1421 Research application/design study C. Zhang, X. Wang, C. Zhao, Y. Ren and
Q. Li are with School of Information Science and Technology, ShanghaiTech
University. Quan Li is the corresponding author. E-mail:
<EMAIL_ADDRESS>T. Zhang is with
Geek+. E-mail<EMAIL_ADDRESS>Z. Peng is with School of Artificial
Intelligence, Sun Yat-sen University. E-mail<EMAIL_ADDRESS>X.
Fan is with School of Entrepreneurship and Management, ShanghaiTech
University. E-mail<EMAIL_ADDRESS>X. Ma is with the Hong Kong
University of Science and Technology. E-mail<EMAIL_ADDRESS>Biv et al.:
Global Illumination for Fun and Profit

# PromotionLens: Inspecting Promotion Strategies of Online E-commerce via
Visual Analytics

Chenyang Zhang  Xiyuan Wang  Chuyi Zhao  Yijing Ren
Tianyu Zhang  Zhenhui Peng  Xiaomeng Fan  Xiaojuan Ma  and Quan Li

###### Abstract

Promotions are commonly used by e-commerce merchants to boost sales. The
efficacy of different promotion strategies can help sellers adapt their
offering to customer demand in order to survive and thrive. Current approaches
to designing promotion strategies are either based on econometrics, which may
not scale to large amounts of sales data, or are spontaneous and provide
little explanation of sales volume. Moreover, accurately measuring the effects
of promotion designs and making bootstrappable adjustments accordingly remains
a challenge due to the incompleteness and complexity of the information
describing promotion strategies and their market environments. We present
PromotionLens, a visual analytics system for exploring, comparing, and
modeling the impact of various promotion strategies. Our approach combines
representative multivariant time-series forecasting models and well-designed
visualizations to demonstrate and explain the impact of sales and promotional
factors, and to support “what-if” analysis of promotions. Two case studies,
expert feedback, and a qualitative user study demonstrate the efficacy of
PromotionLens.

###### keywords:

E-commerce, promotion strategy, time-series prediction, “what-if” analysis,
visualization

K.6.1Management of Computing and Information SystemsProject and People
ManagementLife Cycle; K.7.mThe Computing ProfessionMiscellaneousEthics  Case
I: (1) Select a product within the red circle among all 361° sneakers in the
Product Overview. (2) Browse the product description of the selected product
at the buttom of the scatter chart. (3) View the annual data of the selected
product in the Promotion Overview. (4) Adjust the time period selector in the
Sales Prediction View. (5) Look through the dotted lines and find non-
overlapping zones. (6) Change the promotion strategy in the Strategy View –
exchange the value discount that exists with the same discount rate and
percentage discount for the time period. (7) Discover that sales have
decreased. (8) Conclude that the value discounts may be appropriate for the
selected product. (9) Compare similar products in the glyphs, word clouds and
box plots in the Competitive Analysis View, and select another XTEP product.
(10) Adjust the time selector to fix a time period for the newly selected
product. (11) Replace the existing percentage discount in the Strategy View
with the same discount rate and value discount for the time period. (12) Find
that sales have decreased. (13) Conclude that the percentage discount may be
appropriate for the selected product.

Introduction

E-commerce has boomed over the past few decades as more and more transactions
can be completed online. The boom has brought competition to e-retailers, who
must constantly gain new ideas in promotions to retain and grow customers.
Previous studies have shown that promotions in the Internet environment are
likely to encourage people to buy goods [12, 16, 18, 19, 57]. Sales rocketed
over the periods of successful e-commerce promotions, such as Black Friday,
Cyber Monday, or Singles Day [37, 46, 55]. However, simply adding discounts to
merchandise is not always desirable, and different promotion strategies and
combinations can significantly affect or even change the results. Research on
giveaways suggests that if customers pay more than they expect, the
corresponding promotion strategy may backfire and reduce sales [8]. Similarly,
some researchers have noted that even though both promotions can save buyers
the same amount of money, there is still a preference for promotional terms
between categories [39], leading to different performance in sales statistics.

Some previous studies have provided reasonable and explainable predictions for
sales volume by comparing different machine learning (ML) and deep learning
(DL) models to visually dig into the reasons behind each rise in sales [31,
43, 47, 54]. However, to the best of our knowledge, most of these works have
focused on the statistical characteristics of time series data rather than
incorporating the promotion strategies that most e-commerce retailers must
consider in order to gain more attention and expect higher profits. On the
other hand, researchers in management and economics have proposed a number of
qualitative and quantitative theories for various effects induced by
promotions, such as the frequency of hosting discount events [17], the art of
recommendation using complementarity [50], and the substitution effects [29].
However, few of them are able to scale to larger data volumes, support
interactive guidance of promotions, and incorporate predictive capabilities
[14]. Notably, we considered the interests of both parties and tried to answer
the following questions: 1) Which promotion strategies are more effective and
what are their features? 2) What would happen to sales volume if other
promotion strategies were chosen instead of traditional ones? 3) What
combinations could bring the highest sales?

Nonetheless, examining promotion strategies and analyzing their correlation
with sales volume is not straightforward. First, future trends in sales volume
can be influenced by a variety of factors, such as the inherent attributes and
time-series characteristics of the merchandise [47, 54]. However, the analysis
of the discount impact would not be convincing if we could not first de-
correlate different factors. Second, the diversity of promotion strategies,
e.g., “event sponsoring” and “offering refunds”, also makes it more difficult
to accurately understand and predict sales, and therefore requires a great
deal of effort to properly quantify and normalize different kinds of promotion
strategies. Third, e-merchants usually decide their promotional policies based
on their past sales statistics and business experience with specific items.
They, especially small and medium-sized e-merchants rarely have the
opportunity to quantitatively review the promotion strategies of other
merchants. In addition, the risk of betting once on a new promotion strategy
prevents them from trying other strategies or combinations that might produce
better results than the current situation. Little research has been done on
the “what-if” analysis [20] of promotion strategies.

In this study, we examine promotion strategies in online e-commerce by
considering time-series sales data and promotional factors. Notably, we first
conduct an observational study of the current practices of our collaborating
experts to identify their main needs and concerns regarding the analysis of
promotion strategies for e-commerce platforms. We then streamline the analysis
of promotions by leveraging multiple interpretable machine learning models to
identify key factors that have a significant impact on sales volume. We
support domain experts in comparing the performance of different products in
terms of sales volumes, products descriptions, promotion activities, and
related features. We further provide an interactive bootstrap mechanism of
promotional features to determine the response of machine learning models. We
further propose a visual analytics system, PromotionLens, to help end-users
select, compare, and combine different promotion strategies. Specifically, it
helps users to select target items, check out key promotional factors, and
make promotion choices through “what-if” analysis. Two case studies,
interviews with domain experts, and a qualitative user study validate the
effectiveness of our approach. We summarize the main contributions as follows:

  * •

We consider time-series correlation and quantitative promotion strategy
factors for sales volume forecasting.

