1223 Research Y. Wu, Y. Xu, S. Gao, and Q. Li are with the School of
Information Science and Technology, ShanghaiTech University, and Shanghai
Engineering Research Center of Intelligent Vision and Imaging, China. W. Song
and X. Fan are with the School of Entrepreneurship and Management,
ShanghaiTech University. Q. Li is the corresponding author. E-mail:
<EMAIL_ADDRESS>X. Wang is with
Weill Cornell Medical College, Cornell University. E-mail:
<EMAIL_ADDRESS>connect.ust.hk}. Z. Nie is with Be Friends
Holding Limited. E-mail<EMAIL_ADDRESS>
# LiveRetro: Visual Analytics for Strategic Retrospect in Livestream
E-Commerce

Yuchen Wu0009-0005-8333-4405  Yuansong Xu  Shenghan Gao  Xingbo
Wang0000-0001-5693-1128  Wenkai Song  Zhiheng Nie
Xiaomeng Fan0000-0002-6302-8786  and Quan Li0000-0003-2249-0728

###### Abstract

Livestream e-commerce integrates live streaming and online shopping, allowing
viewers to make purchases while watching. However, effective marketing
strategies remain a challenge due to limited empirical research and subjective
biases from the absence of quantitative data. Current tools fail to capture
the interdependence between live performances and feedback. This study
identified computational features, formulated design requirements, and
developed LiveRetro, an interactive visual analytics system. It enables
comprehensive retrospective analysis of livestream e-commerce for streamers,
viewers, and merchandise. LiveRetro employs enhanced visualization and time-
series forecasting models to align performance features and feedback,
identifying influences at channel, merchandise, feature, and segment levels.
Through case studies and expert interviews, the system provides deep insights
into the relationship between live performance and streaming statistics,
enabling efficient strategic analysis from multiple perspectives.

###### keywords:

Livestream E-commerce, Visual Analytics, Multimodal Video Analysis, Marketing
Strategy, Time-series Modeling.

In Case I, the streamer’s strategic analysis comprises: 1 Select the session
with the highest transaction amount per thousand views (GPM). 2 Identify poor
sales performance of the “Marker Pen” in the A Session View. 3 Discover that
the atmosphere pitch dominates the Text channel during that period in the B
Segment View. 4 Observe a low GPM in the C Exploration View. 5 Observe
negative contributions from the Text and Frame channels in the merchandise-
level summary. 6 Find a scarcity of facial expressions and a high number of
close-ups in the Performance Inspection. 7 Observe in the live replay that the
streamer was drawing a small rabbit on paper. 8 Observe a peak in the Like
value during that time period. 9 Discover the positive contribution of the
atmosphere pitch to the target. 10 Observe low Departure and 11 negative
contribution. 12 Find an abundance of semantically similar comments in the
Comment Reference part. 13 Draw the conclusion that streamers should maintain
a balance in the utilization of various sales pitches, which is appended as
Drawbacks in the D Record View. 14 Identify a period where the GPM initially
decreased but quickly rebounded. 15 Observe a transition from the atmosphere
pitch to the selling pitch and order pitch during this period. 16 Review video
content revealing the streamer providing historical tidbits, and 17 all three
sales pitches found to have a positive impact. 18 Summarize the combination of
these three sales pitches as a wise choice and added as Highlights to the
Record View.

##  1 Introduction

Livestream e-commerce has emerged as a novel channel that blends entertainment
with instant purchases by integrating live streaming with e-commerce, enabling
viewers to make purchases while they watch. This presents a significant
opportunity for value creation for retailers, brands, and digital platforms.
However, the pressing challenge is how to effectively market in livestream
e-commerce. Empirical studies have demonstrated that marketing strategy plays
a critical role in the conceptual core of marketing practice in this domain
[44, 67, 50]. An example of a marketing strategy in the context of live
e-commerce is to limit the time of sale and the number of units available.
However, achieving successful marketing outcomes entails more than merely
employing plausible strategies. Issues often arise during the execution phase,
where “seemingly viable strategic plans fail to effect change” [46]. Such
problems emanate from various realities, including the organization’s
distinctive execution capabilities and constraints [5], genuine core
competencies [22], and evolving internal operations of the organization, among
others [25, 49]. In essence, the dichotomy between marketing strategy
development and implementation is often the underlying cause of many execution
challenges [8, 21]. As a result, a comprehensive strategic retrospect is
necessary to examine actual strategy execution and devise tailored
optimizations for further exploitation.

Previous studies on livestream e-commerce have endeavored to illustrate the
motivations for usage among streamers and consumers [7, 23], streamer
characteristics [79, 71], and the factors that influence both usage intention
and purchase intention [34, 70]. Some studies have also examined marketing
strategies in livestream e-commerce from the perspective of sellers [65, 20].
However, most of these studies have primarily focused on developing
theoretical models and providing implications as general guidelines, rather
than providing a holistic and detailed assessment of strategic operations.
Additionally, many of these studies have employed empirical investigations
(e.g., online questionnaire) to examine perceptions and validate hypotheses,
easily influenced by subjectivity and lead to biases [45, 1, 40] . To aid in
comprehending the implications of the practical execution of live e-commerce
strategy and to facilitate strategic retrospectives, commercially available
analytical tools111https://zhitou.feigua.cn; https://www.changuanjia.com are
developed and subscribed to by professional streamers. However, these tools
only provide rudimentary presentations of live replay and statistics, e.g.,
raw video and multi-line charts. Several studies have established that in live
streaming and retail marketing, streamers/salespeople have a significant
impact on viewers/customers [17, 32, 33, 42]. This indicates that, as the
focus of strategy execution, the live performance of streamers has a profound
influence on streaming statistics [62, 36]. Nonetheless, none of these tools
explicate the connection between live performance and streaming statistics in
an intuitive manner, leaving streamers with a “click one, watch one” back-and-
forth approach to retrospective analysis. Thus, a systematic analytical
approach is required to decode the relationship between live performance and
streaming statistics to conduct strategic reviews and effectively evaluate
actual marketing strategy implementation.

Visual analytics are effective approaches for analyzing video and time-series
data. However, it remains challenging to conduct comprehensive strategic
retrospect in livestream e-commerce. First, the characteristics of livestream
e-commerce data make it challenging to process. The need for multi-modal
analysis naturally arises from a comprehensive analysis of livestream
playback. Unlike previous studies focusing on presentation videos [66, 75, 74,
39] or user behavior videos [55, 3, 63], a single session of livestream
commerce can last for several hours without interruption. In addition,
streaming statistics encompass multiple metrics such as viewer count and order
volume, rendering the data high-dimensional and prolonged. Second, existing
approaches cannot handle multi-modal video analysis and synchronous feedback
simultaneously. Previous studies have centered on examining information
present within the video, including speech [72], emotion [75, 73], gesture
[74], and intra-modal correlation [66, 60]. Nevertheless, livestream
e-commerce involves a process where streamers deliver information, and viewers
receive information while simultaneously responding through activities such as
making purchases and joining the live stream. This specificity of the
livestream context renders existing systems inadequate to meet the demands of
joint analysis of live performances and feedback. Third, multi-modal features
and time-series feedback have distinct temporal granularity and dynamics.
Streaming statistics obtained from the back-end of digital platforms exhibit a
coarser granularity than multi-modal features like speech rate. Additionally,
there are variations and discrepancies in granularity across these modalities,
posing significant difficulties for conducting joint analysis. In summary, the
comprehensive evaluation of marketing strategies in livestream e-commerce is a
challenging task due to the absence of an effective approach for joint
analysis of multi-modal performance and synchronous feedback, the high-
dimensional and prolonged nature of livestream e-commerce data, and the
distinct granularities of livestream performances and streaming statistics.

