Analysis Of Consumer Behavior Through Millenial
Millennial: The consumers can be divided by three categories, which are baby boomers, generation X and generation Y. The researchers emphasized that the group only can be categorized by using the era in which individuals were born and when they came of age the generation of young people is called millennial (Dhanapal, et al. , 2015). Lee & Kotler (2016) said that the millennial generation are approximately 17 until 37 years old, they born between 1980 until 2000 (Moreno, et al. , 2017). According to forrester research in United States, the majority user who using internet technology is Y generation (Dhanapal, et al. , 2015). The millennials generation known as unique generation because their certain distinctive characteristics, especially for the Information and Communication Technologies (ICT) which affect their daily life (Moreno, et al. , 2017). Another researcher argued that Generation Y constitute as the large population. Many consumer industries make the millennial becoming an attractive target because their as large population now and their power of purchase the product.
Therefore, the millennials group is an important thing in the development of e-commerce (Moreno, et al. , 2017). The other researcher said when technology research study involved generation Y into research, they become a homogeneous group because amount of user on millennial group. The millennials are frequently categorized as generation that are comfortable with technology, heavy users of technology and always connected with any devices¬ (Leon, 2018). The millennials generation also known as Generation Y. Nowadays, many people around 23 year-olds live in United States comparing with other age groups. It becomes a clear understanding that millennial is a critical group since they are aging into workforce and will be a future leaders of business organizations, the largest group of costumers in the future (Weber, 2017). A review of the popular literature from Ng, Schweitzer & Lyons (2010) suggests that the Millennials “want it all” and “want it now”, in terms of good pay and benefit, rapid improvement, work/life balance, challenging and attractive work, and giving a contribution to the society (Ordun, 2015).
One of researcher said that millennials make purchase decision easily comparing with the other generation. It is because they get more information easily by electronic and consult with people who have purchased. However, they are not loyal to the brands, means they do not stay with same brands category. They do not prefer to have product in same particular brands as previous generation. Rahman (2015) explained that applying the theory of uses and gratuities will help to identify the motivational factors that influence the millennials in digital media in order to social media research. The young people (millennials) moving away from conventional media to interactive media, they find that media is more useful and enjoyable. In addition, Rahman (2015) argued that sellers supposed to have an explicit message, efficient technology and understanding their demands when approached to the millennial consumers. The limitation of the paper is it has not analyzed the bibliographic and consumer behavior, therefore it will be better to be research in further study (Moreno, et al. , 2017).
Internet technology: Nowadays, internet technologies are used by customer for purchasing different types of product extensively. It is called e-commerce. The system automated interface the process between customer and retailers, retailers and distributors, distributors and factories, and factories and their suppliers. The face of most business function is changed by e-commerce in competitive enterprises (Bhate & Pasha, 2014).
E-commerce: Online shopping is a form of electronic commerce. Consumer can shop directly any goods and services through it. The consumers can find anything such as the specific item, model, or brand while it is available on the online store (Saju & Saraswathy, 2018). Online shopping is increasing more and more. Statista (2017) viewed that 1. 61 billion people purchasing online in worldwide, amounting to USD1. 9 trillion in 2016. More than two billion people who purchase product online estimate increasing in 2019 and the following year for the global e-retail sales (Wong & Wei, 2018).
The face of business might be changed by e-commerce. It serves a good customer management, a lot of options of product, new marketing strategy and the good operations. One of the tool that can be used for analysing the data is data mining. It can be used with many different dimensions or angels, categorize it, and summarize the relationship identified (Bhate & Pasha, 2014). Another researcher said that millennials is hard-working individuals, ready and want to apply their abilities focusing on success and achievement in the work environment. Therefore, the millennials generation will be attached as customers by several business organization. The company will more focus to understand and compiled the strategic responses of customers (Weber, 2017).
Butuh Keterangan Butuh Data
However, the large scale of real time data is not easy to produce. Therefore, using electronic form can help to gather customer information. After obtaining the data, it can be processed using data mining (Bhate & Pasha, 2014). Data mining can be used for knowing about the hidden information in algorithm data. It helps to filter useful information for better decision making in company. Market research, industry research or competitor analysis can be analysed using data mining. The output of data mining is often having the right estimation (Pahwa, et al. , 2017). Another tool that can be used for analysing the consumer behaviour is big data analysis. It is an effective tool to deal with consumer behaviour and utilize the opportunities. It also can know the particular consumers who have unique behaviours and habits. In the journal, researcher want to know what influence does the of big data analysis have on the current operations and how does the sellers use it and what benefit might be obtained for their merchants. The first step in the paper was finding secondary data what is the benefit for BDA in market research. The online shopping and BDA can be used as a knowledge. Next step, questionnaires was collected, it was approximately 450 questionnaires.
