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The Impact of Big Data Analysis on Consumer Behavior

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Based on the new environment of big data, this paper expounds the connotation and characteristics of big data, and analyzes the characteristics of consumer behavior under the application background of big data analysis technology. With the help of AISAS model, which is used to analyze consumer behavior in the network economy, and in combination with the influence mechanism of big data analysis on consumer behavior decision-making process, we have constructed a consumer behavior model under the background of big data and tested it by means of questionnaire survey. The results show that the factors that affect consumer decision-making include external factors and internal perception, while big data affects consumer internal perception through the impact of external factors, thus affecting consumer decision-making. At the same time, consumer information sharing is also conducive to improving the accuracy of big data analysis.
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The Impact of Big Data Analysis on Consumer Behavior
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ICSP 2020
Journal of Physics: Conference Series 1544 (2020) 012165
IOP Publishing
doi:10.1088/1742-6596/1544/1/012165
1
The Impact of Big Data Analysis on Consumer Behavior
Caihua Zhang* Tongxin Tan
School of Economics and Trade, Xinhua College of Sun Yat-sen University
(Dongguan) , Guangzhou 510520, China
*Corresponding author. Email: zhangcaihua2000@163.com
Abstract. Based on the new environment of big data, this paper expounds the connotation and
characteristics of big data, and analyzes the characteristics of consumer behavior under the
application background of big data analysis technology. With the help of AISAS model, which
is used to analyze consumer behavior in the network economy, and in combination with the
influence mechanism of big data analysis on consumer behavior decision-making process, we
have constructed a consumer behavior model under the background of big data and tested it by
means of questionnaire survey. The results show that the factors that affect consumer decision-
making include external factors and internal perception, while big data affects consumer
internal perception through the impact of external factors, thus affecting consumer decision-
making. At the same time, consumer information sharing is also conducive to improving the
accuracy of big data analysis.
1. Introduction
In the era of big data, data informatization has penetrated into all levels of society. Especially for the
participants in the network economy, big data is playing an increasingly important role. Taking
consumer demand as the center has become one of the operating rules of modern market economy.
Therefore, it is of great significance to discuss consumer behavior under the background of big data.
Over the years, many scholars at home and abroad have studied consumer behavior in the network
economy with fruitful results. The focus of the research is mostly on how to construct the online
consumer behavior model and explore the consumer behavior model.
2.Consumer Behavior Analysis in Big Data Era
2.1Connotation and Characteristics of Big Data Analysis
The Big data refers to the large amount of data involved in the data collection, and conventional
analysis tools cannot complete the acquisition, processing and collation of data in a short period of
time. Different from sample analysis, big data analysis is to process all data comprehensively,
comprehensively and professionally and obtain effective information from it. It is generally believed
that big data analysis has five characteristics: Huge amount of data, ultra-fast calculation speed,
diversified data types, low value density and high information authenticity. At present, big data
analysis has been widely used in all walks of life and has gradually become an indispensable
productive force, promoting the efficient allocation and utilization of means of production and rapidly
promoting the improvement of social production efficiency.
ICSP 2020
Journal of Physics: Conference Series 1544 (2020) 012165
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doi:10.1088/1742-6596/1544/1/012165
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2.2For Consumer Behavior Characteristics in Big Data Era
2.2.1Consumers' Behavior Choice Is More Rational. The advent of the big data era has changed the
way consumers obtain product information, and the information they know is more sufficient and
accurate. In the traditional market model, consumers mostly know a certain product or brand through
advertisements and lack other information support, which will restrict consumers' rational decision-
making. In the era of big data, consumers can fully grasp the product information through massive
analysis data, deeply understand the product attributes, and continuously upgrade from the situational
involvement of products to long-term involvement. Therefore, consumers will continuously generate
positive internal perception and promote the occurrence of consumer purchasing behavior. Nowadays,
there are many consumers with high product involvement in the market. Such buyers will use network
information search and comparison, as well as other user evaluations, to comprehensively evaluate
factors such as product cost performance, brand advantages and their own needs, and finally make
more rational purchase decisions[1].