  * •

We propose a visual analytics system with new features to support inspection,
comparison, and “what-if” analysis of multiple adjustments to promotional
policies.

  * •

We conduct two case studies, interviews with marketing researchers and
e-merchants, and a qualitative user study.

##  1 Related Work

###  1.1 Visualizations in E-commerce

Recently, e-commerce has paid increasing attention to big data analysis [3],
in which visualization techniques have been used in depth [21, 34, 59]. For
example, Huang et al. [26] developed a visual analytics system to classify
customers into categories in order to explore shopping patterns in each
category. VAET extracts timestamped events and anomalies in time series to
explain the correlations between salient transactions and temporal events
[53]. DAV is a system for visually correlating product affinities for large
amounts of e-commerce transaction data [22]. Ko et al. [32] developed a system
for analyzing competitive advantage using point of sale data. Brainerd et al.
[7] developed an interactive, scalable visualization tool to analyze user
behavior by using clickstream data on e-commerce websites. Kohavi et al. [33]
reviewed the architecture and discussed lessons and challenges of mining
retail e-commerce data. In this work, we focus on exploring the relationships
between various promotion strategies and the sales performance of e-commerce
items. To the best of our knowledge, PromotionLens is the first visualization
system that supports users in quantifying, comparing, and performing “what-if”
analysis of promotion strategies based on interpretable time-series ML models.

###  1.2 promotion Strategy Analysis

Studies focusing on the analysis of the correlation between promotions and
sales amounts have been proposed from different perspectives dealing with
different complex situations. For example, Agmeka et al. [1] started from the
relation of brand reputation and actual payment actions, concluding that
marketers who do discount more often will earn themselves a good reputation
and higher selling amount. Other scholars focused on the choice of promotion
strategies [28]. Some have studied the effect of promotion duration [36],
while others have examined whether complementary strategies are better than
current strategies across products. There are also studies on discount
pricing, which focus on designing optimal discount policies to maximize the
firm’s profits [5, 56], mainly seeking optimal discount pricing strategies to
attract consumers in order to increase revenue. According to [36], in the era
of e-commerce marketing, various promotional policies are intensively used and
consumers are more rational in their online shopping. For example, research on
giveaways suggests that if customers pay more than they expect, the
corresponding promotion strategy may backfire and reduce sales [8]. Similarly,
some researchers point out that even though both promotions can save buyers
the same amount of money, there are still preferences for promotion terms
between different categories of goods [39], leading to different performance
in sales statistics. Therefore, combining these factors and sales volume has
generated new meaningful research questions in e-commerce. However, most
traditional approaches usually make conclusions more qualitatively, which is
difficult for machines to understand and quantify into the same unit. In order
to create a system that can reasonably consider the effects of a wide variety
of promotion strategies, we characterize the design requirement in an
e-commerce scenario and seek to quantify the discount measures in the dataset
into features of the same unit, which is essential to provide a reasonable
result.

###  1.3 Time-series Forecasting and Evaluation

Anticipating upcoming trends is one of the hottest topics in the e-commerce
industry, usually based on time series forecasting with past statistics, and
plays a key role in e-commerce decision making. According to the model
taxonomy [2], time series models can be classified into four groups, namely,
auto-regressive models, exponential smoothing (ES) models, machine learning
models, and neural network models. As models evolve, the challenge of
receiving information, classifying it, and selecting the appropriate model
become increasingly difficult for human analysis. As a result, researchers
have started to propose various methods to help users understand the output
and compare different predictive machine learning models. For example, DFSeer
[48] imports automatic model selection methods in time series models to help
users make better prediction choices. However, it still cannot support making
forecasting based on multiple variables, which is inconsistent with most cases
in practice. Therefore, multivariate time series models have been proposed to
adjust for chance and make more realistic forecasts. For example, Lewis et al.
[35] proposed an autoregressive model fitting method that can give
satisfactory results for bivariate ARMA models. Recently, the development of
deep neural networks equipped with e.g., recurrent neural networks (RNNs)
[11], multi-layer perception (MLP) [10], and convolutional neural networks
(CNNs) [51] has taken the time series prediction to a new level. In this work,
we employ some of the most representative machine learning algorithms for
future prediction based on historical commodity data.

In addition to prediction, various evaluation methods have been proposed in
the machine learning, visualization, and human-computer interaction
communities to compare the performance of different time series prediction
models. Notably, the most basic approach is to use various metrics such as
mean squared error (MSE) [4, 27], scale-dependent measures such as root MSE
(RMSE) [9, 30], percentage errors such as mean absolute percentage error
(MAPE) [6, 44]. Although these basic methods can evaluate different
multivariate prediction machine learning models, they do not provide detailed
explanations about model comparisons, evaluation, and feature importance.
Recently, Xu et al. [54] proposed mTSeer, a visual analytics system to compare
and evaluate different multivariate time series forecasting models in a
bootstrappable manner. Similar to their work, we use the importance of various
features to explain the sale volume forecasts. However, we differ from mTSeer
in that we further support “what-if” analysis, allowing domain experts to
fine-tune, e.g., change, combine the features extracted from promotion
strategies and learn the response of the model.

##  2 Observational Study

###  2.1 Experts’ Conventional Practice and Bottleneck

To understand how e-commerce retailers develop and determine promotion
strategies, we work with a panel of five domain experts from a partnering
retail company and a local university’s entrepreneurship and management
departments. The group includes a researcher whose expertise involves consumer
information processing and awareness (E1, female, age: $34$), an e-commerce
platform manager (E2, male, age: $36$), a retail data analyst (E3, male, age:
$25$) and two promotion strategy marketers (E4, female, age: $27$ and E5,
male, age: $28$). A considerable part of E2 – E5’s work is to develop a
reasonable and practical promotion plans before the promotion season (e.g.,
Singles’ Day, $618$, and Black Friday), so as to increase product sales and
try to help e-commerce retailers achieve their profit targets. The retail
platform manager (E2) said that for small and medium-sized retailers, the
discount sale season is one of the best opportunities to boost sales and
revenue. The experts shared the typical process most retailers use to develop
a promotional plan as follows. First, prior to the discount promotion season,
retailers will do a “pre-promotion” campaign to increase sales, and prices
will be as low as possible. As a result, customers can potentially be absorbed
into the promotional merchandise and may help promote the discount campaign
among the target consumers. Through this opportunity, retailers can also get
to know their target consumers and do more personalized marketing. They will
also promote the merchandise in other unofficial channels, such as social
media, to increase the retailers’ exposure. The next step is to put the
discount promotion strategy into action. Merchants will consider current unit
prices, measure discount rates and corresponding plans, and estimate the
associated sales volume. They also partner with other channels to drive
customer flow, e.g., using competitive search rankings provided by the
platform to increase exposure.

