Abstract
Many ads are engaging, but what makes them engaging may have little to do with the product. This problem can be particularly relevant to influencer advertising if influencers are motivated to promote themselves, not just the product. We develop an algorithm to measure the degree of effective engagement associated with the product and use it to predict the sales lift of influencer video advertising. We propose the concept of product engagement score, or PE-score, to capture how engaging the product itself is as presented in a video. We estimate pixel-level engagement as a saliency map by training a deep three-dimensional convolutional neural network on video-level engagement data and locate pixel-level product placement with an object detection algorithm. The PE-score is computed as the pixel-level, engagement-weighted product placement in a video. We construct and validate the algorithm with influencer video ads on TikTok and product sales data on Taobao. We leverage variation in video posting time to identify video-specific sales lift and show that the PE-score significantly and robustly predicts sales lift. We explore drivers of engagement and discuss how various stakeholders in influencer advertising can use the PE-score in a scalable way to manage content, align incentives, and improve efficiency.
Marketing Science, ePub ahead of print, December 20, 2024, https://doi.org/10.1287/mksc.2021.0107
83 Pages, Posted: 30 Mar 2021, Last revised: 20 Dec 2024
Jeremy Yang, Harvard University
Juanjuan Zhang, Massachusetts Institute of Technology (MIT) - Sloan School of Management
Yuhan Zhang, Beijing Technology and Business University
Date Written: August 17, 2023
Keywords: Influencer Advertising, Video Advertising, Entertainment Commerce, Creator Economy, Sales Conversion, Incentive Alignment, Computer Vision, TikTok
Summary and Analysis of “Engagement that Sells: Influencer Video Advertising on TikTok”
By Jeremy Yang (Harvard), Juanjuan Zhang (MIT), Yuhan Zhang (BTBU)
🧠 Core Insight:
Not all engagement is good engagement—especially in influencer video ads. What matters most for actual sales lift is whether the product itself is what’s engaging viewers. This paper introduces a novel, scalable algorithm to calculate a Product Engagement Score (PE-score) that predicts which influencer TikTok videos will actually drive purchases—not just rack up likes.
🔍 Key Concepts:
- The Problem: Misaligned Incentives
- Influencers are incentivized to promote themselves, not the product.
- High engagement (likes, shares) ≠ high conversion.
- Sellers lack tools to assess if a video’s engagement is product-relevant.
- The Solution: Product Engagement Score (PE-score)
A score that reflects how engaging the product itself is within the video, computed in 3 steps:
- Engagement heatmap: Pixel-level saliency using a deep 3D convolutional neural network (CNN) trained on engagement data (e.g., shares).
- Product heatmap: Object detection (via SIFT) marks where/when product appears in video.
- PE-score: Normalized dot product of both maps = engagement-weighted presence of the product.
📊 Key Findings:
🧪 Validation:
- Dataset: 2,685 influencer video ads on TikTok, sales tracked via Taobao.
- Result: PE-score significantly predicts sales lift.
- Engagement alone: No correlation with sales.
- Product placement alone: No correlation.
- PE-score: Strong correlation, even controlling for influencer popularity, price, etc.
- Influencers advertising their own products scored 31% higher PE-scores on average → aligns incentives = higher effectiveness.
🧠 Behavioral Insights:
- Peak viewer attention: First 6 seconds.
- Product + human face = higher attention.
- Facial expressions (happy, sad), novelty, and dynamic motion boost saliency.
💡 Practical Applications:
For Brands:
- Screen influencer content pre-release using PE-score to predict conversion.
- Write contracts based on PE-score thresholds, not just engagement.
For Influencers:
- Use PE-score feedback to optimize ad content before publishing.
- Better negotiate fees with proof of performance potential.
For Platforms:
- Improve ad attribution models.
- Power recommendation systems that favor truly effective content.
🤖 Technical Highlights:
- Uses transfer learning with Xception CNN architecture.
- Analyzes videos frame-by-frame and pixel-by-pixel (after reducing size/FPS).
- Model trained on >16,000 TikTok ads, tested on a separate panel with real sales data.
- All computations can be done before ad release, making it scalable for real-world use.
🔁 Strategic Implications:
This paper helps shift performance marketing from proxy metrics (likes, shares) to true outcome metrics (sales lift). It’s a call for alignment between content creation and commerce, especially as platforms like TikTok blur the line between entertainment and shopping.
🧭 TL;DR (for Decision Makers):