2.2.2Consumers' Demand Continues to Escalate. The popularity of big data is slowly changing the
behavior of online and offline traders. Consumers have higher and higher requirements for choosing
online shopping. They not only need the function and quality of products, but also satisfy their
pleasure and experience of online shopping, that is, they pay more and more attention to personalized
services provided by merchants. The value of trading activities includes the use value of the product
itself and the purchase experience value. Furthermore, sometimes the utility brought by the experience
to consumers plays a decisive role in the purchase decision. Big data promotes personalized marketing
of e-commerce, while consumers are demanding more and more innovative and personalized services.
2.2.3 Consumers' Trust in the Commercial Functions of Social Media has Increased. Nowadays, the
commercial functions of social media are continuously explored and utilized, and the commercial
value is increasingly prominent. And innovative business models appear in social media and are
gradually accepted and recognized by consumers. Through social media, enterprises can master more
and more comprehensive personal information of consumers, so as to be able to accurately analyze
their personal preferences, habits and other information, so as to better meet the deep needs of
consumers or tap the potential needs of consumers. Enterprises can analyze people's habits, beliefs and
preferences through social media, and can be accurate to a certain extent, thus forming an almost
invasive intimate relationship with consumers and better meeting the deep needs of consumers. And
consumers also expect their needs to be paid more and more attention, discovered and satisfied, so
they also trust and support the commercial promotion of social media more and more.
3.Construction of Consumer Behavior Model under Big Data Environment
3.1.AISAS Model Analysis Framework Based on Big Data Analysis
In the theory of consumer behavior research, AISAS model proposed by Dentsu Company is more
suitable for analyzing consumer behavior choices in the era of network economy. According to the
theory, consumers go through five stages from coming into contact with product or service
information to finally completing the purchase behavior: A (Action), I (Interest), S (Search), A
(Action), S (Share). It is developed according to the traditional AIDMA mode. On the one hand, both
of them describe a series of behavioral changes in the process of consumer selection. On the other
hand, the difference is that in AISAS mode, two "s" with network characteristics-search and share-
have been added, which reflects the importance of search and share in the Internet era, instead of
unilaterally transmitting information and inputting ideas to consumers, which highlights the influence
of the Internet on people's lifestyle and consumption behavior[2].
ICSP 2020
Journal of Physics: Conference Series 1544 (2020) 012165
IOP Publishing
doi:10.1088/1742-6596/1544/1/012165
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Figure 1: Comparison of Consumer Behavior Models
Factors that affect consumer behavior are usually divided into two aspects: Stimulation of external
factors and internal perception. External factors mainly include product promotion, marketing methods,
product price, sales volume, brand, user evaluation, etc., which will stimulate consumers' internal
perception and value judgment, thus affecting consumers' purchase decisions to a certain extent. The
value of big data analysis lies in transforming complex and huge low-density information data into
reference data with high commercial value through analysis and processing. On the one hand, big data
analysis can help enterprises to better understand consumer demand, determine clear and targeted
market strategies, and create more competitive advantages; On the other hand, the results of big data
analysis are also used to improve and optimize external factors that affect consumer behavior, guide
consumers to make optimal decisions and maximize utility[3].
This paper combines big data analysis with AISAS model to build a consumer behavior analysis
framework to explain how big data affects consumer behavior, as shown in Figure 2. The impact of
big data on external factors produces external stimulation to consumers through product promotion,
marketing methods, information screening, data search and other links, and acts on attention, interest,
search and other behaviors, affecting consumers' internal perception and finally making purchase
decisions[4]. When the purchase behavior is over, the sharing of purchase experience has also become
an important source of information for further big data analysis, which goes back and forth and
continuously affects consumers' behavior choices.