To address the above challenges, we have developed an interactive visual
analytics system, LiveRetro, to facilitate strategic retrospect in livestream
e-commerce, and provide a platform for evidence-based research in marketing.
Our work makes the following contributions. First, we identify seven design
requirements that support a comprehensive strategic retrospect in livestream
e-commerce and informative computational features that facilitate the analysis
of live performance in this domain. Second, we present LiveRetro, a visual
analytics system designed around the identified requirements, and that
incorporates enhanced visualizations and rich interaction to support the
retrospective analysis of livestream e-commerce strategies from a multifaceted
and empirical perspective. Third, we present two case studies and expert
interviews that demonstrate the effectiveness of LiveRetro. In summary, we
present, LiveRetro as the first research effort that uses a visual analytics
approach to perform joint analysis of video content and synchronous feedback,
aiming to decipher the intricate correlations in livestream e-commerce. Our
study is intended to stimulate discussion and inspire further research into
the application of visual analytics to the analysis of livestream and the
livestream e-commerce domain.

##  2 Related Work

###  2.1 Livestream E-Commerce Analysis

Livestream e-commerce has been extensively studied across various dimensions,
including its characteristics and impacts [34, 56, 64], customer motivations
[28, 30, 43, 79], and streamers’ and sellers’ engagement tactics [20, 47, 65].
Some studies have focused on strategy discovery and validation [65, 50, 68,
67], such as the utilization of personalities, game-prizes, and shows as part
of the Persuasion-based Approach to elicit interest and engagement from
viewers [65]. However, none of these studies have addressed the practical
strategy execution in livestream e-commerce. This study contributes to the
existing literature by identifying the influences of various performance
features based on streamers’ own live performance and streaming statistics,
providing personalized and concrete insights.

The prevailing research methodology in the field typically involves empirical
studies conducted through online questionnaires [19, 24] and qualitative
research methods [35]. While some studies have attempted to address
subjectivity and biases in data collection [28, 16], they have mainly focused
on monitoring limited components of livestream e-commerce, such as online
counts and streamers’ replies. In contrast, this study adopts a more direct
approach by gathering statistics and feedback from the live streaming back-end
and extracting features from live replays. This evidence-based platform
enables a more comprehensive and objective analysis of the livestream
e-commerce phenomenon.

###  2.2 Visual Analysis of Videos

In the past decade, various visual analytic approaches have been proposed to
analyze different types of videos, including movies [29, 38], news [27],
presentations [75], education [52, 73], and user experience. Specifically,
many researchers analyze human behavior and its dynamics in videos, including
emotion [75, 39], voice modulation [72], body language [31, 74, 66], and word
use and delivery [61]. Some researchers leverage video events to evaluate user
behavior, such as clickstream for learning behavior analysis in the MOOC
platform [52] and user comment posting [10] for studying viewers’ online
participation. Two recent studies have proposed innovative approaches for
evaluating user experience. Soure et al. [55] developed a tool called CoUX
which facilitates the review of think-aloud usability test videos. Similarly,
Batch et al. [3] introduced uxSense, which utilizes machine learning
techniques to extract user behavior from audio and video recordings as
parallel time-stamped data streams. Notably, Tang et al. [58] proposed a risk-
aware framework, VideoModerator, for multi-modal video moderation in
e-commerce.

However, these approaches mainly focus on exploring information within the
video and are not directly applicable to analyzing livestream e-commerce data,
where streamers’ live performance and viewers’ feedback should be considered
simultaneously. Our system integrates ML modeling, explanations, and
visualization to enable a joint analysis of video content and synchronous
feedback.

###  2.3 Time-series Forecasting and Explanatory Visualization

Time-series forecasting is widely used in e-commerce to predict sales and
product prices, with traditional statistical forecasting methods such as ARIMA
models [78] and machine learning approaches like multi-layer perception (MLP)
[77] and recurrent neural networks (RNNs) [14] being employed. Nonetheless,
improved prediction performance alone is insufficient for decision-makers who
require trust, adoption, and regulatory compliance. Therefore, studies have
explored integrating explanatory visualizations with forecasting, such as
MultiRNNExplorer [51] and mTSeer [69], which enhance line charts with feature
contributions to explain and evaluate multivariate time-series forecasting
models. Other studies, such as PromotionLens [76] and RISeer [9], have also
utilized feature contributions to explain sale volume forecasts and evaluate
regional industrial structure.

Previous explanatory designs mainly provide feature-level explanations for
predictions. To overcome the challenge of aggregating multiple feature
contributions from diverse channels, we use representative ML models to model
streaming statistics and generate feature contribution summaries at multiple
levels. We augment the mTSeer design by adding partitioned stacked bars to
display the internal distribution and using a “lollipop” design to segment the
summary for each merchandise based on its temporal periods.

Figure 1: A viewer interface for livestream e-commerce on TikTok with five key
components: the Account, the Streamer, the Merchandise, the Comments, and the
Purchase Interface.

##  3 Observational Study

###  3.1 About Livestream E-Commerce Platforms

Livestream e-commerce platforms, such as TikTok, have a viewer interface (Fig.
1) that includes five key components: Account, Streamer, Merchandise,
Comments, and Purchase Interface. Account serves as a unique identifier,
Streamer functions as salespeople, Merchandise is the promoted product,
Comments provide real-time feedback, and the Purchase Interface enables
immediate purchases.

###  3.2 About the Collaborative Team and User Interviews

We worked with three experienced livestream e-commerce streamers, E1, E2, and
E3, who are among the top 1% of sales on TikTok. They have been using
commercial analytical tools in their daily analytical routine for over 14
months. We also collaborated with two marketing researchers from a local
university, E4 and E5, who have publications in the field of marketing in
livestream e-commerce. To identify appropriate analytical approaches and
requirements for conducting a strategic retrospect in the context of
livestream e-commerce, semi-structured independent interviews lasting
approximately $40$ minutes were conducted with the five participants. The
interviews aimed to elicit 1) information about the participants’ current
practices and bottlenecks related to strategic retrospect and marketing
research, 2) specific concerns regarding livestream e-commerce strategy, and
3) proposed features for an approach that facilitates strategic retrospect in
e-commerce.

Current Practices. As per the feedback from the streamers, livestream
e-commerce streamers follow a specific set of steps for conducting a strategic
retrospect after their live streaming session. First, they identify the
highlights and mistakes of their performance during the session, such as
identifying any interaction issues or mistakes that were made. Second, they
comb through the streaming statistics to examine their live performance and
detect any significant variations. Finally, they solicit feedback from viewers
to gain insight into their reactions towards the live performance and the
demand for the merchandise. In contrast, marketing researchers typically
construct conceptual models and formulate hypotheses based on relevant
literature, conduct surveys, and use confirmatory factor analysis [6] and
structural equation modeling [59] to test their hypotheses and derive
theoretical and practical implications based on their findings.