Although domain experts have designed and implemented a number of marketing
campaigns and strategies, they all agree that the current promotional programs
are based only on past retail experience and crude sales data analysis. There
are more factors to consider, and the experts mainly encounter the following
problems. First, the experts mentioned that in most cases, they would discount
at half price, which is convenient and has the potential to increase sales to
some extent. However, on the other hand, promotion strategies like “half
price” can lead to a sharp drop in margins and destroy profitability.
Therefore, the retail team needs a demand forecasting model to help them get a
handle on the relationship between sales and prices at any given time, as well
as other possible factors, “such as seasonality and promotions,” said E2.
Second, retail experts said that promotional programs should be designed
differently depending on the different price elasticities (i.e., the degree of
change in market demand caused by price changes of the items sold). For
example, for items with zero price elasticity, considerable discounts will not
increase sales, but will erode margins. For those goods with high price
elasticity, a small discount can greatly increase sales while margins or sales
are slightly reduced. Therefore, designing promotion strategies should pay
close attention to individual items. Third, identifying a specific promotional
plan during the traditional e-commerce promotional season is more like a
gamble of opportunity. There is no way to experience other promotional
programs or to anticipate their corresponding impact on sales. “Promotions
mean higher customer flow and sales, and it would be constructive if we could
predict ahead by trying other promotion strategies,” said E5.

Figure 1: The architecture of PromotionLens consists of the back-end engine
and front-end visualization. (A) The Product Overview helps users select
products they are interested in, discover relationships between other
products, and get information about their titles, brands, and stores. (B) The
Promotion Overview shows the development of a product’s promotions and how
they relate to sales over the last two years. (C) The Sales Analysis View
shows the forecast of the three models and the duration of the promotions. (D)
The Competitive Analysis View shows the data of the five most similar
competitors for comparison. (E) The Strategy Setting View allows to set
customized promotion strategies.

###  2.2 Experts’ Needs and Expectations

To ensure that our approach was well suited to the tasks and requirements of
the general field, we further interviewed all experts (E1 – E5) to identify
their main concerns about developing promotion strategies. At the end of the
interviews, the need for a highly interactive visualization system emerged as
a key theme in the feedback collected. While individual expectations for such
a system had different emphasis, specific design requirements were expressed
across the board.

R.1 Enable interactive comparison of products. Marketers often compete with
peers selling similar products and need information on other complementary
products to make promotional decisions accordingly. Therefore, experts
expected that our approach can provide an overview of available product data,
including but not limited to product ID, category, and brand, to help them
observe relationships with other items and quickly retrieve specific products
for further analysis.

R.2 Predict future trends for individual items. Traditionally, when estimating
and forecasting sales, our domain experts obtain historical sales data from
Enterprise Resource Planning (ERP) system and invite some univariate time
series models to make forecasts. “The system doesn’t perform as well as
expected, especially during promotional seasons,” said E3. “We can empirically
add a linear factor determined by promotion discount rates, but the results
are often unsatisfactory,” said E2. E1 also said that multiple variables
combine to determine the amount of sales, of which “some are difficult to
quantify.” She also commented that “promotion is one of the biggest factors
that motivate shoppers to press the ‘buy button’.” Therefore, our approach
should support multivariate time series forecasting, consider quantifiable
factors, and add promotion features to adjust the prediction results.

R.3 Understand the factors that influence sales volume throughout the product
lifecycle. According to the feedback from the domain experts, it is important
not only to get the best promotion strategy, but also to find out what causes
the diversity of results. E2 and E3 were particularly interested in some rare
and unexpected situations in the timeline. Therefore, equipping the
visualization system with factor analysis allows them to easily observe and
speculate on the influencing factors. E1 also mentioned that advanced
analytics should get feedback during the sales evolution of the merchandise
and that “retailer should be aware of influencing factors that may affect the
sales volume for the whole sales period and not be limited to a certain
promotion phase.”

R.4 Identify the similarities and differences of existing promotion
strategies. A promotion is a short-term incentive activity designed to
encourage people to buy a product or service. Although there are many dazzling
promotions such as ‘‘coupons”, ‘‘gifts”, and ‘‘station-wide discounts”,
according to E4 and E5, there are many misconceptions in designing promotion
strategies. For example, they said that it is easy for some stores to make
promotions regular. “Promotions should be short-term. If they become routine,
product prices become everyday prices, which translates into no promotions,”
said E5. “Once merchants stop promotions, customers will hesitate and refuse
to buy until they are promoted,” which can also cause significant damage to a
brand’s reputation. In addition, many existing promotion strategies are
relatively similar, and some retailers simply follow their competitors without
thinking about why such promotion strategies work. “Some merchants have only
one promotional policy, for example, ‘always discounts’,” said E4. Therefore,
domain experts asked our system to support a detailed exploration of the
similarities and differences of common promotion strategies available on
e-commerce platforms.

R.5 Support “what-if” promotion simulations for each commodity. According to
E1, there is always a strong need and demand for promotion strategy
development. Although previous studies have conducted theoretical and
mathematical simulations of specific promotional behaviors, experts still
wanted a system that can: enable interactive adjustment of promotional
policies, demonstrate the effects of different promotion strategies on a
specific commodity, and summarize the effects of promotional behaviors through
the performance on various commodities. Thus, our system should support “what-
if” promotion simulations to examine the model’s response for each commodity.

##  3 Approach Overview

We propose PromotionLens, a visual analytics system for evaluating promotion
strategies based on time-series predictive models. Figure 1 shows the
architecture of PromotionLens, which consists of a back-end prediction engine
composed of a data processing module and a forecasting module, followed by a
front-end visualization. To be precise, before building the sales forecasting
module, the data processing module quantifies the qualitative text
descriptions in the raw data and transforms them into features that are
acceptable to the data acceptance port for time series forecasting. After
that, various predictive machine learning models are first trained based on
the inputs, and then the prediction module is employed for sales prediction
and interpretation. At this point, we reach the feature importance of each
model. We provide several elaborate visualizations in the subsequent front-end
visualization to enable domain experts to make interactive comparisons between
products, predictive models, and promotion strategies.

##  4 Back-end Engine

In this section, we describe the mechanism of how the back-end engine works,
including adapting raw data collected from a large e-commerce platform into
quantifiable features that can be used in predictive models and to forecast
sales under specific promotion strategies.

