect Product Tagging)

url = "https://luckdata.io/api/douyin-API/get_pa29"
params = {
"type": "items,cnt,trends,author",
"item_id": "7451571619450883355"
}
res = requests.get(url, params=params, headers=headers)
video_detail = res.json()

This allows us to assess whether the video is commerce-enabled (e.g., contains product links or tags).

Section 3: Building an Influencer Commerce Scoring Model

Based on the data, we can construct a simplified scoring function to measure both engagement and commerce potential:

def score_creator(play_count, like_count, comment_count, has_link):
interaction_rate = (like_count + comment_count) / play_count
link_bonus = 1.2 if has_link else 1.0
score = interaction_rate * 100 * link_bonus
return round(score, 2)

This score increases when an influencer shows high interaction and has product links, indicating potential for driving conversions.

Section 4: Case Study — Comparing Three TikTok Influencers

Assume we collected data from three TikTok influencers over the past 7 days:

Despite having the most followers and views, the third influencer lacks both engagement and commerce intent, making them less suitable for product campaigns.

Section 5: Real-World Use Cases and Advanced Applications

1. MCN Agencies

   • Build internal KPI systems to evaluate influencer performance

   • Assess monetization potential for pricing and collaboration decisions

   • Guide influencers on improving content and commerce alignment

2. Brand & E-commerce Teams

   • Accurately match influencers with relevant product categories

   • Use scoring models to guide budget allocation

   • Continuously monitor performance to refine campaign strategies (e.g., A/B testing)

3. SaaS & Data Platform Teams

   • Integrate APIs into internal systems

   • Build influencer dashboards with BI tools

   • Offer data-driven services for clients to manage influencer relationships

✅ Conclusion: From Intuition to Data-Driven Influencer Marketing

As influencer costs rise and ROI becomes more uncertain, relying solely on intuition is no longer sustainable.

By leveraging APIs and Python, we can:

✅ Rapidly collect influencer data
✅ Build scoring models to evaluate commerce potential
✅ Make precise decisions to improve conversion outcomes

Shift from guesswork to data, and ensure every marketing dollar delivers maximum value.

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