=Paper=
{{Paper
|id=Vol-2578/DARLIAP11
|storemode=property
|title=Exploring the Auto Model Competition Patterns in China's Auto Market based on Complex Networks Theory
|pdfUrl=https://ceur-ws.org/Vol-2578/DARLIAP11.pdf
|volume=Vol-2578
|authors=Sheng Zhang,Haoyang Che,Jiacai Zhang,Yucong Duan
|dblpUrl=https://dblp.org/rec/conf/edbt/ZhangCZD20
}}
==Exploring the Auto Model Competition Patterns in China's Auto Market based on Complex Networks Theory==
https://ceur-ws.org/Vol-2578/DARLIAP11.pdf
Exploring the Auto Model Competition Patterns in China’s
Auto Market based on Complex Networks Theory
Sheng Zhang Haoyang Che
College of Artificial Intelligence College of Artificial Intelligence
Beijing Normal University Beijing Normal University
Beijing, China Beijing, China
zsheng_2018@163.com chehy@hotmail.com
Jiacai Zhang∗ Yucong Duan
College of Artificial Intelligence College of Information Science and Technology
Beijing Normal University Hainan University
Beijing, China Haikou, China
jiacai.zhang@bnu.edu.cn duanyucong@hotmail.com
ABSTRACT have accumulated more than 500 million users, 100 million sales
Understanding the competition pattern of auto models is critical leads, and billions of user behavior data.
for stakeholders including automakers and dealers. However, the In order to solve the problems of traditional methods, we pro-
traditional methods mainly rely on the experience and analytical pose a novel method from the perspective of complex networks,
dimensions of the analyst, which lack reliable methodology and using the sales lead data of auto models from VAWs to build an
ignore the value of user behavior. In this paper, we propose a auto model competition network, and explore and analyze the
novel method based on complex network theory, construct an auto model competition pattern of China’s auto market. Figure
auto model competition network with users’ sales leads, and 1 outlines our framework, which consists of three parts: data
analyze the static characteristics of the network. Besides, by us- preprocessing, network construction, and competition pattern
ing different community detection algorithms and constructing analysis. Among them, competition pattern analysis includes
predictive models, we discovered that there are six major com- network visualization, characteristic analysis, and community
munities in the network, and that price, popularity, model level, structure analysis. Compared with the traditional method, our
as well as model asset ownership, are the main factors affecting method has the following advantages: First, our model is based
community division. on a complex network and has a solid theoretical foundation.
Second, we use the sales lead data of auto models, which is more
valuable than data such as car sales. It comprehensively reflects
1 INTRODUCTION the preferences of users and the comparison of different mod-
China’s auto sales declined for the first time in 2018 [17]. This els. Lastly, we have established a complete analysis framework,
is undoubtedly putting tremendous pressure on stakeholders, which can improve the efficiency and reliability of the analysis.
including automakers and dealers. It is extremely important to By applying our model to 6,152,335 sales leads of 1069 auto
understand the competition pattern of auto models, which can models in January 2019, we have two main contributions:
help them to recognize market needs, identify emerging competi-
tors, and develop targeted auto production and sales strategies. • We constructed auto model competition networks, per-
In terms of the competition patterns analysis, traditional meth- formed visualization and network characteristic analysis,
ods are often limited to strategic management and market analy- revealing the characteristics such as intensified competi-
sis, such as SWOT analysis [8] and the Porter Five Forces model tion and small-world phenomenon.
[13]. However, these methods mainly rely on the experience and • We found six major communities using community de-
intuition of analysts, and lack reliable methodology. In addition, tection algorithms, and built prediction models based on
the analysis dimension is often confined to car sales and user them. We found that price, model level, and popularity
feedback, ignoring the value of other user behaviors. Thus, it were the main factors to affecting community division.
may cause unstable performance in pattern interpretation. The rest of this article is organized as follows. Section II in-
At the same time, with the advent of mobile Internet, vertical troduces the related work of strategic management, marketing
auto websites (VAWs) have become an important channel for and complex network in auto competition pattern analysis. In
people to obtain car information and buy cars. More and more section III, we describe the dataset and data preprocessing steps.
users will browse the car information on VAWs and leave their In section IV, we construct the auto model competition network
sales leads (customer’s personal information, including name and in January 2019 and perform the network visualization. Section
phone number, for sales purposes) before purchasing a car, so V analyzes the static characteristics of the network. In section
that dealers can contact them to make an appointment for a test VI, we divide the community structure of the network, and find
drive. After more than a decade of accumulation, leading websites the main factors affecting community division by constructing
∗ corresponding author predictive models. Section VII concludes the paper.