###  4.1 Data Processing

We used a dataset collected from a e-commerce platform with hundreds of
millions of consumers, through a partnership with retail companies, to cover
the top $500$ products in the sportswear market in terms of sales volume over
a two-and-a-half-year period from January $2019$ to July $2021$. It is worth
noting that there are three types of data: 1) Sales Volume. The daily sales
volume and cumulative sales volume of different products are recorded; (2)
Product Description. Daily information such as product name, price before
discount, category, brand, and merchant to which the product belongs is
recorded; (3) Promotions. All promotions experienced by each product are
recorded, including a detailed description of the event (e.g., $10 Off Orders
Over $69), the start time, and the end time. Before modeling, several non-
numeric dimensions in the original dataset need to be quantified, such as:
category, brand, product name, and promotion details. Some of these attributes
have a limited range of values and are relatively fixed. It is acceptable to
simply map them to numbers to maintain information. However, with respect to
titles and promotion details, which are a series of descriptive words or
semantic sentences, it is expensive to use a one-to-one mapping because these
words may be different for different products at different times. Therefore, a
well-designed quantification approach to product names and promotional details
is needed.

Designing appropriate product titles is one of the most fundamental ways to
attract potential customers by hitting their searches. The resulting titles
are crucial for machine learning models to distinguish between various
products. As mentioned earlier, product titles are composed of descriptive
words that may not be logically connected to each other and can therefore be
treated as a set of words rather than parsed from the perspective of a
complete sentence. For this purpose, we utilize a bag-of-words (BOW) [58]
method to obtain the information hidden in the titles. Specifically, we first
create a codebook of all the words that appear in product titles. Then, we
split each title into a set of terms and compare them to the codebook to
determine which words appear in the title. In this way, each title can be
encoded as a vector with either $0$ or $1$, where $0$ indicates the
corresponding keyword in the codebook is missing from the title and $1$
indicates that the keyword is present in the title. It should be noted that
the dimensionality of the constructed vector is quite high for a title with
only ten words, which is too sparse for the model to learn features from the
title. To solve the sparsity problem, we follow the idea of word2vec [15] and
reduce the title dimension to $8$ after several experiments, i.e., we
calculate the number of semantically valid words in $100$ titles. The results
show that eight words are sufficient to describe a product.

Although promotions can be diverse, they usually follow some basic rules that
allow us to translate textual information into numerical values. Based on
these rules and the recommendations of experts, we classify all promotions
into two categories, including six promotion types (Table 1), for precise
quantification. For direct discounts (value discount, percentage discount, and
flash sale), we extract two key features. (1) the discount rate $k_{d}$, which
is the ratio of the discounted price to original price; (2) the trigger amount
$p_{t}$, which is the minimum amount to receive the discount. For indirect
discounts (i.e., loyalty points, free shipping, and interest-free
installment), where the reward is not a direct price reduction, the reward and
the original price of the product are taken out as features. In addition,
since the effect of a promotion may exist before the campaign starts or after
it ends, we track the entire lifecycle of the promotion by setting a status
flag.

Table 1: Promotion Types. Category | Type | Form
---|---|---
Direct Discount |  Value Discount | $10 Off Orders Over $69
Percentage Discount | 20% Off
Flash Sale | 30% Off in 4 Hours
Indirect Discount |  Loyalty Points | 100 Loyalty Points Back
Free Shipping | Free Shipping on Orders Over $99
Interest-free Installment | 6 Months Interest-free Installment

###  4.2 Forecasting

In order to select the most appropriate model(s), we conduct a series of
experiments and derive Table 2. To take full advantage of the historical time
series data, as suggested by E.1, we enter the historical average sales into
the model, including the average daily sales for the last month, quarter, half
year and full year. Considering the performance and the interpretability of
the model, we select three representative multivariate time series forecasting
models as candidate forecasting techniques (i.e., RandomForest [25], XGBoost
[13], and MLP [23]). Our system can easily integrate other types of time
series forecasting models, e.g., Vector Auto-Regressive Model (VAR), RNN, and
CNN. Feature importance measures the impact of each feature on the prediction
results, and explains the model’s decision by calculating the additivity of
each feature. We apply SHapley Additive exPlanations (SHAP) [38], an
interpretation method that calculates Shapley values at the instance level, to
estimate the feature importance for the sales prediction results. The Shapley
values of these features add up to five values (i.e., descriptions, price,
temporal information, competitive information, and promotion), indicating the
power from the five relevant aspects. It is worth noting that the Shapley
value can be positive or negative, indicating that the feature contributes
positively or negatively to the prediction.

Table 2: Model performance. Models | RMSE | MAPE
---|---|---
Linear | 4889.36 | 97.21%
RandomForest |  344.93 |  35.21%
XGBoost |  322.34 | 57.69%
MLP | 389.83 |  31.29%
GradientBoosting | 626.87 | 71.64%

##  5 Front-End Visualization

The fundamental design principle of PromotionLens is to leverage or enhance
familiar visual metaphors so that experts can focus on analysis. We follow the
mantra “overview first, zoom and filter, then details-on-demand” [45], to
guide domain experts to explicitly explore, compare, and model the impact of
various promotion strategies. Based on these principles and the previously
mentioned requirements, we design and develop the PromotionLens interface,
which consists of four main views: the Product Overview enables domain experts
to select products of interest and understand their basic information,
including their sales characteristics, product descriptions, and promotion
profiles (R.1); the Sales Prediction View displays the sales volume predicted
by the multivariate forecasting model and lists the promotions in order, along
with the influencing factors (R.2, R.3); the Strategy Setting View gives a
detailed description of the promotion, allowing the domain experts to
configure different promotion strategies and simulate their corresponding
impact on sales volume (R.4, R.5); the Competitive Analysis View shows the
characteristics of competing products, including their promotional
information, and thus helps to compare promotion strategies and motivate
domain experts to find better ones (R.1, R.4).

Figure 2: The left part of the figure shows the meaning of each section. The
right part shows the details and manner of the promotion (a) and its design
alternatives (b): (a) linear bar chart, (b) stacked line chart. Figure 3: (a)
Composition of the time horizon, (b) Visual encoding of prediction, (c) Close-
up view for the sales prediction, and (d) Dotted line graph.