Attention
Interest
Desire
Action
AIDMA
Attention
Interest
Searchh
Action
Share
AISAS
ICSP 2020
Journal of Physics: Conference Series 1544 (2020) 012165
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doi:10.1088/1742-6596/1544/1/012165
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Figure 2: Impact Mechanism of Big Data Analysis on Consumer Behavior
3.2. Research Assumptions
Based on this, the consumer behavior model in the big data environment can be expressed by the
following formula:
Y=AX1(X2)+BX2+CX3+ε 1
In Equation (1), X1, X2 and X3 respectively represent the external stimulus factors, internal
perception and consumption experience sharing of consumer behavior; Y represents the purchasing
behavior of consumers; ε represents the error matrix; A, B and C respectively represent the influence
coefficients of influencing factors on consumer behaviour[5]. So, how does big data affect consumer
behavior through the penetration of these influencing factors? How much is the correlation between
consumer behavior and influencing factors? In view of these problems, this paper puts forward the
following assumptions[6]:
H1: Big data analysis is conducive to improving product promotion paths and marketing methods,
and can increase consumers' attention.
H2: Diversification of types of big data analysis is conducive to consumers' comparative analysis
and screening out products of interest.
H3: Big data mining helps consumers to conduct all-round information search on target products,
generate more rational internal perception and make final consumption decisions.
H4: After the consumer's purchasing behavior is completed, the sharing of product consumption
experience will form a new big data supplement, further affecting the consumer's purchasing behavior
and forming a virtuous circle.
H5: Good internal perception will positively affect consumers' purchase intention.
3.3 Questionnaire Design and Result Analysis
Based on the above analysis, this paper conducts a questionnaire survey on consumers by means of
questionnaires. The survey objects include employees of enterprises, housewives, college students and
other groups with strong consumption ability. A total of 380 questionnaires were distributed and 352
valid questionnaires were recovered[7].
First of all, we analyzed the reliability and validity. Validity analysis refers to the effectiveness of
measurement, which refers to the degree to which the means and tools in the questionnaire survey can
accurately measure the object to be measured. In KMO test, the higher its value, the more it shows that
Big Data Analysis
Product Promotion
Marketing Means
Information Filtering
Big Data Search
Attention
Interest
Search
Action
External Stimulus Internal Perception
Share
ICSP 2020
Journal of Physics: Conference Series 1544 (2020) 012165
IOP Publishing
doi:10.1088/1742-6596/1544/1/012165
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the survey object is the object to be studied, that is, the more the results of the survey scale can show
the real characteristic validity to be measured. The following results are obtained by using SPSS
software:
Table 1 Reliability and Validity Analysis
Indicators
Action
Attention
Interest
Search
share
α
0.907
0.911
0.878
0.892
0.921
KMO
0.812
0.774
0.769
0.795
0.804
As can be seen from the above table, the α coefficient values of each variable in the model are
greater than 0.8, indicating that the internal consistency is high and the reliability meets the conditions.
KOM values are above 0.7, and the availability level is also high.
After we carry out regression analysis on the hypothesis model, the verification results are as
follows:
Table 2 Analysis Table of Model Verification Results
Assumption
Coefficient
T value
Conclusion
H1
0.635
9.728
Support
H2
0.783
14.304
Support
H3
0.867
12.626
Support
H4
0.846
14.572
Support
H5
0.857
12.435
Support
As can be seen from the above table, the five assumptions in the model are all valid. Big data will
act on the whole process of consumers' purchase decision-making and affect consumers' internal
perception through external stimulation. At the same time, consumers' subsequent consumption
experience sharing will continuously optimize and update the basic database of big data analysis. In
addition, we can also see that the screening of relevant information and data search have a significant
impact on consumer behavior.
4. Conclusion
With the development of big data analysis technology in the modern network economy, many network
platforms or enterprises participating in e-commerce will collect personalized behavior information of
consumers, and use big data analysis and processing technology to integrate and extract effective
information, and make targeted recommendations to consumers accurately. Under the attraction of this
"tailor-made" promotion mode and marketing methods, it is easier to stimulate consumers' interest and
even change consumers' demand preferences, thus significantly improving the substitution effect of
related commodities[8]. At the same time, as the era of big data makes product information
quantitative, transparent and accessible, consumers can also independently collect relevant information
of intended products, read relevant big data analysis conclusions, realize rational consumption, and
achieve the expected utility value to the greatest extent[9].