Bottlenecks. Commercial streaming tools offer live replay and streaming
statistics for professional streamers, but users are limited to selecting
specific points in time to watch the original video. E1 mentioned that “I have
been trying to figure out what are the most selling features, but it’s pretty
hard for me to just watch the videos and digest them over and over again.”
This can make conducting strategic retrospect inefficient and challenging.
Marketing researchers also face constraints when relying solely on empirical
research for broader research scope and solid reasoning. “Inductive reasoning,
which forms the basis of empirical research, can obscure exploration of
reality and limit the reliability and universal applicability of
generalizations”, said E5. As such, streamers and researchers alike value the
development of an evidence-based platform for expanding research scope,
validating existing theories, and conducting comprehensive strategic
retrospects.

Analytical Aspects and Approaches. Experts were interviewed individually to
determine the main analytical aspects of the strategic retrospect, and a
consensus was reached on three core elements: streamer, viewer, and
merchandise. According to E2, “these are three golden elements of every live
streamer”; “the streamer promotes certain merchandise, and the viewer is
driven by the streamer’s live performance to purchase the merchandise. These
three form the core of the livestream e-commerce session”. We asked the
experts to provide their professional opinions on various aspects of the
analysis. E1 highlighted the importance of live performance analysis for
streaming from the streamer side. E5 expressed interest in exploring the
impact of live performance from different channels, such as audio and frames.
Therefore, we discussed and identified a multi-modal approach for live
performance analysis with the researchers. Unfortunately, the literature in
the livestream e-commerce field only describes the characteristics of
streamers as abstract notions [79, 71, 20] (e.g., expertise, attractiveness).
Therefore, we referred to relevant literature [65, 54] and conducted a focus
group discussion with all experts to further brainstorm and identify
modalities and computational characteristics of each modality. We identified
several key attributes for the Audio, Text, and Frame channels in the context
of livestream e-commerce, including volume, pitch, speech rate, pause, six
types of sales pitches, facial expression, and camera position (Tab.1 in the
appendix for details). It is noteworthy that the six types of sales pitches we
identified are commonly utilized in livestream e-commerce, which was
corroborated by all streamers, but had not been previously recognized in the
literature. From the viewer side, E3 suggested summarizing viewer comments,
while E1 emphasized the importance of understanding viewers’ interaction
status. Regarding the commodity aspect, E2 specified two sub-aspects, namely
the selection and the permutation of commodities. In addition to these three
aspects, all experts showed enthusiasm for exploring the correlation between
live performance and streaming statistics. E2, E4, and E5 further suggested
investigating the influences of these three aspects and more detailed features
on specific targets (e.g., sales, likes).

###  3.3 Design Requirements

We derived seven design requirements from user interviews and expert feedback,
categorized into four groups: streamer retrospect ( Streamer), viewer
retrospect ( Viewer), merchandise retrospect ( Merchandise), and correlation
exploration ( Correlation).

Streamer

R1. Summarize the performance of each streamer in a live session. Livestream
e-commerce often requires multiple streamers to host extended live sessions,
making it essential to assess and combine each streamer’s performance in terms
of sales and viewer response for further analysis and reflection.

Streamer

R2. Capture features of streamers’ live performance from multiple channels.
Experts agreed that relying on repetitive raw video observation to evaluate
streamers’ live performance is tedious and time-consuming. A more efficient
and effective approach is required to examine streamers’ live performance.
Therefore, they suggested using features from multiple channels to create a
more comprehensive and detailed picture of the streamer’s performance.

Viewer

R3. Summarize viewers’ comments for the chosen time period. Viewer comments
during live streams offer direct feedback on the streamer and the promoted
merchandise and are influenced by live room activity and streamer guidance.
Therefore, it is important to synthesize and summarize viewer comments during
the relevant time period to address pre-sales inquiries and post-sales
concerns.

Merchandise

R4. Summarize sales data for each merchandise. During a live session,
streamers promote multiple merchandise and require access to sales data for
each item to analyze and optimize their strategy. High-conversion merchandise
may be promoted longer while underperforming items may be removed from the
promotion.

Merchandise

R5. Visualize merchandise arrangement. Streamers promote merchandise in a
specific order during live sessions, categorizing them as “traffic-type” for
visibility, “welfare-type” for interaction, and “profit-type” for maximum
profit. An overview of the merchandise arrangement is needed to evaluate its
effectiveness in driving sales.

Correlation

R6. Provide an organized joint analysis for live replay and feedback. Experts
agreed on the need to connect live performances with their corresponding
feedback, which is difficult to discern from video analysis alone. Thus, the
system must harmonize multi-modal features and streaming statistics and
present them in an organized way.

Correlation

R7. Identify the influences of different channels and features for a live
session. E2, E4, and E5 expressed a notable interest in examining the impact
of specific channels or features on streaming statistics. They aimed to
determine the key elements that streamers should prioritize in order to
enhance sales, such as providing detailed explanations or creating an engaging
atmosphere.

##  4 LiveRetro

Our approach and workflow for analysis using the LiveRetro system consists of
three components: a data processing module, b modeling engine, and c visual
interface (Fig. 2). The data processing module extracts data from live replay
and retrieves streaming statistics, applies models for feature extraction, and
aligns features and statistics to the same granularity and time interval. The
modeling engine utilizes the aligned performance features to model certain
attributes with multiple models, and the model with the best performance is
automatically selected. Lastly, the visual interface provides multiple
interconnected views for detailed and comprehensive retrospective analysis.

###  4.1 Data Description and Processing

We used a dataset provided by our collaboration team members E1-E3, consisting
of livestream e-commerce data from January 1 to February 28, 2023. This
dataset comprises all live streaming sessions on their account during the time
period, with corresponding videos and streaming statistics. The videos contain
frames and audio capturing the sales pitch of the streamers and their display,
enabling viewers to witness the promotional activity. The streaming statistics
(Table 1), on the other hand, provides synchronous feedback on overall,
viewer, and merchandise aspects. Despite the extended duration of individual
live streaming sessions, which typically surpass ten hours, streamers endorse
a range of approximately 30 distinct products through their shift work. In
consultation with E1 and E2, we segmented each session into multiple clips,
each featuring a complete batch of products.

Figure 2: LiveRetro consists of a data processing module, a modeling engine,
and a visual interface which supports a cyclical workflow.

In this study, we examined three modalities of video data, which include
audio, text, and frame. To extract features from these modalities, we adopted
a two-step process. First, we separated the audio from the video and then
utilized the Google Cloud Speech-to-Text API to transcribe the audio data into
script form. The resulting script was then aligned with the time series data
to facilitate feature extraction. To construct our training dataset, we
selected script data from five different livestream sessions that correspond
to the video data. Prior to the following feature classification task, we
preprocessed the data by removing incomplete sentences as they can adversely
affect classification accuracy. Additionally, we corrected any inaccuracies
resulting from the transcription process. Overall, our approach involved the
extraction of features from the various modalities of video data, followed by
the alignment of these features with the corresponding time series data,
enabling us to accurately classify and analyze the features of interest.