###  5.1 Product Overview

The Product Overview (Figure 1(A)) enables users to efficiently select a
product of interest, observe its general information and examine its
relationship with other products (R.1). Dimensionality reduction techniques
like t-SNE, PCA, and MDS can generate low-dimensional representations and
preserve local similarities to convey neighborhood structures like potential
anomalies and clusters, all of which can be applied to explore and illustrate
patterns in higher-dimensional spaces [24, 42]. Here, we use t-SNE to project
all products into two-dimensional space to see potential clusters and outlier
points. After discussing with domain experts and surveying existing e-commerce
practices, we used the following six metrics: sales median, standard
deviation, quartile range, and the correlation between price, promotion,
season and sales. These metrics form an $n$-dimensional feature vector
$x_{1},...,x_{N}$. We place the corresponding product statistics glyph, each
with six features, in the calculated positions (detailed product statistics
glyphs will be presented in the Competitive Analysis View (Figure 1(D)). To
interact with this view, the user should first set the filters that control
the data, type, and brand of the product. Then, the system will automatically
highlight all products that match the filter criteria for selection. The user
can click on these points to select the target product. In addition, if the
user knows the product ID, he/she can also enter that ID in the search box and
press Search . When a specific product is selected, message tooltip such as
product ID, product name and product category will be displayed.

###  5.2 Promotion Overview

We design a Promotion Overview (Figure 1(B)) to provide the analysis with the
promotions of the selected product and their correlation with the sales volume
in the past periods (R.1, R.3). As shown in Figure 2, there are two rings,
each representing a year. The internal one represents the previous year and
the external one represents the next year. The internal line graph represents
the sales volume of the corresponding promotion offered on that day, and the
external bar graph generates information on “what” and “how many” promotion
strategies were used, with different colors representing a different types of
promotions. Also, the height of the bars of the same color indicates the
number of promotions using that type (Figure 2(a)). During the design
iteration, we replace the alternative design of stacked line charts (Figure
2(b)), because in such a compact chart, stacked line charts can become messy.
We also compare the horizontal design with the radial design. Because
promotions have a cyclical dependency, and a circular glyph can better convey
the cyclical pattern. In addition, the space left for daily promotions is too
narrow to be clearly displayed in a horizontally stacked bar chart, which may
confuse users and increase their learning costs. This radial chart allows us
to compare promotion strategies and sales amounts separately along the radius
for a two-year period.

###  5.3 Sales Prediction View

The Sales Prediction View in Figure 1(C) helps users understand the detailed
correlation between the five most important factors (i.e., price, competition,
temporal variation, title and promotions) and product selling amount. In
particular, at the top of the view is a visualization of the time, which shows
the forecast profiles for the three models (Figure 3(a)), making it easy for
the user to adjust the length by dragging the gray slider to determine the
time horizon to examine in detail. The main view of the Sales Prediction View
shows the results of the three forecast models (R.2) and their interpretation
of sales changes. As shown in Figure 3(b), the graph for each model contains
four parts: (1) the black dashed line (Figure 3(b).1) indicates the ground
truth sales volume for the selected time horizon; (2) the pink solid line
(Figure 3(b).2) shows the actual prices; (3) the purple solid step lines
(Figure 3(b).3) represents the comparison of the predicted sales amount with
the real amount; (4) five colored bars representing the importance of five
characteristics are attached above and below the model prediction line (Figure
3(b).4) (R.3). It is worth noting that, as shown in Figure 3(b).3, the daily
predicted sales amount is represented as a step line rather than a curve or
line segment, so that we can use the step space as the x-axis of daily feature
importance. According to Figure 3(c), inspired by the design of mTSeer [54],
the five feature importance are calculated, normalized, and represented as a
stack of bars (R.1, R.3). Bars above the step lines indicate the positive
impact on the forecast and vice versa. Further than mTSeer, at the bottom of
the Sales Prediction View is a dotted line graph (Figure 3(d)), which implies
the promotion time coded by the length of the dotted line. All the charts
mentioned above have the same X-axis, i.e., the time axis. After selecting a
specific time horizon, the user can compare the performance of different
models and pick up the chart that most closely resembles the ground truth
prediction in order to observe the importance of its features and the
promotions on the selected days. The user can also check the newly plotted
forecasts caused by the updated promotional activity settings in the Strategy
Setting View (Figure 1(E)).

###  5.4 Strategy Setting View

The Strategy Setting View is designed to meet the user’s need for promotion
strategy assumptions (R.5). When a product is selected in the Product
Overview, all promotions within its time frame specified by the user in the
sales prediction view are listed here. Users can use the category filters
above to quickly locate specific promotions. For each campaign, users can
press the Edit button to adjust the campaign status by changing the
description in the box with the formatting in Table 1, or by enabling and
disabling the checkboxes. Users can also modify the duration of each promotion
by dragging the position and length of the slider below each term. Pressing
the Delete  button will delete an existing campaign, while pressing the Add
button will add a campaign. When all promotions are set up, the user can re-
run the model by pressing the Refresh  button and the system will draw a new
step line in purple in the Sales Prediction View and attach an updated feature
importance bar to it.

###  5.5 Competitive Analysis View

The Competitive Analysis View (Figure 1(D)) provides a comparison between the
selected product and several of the most similar products in the same category
(R.1), which can be considered as potential competitors. In this work, experts
recommend comparing at least five other products of the same type. With this
view, the user can find similarities and differences between the target
product and its competitors, and thus build a corresponding promotion strategy
solidly and confidently.

To select the top five most similar products, we calculate the Euclidean
distance between the target product and the other five products in the same
category in the Product Overview (Figure 1(A)). To help users get a general
idea of the products and compare the differences between them, we designed a
novel glyph called product statistics glyph to encode the statistical
attributes of the products (e.g., stability of sales amount, price elastics).
As shown in Figure 4(1a), the product statistics glyph is divided into two
halves. For the right half, we code median (A), standard deviation (B), and
interquartile range (C) for the product sales amount. These three attributes
are encoded as half-donut plots with the same segmentation angle because they
are different features and all are positive. The height of the split donut
shows the attribute value. The higher it is, the larger the corresponding
value is. For the left half, we designed three donut plots representing the
correlation coefficients of sales amount with price (D), promotion strength
(E), and season (F), respectively. Six colors are used in the glyph to
represent the six attributes of each product. Since their values range from
$-1$ to $1$, we set a zero point on the horizon and define the part above the
horizontal line as positive and below as negative, so that users can clearly
distinguish the positive and negative effects of each of these three
attributes. All values here are clamped and normalized with interquartile
ranges to avoid outliers that would cause all other values to show up near
zero.

Figure 4: Competitive Analysis View: (1) Product statistics glyph design; (2)
Box plot design for performance measurement.

