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The Role of Big Data Analytics in Improving E-commerce Performance

Discuss about the Data Analytics for E-commerce Company.

Business organizations operate on large amounts of data that are stored on the firm’s databases (Hartmann, Zaki, Feldmann & Neely, 2014, 25). The IT departments are usually responsible for maintaining and providing business data for functional use in the companies. Consequently, most business organizations have transformed their business operations online due to the technological advancements. Thus IT personnel in the companies get involved in analyzing and predicting the business patterns of the organizations, basing on the data kept in the databases. Accordingly, with the internet-based operations companies rely on data analytics to develop business models that are used to predict the operations of the firms (Woerner, & Wixom, 2015, 61). In using data to predict business performance, data scientists get involved in analyzing the key operations. Data analysts work on a variety of business data to understand and determine solutions to the problems affecting the firm’s operations.

The major problem affecting the performance of the e-commerce company is the decrease in profits due to increasing competition and unpredictability in customers’ expectations. This is in spite of the company leading in the market with a variety of product selections. With a wide range of product segments, it implies that the company has a vast amount of data on the sales made. Therefore, the e-commerce company needs to analyze the sales data for each product segment and the geographic region information, so that it can improve its performance. The results of data analysis will be used in developing business models which can be employed in making decisions regarding customers and the products sold. Data analysis strategies will work out to explore the possible geographic region the company can target to add new customers and increase the sales made. In addition, the analytics intent to innovate new business strategies and the products which should be prioritized so that it can retain more customers.

Big data refers to a collection of massive and complex data volume that comprises of huge amounts of data and data management capabilities (Anuradha, J., 2015, 321). This term was coined because of the continuous generation of information from the different emerging technologies. Researchers developed the interest in the field of big data analytics to help in stimulating economic growth as well as predicting future trends for investors (Lee, Kweon, Kim & Chai, 2017, 57). In online transactions, data analytics is based on information captured from internet clicks, social media information, and mobile transactions as well as other customer purchase transactions (Li, Xing, Liu, & Chong, 2017, 30).

Seven Big Data Analysis Techniques for Developing Business Models


According to the research study by Koirala, e-commerce firms emerged as the fastest groups of businesses to adopt big data analytics (Akter, & Wamba, 2016, 180). Adoption of big data in companies led to the reduction in cost and improvement of business performance due to the vast storage and processing capacities of advanced technologies. E-commerce firms such as Amazon and Netflix rely on big data information to improve the business operations. Data captured by e-commerce firms is mainly categorized into transaction activity data, click-stream data, video data, and voice data in tracking consumer shopping behaviors (Voytek, 2017, 1231). Researchers analyzed the performance of giant retail companies and established that firms such as Amazon generated almost 30% sales through data analytics (Lee, Kweon, Kim & Chai, 2017, 978).

According to the efficient-market hypothesis, product prices reflect all information and events of the online business operations performed 9 Arthur, 2018, 42). For instances, if firms invest in big data analytics, information on the investment value gets reflected in the stock prices. Investments made in big data analysis positively affects the stock prices, therefore, leading to improved business performance (Wamba et al., 2017, 357). Several research studies indicate that companies which adopt data analytics experience improvements in business operations due to the speed of information processing.

According to Faed (2013), customer relationship management can be controlled by analyzing data on online transaction platforms. Information can be captured basing on the behavioral and non-behavioral reactions of customers who engage with the business firms. Accordingly, several studies indicate that customer complaints on online platforms are crucial in improving products quality (Kwon, Lee & Shin, 2014, 390). Consequently, data captured from customer reactions can be analyzed effectively to enhance the loyalty of clients to the business.

In data analytics, the whole process majors on the analysis of vast amounts of data presented. The statistical data analysis approaches are applied in studying the behavior of the market variables. In order to apply the statistical tools in data analysis, the data is modeled in a way that the responses can be explained. Thus, data generated from the statistical methods are applied in developing statistical models from which assumptions are made relating to the normality, and randomization of data.

Generally, there are about seven big data analysis techniques that can be applied in developing a business model in order to predict the performance behavior (Lin, 2015, 48). These strategies include association rule learning, classification tree analysis, genetic algorithms, machine learning, regression analysis, sentiment analysis, and social network analysis (Suthaharan, 2016, 56). Association rule learning method is used to establish the correlation between the data variables for big data firms. The methodology is normally employed to assist in extracting information on customers who visit the websites and placement of products in a better proximity. Classification tree analysis is employed in data analytics to determine the category in which specific data sets belong in. consequently, genetic algorithm methodology algorithm methodology outlines the evolution of business mechanisms in solving problems which usually require optimization.