© 2020 Copyright for this paper by its author(s). Published in the Workshop Proceed-
ings of the EDBT/ICDT 2020 Joint Conference (March 30-April 2, 2020, Copenhagen, 2 RELATED WORK
Denmark) on CEUR-WS.org. Use permitted under Creative Commons License At- Many investigations have researched the auto market competi-
tribution 4.0 International (CC BY 4.0)
tion pattern from different aspects. In this section, we will classify
Figure 1: Framework Overview
the related work into strategic management and marketing anal- Table 1: The Original Dataset Schema
ysis, and complex network.
Strategic management and marketing analysis are the most Field Description Example
common methods to study the competition pattern in the auto
ID Row ID RID_00000001
market. Study [4] applied SWOT analysis and the five-force
User Anonymous user ID UID_111111111
model, studied the competition pattern of Chery Automobile,
Province User province Guangdong
and pointed out the huge threat posed by other brands entering
City User city Shenzhen
the low-end model market. Study [14] analyzed the competi-
Source Sales lead source type Mobile, Web, other
tive environment, opportunities and challenges faced by FAW-
Time Sales lead time 2019-01-01 00:00:00
Volkswagen’s new energy models based on the PEST model and
Brand Brand of sales lead Volkswagen
the SWOT model. However, these methods highly depend on
Model Model of sales lead Volkswagen Lavida
analysts’ experience and intuitions instead of a solid method-
Style Style of sales lead Volkswagen Lavida 1.4L
ology foundation, which could perform less stable in bidding
presentation and pattern interpretation.
Another method is complex networks based on graph theory.
In recent years, the research of complex networks has expended leaving sales leads, improving the authenticity of the data. The
from the fields of physics and computers to society and tech- overlapping sales lead refers to the situation that different mod-
nology. Numerous theoretical studies and empirical analyses els have the same sales leads, which means that users may be
have also emerged [1, 5, 15]. In the auto field, Lijuan Zhang et al. interested in multiple models at the same time, so these models
studied the cooperation network between automakers and parts could be potential competitors. Overlapping sales leads reflect
suppliers, and found the small-world phenomenon of the net- users’ comparison of different models. Compared with other data,
work [16]. Jianmei Yang et al. used the Newman fast community sales leads and overlapping sales leads have higher authentic-
algorithm to divide the network of auto companies into different ity, and timely and accurately reflect the user’s preferences for
communities based on their product categories, and established models (sales leads) and comparisons between different models
a multi-layer network to analyze the confrontation behavior be- (overlapping sales leads).
tween automotive companies [10]. However, these researches are In order to construct the auto brand competition network, we
only from the perspective of automakers and suppliers, without need to process the data into the required form. First, since some
taking user behavior data into consideration. cars have different ids, and/or names of brand, model or style, we
In summary, different from existing researches, we build an need to identify and unify them. After that, duplicate, missing and
effective framework from the complex network perspective, and erroneous entries are eliminated. And because the automakers
use massive sales leads data from VAWs to analyze the auto model and dealers usually analyze the auto model data monthly, we
competition pattern. need to aggregate the sales leads data by month. Besides, shorter
(such as daily) or longer periods (such as annually) may not be
able to accurately or timely reflect the model competition pattern.