Table 1: Time-Series Feedback Retrieved from Platform Back-End.

Overall
---
Sales | Volume | GPM | UV Value
Viewer
Entry | Departure | Like | Comments | Subscribe | Conversion Rate | Average Stay
Merchandise
Cancels | Exposure-Click Ratio | Click-Turnover Ratio

###  4.2 Feature Extraction

By conducting interviews with streamers (E1-E3) and researchers (E4–E5), we
identified the requisite features pertaining to the following three
modalities. These features facilitate a more comprehensive comprehension and
analysis of the streamer’s information, thereby allowing us to gain a better
understanding of the strategies employed.

Audio Feature Extraction. Our analysis of the audio data centers around four
prominent features, namely Volume, Pitch, Pause, and Speech Rate, as described
in Tab.1 in the appendix. We selected these features due to their capacity to
encapsulate a diverse range of audio characteristics. We employed Praat [4] to
directly obtain Volume and Pitch from its audio analysis view. To extract
Speech Rate, we utilized a Praat script that identified the occurrence time
and the number of syllables. We then computed the syllable count per second to
derive the Speech Rate at one-second intervals.

Text Feature Extraction. Following consultation with both streamers and
researchers, our collaboration team has reached a consensus to categorize the
sales pitches employed during live streaming into six categories. These
include the Traffic Pitch, Interaction Pitch, Selling Pitch, Order Pitch, Urge
Pitch, and Atmosphere Pitch, as shown in Tab.1 in the appendix. With the
assistance of E1-E4, we were able to correctly identify and categorize
sentences into their corresponding sales pitch categories. The training
dataset was composed of $3,000$ labeled sentences, with $500$ sentences
allocated to each category. We examined two powerful pre-trained language
models, BERT [13] and ERNIE3.0 [57], known for their exceptional performance
in Chinese language tasks, to classify live-stream sales pitches. Both models
were trained and fine-tuned using labeled sentences, and a 5-fold cross-
validation technique was employed to identify the optimal hyperparameters.
Given the relatively small size of our dataset and the concern of overfitting,
we took careful measures to ensure that the models performed consistently on
both the training and test sets. Upon validation, ERNIE3.0 achieved a higher
accuracy of $91.2\%$ on the test data, outperforming BERT, which achieved an
accuracy of $85.4\%$. As a result, ERNIE3.0 was selected and successfully
implemented for the classification of sentences extracted from five live-
stream sessions.

Frame Feature Extraction. The selection of Facial Expressions and Camera
Positions as streamer video frame features is a thoughtful process. Facial
expressions can provide insights into the streamer’s personality and
significantly impact viewer perception. For example, a genuine smile can
create a warm and friendly impression, enhancing the streamer’s persuasiveness
and credibility. Additionally, camera positions can showcase a product from
different angles and locations, enhancing audience understanding and
maintaining their engagement. In frame segmentation of livestream videos, the
RetinaFace algorithm [12] is employed for accurate face detection, given its
excellent performance in this task. After face detection, the VGG-Face model
[48] is utilized to extract emotional expressions from frames containing human
faces. Considering the similarities between camera positions in film and the
unique characteristics of livestream e-commerce, frames are classified into
two categories: Long-Range View and Close-Up, as detailed in Tab. 1 in the
appendix. The classification is based on the results of face detection and the
proportion of the face area in each frame.

###  4.3 Time-series Modeling

The extracted features were utilized as input for time-series modeling, with
the features aggregated at a one-minute granularity to facilitate analysis.
The feature vector consisted of $21$ dimensions, including six multi-modal
features and $15$ real-time feedback features such as sales amount, sales
volume, number of comments, and viewer average stay. To enable users to select
the target data to be predicted, nine options were provided, including sales
amount, sales volume, UV value (sales amount divided by the number of unique
viewers), GPM (transaction amount per thousand views), number of viewers
entering and leaving, number of likes, number of comments, and average stay
time. Users could choose one option for prediction while using the rest as
input data for prediction. To assess the model’s performance, absolute and
relative metrics, namely mean absolute error (MAE) and mean absolute
percentage error (MAPE), were utilized due to the varying values of the
feature vector. Several non-linear machine learning models, including XGBoost
[11], Random Forest (RF), Long Short-Term Memory Networks (LSTM) [18], and
Multi-Layer Perceptron (MLP), were evaluated using a 7:3 split of training and
test data. The MAE and MAPE were calculated on the test data and combined to
obtain a weighted average. The model with the best performance was selected
based on the composite score, considering both prediction accuracy and
interpretability. The chosen model was then utilized to generate predictions
for the specific target data. The objective of the modeling is to examine the
impacts of various channels and features. For this purpose, SHapley Additive
exPlanations (SHAP) values [37] were employed as the model explanation metrics
due to their suitability, consistency, and flexibility. This approach allows
users to explore feature contributions at different levels and make informed
decisions, such as optimizing their strategies based on identified feature
pairs in streamers and merchandise.

##  5 Visual Interface

Considering the large and complex data generated by livestream e-commerce, we
probed into the behavioral characteristics of retrospective analysis with our
collaborative team and developed a refined set of design principles, in
addition to following the visualization mantra of “overview first, zoom and
filter, then details on demand” [53]. These principles were integrated into
the design process to simplify the system’s visual complexity and reduce
users’ cognitive load during the joint analysis of extended videos and
concurrent feedback.

DP1

Consistent encoding pattern. Given the complexity of livestream e-commerce
data, users may experience a considerable cognitive burden in processing it.
Therefore, to alleviate this burden, a consistent attribute encoding pattern
can be employed across the system. This pattern entails using the same or
similar shapes, positions, and color groups (e.g., purple for audio, blue for
text, and green for frame) at the attribute level for elements sharing the
same attribute.

DP2

Global visual alignment. Users may need to refer to multiple metrics or
statistics simultaneously to conduct a comprehensive analysis of livestream
e-commerce strategies. This process can be simplified by globally aligning the
presentation of different elements at the inter-view level, enabling users to
compare attributes in parallel. For instance, in our Exploration View, we
align the presentation of various elements with the timeline, which features a
vertically synchronized cursor to help users locate different attributes
simultaneously.

DP3

Cross-view time linkage. We create multiple views with different objectives
and capabilities to meet our design goals, providing users with a
comprehensive perspective during strategic retrospect. These views may need to
be navigated to access various information for a given livestream session. At
the intra-view level, time segments are used to connect all the views, with
each view focusing on elements corresponding to the desired time segment, as
described in Section 5.4.