Case Study of Five Business Companies to Predict E-commerce Performance

On the other hand, machine learning methodology involves the application of software in analyzing and predicting data behavior (Zakir, Seymour & Berg,  2015, 34). This methodology will help in evaluating the customer preferences and determining the probability of customer prospects. Accordingly, regression analysis is a statistical methodology that can be applied in data analysis to manipulate the independent and dependent variables in the e-commerce industry. In addition, social network analysis methodology is essential in analyzing big data generated from the e-commerce firm.


In this study, a case study of 5 business companies will be used to predict the performance of the e-commerce company. Correlation statistical method will be applied in analyzing the data from the case study. The data provided is modeled using a Likert-rating scale so that the results generated can be evaluated using statistical methods such as mean, standard deviation, regression analysis and hypothesis testing (Baesens, Bapna, Marsden, Vanthienen & Zhao, 2016, 110). Calculation of the mean is essential in establishing the general trend of the dataset generated on the e-commerce sales made. Consequently, the standard deviation analysis of the data will determine how the data is spread around the mean. Dispersion of data from the mean is used to analyze the customer behavior.

The business companies involved in this study are the consumer lending institution, investment banking institution, securities trading institution, hedge fund institution, and a wealth management institution. The survey study will establish the use of data analysis in improving the performance of the companies. In this survey, literature survey is used to collect data from the companies basing a nominal scale rating. Therefore, the scale comprises; 

(5) Very High Role to Business Success

(4) High Role

(3) Intermediate Role

(2) Low Role

(1) Very Low Role

(0) No Role in Business Success

The scale is rated basing on the business factors which are a need for identification, market segmentation, performance improvement, business model innovation and creation of transparent infrastructure.  After the data is coded, statistical methods are employed in analyzing and determine the behavior of the e-commerce company.

Results from the studies indicate that the big data analytics contribute greatly to the success of the business performance. Analysis of the results indicates that the business companies are yet to fully integrate data science techniques in analyzing business performance. The results are recorded basing on the level to which big data mechanisms have been integrated into the business factors of the business firms studied. Data captured from the studies rates the level of success of the business performance, business model innovations, creation of transparent infrastructures, data integrity, and ethics as well as the process management.

Results of Big Data Analytics in E-commerce Using Existing Literature

The results of the big data analytics in e-commerce using existing literature are presented in the table below.

Big data analytics

Average rating

Percentage

Performance improvement

4

80%

Business model innovation

4

80%

Creation of transparent infrastructure

3

60%

Analytical maturity

2

40%

Data ethics and integrity

2

40%

Process management

3

60%

The sales of e-commerce business are summarized below per geographic region as in the year 2018.

Geographic region

Sales in billions (USD)

North America

249.8

Western Europe

184.3

asia

162.9

Latin America

16.9

Central and eastern Europe

15.2

The Middle East and Africa

0.3

From the table above, it is evident that performance of business companies improved with the adoption of data analytics methods. Consequently, the business model innovation is rated at 80% due to use of data science in running the businesses. This implies that adoption of data science in analyzing business performance can improve on the innovation strategies of business models used. Moreover, the creation of transparent infrastructure and incorporation of data integrity measures is not substantially incorporated in the e-commerce industry.

The geographic region to target new customers

From the geographical e-commerce sales, I recommend the e-commerce company to target the North American, Asia and Western Europe markets. This is because the sales data from the markets is high compared to the other regions. Thus, the company will be able to reach out to the huge customer population in these geographic regions. In addition, the company will be able to increase its sales from the online transactions, thereby realizing an increase in profits.

Products to be prioritized

With the increasing changes in technological features, customers purchase electronic products more than other items on e-commerce platforms. Therefore, the e-commerce company is recommended to prioritize electronic products and clothing in order to acquire more customers. Apart from the above products, the company should also base on household items due to the increased demands.

Impacts of shipping products

Shipping of products will be accompanied by both positive and negative consequences.  Positively, the service will increase the number of customers purchasing from the company. This is due to the delivery of products to the residential regions. However, the shipping services could be accompanied by the delivery of wrong products, products with different features and damaged products (Bharucha, 2017, 150). Thus this could lead to failure of the company in retaining the customers.