After collection, the questionnaire data was analysed using SPSS and MATLAB. In addition, marketing manager of Alibaba was interviewed for getting another information about the impact of BDA on Alibaba (Li, et al. , 2017). One of e-commerce consumer behaviour researcher used web data mining technology to improve the ability for decision making. It is one of big data technology. Web data mining is divided by three types which are web content mining, web structure mining, and web usage mining. Web mining process refers to a consumer browsing which the consumer’s unseen information and visits is summarized and analysed by the Web logs. Creating an effective model to ensure that consumer personalized information recommendation might obtain a better quality of service.
Extracting the future information about consumer is used to establish an accurate model and the consumer preferences merchandise can be noted and managed using the application of modelling techniques (Hongyan & Zhenyu, 2016). Another researcher used fashion online store to analyse purchase intention among millennials. They tried to find the relationship between the purchase intention and attitude towards word of mouth and product types which is available on fashion online store. Getting the data used structured questionnaire. They divided into two parts which demographic profile become a first part and scaled response questions as second part. Both of the hypothesises was supported. Firstly, online word-of-mouth significantly predicted online purchase intention. Secondly, intention to purchase online was significantly influenced by the availability of variety product at online store (Sethi, et al. , 2018). Customer loyalty: Ayaydin & Baltaci (2013) said that compare with the previous generation, millennials spend more but have less loyalty to the brands. They find products and brands which are suitable for their personality, lifestyle, social and community values. They build image, represent their personality and show their values using brands. They advertise favourable and unfavourable brands that they consider good using their technological ability (Moreno, et al. , 2017). For predicting consumer loyalty which is related to another variables, one of researcher used online survey method for collecting the data. The respondents are people who live in Malaysia and used m-commerce services. The result is found that the are efficiency, system availability, fulfilment privacy, satisfaction, trust and commitment are the factors that influence m-commerce customer loyalty in Malaysia in directly or indirectly (Lee & Wong, 2016).
Consumer behaviour: Consumer purchase behaviour is used for customer behaviour analytics which consumer becomes a user, payer and buyer. Now, many organizations do not focus for only one customer but a group of customer or organizational behaviour which can be known easily which customer might be worth it to apply the strategy more specific to the right customer. For a better customer satisfaction, company use analysis of consumer behaviour which accurate profile can know customer needs and interests. It also allows company to offer what the customer want. When customer feel satisfied, they will be come back for more (Pahwa, et al. , 2017). Another consumer behaviour analysis paper use data mining analytics process. It is involved three stages of data mining process. The first is initial exploration which the process includes cleaning data, data transformation and selecting subsets of records. Second is model building or pattern identification. It is a complex process to choose a good performance which apply various model but still using a same data set. The last stage is deployment. The model, which has been choosen in the previous stage, will apply into the data to produce a prediction or estimate the expected outcome (Pahwa, et al. , 2017).
For predicting the next purchase of consumers, one of paper studied about customer online shopping experience using data analytics. They used online purchasing history which contained 110,843 customers and applied in analysis tools. There are pricing data mining, customer segmentation, and predictive analysis. They found online shopping on online travel agency decreasing continuously (Wong & Wei, 2018). Another researcher analysed consumer behaviour in one of particular e-commerce site, it is Amazon. in especially in Kolenchery town. They analysed the significant difference in the rating of Amaazon. in services among the consumers of Kolenchery Town. They used 60 samples which is selected by adopting convenience sampling method. Questionnaires become their primary data. They found clothing becoming a most buying product and consumers feel satisfied with almost all services in the Amazon. in (Saju & Saraswathy, 2018). Information technology: With the development of information technology, the traditional business environment continuously provide new features to respect for the consumer behavior.
Therefore, consumer behavior research group is encountering many new challenges. Firstly, how complicate information needs to collect and interpret correctly in the need of business, next is how to expand the reasonable consumer research method and system to adapt with the consumer behavior which changes rapidly. That are the reasons which make researcher relying on information technology network, they use network platform for getting consumers’ personal and habit data then analyze using statistical and analytical techniques, take information about consumer behavior in consumer behavior preferences, emotional tendencies, another characteristic information, as well as the consumer interaction which is presented by the complex network groups. Some analytical result will be possible to predict and understand consumer needs, create a customer system which is personalized by the system, the management of network resource system and guidance network security control for economy beneficial (Hongyan & Zhenyu, 2016).
A lot of applications have been found by traditional methods for analyzing and predicting customer demands. It will be used for forecasting the total quantity of products that belong to the same group rather than the relationship between the different consumers and product groups. K-means algorithm can segment the customer by clustering and data from various e-commerce website in web log. Therefore, data mining model can be used not only for predicting the product which have to be improved but also for identifying the right customers for a specific product group. In the journal, researcher use k-means alghoritm to transfer data into cluster. They divided becoming six clusters. Each cluster has different segmentation such as age, gender, how many hours are spent when online, online address, language and target customer behavior (Bhate & Pasha, 2014).
