E-commerce (Electronic commerce) is the process of carrying out buying and selling of goods and services through the internet Rowe, F., Truex, D., & Huynh, M. Q. (2012). The transactions are carried out either between businesses, business to consumers, consumer to business or consumer to consumer Gomez-Herrera, E., Martens, B., & Turlea, G. (2014). A number of factors had contributed to the widespread of ecommerce in Indian business industries. To begin with, technology has enhanced and improved the ways activities are carried out in business right through to communication and even security just to mention a few. Rapid growth and wide use of smart phones, tablets, computers and internet has made accessibility of e-commerce become so easy for its users both at rural and urban areas Mata, F. J., & Quesada, A. (2014). Rising in the living standards of the citizens in India is as well a factor that had contributed to the widespread of ecommerce. This project is therefore brought forth to fill in the gaps that have been in the business world. Buying and selling of goods and services have been enhanced ever since the emergence of e-commerce in various parts of the world Yoon, H. S., & Occeña, L. G. (2015). The purpose of this project is to explain the roles, characteristics and the functions of the e-commerce particularly in India. The increased competition in business with each company wanting to increase their market coverage and allowing easy access to their products by their customers had resulted to popularity and spread of e-commerce among businesses in India. As a result therefore, the determining factor of the survival of business organizations would soon be e-commerce in the near future.
Customer touchpoints in the e-commerce industry such as viewing items and making purchase and navigating through the data has shifted through big data to determine the patterns and predictions for the future of the business Trakunphutthirak, R., Cheung, Y., & Lee, V. C. (2017). Data science plays a big role in identifying valuable customers to the business with the aim of ensuring the customer lifetime value. What the customer can bring (revenue) to the business in the future can be determined through customer lifetime value (LTV) modelling Shahin, A., & Mohammadi Shahiverdi, S. (2015). Another role data science plays in the e-commerce is to discover the customers who are likely to churn. Through churn model, data insight implementation of customer retention strategy is provided Coussement, K., & De Bock, K. W. (2013). Data science is important in all the aspects of business and ensures that all the requirements in the business environment are met and kept up to. Among other activities and roles of the data science, it as well drive sales with intelligent products’ recommendations Ta, H., Esper, T., & Hofer, A. R. (2015). The built search engines in the ecommerce business allow the recommendation of products to the customers which is expected would be enjoyed by the customers regarding their purchasing and browsing behaviors. From the data collected, useful information can be automatically extracted from the reviews obtained in the data through the use of natural language processing.
This project was aimed at explaining the importance of ecommerce in the business world and the interactive ways in which it enhance business activities in the industries in India. Great demands arising from the businesses and their customers and how the customers give and obtain information from the industries.
Meeting the above stated objectives will result to value increase in the industries in the ecommerce business in India. A number of values that would be enjoyed from the ecommerce business would be as follows;
This part will offer theoretical structure of the ecommerce business activities including all its components for its efficiency in the business world. All the plans on how the company will make revenue to maximize their profits would all be demonstrated in the model. The relational activities between the customers and the business operators would be structured in the model as it will be shown in the figure 1. Categories of ecommerce that would be shown in the model include; business to business, business to customers, customers to customers and customers to business categories.
Figure 1: Interactive ecommerce model
In this case, the business wholesalers or larger business organizations sell goods and services to the intermediate customers (business retailers) who further sell the products to the ultimate customers. Orders from the company’s website are placed where the company supplies the products in response to the placed order where thereafter the products are then resold to the ultimate customers through the internet in the intermediate or retailers’ website Brennan (2014). The ultimate customers will in this case obtain or purchase products from the retailers’ website.
In this case, the customers obtain the products directly from the large business organizations as directed in the model in the figure 1. The business place their products on the products sheets on the internet where the buyers come through browsing on the internet and choose products of their choice and make their decision to buy the products. This model shows and is enhanced by the widespread of internet accessing gadgets by the consumers. Business respond to orders in accordance to the customers’ demands in the market arising from the data they collect concerning behaviors of the customers on their websites and databases Trimi, S., & Berbegal-Mirabent, J. (2012). Leveraging and fully exhausting the data will help the Indian ecommerce business to greatly fill the market with goods as the consumers’ specifications.
Customers in this category can as well dispose off their properties to other consumers who might raise interest towards the goods. The kind of properties that could be sold by the customers to other customers include household properties, motorcycles, cars and many other properties. Consumers advertise the properties they wish to sell where there might be some cost incurred or at no cost on the websites. From the other end, the customer with interest on the property would view the advertisement and show interest by placing an order towards the property on disposal and finally buys the products. This model shows how the customers can transact business among themselves through the use of internet and the ecommerce at large.
In this category of model, the customers give approach to the business website with different organizations for different services. This enables the customer to place an order concerning the kind or variety of products they would wish to buy from the business. Organization that gives lucrative offers to the specifications of the customers will attract the customers to buy or hire services from the business Armstrong, G., Kotler, P., Harker, M., & Brennan, R. (2015). Websites of the businesses will contain the data both from the business and from the customers where they can be used later for future betterment of the business and maximize the business revenue. Accessibility of the internet in India through the widespread of smart phones and tablets have helped in the development of ecommerce in the country increase the business coverage even beyond the borders of the country to the nearby countries and to the world like the Amazon investment.
