Creating a High-ROI Short Video Advertising Strategy: How to Accurately Evaluate Influencer Effectiveness Using the Douyin API

2025-05-06

In today's rapidly evolving short video marketing landscape, influencer promotions, brand collaborations, and recommendation-style content have become key tactics for brands aiming to capture user attention. However, assessing the real value of influencer collaborations is far more complex than simply organizing a shoot. Which videos gained traction organically? Which influencers have inflated delivery capabilities? What content structure drives the most conversions?

The answers lie in the data.

1. Common Pain Points in Influencer Collaborations

When brands collaborate with influencers, they often face the following challenges:

Blind Selection: Relying solely on follower counts, while ignoring engagement rates and historical conversion performance.
Delayed Data: Receiving campaign performance reports a week later, missing the optimal window for optimization.
Single-Faceted Evaluation: Focusing only on views and likes, ignoring content structure and comment feedback.
Difficult Comparisons: Inconsistent pricing and performance across influencers make it hard to determine who offers the best value.

This calls for a data-driven influencer evaluation system that spans pre-launch, mid-campaign, and post-campaign stages.

2. System Goal: Influencer Value Scoring with Douyin API

With the help of the Douyin API, we can:

Access influencers' content publishing history in real time.
Retrieve engagement data (likes, comments, shares) for historical videos.
Analyze comment sentiment to assess authentic user reactions.
Build performance models such as CTR and estimated ROI.
Quantify influencer collaboration value through a scoring model.

The final output is an “Influencer Collaboration Dashboard” serving these scenarios:

Influencer selection (pre-campaign)
Real-time performance monitoring (mid-campaign)
Reinvestment value evaluation (post-campaign)

3. Pre-Campaign: Building an Influencer Pool and Preliminary Evaluation

In the pre-campaign stage, we first collect basic data on potential influencers.

1. Use City/Industry Rankings to Identify Quality Influencers

import requests
def get_top_authors(city_code='110000', type='rise_heat'):
url = 'https://luckdata.io/api/douyin-API/get_xv5p'
headers = {'X-Luckdata-Api-Key': 'your_key'}
params = {
'type': type,
'start_date': '20250425',
'end_date': '20250426',
'page_size': 20,
'city': city_code
}
resp = requests.get(url, headers=headers, params=params)
return resp.json()['data']

You can construct your influencer list based on different dimensions such as “fastest rising,” “highest engagement rate,” or “originality of content.”

2. Gather Historical Video Performance

By using the API, fetch the latest 30 videos from each influencer and calculate average views, like rates, and comment rates.

Sample Metrics:

Average Views (Exposure Potential)
Like Rate = Likes / Views
Positive Comment Ratio (Sentiment Analysis)
Posting Frequency = Weekly Update Rate

These serve as key indicators of an influencer’s baseline impact.

4. Mid-Campaign: Real-Time Monitoring of Collaboration Videos

Once a collaboration video goes live, set up a short-term real-time monitoring system:

Fetch view and engagement data every hour to draw growth curves.
Track comment keywords and sentiment trends to assess audience reactions.
Compare with peer videos to evaluate relative performance.

Example: Generate Growth Curve for Collaboration Video

def track_video_growth(item_id):
url = f'https://luckdata.io/api/douyin-API/get_pa29?type=cnt&item_id={item_id}'
headers = {'X-Luckdata-Api-Key': 'your_key'}
resp = requests.get(url, headers=headers)
data = resp.json()['data']
return data['play'], data['like'], data['comment']

Store the results in a database and visualize with matplotlib or BI tools:

Hourly growth curve for video views
Like and comment curves over time
Peak engagement periods

This helps:

Adjust media spend pacing
Determine whether additional promotion is needed
Quickly identify potential viral content for amplification

5. Post-Campaign: Building an Influencer Scoring Model

Based on mid-campaign data, assign scores to each influencer based on these dimensions:

Dimension	Metrics	Suggested Weight
Exposure	Avg. Views, View Growth Rate	30%
Engagement	Like Rate, Comment Rate, Share Rate	25%
Feedback Quality	Positive Comment Ratio, Sentiment Volatility	15%
Consistency	Posting Frequency, Content Quality	10%
Cost Efficiency	CPM, CPV, Conversion Rate	20%

After each collaboration, output a report like this:

Influencer	Views	Like Rate	Positive Feedback Rate	Collaboration Cost	Composite Score
Xiao Zhao	420,000	5.2%	81%	¥3,500	87
Da Wang	780,000	2.3%	65%	¥7,500	72
Lily	230,000	8.6%	92%	¥3,000	85

This scoring system supports reinvestment decisions and helps guide influencer teams on areas for improvement.

6. Visualization and Strategy Feedback Loop

All indicators should be aggregated into a dashboard with the following modules:

Real-time performance trends of collaboration videos
Influencer score leaderboard
Radar charts of influencer attributes (Exposure, Engagement, Sentiment)
CPM and CPV trend lines
YOY and MOM comparisons of campaign performance

Visualization enhances team efficiency and provides clarity for executives, enabling more precise budget allocation.

7. The Core Value of LuckData

Building this system is made possible by the support of LuckData’s Douyin API:

Traceable Historical Data Access, ideal for trend modeling
Rapid Access to Creator and Video Performance, enabling efficient scoring
High Stability and Speed, supporting large-scale influencer monitoring
Flexible Point-Based Billing, scalable from small tests to major brand campaigns

By combining Python, PostgreSQL, and BI tools, you can deploy an “Influencer Performance Evaluation System” in just a week—enabling full-cycle, data-driven campaign management from production to launch.

Conclusion

In the short video ecosystem, influencers are the critical bridge between brands and consumers. Fine-grained data analysis and performance evaluation of influencer collaborations directly determine the success of your media strategy. By building a Douyin API-powered influencer evaluation system, you’ll gain sharper insights, faster responses, and higher returns on your marketing investment.

Next, consider expanding this system to cover live-stream influencers, cross-platform creators, or even integrate with your CRM to achieve deeper, closed-loop data management.

If you haven’t started using LuckData’s Douyin API, now is the perfect time to apply for a trial and transform your data into decision-making power : https://luckdata.io/marketplace/detail/douyin-API