3 DATA AND PREPROCESSING For instance, if 100 users left their sales leads in October 2018 on
The original dataset is from one of China’s largest VAWs, which Camry, the model sales leads of Camry in October 2018 are 100.
contains 1 PB anonymous log data from January 2017 to January Finally, we extract and aggregate the same sales leads between
2019. Each entry includes anonymous user ID, province, city, different models as overlapping leads. For example, if ten users
sales lead source and time, as well as the corresponding brand, left their sales leads on Camry and Jetta in October 2018, the
model and style information, which is shown in Table 1. overlapping sales leads between Camry and Jetta in October 2018
As we mentioned before, sales leads refer to users’ information are 10. To study the recent competition pattern, we selected the
for sales use, including names, regions and contact information data for January 2019, including 1069 brands with 6,152,335 sales
of potential customers. If a user leaves his/her information on a leads and 1,129,919 overlapping brand sales leads.
model on a VAW, which indicates that he/she is interested in this Figure 2 illustrates the model sales leads and overlapping sales
car and could be a potential buyer. Because sales leads require the leads distribution in January 2019. In Figure 2 (a), it is obvious that
user’s personal information, users will be more cautious when most models have relatively low sales leads, but a few models such
sales leads from the nodes) and edge weights (the number of
overlapping sales leads from the edges) have been rescaled for
clarity.
Figure 3 gives an overview of the auto model competition
network. Basically, the nodes in the middle have more sales leads
and overlapping sales leads. However, there is a certain distance
between the nodes with the most sales leads (such as Lavida and
Jett), suggesting a potential community structure and they may
belong to different communities.
5 NETWORK CHARACTERISTICS
(a) Sales Leads Distribution ANALYSIS
Degree distribution, average shortest path length and clustering
coefficient are the most common characteristics of a network.
In this section, we will analyze the characteristics of the auto
model competition network in January 2019, and discuss the
interpretation of these characteristics.
• Degree distribution: The degree of a node refers to the
number of edges connected to the node.
The degree distribution is shown in Figure 4 (a). As we can
see, the number of nodes decreases as the degree increase
and decreases almost constantly, except for the beginning
part. Since the degree represents the number of connected
(b) Overlapping Sales Leads Distribution edges of a node, that is, the number of directly adjacent
nodes, which means the degree of a node represents the
Figure 2: Distribution of Sales Leads and Overlapping number of direct competitors of the model it represents.
Sales Leads Therefore, Figure 4 (a) illustrates that as the number of
competitors increases, the number of models decreases.
Among them, the node with the highest degree is Lavida
as Jetta, Lavida and Sylphy have a very high amount of sales leads, (with 809 competitors), instead of the node with the most
ranging from 1 to 165,199. Figure 2 (b) shows the distribution sales leads—Jetta. On the contrary, there are also 28 nodes
of overlapping sales leads between different brands. Similarly, with a degree of 0, that is, isolated nodes without com-
most overlapping sales leads are low, while others are very high petitors. And these models are excluded in the following
such as overlapping sales leads between Jetta and Santana (4,393 discussion. Besides, the average degree is 236.95, which
overlapping sales leads). Figure 2 indicates the number of sales shows that there are nearly 240 competitors per model,
leads between different models is huge, suggesting that there are reflecting the fierce market competition.
different model divisions. • Average shortest path length and diameter: The av-
erage shortest path length is the average distance between
4 NETWORK CONSTRUCTION & all pairs of nodes (if the graph is connected). And diameter
VISUALIZATION describes the maximum path length in a network.
The auto model competition network is essentially a graph. By Due to the large difference in weight between nodes, and
regarding the auto models as nodes (sales leads as size), and the weighted shortest path length cannot be used to de-
competition relationship as edges (if two nodes have overlapping scribe the small-world phenomenon of the network, we
sales leads) which link different models, we can abstract the will ignore the weight of the connected edges (i.e. re-
auto model competition network. In the network, brands with garded as a binary network). And as we mentioned before,
overlapping sales leads are considered to be competitors. And since the original network is not connected, we choose the
the network is built with networkx Python library [7]. largest giant component (LGC network), which is exactly
There are 1069 nodes and 126,650 edges in the network of the original network after removing all isolated nodes.