###  5.1 Session View

The Session View (LiveRetro: Visual Analytics for Strategic Retrospect in
Livestream E-Commerce\- A) provides an overview of live sessions within a
specific time range, along with sales data for the merchandise promoted during
each session (R5). This view presents a table consisting of four columns that
detail fundamental information and key indicators, namely the time, duration,
GPM, and GMV (Gross Merchandise Volume). The display of these four attributes
allows users to obtain a swift summary of each live session. Additionally,
users have the option to sort sessions according to these attributes by
clicking  on the header. Users can expand the row of a specific session to
view details of the merchandise by clicking  on the left. Sales data for each
promoted merchandise is presented in chronological order within an expanded
table along a vertical timeline, with the launch time displayed on the left
and sales details grouped in boxes on the right. Each box lists the thumbnail
and title of the merchandise on the left, while on the right, the overall
Exposure-Click Ratio (i.e., the click number of the purchase interface to
online counts) and Click-Turnover Ratio (i.e., the number of transactions to
click count of the purchase interface) distribution is depicted using two
opposing beeswarm charts. Highlighted dots indicate the exact value of two
attributes for the merchandise. The size of the rectangle on the right encodes
the volume, while the color encodes sales. In terms of design, the amount of
sales for each merchandise follows the same encoding scheme as the Exploration
View ( DP1). Lastly, to facilitate comparison, the area graphs and sales &
volume rectangles are aligned vertically ( DP2).

###  5.2 Segment View

The Segment View (LiveRetro: Visual Analytics for Strategic Retrospect in
Livestream E-Commerce\- C) allows users to investigate the temporal
distribution of streamers’ live performance (R2) for different time segments.
Additionally, it provides a visual representation of the relationship between
GPM and live performance (R7), conveying this information in an intuitive
manner.

Inspired by time curves [2], to unfold the temporal variation pattern in live
performance, we partition live sessions into time segments of varying
granularities (i.e., 1-minute and 5-minute) and project performance features
(detailed in Tab.1 in the appendix) of each segment as a glyph onto a 2D plane
using t-SNE, because it offers “the best overall quality in terms of producing
low errors on average” [15]. To construct the feature vector, we extract each
audio feature by taking its minimum, median, and maximum values (e.g.,
$V_{volume}=[Volume_{min},Volume_{median},Volume_{max}]$), text features are
extracted by calculating the total number of words in each pitch’s sentence,
and facial features are extracted by determining the primary expression
category and its frequency. We normalize each group of features before
concatenation them to prevent bias, resulting in a 25-dimensional vector. Each
selected glyph point is then connected by curves in chronological order, with
the hue of the curves varying.

To effectively display the performance features of each time segment, we
design a glyph based on a pie chart. Within the glyph, the central pie chart
displays the relative value of GPM. Both the pie’s and outer ring’s colors
represent the chronological order. To encode audio, text, and facial features,
this glyph is equally divided into three sectors of a circle. Specifically,
the top-left sector represents audio features, the top-right sector represents
text features, and the bottom sector represents facial features. As these
features are heterogeneous, we adopt different designs to differentiate each
group of features. Audio features are encoded in a rose chart-based design,
where the petal length represents the feature’s average value of the time
segment. Text features are encoded in a pie chart-based design, where the
angle of the pie represents the relative amount of corresponding pitch in the
time segment. Finally, facial features are encoded in a donut chart-based
design, where the color represents the type of primary expression and the
angle of the arc represents the intensity.

Figure 3: Glyph designs for Segment View. (A) An alternative based on radar
chart and donut chart; (B) An alternative based on pie chart and rose chart;
(C) Our current design.

Design Alternatives. During the iterative design process, we explored two
alternative designs (Fig. 3\- A B) for our glyph-based visualization. The
first alternative design employed a combination of a radar chart and a donut
chart, with the central pie chart representing the GPM, and two concentric
donut charts representing the proportions of sales pitches and facial
expressions. Additionally, the outer radar chart was used to encode the value
of four audio features. However, this alternative design lacked a clear
representation of the time attribute and suffered from visual obscurity. In
the second alternative design, we used a pie chart and a rose chart to
represent the chronological order and the GPM and features from three
channels, respectively. However, our users reported that it was difficult to
distinguish between the feature groups. This led us to explore different
design options for the features from the three channels, resulting in the
current design (Fig. 3\- C). In this design, we use different visual encodings
to represent the audio, visual, and textual features, respectively.

###  5.3 Exploration View

The Exploration View contains Performance Inspection, Correlation Modeling,
and Comment Reference (R1-R4, R6-R8), aligned vertically with a synchronized
cursor ( DP2) for concurrent analysis.

####  5.3.1 Performance Inspection

The Performance Inspection part (Fig. 2\- C1) facilitates users to inspect the
overall performance of streamers (R1) as well as their fine-grained
performance across multiple channels, including audio, text, and frame (R2).
Both representations of overall performance and fine-grained performance are
also vertically aligned ( DP2).

At the top is a legend panel that dynamically switches its legends according
to the selected channel. Adjacent to the legend panel is a button group. Below
the legend panel, a tree metaphor is employed to summarize the performance of
streamers. The “root” denotes the time a streamer has occupied, while the
“trunk” displays their portraits and names. The three “branches” represent
summaries of streamers’ audio features, text features, and facial features,
respectively, in a clockwise arrangement. Importantly, these summaries are
encoded using detached glyph patterns that are aligned in similar positions to
those in the Segment View ( DP1). If multiple streamers host the session, a
radar chart is displayed to compare their performance across multiple metrics,
including Average Online Rate, Views, Attractiveness, Average Stay, and
Conversion Rate. Finally, at the bottom of the display, the fine-grained
performance of the aforementioned channels is presented.

Audio. Inspired by SpeechLens [72], we explored the use of volume charts to
encode audio features. However, due to the rapidly changing dynamics of audio
features, such as volume and pitch, a coarse-grained representation proved
insufficient in capturing the nuances of variations within the time segments.
Consequently, we opted to use connected box charts instead (LiveRetro: Visual
Analytics for Strategic Retrospect in Livestream E-Commerce\- 1), which
allowed us to represent the variations in volume, pitch, and speed in a more
detailed and comprehensive manner. Additionally, a bar chart was included at
the bottom of the visualization to depict the total number of pauses in each
time segment, with a white line indicating the maximum pause duration.

Text. The streamgraph chart (LiveRetro: Visual Analytics for Strategic
Retrospect in Livestream E-Commerce\- 2), a prevalent technique for
visualizing continuous data, has been employed in our study to illustrate the
temporal distribution of sales pitches. Its fluid and cohesive shape, along
with its suitability for analyzing the relative proportions of the entire
dataset, make it a particularly fitting choice for this purpose.

Frame. Given that the camera positions are integral to the captured facial
expressions, we have presented two opposing visualization side-by-side. The
stacked area chart positioned above portrays the temporal evolution and
variability in the quantity and dominant type of sales pitches, while the
adjacent area chart below depicts the total number of instances in which the
camera was focused in a Close-up view, with the corresponding timestamps
arranged in columns from top to bottom.

Furthermore, users have the ability to navigate between these channels by
selecting the respective tabs located on the left-hand side.

