Benefit Appeals in Livestreaming E-commerce:

The Moderating Role of Influencers’ Beauty and Body Motion

ABSTRACT

Benefit appeals represent a commonly used advertising tactic for improving customer response. However, the effectiveness of benefit appeals in the context of multimedia livestreaming e-commerce remains unverified. To address this issue, we developed a multistudy framework that employs 10,971 livestreaming video clips and TikTok sales data. In Study 1, to preliminarily establish the correlation between benefit appeals and sales, we conducted exploratory research leveraging interpretable machine learning. In Study 2, to examine quasi-causality, we conducted an econometric analysis utilizing propensity score matching (PSM). The results demonstrate that benefit appeals significantly increase sales, with the influencer’s facial beauty and body motion amplifying this effect. In Study 3, to provide causal evidence, we launched two randomized online experiments. The experimental results corroborate the findings of Study 2 again and reveal the mediating role of increasing customer engagement from a psychological perspective. Our study is among the first to quantify influencers’ verbal (benefit appeals) and nonverbal cues (beauty and body motion) via machine learning and to examine their sales effects in real-world business, enriching the literature on benefit appeals in the context of multimedia livestreaming e-commerce.

1. Multimedia Data Processing:

1.1 Flow Chart of Multimedia Data Processing

In this research, we derive unstructured video data and corresponding structured e-commerce data from TikTok. Machine learning techniques (computer vision and natural language processing) automatically extract verbal and nonverbal variables. Then, the extracted variables are taken as inputs for interpretable machine learning, XGBoost, and SHAP.

Figure 1. Flow Chart of Multimedia Data Processing

1.2 The Measurement of Beauty

Influencers’ beauty is detected by the beauty score detection API provided by Face++. Utilizing 83 facial landmarks that mark the outline of the face, eye, eyebrow, lip, and nose contour, Face++ outputs beauty scores automatically. Figure 2 displays an example of facial landmarks and beauty score detection interface on Face++.

Figure 2. Beauty Score Detection Interface

1.3 The Measurement of Body Motion

The body motion is obtained through the dense optical flow algorithm. By analyzing two adjacent frames of a live streaming video, this algorithm computes a displacement vector for each pixel in the image, generating an optical flow map (see Figure 3). Video 1 demonstrates an example of dense optical flow, with the intensity of the red areas on the right indicating the magnitude of the influencer’s body motion.

Figure 3. The Measurement of Body Motion

Video 1. Example of Body Motion Measurement

2. Interpretable Machine Learning

Interpretable Machine learning methods, eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) were applied to explore the the importance, effect, and interactions of variables.

Figure 4. Density scatter plots of the SHAP values

Figure 5. SHAP Interaction Plot

3. Experimental Stimuli in Study 3

In Study 3, two 2x2 experiments, study3a and study3b, were conducted to test the causality effect. We display the transcript stimuli of benefit appeal, beauty, and Body motion in the following sections.

3.1 Benefit appeals in influencer’s transcript stimuli

To create different extents of using benefit appeal (high-benefit versus low-benefit), we constructed a pair of live streaming transcripts emphasizing benefit appeal (high-benefit) and using fewer benefit appeals (low-benefit) respectively. The content emphasizing benefit appeal in the high-benefit group is marked in blue.

3.2 Beauty of the influencer in picture stimuli

To manipulate the beauty of the influencer, makeup and beautification techniques were applied to the face of the same influencer. The portraits of the influencer in high-beauty condition and low-beauty condition are shown in Figure 6.

Figure 6. The manipulate of influencer's beauty

3.3 Body motion of the influencer in video stimuli

To manipulate the influencer’s body motion, we asked the influencer to behave in different magnitudes of body motion (high-body motion versus low-body motion). We originally recorded four videos in Chinese (high-benefit × low-body motion, high-benefit × high-body motion, low-benefit × low-body motion, and low-benefit × high-body motion). These four videos have been converted into English versions. All eight Chinese and English videos are shown below:

(1) Chinese Version

Group1
Benefit appeal: high
Body motion: high
Group2
Benefit appeal: high
Body motion: low
Group3
Benefit appeal: low
Body motion: high
Group4
Benefit appeal: low
Body motion: low

(2) English Version

Group1
Benefit appeal: high
Body motion: high
Group2
Benefit appeal: high
Body motion: low
Group3
Benefit appeal: low
Body motion: high
Group4
Benefit appeal: low
Body motion: low