ICSP 2020
Journal of Physics: Conference Series 1544 (2020) 012165
IOP Publishing
doi:10.1088/1742-6596/1544/1/012165
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The Internet provides a platform for every consumer to share the experience of purchasing and
using products. After processing these low-density information through big data analysis technology,
an objective product evaluation system can be constructed to help consumers make wiser shopping
decisions.
References
[1] Li Zongwei. Research on the Model Construction of Online Comments Affecting Consumers'
Purchase Decision [J]. Innovation, 2013, 7 (5).
[2] Wang Yongzhou, Deng Yan.Analysis of Consumer Purchase Decision Behavior Based on
Big Data Forecast [J]. Business Economics Research, 2016 (23).
[3] Zhu Guangting, Zhu Junxuan. Research on Internet Consumer Behavior in Big Data
Environment [J]. Statistics and Decision, 2014 (23).
[4] Wang Lili. Research on Consumer Information Search Behavior from the Perspective of
Internet [D]. Shandong University, 2017.
[5] Li Zhicheng, Liu Meilian. Research on Consumer Behavior in E-commerce Environment [J].
Chinese Management Science, 2002 (6).
[6] Tang Renmin, Wang Daiqiang. Consumer Behavior Analysis Based on a Big Data
Application Architecture [J]. Microcomputers and Applications, 2014 (20).
[7] David C.Edelman& Marc Singer.Competing on Customer Journey[J].Harvard Business
Review.
[8] Christopher Trepel, Craig R Fox, Russell A Poldrack. Prospect theory on the brain?Toward a
cognitive neuroscience of decision under risk[J].Cognitive Brain Research,2005.
[9] Zilong Fang,Pengju Li.The Mechanism of“Big Data Impact on Consumer
Behavior[J].American Journal of Industrial & Business Management,2014,4(1).
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... Even firms can develop data-driven business models that retrieve and sell external data to meet customers' demands [72]. Big data analysis increases customer attention and purchasing behavior [50]. The difficulty of obtaining data from various channels on customer engagement is one of the most critical challenges [48]. ...
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Research on the Model Construction of Online Comments Affecting Consumers' Purchase Decision
  • Li Zongwei
Li Zongwei. Research on the Model Construction of Online Comments Affecting Consumers' Purchase Decision [J].
Analysis of Consumer Purchase Decision Behavior Based on Big Data Forecast
  • Wang Yongzhou
  • Deng Yan
Wang Yongzhou, Deng Yan.Analysis of Consumer Purchase Decision Behavior Based on Big Data Forecast [J]. Business Economics Research, 2016 (23).
Research on Internet Consumer Behavior in Big Data Environment
  • Zhu Guangting
  • Zhu Junxuan
Zhu Guangting, Zhu Junxuan. Research on Internet Consumer Behavior in Big Data Environment [J]. Statistics and Decision, 2014 (23).
Research on Consumer Information Search Behavior from the Perspective of Internet
  • Wang Lili
Wang Lili. Research on Consumer Information Search Behavior from the Perspective of Internet [D]. Shandong University, 2017.
Research on Consumer Behavior in E-commerce Environment
  • Li Zhicheng
  • Liu Meilian
Li Zhicheng, Liu Meilian. Research on Consumer Behavior in E-commerce Environment [J].
Consumer Behavior Analysis Based on a Big Data Application Architecture
  • Wang Tang Renmin
  • Daiqiang
Tang Renmin, Wang Daiqiang. Consumer Behavior Analysis Based on a Big Data Application Architecture [J]. Microcomputers and Applications, 2014 (20).
Edelman& Marc Singer. Competing on Customer Journey[J]
  • David
Research on the Model Construction of Online Comments Affecting Consumers’ Purchase Decision [J]
  • Li