####  5.3.2 Correlation Modeling

The Correlation Modeling part (Fig. 2\- C2) provides multi-level summaries of
feature contribution, enabling a thorough understanding of channel and feature
effects (R8). It is organized in a top-to-bottom order. First, users have the
option to select a specific target for inspection by utilizing the selection
box located at the top right corner of the interface. In the channel-level
summary (Fig. 4\- A), we have presented the feature contribution of audio
features, text features, and frame features (Fig.1 in the appendix), as the
contribution of the corresponding channels. Regarding the design, the dotted
step line (Fig. 4\- 1) represents the raw value of the selected target, while
the solid step line (Fig. 4\- 2) represents the predicted value of the target
in comparison to the raw value. Inspired by mTSeer [69] and PromotionLens
[76], we normalized and visualized the contribution of the four aspects using
stacked bars. Similar to their approach, we positioned the stacked bars near
the step line, where bars above the line represent a positive effect on the
target, and vice versa (Fig. 4\- 5). Each stacked bar consists of a solid and
translucent part, representing the positive and negative proportions (Fig. 4\-
3 4) of feedback in the corresponding channel, respectively. Users can
interact with the legends in the header to focus on specific channels, and
they can click on bars for detailed insights on channel contributions at
different levels. Below the channel-level summary is a chronological
arrangement of products based on their respective launch times. Each
merchandise thumbnail is linked to its corresponding point on the predicted
step line, thereby enabling a more streamlined identification of the temporal
duration occupied by each merchandise. The part beneath the merchandise
arrangement exhibits merchandise-level summary (Fig. 4\- B). The bar below
each product thumbnail signifies the price (Fig. 4\- 6 7), while the donut
chart provides a condensed summary of the contribution of the product’s
occupied time. The transparency and angle of the arc encode the polarity
(positive or negative) and relative proportion of the summarized effect of a
particular channel. Additionally, a dotted line (Fig. 4\- 8) connects the
thumbnail and donut chart, with its length indicating the averaged target
value of the occupied time. Furthermore, the encoding color, transparency, and
line style used to represent merchandise-level summary is identical to that
used in the overview-level ( DP1), and these three parts are also vertically
aligned ( DP2). The bottom part shows more detailed contribution summaries for
specified channels at the feature-level and segment-level. The left section
presents clusters of bars exhibiting the feature-level summary (LiveRetro:
Visual Analytics for Strategic Retrospect in Livestream E-Commerce\- 5), which
is derived by aggregating the positive and negative contribution values of the
relevant indicator separately, for all time segments. On the right, each row
of bars indicating the segment-level summary (LiveRetro: Visual Analytics for
Strategic Retrospect in Livestream E-Commerce\- 6) for the corresponding
feature. It is noteworthy that the color of the leftmost line denotes the
selected channel, and the color scheme for feature-level and segment-level
summaries are identical to that of the Performance Inspection part ( DP1).

Figure 4: Contribution summary designs for Correlation Modeling part. (A)
Design for the channel-level summary; (B) Design for the merchandise-level
summary. (B1) An alternative of merchandise-level summary based on bar groups
design. (B2) An alternative of merchandise-level summary based on a
“cylinder”-like design.

Design Alternatives. We explored two alternative designs for our merchandise-
level summary. The first alternative (Fig. 4\- B1) displayed the contribution
of three channels for each merchandise as bar chart groups. However, this
design did not effectively show the proportional relationship between each
channel and lacked price information and averaged target details. Hence, we
pursued a second alternative (Fig. 4\- B2) using a "cylinder"-like design,
with the upper pie indicating contribution proportion and the bar below
representing the price. The positions of the red "pistons" displayed the
averaged target of each merchandise. However, user feedback revealed
difficulties in capturing the variation of averaged targets using this
representation. Consequently, we established a connection between the
contribution representation and the averaged target and chose to use a donut
chart instead of a pie chart to minimize visual obstruction. These
modifications resulted in our current design, which aims to provide a
comprehensive and informative summary of the merchandise-level data.

####  5.3.3 Comment Reference

The Comment Reference part (Fig. 2\- C3) facilitates users in retrospectively
accessing the real-time comments that were generated during live streaming, as
articulated in requirements R3 and R6. It includes a timeline and a summary of
comments. In order to present both the overarching temporal patterns of the
comments and informative details contained therein (such as opinion on
merchandise), the summary is presented using a volume chart-based design with
four components: a background volume chart, a zig-zag curve, a foreground with
individual comment data points, and a floating keyword box. The background
volume chart illustrates the trend of comment intensity, and a zig-zag curve
that is bounded by the volume chart is constructed along the timeline. To
elucidate the semantic distribution of the comments (Fig. 5), we first create
embeddings for all comments within the clip using a pre-trained BERT model.
Subsequently, we project these embeddings onto a 1D virtual axis using t-SNE,
and assign colors to each dot using a gradient palette, from left to right
along the virtual axis. Finally, all colored dots are mapped back onto the
zig-zag curve in chronological order. Furthermore, as the vertical cursor
hovers over a particular time segment, the keyword box at the bottom floats
synchronously, exhibiting the extracted keywords within that time segment in
descending order of weight from left to right. Notably, the detailed matrix
and comment summary are also aligned vertically with the timeline ( DP2).

Figure 5: Comment dots are colored by creating embeddings using pre-trained
BERT, projecting them onto a 1D axis with t-SNE, and assigning colors from a
gradient palette.

Design Alternatives. We explored two alternatives for comment summary design.
The first alternative (Fig. 6\- A) utilized a word cloud with a colorfield
background to indicate comment intensity along the timeline. However, this
design did not effectively convey accurate semantic information for certain
time segments. The second alternative (Fig. 6\- B) employed a beeswarm chart
with colored dots representing the semantic distribution. While this design
captured semantic variations within and between time segments, it did not
reveal the sequence of comments. Consequently, the current design (Fig. 6\- C)
was developed by combining the two alternatives. In this design, the dots
representing comments are arranged along a zig-zag curve to maintain
chronological order, and a floating keyword box is positioned below the curve
to facilitate the identification of the most relevant keywords.

Figure 6: Comment summary designs for Comment Reference part. (A) An
alternative based on word cloud and colorfield design; (B) An alternative
based on beeswarm chart; (C) Our current design.

###  5.4 Interaction Workflow

Incorporating a Time Linkage Mechanism, LiveRetro synchronizes multiple views
within specific time segments ( DP3). This mechanism seamlessly integrates
interactive operations among the views in the cyclical workflow. LiveRetro
offers users multiple options to activate the time linkage features, including
clicking on a merchandise box in the Session View, lassoing or clicking glyph
points in the Segment View, and clicking or brushing in the Exploration View.
Upon selecting time segments, the system responds by locating and highlighting
the corresponding merchandise in the Session View, highlighting the
corresponding glyph points and time curves in the Segment View, moving the
vertical cursor in the Exploration View to the corresponding segment, and
causing the live replay to jump to the corresponding time. As shown in Fig.
2\- c, to complete the workflow cycle, users first select a specific time
range in the Session View, then proceed to examine merchandise sales data and
designate a particular clip by clicking . Next, users transition to the
Segment View to determine segment granularity and analyze performance
distribution through lassoing and semantic zooming techniques. Subsequently,
users navigate to the Exploration View to further inspect performance using
hovering and tooltips. Following the modeling and reference parts, users can
the current selection or save selected segments as  or  by clicking the top
right buttons. Finally, users can brush or click the Exploration View to
proceed to the next circulation back to the Session View or to move to the
Record View for a summary of the entire process.