January 2019. Among them, there are 28 isolated nodes (i.e., no The average shortest path length of the LGC network is
edges). And Figure 3 shows the network of January 2019 without 1.82, and the diameter is 4, which are really small compared
isolated nodes. The size and color of nodes reflect the number of to the number of nodes (1041 nodes). Figure 4 (b) shows
sales leads for the model, and the thickness of edges represents the distribution of the shortest path length between all
the amount of overlapping sales leads. To be specific, if the size of node pairs in the network. Obviously, most nodes have
the node is larger and the color of the node is redder, it has more direct competition (the shortest path length is 1, 23.4%) or
sales leads. And if the thickness of the edge is thicker, the color of common competitors (the shortest path length is 2, 71.1%).
the edge is redder, there are more overlapping sales leads between Only less than 0.5% of the shortest path length equals to
the two nodes, and their competition is fiercer. In addition, the the diameter of the network (length = 4).
figure is drawn using Gephi and its built-in ForceAltas2 layout • Clustering coefficient: The clustering coefficient mea-
algorithm [2, 9]. Non-overlap option was chosen to ensure the sures the situation of interconnection between neighbor
nodes do not overlap. And all the node sizes (the number of nodes of nodes in the network.
Figure 3: Auto Model Competition Network
Figure 4 (c) depicts the distribution of clustering coeffi- and dealers, it is important to understand the actual division of
cient in the network. The clustering coefficient of most auto models in the auto market, identify current and even poten-
nodes is between 0.4 to 0.8, which indicates that most of tial competitors, and assist them in formulating future production
the models with common competitors are also competi- and marketing strategies. Besides, we have initially determined
tors, and there is a relative obvious clique effect. And the that there is a certain community structure in the auto model
average clustering coefficient is 0.64, significantly higher competition network. Therefore, in this section, we will first
than corresponding random network. detect the community structure of the network, and then build
In summary, low average shortest path length and high prediction models based on the communities to find key features
clustering coefficient imply the network possesses the that affect community division and users’ choice.
small-world phenomenon. It means that although most
nodes are not connected to each other, most nodes can be
reached in a few steps. And it is likely to contain cliques
or sub-networks, which implies that the network may
6.1 Community Structure Detection
contain multiple communities, and this will be discussed The community structure was proposed by Girvan and Newman
in section VI. in 2002 [6]. Generally, a community represents a group of nodes
In conclusion, the auto model competition network presents with similar characteristics, and there may be multiple communi-
the differences in degree distribution and small-world phenome- ties in a network. According to the definition, the nodes within
non. Corresponding to the real world, they illustrate the fierce a community are more closely connected, while the nodes of
market competition, and potential multiple communities. different communities are loosely connected. At present, many
community detection algorithms have been proposed, such as the
6 COMMUNITY STRUCTURE AND GN algorithm [6], the fast Newman algorithm [11], and the Lou-
vain algorithm [3]. At the same time, Newman et al. also proposed
PREDICTION a modularity function to evaluate the quality of community struc-
In fact, the auto models already have different classifications ture division in the network [12]. This value is between [-1/2, 1],
according to auto brand, usage, nationality, price range and so and the closer is it to 1, the better the community division effect.
on. However, these classifications can only represent the model’s In fact, the value in practical applications is generally between
own attributes, and cannot comprehensively reflect the users’ 0.3 to 0.7 [12].
evaluation and actual division in the auto market. For automakers
(a) Degree Distribution (b) Shortest Path Length Distribution (c) Clustering Coefficient Distribution
Figure 4: Distribution of Degree, Shortest Path Length & Clustering Coefficient
Table 2: Comparison of Community Detection Algorithms Table 3: Features to Predict Community Division
Modularity Number of Computation Fields Description Example
Algorithm
Score clusters Time (s)
Number of sales
Num_leads 1000
Fast Newman 0.031 282 7.652 leads of the model
Louvain 0.329 6 4.412 The highest price 16.28
Price_high
of the model (in 10,000 CNY)
The lowest price 11.08
Price_low
In this section, we use the Fast Newman and Louvain algo- of the model (in 10,000 CNY)
rithms for community detection, both of which are greedy algo- The model Minicar
Model_level
rithms based on modularity maximization. And the algorithms’ classification (14 kinds in total)
results are shown in Table 2 (edge weights are considered here). The country of Germany
Country_name
Obviously, the Louvain algorithm performs better, not only has the model (10 countries in total)
a higher modularity score, but also has a shorter computation Domestic
Model asset
time. Besides, the interpretability of 6 clusters of 1041 nodes is Country_class (or imported/
ownership
significantly higher than that of 282 clusters. Figure 5 shows the joint venture)
community detection results of the Louvain algorithm, where The brand of Volkswagen, . . .