In addition to the statistical glyphs for the products, a word cloud is
constructed using all keywords for the six products, with the word size
proportional to the average sales amount of all products containing the word.
Two box plot-based graphs are also shown in Figure 4(2), where users can
compare the sales amount performance and price levels of all six products
individually (R.4). For ease of observation, we divide the time into a fixed
number of intervals, i.e., ten boxes in this example, and we split the time
equally on the x-axis regardless of how long the user in the sales prediction
view sets the time. The total sales amount is calculated for each box on the
exact date the box is positioned in the same category of the selected product.
Below them is a dotted line chart, the same as the one in Figure 1(C), showing
the promotion duration.

Design alternatives. During the iterative process of our system, we went
through several design alternatives for the product statistics glyphs.
Intuitively, we decided to use the glyph based on a donut chart (Figure 4(1)).
It is divided equally into six parts, each representing a specific statistical
property. Although the glyph can perfectly combine the above six data, it is
difficult to set a standard for them due to the different ranges between
correlations, medians, deviations, and quartiles (i.e., correlations range
from $-1$ to $1$, while the range between medians, deviations, and quartiles
is only positive). Initially, we drew positive bars outwards to the center
circle and negative bars inwards (Figure 4(1b)). However, when we tried to use
this glyph in the Product Overview, the normalized bars became too small to be
observed. A possible solution is to have the bars all outward and distinguish
negative from positive numbers by shades of color (Figure 4(1c)), i.e., light
shading for positive numbers and dark shading for negative numbers. However,
this glyph sets up too many rules for each statistic and can be quite
confusing for users to get started quickly. Finally, after discussions with
domain experts, we decided to use the pie-shaped bars for purely positive
values and ring-shaped bars for numbers in the $[-1,1]$ range.

###  5.6 Interactions Among the Views

Rich interactions are integrated to facilitate effective in-depth analysis.
(1) Zooming. The system supports zooming in on the product overview, enabling
users to locate and distinguish overlapping glyphs. (2) Filtering. The line
elements in the Sales Prediction View can be toggled off when the
corresponding legend is disabled, so that other lines can be more clearly
observed. In the Product Overview, users can filter products by category and
brand to focus on specific products. (3) Brushing. The time range in the Sales
Prediction View and the Strategy Setting View can be adjusted interactively.
(4) Parameter editing. Users can edit promotions by modifying the text
description in the input box or enabling and disabling the checkboxes in the
Strategy Setting View to perform a “what-if” analysis of the corresponding
promotion strategy. (5) Highlighting and linking. When the product statistics
glyph in the Competitive Analysis View is selected from the base, the system
highlights the corresponding word and line graph in the box-based glyph. Thus,
the user can have a clear view of the selected competitive product. (6) Hover
on and tooltip. If we hover over the model forecast graph, detailed
information (such as date, sales volumes, price, etc.) is displayed. Also, if
the mouse hovers over a promotion in the Sales Prediction View, it will show
the details of the promotion.

##  6 Evaluation

###  6.1 Case Study

####  6.1.1 Case I: There is No One-size-fits-all Promotion

In the first case, the experts wanted to determine whether similar products
always shared similar optimal promotion strategies. “Footwear is a typical
category of e-commerce marketing products, so we can start with relatively
affordable shoe brands, such as 361°”, said E2. Following this, in the Product
Overview, E2 used the drop-down boxes to filter the categories and brands to
Sneakers and 361°, and he found that one product had a higher Median and STD
than the others, indicating this shoes is well-selling (PromotionLens:
Inspecting Promotion Strategies of Online E-commerce via Visual Analytics(1)).
Therefore, this product with ID 556616451288 is then selected as the target
product and its details are displayed in the information panel
below(PromotionLens: Inspecting Promotion Strategies of Online E-commerce via
Visual Analytics(2)). In the Promotion Overview, E2 found that percentage
discounts and value discounts were frequently applied between April and
October 2020 (PromotionLens: Inspecting Promotion Strategies of Online
E-commerce via Visual Analytics(3)). “Percentage or value, which strategy is
better?”, intrigued by this, E2 adjusted the time slider in the Sales
Prediction View to dive into this period (PromotionLens: Inspecting Promotion
Strategies of Online E-commerce via Visual Analytics(4)).

To estimate the degree of impact of the promotion activity, firstly, E2
defined the growth rate for a promotion: denoting the sales volume at the
beginning of the promotion as ${V_{t}}$, and the sales volume on the previous
day as ${V_{t-1}}$, the growth rate is defined as
$\frac{V_{t}-V_{t-1}}{V_{t-1}}$, picking the sales growth on the first day as
a proxy for the promotion impact.

In order to compare the two promotion strategies, E2 needed to ensure two
things: 1) no multiple promotions are applied simultaneously; and 2) there is
no overlap of the same promotion that interferes with his judgments. With
dotted line graph, E2 found 5 non-overlapping zones for each promotion
strategy (PromotionLens: Inspecting Promotion Strategies of Online E-commerce
via Visual Analytics(5)) and calculated the average growth rate. The result
shows that the growth rate of the value discount is $0.2$ and the one of the
percentage discount is $0.15$, which means the value discount should be more
effective for 361° sneakers.

To verify this assumption, E2 conducted a simulation by transferring other
promotions with the same strength to the selected product to see if there was
a better promotion that can further increase the sales amount. E2 picked the
value discount starting from $1^{st}$ October, $2020$ (PromotionLens:
Inspecting Promotion Strategies of Online E-commerce via Visual Analytics(6)),
which is “30 CNY off orders over 300 CNY”, and changed it to “$10\%$ off”
(same discount rate 0.1). Then, E2 reran the prediction models (PromotionLens:
Inspecting Promotion Strategies of Online E-commerce via Visual Analytics(7)).
Compared with the data before the modification, he found that the prediction
results of both models decreased (XGBoost was excluded due to its high outlier
rate), indicating that the value discount was more suitable for 361° sneakers
(PromotionLens: Inspecting Promotion Strategies of Online E-commerce via
Visual Analytics(8)).

To explore further, E2 turned to examine whether similar products (i.e. the
product’s competitors) share the same optimal promotion strategy with it.
Specifically, in the Competitive Analysis View, he explored the competitors’
glyphs, associated word clouds, and box plots, respectively. The product of
XTEP sneakers with product ID 40527285607 was selected because its glyph,
keywords in the word cloud, and the degree of curve fluctuation in its box
plot was most similar to that of our target product (PromotionLens: Inspecting
Promotion Strategies of Online E-commerce via Visual Analytics(9)). E2 found
that from March to June, $2020$, XTEP sneakers had a value discount promotion
of $0.27$ growth rate and a percentage discount promotion of $0.35$ growth
rate, which meant that the percentage discount seemed appropriate. Therefore,
he used the same procedure as for the 361° sneakers (PromotionLens: Inspecting
Promotion Strategies of Online E-commerce via Visual Analytics(10–11)),
replacing the original percentage discount with the value discount for the
XTEP sneakers, but maintaining their original strength. The prediction results
of two models decreased and one remained still (PromotionLens: Inspecting
Promotion Strategies of Online E-commerce via Visual Analytics(12)),
indicating that the percentage discount is more suitable for XTEP sneakers
(PromotionLens: Inspecting Promotion Strategies of Online E-commerce via
Visual Analytics(13)). Therefore, E2 concluded that not all products have the
same optimal promotion strategies, even though there are many commonalities,
“there is no one-size fits-all”. This case also validates the ability of our
system to support retailers in making predictions and comparisons about
promotions.