Record View (LiveRetro: Visual Analytics for Strategic Retrospect in
Livestream E-Commerce\- D). To meet user feedback requesting follow-up
references, we introduced a summarized performance feature with four
attributes: corresponding target, selected time segments, summarized
performance using the Segment View and Exploration View glyph pattern ( DP1),
and vertically aligned timeline thumbnails for easy comparison ( DP2). Users
can delete specific selections with the  icon and export all preserved
selections, along with performance information, for further analysis .

##  6 Evaluation

###  6.1 Case I: Strategic Retrospect for Streamers

In Case I, a retrospective analysis was conducted on the livestream sessions
delivered by streamer E1. The focus of the analysis was centered around the
streamer’s efforts to reflect on past failures and employ appropriate
promotional techniques for maximum efficacy.

“What were the reasons for the failure of the entertainment?” Following the
interaction workflow, E1 opted to investigate the livestream sessions from the
past ten days. E1 selected the session with the highest GPM value (LiveRetro:
Visual Analytics for Strategic Retrospect in Livestream E-Commerce\- 1) and
examined the merchandise sales data in the Session View. It was discovered
that the merchandise “Marker Pen” had poor sales performance 2, with low
Exposure-click ratio and Click-turnover ratio. Curious about the reason for
this, E1 accessed the corresponding period’s merchandise box to view the
Segment View with the one-minute granularity selected, it was observed that
the atmosphere pitch was dominant in Text channel during that period 3. This
prompted his interest in exploring the relationship between these features and
sales data. To gain a better understanding of the sales data, E1 turned to the
Exploration View to investigate further. It was realized that the sales amount
and sales volume in the Session View were not entirely comprehensive due to
the varying prices of different products. Thus, E1 opted to utilize GPM as the
target in the Correlation Modeling part. However, during that period, a low
GPM value 4 was still obtained with significant negative contributions from
the Text and Frame channels displayed in the corresponding merchandise-level
summary 5. To understand this further, E1 turned to the Performance Inspection
part to examine these multi-modality features. It was observed that there were
very few facial expressions during this period, and a large number of close-
ups in the camera position 6. After analyzing the video content, it was
discovered that the streamer was drawing a small rabbit on paper to convey New
Year’s greetings to the viewers 7, with many atmosphere pitches in the Text
features. It was believed that this “talent show” captured the viewers’
attention, leading to fewer orders.

Drawing upon this assumption, E1 formulated a hypothesis that the feedback
provided by the viewer would be favorable. As a result, E1 modified the target
data to reflect a positive sentiment by replacing it with the term Like, and
subsequently observed a peak in the Like value during that time period 8.
Additionally, the inclusion of Text features appeared to have a positive
impact on this outcome. E1 conducted further examination of the Correlation
Modeling part by clicking on the blue stack bar to direct to the detailed
contribution summaries below, which led to the discovery that the atmosphere
pitch has a positive contribution to the target data 9, as determined by its
SHAP values. In order to gain a more thorough understanding, E1 also
investigated the situation when the target was Departure 10. This yielded a
comparable result, with the exception of a dip in the departure value and
negative contribution of the atmosphere pitch 11, which was sensible given
that low departure values indicate high popularity. Finally, E1 analyzed the
Comment Reference part 12 and found that the abundance of semantically similar
comments, such as those containing terms like “support, talented, excellent,
like”, during this time period. The prevalence of such comments suggests that
viewers responded positively to the content. This further reinforced E1’s
initial belief. Upon analyzing the data of the three targets, it was
determined by E1 that the use atmosphere pitch should be moderate, as
excessive employment may result in increased viewership but decreased sales.
Consequently, a decision was made to maintain a balance in the utilization of
various sales pitches during subsequent sessions. For ease of future
reference, E1 appended this period to the Record View under the category of
Drawbacks 13.

“Are there alternative methods that would yield better outcomes?” After
examining the Drawbacks, E1 developed an interest in the methods that led to
the high sales periods. As such, he turned his attention to the GPM and
scrutinized the raw value peaks, uncovering a surprising period where the GPM
value initially decreased but quickly rebounded and peaked with a significant
positive contribution from Text channel 14. This discovery prompted E1 to
investigate further in the Performance Inspection part. While analyzing the
sales pitch employed predominantly during this time, E1 discovered that it had
transitioned from the atmosphere pitch to the selling pitch and order pitch
15. Upon reviewing the corresponding video content 16, he noted that the
streamer began by providing a historical tidbit relevant to the product to
capture the viewers’ interest before transitioning to promoting the product.
Subsequently, E1 delved deeper into the Correlation Modeling part and
meticulously analyzed the Text feature, ultimately concluding that all three
sales pitches mentioned above had a positive impact on the target data during
this period 17. Upon comparison with the aforementioned Drawbacks, E1 realized
that the optimal employment and arrangement of diverse sales pitches were the
primary drivers behind the GPM peaks. He noted that the judicious use of the
atmosphere pitch to attract viewers’ attention followed by a seamless
transition into selling pitch and order pitch for promoting the products
proved to be a wise choice. Finally, E1 appended this period to the Record
view under the category of Highlights 18 for future reference.

Takeaway message. By employing predictive modeling and conducting case studies
involving streamers, we have discovered that sales pitches exert a significant
influence on streaming statistics. This underscores the importance for
streamers and researchers to place greater emphasis on sales pitches,
particularly their combinations, in order to achieve optimal results and
outcomes.

###  6.2 Case II: Enlightenment for Marketing Researchers

In Case II, E5 exhibited significant interests in the viability of
conventional marketing and communication theories in this scenario.

Figure 7: Case II: Enlightenment for Marketing Researchers. 1 Identifying
effective glyphs for combining different sales pitches, 2 peak in Like value
during the period, with a positive contribution from the Text channel, 3
declining trend and a negative contribution from the Text channel in
Departure, 4 observe the keywords like “purchased, like, delicious, fond”. 5
Increasing trend in GPM value during the period, with a potential S-O-R
paradigm, 6 various sales pitches were used, and 7 the streamer was
demonstrating milk’s quality by scooping and savoring it.

The S-O-R paradigm in livestream e-commerce. According to Jacoby’s [26]
Stimulus-Organism-Response (S-O-R) model, external stimuli can affect the
cognitive and emotional states of individuals, which in turn can influence
their behavioral responses. In the context of livestream e-commerce, E5 aims
to investigate the applicability of the S-O-R paradigm. Particularly, E5
employed the S-O-R model as a theoretical framework to investigate the
interplay between the streamer’s performance, feedback from viewers, and sales
data, which were respectively interpreted as the Stimulus, Organism, and
Response. Given that the sales pitch is a crucial persuasive factor that
directly affects a viewer’s purchase decision, E5 focused on the Text channel
during the Stimulus process. Initially, E5 analyzed the usage of sales pitch
in the Segment View and identified glyphs that combined different sales
pitches (Fig. 7\- 1). To understand the impact of these glyphs on viewer
feedback, E5 examined one of them in detail using the Exploration View and
used Like as the target in the Correlation Modeling part. The results showed
that the Like value peaked during this period, with a positive contribution
from the Text channel 2. To validate this finding, E5 changed the target to
Departure, which revealed a declining trend and negative contribution from the
Text channel 3. Additionally, E5 investigated viewer feedback further by
analyzing the Comment Reference part and discovered a high intensity of
comments containing keywords like “purchased, like, delicious, fond” 4, which
suggested positive viewer feedback. Finally, E5 examined the sales data by
changing the target into GPM to explore the response. As expected, the GPM
value demonstrated an increasing trend during this period 5. Following the
aforementioned exploration process, the feasibility of the S-O-R model in the
context of livestream e-commerce was ascertained by E5. The finding has
motivated E5 to conduct additional research on livestream e-commerce using
this model. In general, E5’s investigation of the S-O-R model has uncovered
new possibilities for comprehending the intricate dynamics of livestream
e-commerce. This could lead to more efficient and specific strategies in this
field.