New innovative ideas to improve profits

To improve the profits of the e-commerce company, the company should incorporate new ideas into its operational strategies. As an innovative strategy, the company must make categorize the products data to ease the searching process. The company can also prioritize that product and develop features that display the latest products within the firm's stores. Another strategy is to advertise the company's products on the online social platforms.

Recommendation for E-commerce Company Based on Geographical Sales Data

Implementation Plan

When implementing the e-commerce innovative strategies, the company should target at increasing the number of customers and retaining the current customers. The implementation plan should involve creating advertisement features, training the personnel and improving on the company's security features. The company needs to implement products with high demand in acquiring more customers. This will involve adding functional features to the e-commerce website that display the most purchased products. Moreover, the implementation plan will involve adding security features to the e-commerce platform in order to improve the integrity of data. After incorporating these features, the company will have to develop advertisement features on the social platforms so that more customers are reached out. Then the shipping service will be adopted by building more store points in the geographical markets with high sales.

Conclusion

Business operations in e-commerce companies involve processing of large amounts of data. As a result, data science analysis concepts are employed in the processing of huge information and developing business models from the results. The e-commerce company in this study is faced with performance constraints that have considerably decreased product sales and profits generated. Data mining strategies were used in the study to examine mechanisms of improving the business performance. From the previous studies done, it is evident that data analytic techniques will improve the business performance of the company. Data analysis techniques provided information on the geographical location and products information which will relevantly help the company to operate on new business ideas

References

Faed, A., 2013. An intelligent customer complaint management system with application to the  transport and logistics industry. Springer Science & Business Media.

Anuradha, J., 2015. A brief introduction to Big Data 5Vs characteristics and Hadoop technology. Procedia computer science, 48, pp.319-324.

Lee, H., Kweon, E., Kim, M. and Chai, S., 2017. Does Implementation of Big Data Analytics

Improve Firms’ Market Value? Investors’ Reaction in Stock Market. Sustainability, 9(6), p.978.

Akter, S. and Wamba, S.F., 2016. Big data analytics in E-commerce: a systematic review andagenda for future research. Electronic Markets, 26(2), pp.173-194.

Wamba, S.F., Gunasekaran, A., Akter, S., Ren, S.J.F., Dubey, R. and Childe, S.J., 2017. Big data

analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, pp.356-365.

Lin, C.Y., 2015. Big Data Analytics. Lecture Notes, University of Columbia.

Baesens, B., Bapna, R., Marsden, J.R., Vanthienen, J. and Zhao, J.L., 2016. Transformational

Issues Of Big Data And Analytics In Networked Business. MIS quarterly, 40(4).

Zakir, J., Seymour, T. and Berg, K., 2015. BIG DATA ANALYTICS. Issues in InformationSystems, 16(2).

Suthaharan, S., 2016. Big data analytics. In Machine Learning Models and Algorithms for Big

 Data Classification (pp. 31-75). Springer, Boston, MA.

Voytek, B., 2017. Social Media, Open Science, and Data Science Are Inextricably Linked.Neuron, 96(6), pp.1219-1222.

Kwon, O., Lee, N. and Shin, B., 2014. Data quality management, data usage experience and

acquisition intention of big data analytics. International Journal of Information Management, 34(3), pp.387-394.

Arthur, W.B., 2018. Asset pricing under endogenous expectations in an artificial stock market. In

The economy as an evolving complex system II (pp. 31-60). CRC Press.

BHARUCHA, J.P., 2017. Issues in the Home Delivery Model in India. International Journal ofSupply Chain Management, 6(3), pp.145-151.

Li, Q., Xing, J., Liu, O. and Chong, W., 2017. The Impact of Big Data Analytics on Customers?

Online Behaviour. In Proceedings of the International MultiConference of Engineers and Computer Scientists (Vol. 2).

Woerner, S.L., and Wixom, B.H., 2015. Big data: extending the business strategy toolbox.

Journal of Information Technology, 30(1), pp.60-62.

Hartmann, P.M., Zaki, M., Feldmann, N. and Neely, A., 2014. Big data for big business? A

taxonomy of data-driven business models used by start-up firms. A Taxonomy of Data-Driven Business Models Used by Start-Up Firms (March 27, 2014).

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