The challenges that could arise from the ecommerce model are that the businesses and their customers are not shown to meet at any given point. In response to that, the model does not offer the methods that could be used to enhance face to face communication between business operators and their customers since some goods might require physical meetings.
In this section, data that was used in creating the model will be discussed to bring clarity for their use in the model. Some of the characteristics of data would were;
Volume – data as collected from all the accessed customers in various parts of the country and across the borders are large and enormous. Value of the data can be determined through the size of collected data. Considering dealing with data collected from the business websites and databases is their volumes.
Variety – this is another key aspect of the data collected in the ecommerce businesses. The collected data come from various sources with varied nature i.e. structured, semi structured and unstructured data. At the time of analysis, unstructured data such as the emails, photos audios etc. are in the present days collected by ecommerce businesses.
Velocity – this focuses on the speed at which data used in the ecommerce businesses are generated from the sources and manipulated to meet the demands in the market. The data flow is continuous and emerge from social media, networks, mobile devices and many others.
Variability – this addresses the inconsistency portrayed by the data collected thus resulting to a challenge when it comes to handling them at any given point as they are sourced.
For proper and complete data processing, good system for computations of the data is necessary. Upon the collection of data, good networking needs to be available to enhance the data processing. Ecommerce is therefore supposed to be supported through putting up strong network coverage that will improve the speed of data collection and its processing.
According to the nature of electronic data collected in the ecommerce business, traditional methods of data analysis cannot be employed in full data visualization as they stream from the customers Procter, R., Vis, F., & Voss, A. (2013). Tools like the Hadoop and Spark are needed to deal with such data due to their volumes. These platforms are important when handling electronic data like those from ecommerce business since they control the cost spent on data by creating clusters of any required size within a short time Waller, M. A., & Fawcett, S. E. (2013). Apache flink is another tool that is important in the analysis of these kind of data due to their ability in handling and operating data in rows efficiently and with ease. Batch processes are easily handled by Flink by giving them treatment as special case from the streaming data. In case of unbounded streams of data, Apache Storm is made into use since it stresses on the functional programming that is seen compatible with all other programming languages.
When it comes to making decisions, businesses take seriously data collected from their customers to ensure that they are in line with the requirements of their customers. To get on the run, ecommerce operators need resources like the complete MATLAB Mastery bundle. It’s a computing environment and programming language that makes data coding, analysis and visualization easily accessible simplifying complicated problems with the use of just fewer complicated codes.
The analytic toolkit is another major requirement resource when dealing with data processing and the analytics tools such as SAS, R Studio etc. are used by the analysts in modelling the data. Among other tools are, the python, data visualization with tableau desktop. Python uses programming language that makes it easy to interact with data thus solving complicated problems that might be arising from the data.
From the above discussed tools that can be used for collection and handling of electronic data, the proposed data analysis for data collected in this project will be predictive data analysis that will be showing changes in the ecommerce business over time and frequency statistics to view categories of customers with same preferences in the industry. Considering the preferences of the customers is important at the time of making decision that touch the customers’ needs.
References
Akter, S., & Wamba, S. F. (2016). Big data analytics in E-commerce: a systematic review and agenda for future research. Electronic Markets, 26(2), 173-194.
Armstrong, G., Kotler, P., Harker, M., & Brennan, R. (2015). Marketing: an introduction. Pearson Education.
Brennan, R. (2014). Business-to-business Marketing (pp. 83-86). Springer New York.
Coussement, K., & De Bock, K. W. (2013). Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning. Journal of Business Research, 66(9), 1629-1636.
Gomez-Herrera, E., Martens, B., & Turlea, G. (2014). The drivers and impediments for cross-border e-commerce in the EU. Information Economics and Policy, 28, 83-96.
Kapoor, R. V., Lo, J. W., & Ng, J. W. (2016). U.S. Patent No. 9,331,871. Washington, DC: U.S. Patent and Trademark Office.
Mata, F. J., & Quesada, A. (2014). Web 2.0, social networks and e-commerce as marketing tools. Journal of theoretical and applied electronic commerce research, 9(1), 56-69.
Procter, R., Vis, F., & Voss, A. (2013). Reading the riots on Twitter: methodological innovation for the analysis of big data. International journal of social research methodology, 16(3), 197-214.
Rowe, F., Truex, D., & Huynh, M. Q. (2012). An empirical study of determinants of e-commerce adoption in SMEs in Vietnam: An economy in transition. Journal of Global Information Management (JGIM), 20(3), 23-54.
Shahin, A., & Mohammadi Shahiverdi, S. (2015). Estimating customer lifetime value for new product development based on the Kano model with a case study in automobile industry. Benchmarking: An International Journal, 22(5), 857-873.
Ta, H., Esper, T., & Hofer, A. R. (2015). Business?to?Consumer (B2C) Collaboration: Rethinking the Role of Consumers in Supply Chain Management. Journal of business logistics, 36(1), 133-134.
Trakunphutthirak, R., Cheung, Y., & Lee, V. C. (2017). Conceptualizing Mining of Firm’s Web Log Files. Journal of Systems Science and Information, 5(6), 489-510.
Trimi, S., & Berbegal-Mirabent, J. (2012). Business model innovation in entrepreneurship. International Entrepreneurship and Management Journal, 8(4), 449-465.
Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77-84.
Yoon, H. S., & Occeña, L. G. (2015). Influencing factors of trust in consumer-to-consumer electronic commerce with gender and age. International Journal of Information Management, 35(3), 352-363.
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