Role of streamers from the perspective of McGuire’s Communication Persuasion
Matrix. McGuire’s Communication Persuasion Matrix [41] encompasses input
communication variables such as source, message, channel, receiver, and
destination, and output mediational steps that underlie the persuasion process
in the public communication campaigns. In this regard, E5 exhibited keen
interest in investigating the distinctive source factor, streamers, within the
context of marketing communication in streaming media. Building upon previous
explorations, E5 examined the Performance Inspection part in the Exploration
View and observed various sales pitches being used during this period 6.
However, E5 recognized the need to delve deeper into the content of these
sales pitches to explore the persuasive impact of streamers as communication
factors. Consequently, E5 brushed this period for further exploration of
details. By referencing video content, E5 discovered that the streamer
employed narrative and presentation techniques to market milk, such as
demonstrating its quality by scooping it and savoring it 7. E5 posited that
such a personalized presentation of the product could engender an authentic
sense and foster credibility with prospective customers, thereby augmenting
the streamer’s persuasive effectiveness as a source. Moreover, the streamer
customized the explanation of milk’s benefits for different age groups,
leading E5 to hypothesize that this approach could enhance viewer engagement
and foster a sense of being understood, ultimately cultivating stronger
connections with the viewer as a source. By combining the aforementioned peaks
in GPM and Like values within the Correlation Modeling part with the
corresponding video content, E5 discerned that the streamer’s persuasive
approach yielded remarkable outcomes. Moreover, E5 identified that this
process conforms to McGuire’s Communication Persuasion Matrix, which
emphasizes the importance of source characteristics in persuading viewers. E5
believed that these findings could prove valuable to businesses seeking to
employ streaming media as a marketing communication tool, as they offered a
framework for crafting compelling and impactful content that resonates with
diverse viewers.

###  6.3 Expert Interviews

To assess the effectiveness of LiveRetro compared to a commercial system
previously utilized by streamers (Fig.1 in the appendix), we conducted semi-
structured interviews with E1-E5, which included a 15-minute tutorial and
lasted about an hour in total. During the sessions, participants used both
systems for approximately 50 minutes to conduct strategic retrospective
analyses on randomly selected sessions.

System Performance and Design. Experts confirmed LiveRetro’s effectiveness in
strategic retrospective analysis in livestream e-commerce and correlating
video content with synchronous feedback. E1 and E3 praised its multi-level
contribution summaries for quickly revealing performance features that
influence the target. E3 noted that LiveRetro provides a more holistic
perspective and accelerates retrospective analysis, while E4 found it helpful
in examining marketing theories in the context of livestream e-commerce.
Experts confirmed that the visualizations are valuable, easy to understand,
and with fluid interactions. They favored the Time linkage Mechanism and the
vertically synchronized cursor for their “dynamic and immediate access to
information”. The Exploration View was also appreciated, with E5 finding the
Performance Inspection intuitive and potentially “saving weeks of work”. E1
and E2 found the Correlation Modeling highly useful for exploring feature
contributions from different channels, while E3 perceived it as complex due to
the amount of information visualized.

Learning Curve. The experts noted that LiveRetro, with more components, took
some time (usually a 20-minute trial) to get accustomed to, in contrast to the
commercial system. However, they found LiveRetro to be incredibly useful for
understanding live streaming practices. They gained valuable insights into the
relationship between live performance and synchronous feedback and expressed
interest in using it for retrospective analysis and theory validation in the
future.

##  7 Discussion and Limitation

Lessons Learned. In our collaboration with experts, we learn three important
lessons. First, livestream e-commerce holds significant untapped potential for
exploration, encompassing various unexplored scenarios, factors, and
relationships. Second, specific guidelines for designing visual interfaces in
this domain are essential, as existing literature can result in overwhelming
interfaces. Through expert input, we provide tailored principles for future
design. Third, achieving “Alignment” in joint video content and synchronous
feedback analysis is paramount. Our system ensures alignment at multiple
levels, enhancing organization and cognitive efficiency.

Practical and Theoretical Inspirations. Our study reveals that family life
segments in live streaming have a higher average viewer engagement time
compared to informative segments. This finding leads our streamers to focus on
emotional resonance to increase viewer retention, and marketing researchers
appreciate our categorization of sales pitches. Our work encourages further
research on the effects of different sales pitches and moves beyond general
streamer characteristics.

Generalizability and Scalability. LiveRetro enables comprehensive and
strategic retrospective analysis of livestream e-commerce and has been
successfully tested on TikTok with streamers and marketing researchers. It can
also be applied to other platforms like CommentSold, Bambuser, and Instagram
Live for retrospective analysis. The system can be extended to support joint
analysis of video and synchronous feedback in different domains. However,
scalability issues arise with feature extraction and visual designs. To
address this, techniques such as GPU acceleration and parallel computing can
be utilized for efficient facial expression extraction. Moreover, visual
clutter in the Segment View can be reduced by implementing semantic zooming
and click-to-focus features for specific segments of interest.

Limitations. Due to data regulations, streaming statistics on TikTok have a
maximum sampling rate of one minute, necessitating the adoption of the same
granularity for performance features. However, finer granularity, like two-
second intervals, could improve real-time feedback and capture intricate
correlations. The current modeling approach lacks consideration of
merchandise-specific characteristics, such as category, popularity, and cost-
effectiveness, which can influence feedback. Additionally, case studies
involved our collaborative team, necessitating further testing to strengthen
the system’s usability and effectiveness.

##  8 Conclusion and Future Work

This study develops design principles and a visual analytics system called
LiveRetro for retrospective analysis of livestream e-commerce, incorporating
computational features and feedback from streamers, viewers, and merchandise.
LiveRetro features enhanced visualization and multiple time-series forecasting
models to model specific targets and identify performance influences at
different levels. Two case studies and expert interviews demonstrate
LiveRetro’s ability to provide deep insights and enable efficient
retrospective analysis. Future plans include incorporating merchandise factors
and providing comparative analysis at market and account levels.

###### Acknowledgements.

This work is partially supported by the Shanghai Frontiers Science Center of
Human-centered Artificial Intelligence (ShangHAI) and Key Laboratory of
Intelligent Perception and Human-Machine Collaboration (ShanghaiTech
University), Ministry of Education.

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