Figure 5: Case II: (1) Select a product that has a weak correlation with the
season but a strong correlation with the promotion. (2) Observe and examine
the data for July. (3) Promotion before and after the conversion. After the
conversion, the forecasting sales decreased at the beginning of June and
showed an increase at the end of June. (4) It shows low seasonal relevance but
high price and promotion relevance for this product.

####  6.1.2 Case II: Making Rational Long-Term Promotion Strategy

While exploring the dataset, E1 noticed an interesting pattern in the
Promotion Overview: for some products, the promotion strategy of doing a value
discount at the beginning of the promotional season and a percentage discount
at the end usually performs better than applying both promotions the other way
around, which aroused her enthusiasm to find out why. E1 first identified a
cluster that was weakly correlated with the season but strongly correlated
with the promotion in the Product Overview. Therefore, she selected product
610039588421 in the cluster since its Median (brown) sector is significantly
higher than others (Figure 5(1)). Then, in the Promotion Overview, she found
there are a value discount at the beginning of June $2021$ and a percentage
discount at the end of the month, while the interference of loyalty points is
excluded since this promotion activity continues all month long (Figure 5(2)).
“This is exactly the target pattern, value discount at the beginning and
percentage discount in the end”, E1 then adjusted the time slider in the Sales
Prediction View to cover June, $2021$.

Using the Strategy Setting View, E1 switched promotions so that they appear in
opposite periods (i.e., shifted the value discount to the end of this month
and the percentage discount to the beginning) and recorded the forecastings
before and after the switch. She found that the switch leads to a decrease in
the amount of sales at the beginning of the month but an increase at the end
of the month compared to the chart before the switch (Figure 5(3)). There are
both advantages and disadvantages to each of the two strategies, so “why do
merchants tend to use the strategy value discount at the beginning and
percentage discount in the end?” E1 implied that this strategy could be the
best way for the corresponding retailers through their business experience. E2
confirmed that this commonly used strategy is related to the e-commerce
schedule that seeks high sales volume at the beginning of the promotional
season and requires stable sales volume at the end, because the merchants must
consider the cost of storage carefully.

“Are there other factors that may favor this long-term promotion strategy?” By
looking at the glyphs in the Competitive Analysis View, E1 confirmed that the
product is neither highly seasonal nor price elastic, but relatively more
sensitive to changes in promotions (Figure 5(4a)). She also looked at its word
cloud and found that it had very few keywords and most of them had little
impact on sales, which helped the expert rule out the advertising factors with
sales (Figure 5(4b)). In the box plots, the sales trend for this product and
its competitors in June $2021$ were very similar, which indicated that this
product could be considered as a representative for the overall performance of
the same category. In addition, the price of this product is relatively low
(i.e., RMB $100$) and does not fluctuate significantly, which eliminates the
interference of price changes on sales (Figure 5(4c)). “Most people receive
their salaries at the beginning of the month, which is the time they feel most
confident about spending money.” With more free money at their disposal,
people are more likely to make impulse purchases. Therefore, doing a value
discount at the beginning of the promotional season and a percentage discount
at the end should be the best option for this product, “value discounts
require consumers to spend more money to meet the conditions of the offer, so
it is easier to make people spend emotionally; in contrast, percentage
discounts are a more rational choice”. In addition, this interactive
exploration process evoked her interest, “while more cases are needed to
summarize the hypothesis, PromotionLens has pointed the way,” commented E1.
All in all, this case shows us that PromotionLens can not only help users make
relatively optimal promotion strategies during the long promotion season, but
also capture specific patterns that ML models may fail to extract on their
own.

Figure 6: Results of user perceptions of PromotionLens. $1-7$ represents
“strongly disagree” to “strongly agree” for each statement.

###  6.2 User Study

We conducted a user study to evaluate PromotionLens in three aspects: workflow
effectiveness, ease of visual design and system usability.

Participants. We recruited $12$ participants ($4$ female, $8$ male,
$age_{mean}=23.46$, $age_{sd}=2.93$) through word-of-mouth, consists of
students and faculties from the majors of Computer Science, Economics, and
Design in an university. They were all loyal online shoppers, each with
approximately RMB $2000$ in annual expenditures. We selected participants with
a strong interest in online shopping because they could provide us with
understandable insights and help verify system usability. The study was
conducted face-to-face, with users participating in tutorials, experiencing
the system, performing tasks, and filling out questionnaires. Upon completion,
each participant receives a $\$10$ voucher for the online e-commerce platform.

Tasks. In our user study, each participant was asked to complete two tasks in
turn. The purpose of task 1 was to examine whether the system was capable of
helping users understand the model. In this task, users were asked to find the
period in which a promotion strategy had the greatest impact on sales volume
and then explore the impact of each promotional activity. The goal of task 2
is for users to develop a new promotion strategy. Participants can customize
promotion strategies and check their influence on predicted sales. As our
participants are not e-commerce experts or practitioners, we mainly examine
the integrity and usability of the system design. For more discussion on
system effectiveness, please see section subsection 6.1 and subsection 6.3.

Procedure. The whole process lasted about one hour. Before we presented our
system, all participants were informed that their reactions and feedback about
our system would be collected anonymously in a questionnaire. Then, we
conducted a tutorial session for about $20$ minutes, during which we
introduced the workflow of our system, explained all the operations for each
view, and told the users which types of features were worthy of attention. We
then allowed participants to pre-operate the system and ask us further
questions for around $10$ minutes. When the participants felt ready, they were
given the two tasks mentioned above, which they needed to complete
individually. After completing these two tasks, we would distribute a
questionnaire with questions using Likert scale criteria [41], with $1$
representing “strongly disagree” and $7$ representing “strongly agree” (Figure
6). The items measured in the questionnaire included $6$ items on the
effectiveness of our system workflow, $3$ items on visual design convenience,
and $4$ items on system usability [52, 47]. Other comments that did not
participate in the questionnaire were also recorded for reference.