Brand_name
different colors represent different communities. Although the the model (130 brands in total)
number of nodes in each community is different, the nodes within
the same community are all in proximity. A detailed interpreta- Table 4: Model Prediction
tion of the community division will be in the next part.
6.2 Community Prediction Model Accuracy Precision Recall F1 Score
Based on the community structure detected in the previous sec- Random
0.8119 0.8290 0.8120 0.8036
tion, we constructed several predictive models to find the key Forest
features that affect community division. XGBoost 0.8220 0.8420 0.8220 0.8203
First, we need to propose several features that may influence
the community division of the auto model competition network,
including the number of sales leads, the highest price of the 170K to 240K CNY. Community 2 is mainly popular compact
model, the lowest price of the model, the model classification, the cars between 120K to 170K CNY. Community 3 is basically some
country of the model, the model asset ownership and the brand cheap cars, including mini cars, compact cars, small cars and
of the model, as shown in Table 3. Among all these features, the SUVs. Community 4 has the most models, which are all expen-
first three features are numerical variables, and one-hot encoding sive cars, such as SUVs, medium cars, medium and large cars,
is used on the rest four features. large cars, luxury cars and sports cars. Community 5 does not
Then Random Forest and XGBoost with 5-folds cross-validation include sedans, but MPVs, trucks, pickups, vans, buses and so on.
are applied to these features and community labels. The metrics Finally, Community 6 is mainly domestic SUVs.
and performances are shown in Table 4, which are all mean val- Combined with Figure 5 and the community characteristics
ues with 5-folds cross-validation. Obviously, the XGBoost has above, we have several findings: First of all, the compact cars
better performance in all metrics. And we find that the most within 120k to 170k in China are the most popular ones (i.e.
important features are price (‘price_low’ and ‘price_high’), popu- community 2) with the highest average sales leads. Second, SUV
larity (‘num_leads’), model level (‘model_level’), and model asset is the most popular model classification, appearing in almost
ownership (‘country_class’). every community. And domestic SUVs and imported and joint
Therefore, according to the community division and key fea- venture SUVs are in different communities. Finally, we find that
tures, we can summarize the characteristics of all the 6 com- the high price community (community 4) has the largest number
munities, illustrating in Table 5. To be specific, community 1 is of models, but the average number of sales leads is the minimum
mainly imported or joint-venture SUV with price ranging from in sedans (excluding community 5).
Table 5: Community Characteristics
Community Number Color Number of Models Average Number of Sales Leads Characteristics Example
1 Dark Green 104 10646.30 Mainly SUVs (imported or joint-venture) CR-V
2 Pink 48 20834.48 Mainly popular compact cars Lavida
3 Light Green 204 5598.01 Low price (mini/compact/small cars and SUVs) Jetta
High price (mainly SUV/medium/medium and
4 Violet 312 4861.70 Accord
large/large cars, Luxury cars, and Sports cars)
5 Blue 203 2271.48 Not sedan (MPV/truck/pickup/van/bus. . . ) WulingHongguangS
6 Orange 149 6187.33 Mainly domestic SUV Haval H6
of comprehensive data, and lack of a complete analysis frame-
work. However, this paper only researches the characteristics
and community structure of the auto model competition network
in January 2019 in detail, and the subsequent work will further
study the dynamic characteristics and community structures.
ACKNOWLEDGMENTS
This research was funded by the National Key Technologies RD
Program (2017YFB1002502), and the General Program (61977010)
of Nature Science Foundation of China. This work was also sup-
ported by the project of Beijing Advanced Education Center for
Future Education (BJAICFE2016IR-003). We would also like to
thank Mr. Ran Feng for his suggestion on data preprocessing.
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