Results. The questionnaire results are presented in Figure 6. In general,
participants rated PromotionLens highly. In terms of workflow effectiveness,
participants felt that PromotionLens could display sales volume and promotions
(Q3) and that they could easily select and view information about competitors
(Q5). In addition, the visual design of the system generally facilitated the
provision of sufficient information to examine promotion strategies (Q8) and
aided exploration and decision making (Q9). However, some were concerned that
the visual design was not very green hand friendly (Q7), with the main
concerns being that “information is a bit overwhelming for first-time users,
but should not be too difficult after getting familiar with the system”.
Regarding system usability, participants found it quite useful to be able to
select the best model based on the predictions (Q10), to observe the
differences between competing products (Q11), to edit promotions as desired
(Q12), and to reasonably form a workflow (Q13).

###  6.3 Expert Interview

We conducted semi-structured interviews with E1 – E5 to check whether
PromotionLens helped explore the impact of promotion strategies.

System Performance. All experts agreed that the system runs smoothly and the
visualization is decent and organized. “The system is useful for the seller’s
promotion strategy development”. Our approach proposes an innovative “what-if”
analysis, which is helpful for online retailers like E4 and E5. In discussing
with experts what most enlightened them about our system, E1 said that
extracting “unexpected” factors such as seasonality, which data engineers may
not normally notice, may reveal new associations, allowing researchers like
her to validate existing associations or propose new marketing theories.

Learning Curve. The average time spent presenting our system to domain experts
was about $15$ minutes. After the presentation, we gave the experts $20$
minutes to freely explore each view in PromotionLens, and they all found the
system easy to work with. “The information provided by PromotionLens was
sufficiently reasonable for most of our analysis work, and our previous
suggestions and requests were taken into account”. The experts also said that
with a basic tutorial, most users would get started on their own with their
customized learning habits. During the discovery stage, E2, working as a
product manager (PM), needed to contact the management team to raise concerns
that some users may be intimidated and frightened by the unfamiliar workflow.
“However, if they are given a tutorial session to show them the functionality
of each view, they would soon become experts.” For further iterations, he
suggested that we implement a report generation feature to summarize the
identified patterns and outliers so that users without much technical
background can get results quickly and efficiently.

Generalizability and Scalability. In the interviews, we asked about which
components in PromotionLens could be directly transferred to other scenarios;
three aspects were considered valuable: (1) Visualization. E1 commented that
the designs in the Product Overview and the box plot used in PromotionLens are
also widely used in other visualization systems, so they should be easily
generalized to other similar scenarios. (2) Data. E3 noted that currently, our
system only works on $500$ products, but other product data can be easily
inserted because they share the same format. Therefore, our system has a
strong potential to include multiple products. (3) Algorithms. E3 found it
helpful that PromotionLens has an explicit procedure to handle the dataset and
the system is able to switch or add new models when necessary. In addition, E1
suggested that our system could be extended to explore additional features
beyond promotion strategies, e.g., exploring the relationship between seasonal
purchase and product sales volume. Since we have only entered the top $500$
best-selling products in the system, experts noted that scalability could be
an issue for the Product Overview and that “if the dataset contains thousands
of products, the view should be very crowded”. We can hide the filtered
products instead of highlighting the selected products. If the view is still
crowded, we can use multiple hierarchies to reduce the visual clutter.

##  7 Discussion and Limitation

PromotionLens considers most of the explicit factors that can influence sales
volume; it combines time, product name, competitors, and historical sales
volume with promotion strategies and gives a feature importance analysis along
with the forecast. However, more implicit factors might also lead to
fluctuations in sales volume. For example, social factors (e.g., news events,
macroeconomics) may affect the sales volume of certain categories of products.
In addition, according to Forbes, on-site promotional sales are becoming
increasingly popular in online shopping [40], thus making it more important
for e-commerce to weigh in when considering promotion strategies. Researchers
can attach more implicit factors to our work for further study.

Intention of Promotions. Usually, there is only one criterion to evaluate a
promotion strategy: whether it significantly increases sales. However, every
product has its own life cycle. promotion strategies should be designed to
serve the trends at each stage. A roller-coaster sales curve is the last thing
a retailer wants, because rapid rises and falls can put tremendous pressure on
warehousing, which will exponentially increase the cost of the product.
Similarly, rapid sales growth may leave products out of stock, and producing
another batch would cost extra and increase the risk of overstocking.
Therefore, rational sellers tend to have a “hat-like” sales curve [49] (i.e.,
introduction, growth, maturity, and decline phases) and maximize profits. This
lifecycle inspires future iterations where we can implement another input
panel that allows PromotionLens to accept data on e.g., manufacturing cost,
storage cost, to help retailers find the best promotion strategies.

Inspirations for Marketing. The emergence of monthly promotion model inspired
our domain experts to hypothesize that factors such as buyers’ financial
status and buyers’ spending habits may influence the effectiveness of
promotion strategies and further affect sales volume. Thus, PromotionLens is
able to reveal hidden relationships and interesting phenomena for further
academic research in these fields.

Limitation. There are several limitations to this work. First, our system only
works for the top $500$ best-selling sports products from a few well-known
brands. However, in addition to these brands, there are many mid-sized or
small stores on this platform that need help with promotional production.
Large e-commerce stores have a large volume of sales every day, while sales
data for smaller retailers can be very sparse and our system needs further
testing. Second, when quantifying promotions, the system does not take into
account the limitations between promotions, such as not being able to use
value discounts when users choose percentage discounts. A more comprehensive
quantification method needs to be applied to our system in the future. Third,
we recruit participants (mainly students) who lack expertise to do the user
study, resulting in limited validity of the quantitative evaluation of “the
effectiveness of the workflow”. Fourth, for simplicity, we narrow the scope of
the study to footwear and apparel, and as the amount of data expands, we can
study different marketing models in more areas.

##  8 Conclusion and Future Work

In this study, we present PromotionLens to support retailers and researchers
in exploring, comparing, and modeling their promotion strategies. It combines
representative multivariant time-series forecasting models and visualizations
to demonstrate and explain the impact of sales and promotional factors, and
also supports “what-if” analysis of promotions that can inspire marketing
researchers and help online retailers. In the future, we will access data on
manufacturing cost and storage cost to maximize profits for users. We will
also integrate other heterogeneous data, such as news events and
macroeconomics influences, to reveal causes more accurately.

###### Acknowledgements.

We are grateful for the valuable feedback and comments provided by the
anonymous reviewers. This work is partially supported by the Research Start-up
Fund of ShanghaiTech University, the Fundamental Research Funds for the
Central Universities, Sun Yat-sen University under Grant No.: 22qntd1101, and
HKUST-WeBank Joint Laboratory Project Grant No.: WEB19EG01-d.

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