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Business Intelligence Assignment: Utilization Of Predictive Modelling In E-Commerce Industry


Prepare a business intelligence assignment illustrating about the utilisation of predictive analytics and modelling in e-commerce industry.


The research on business intelligence assignment signifies that Business Intelligence (BI) is a technology that provides the ability to manage huge sets of data and gain meaningful information out of the same. There are different forms of analysis that can be conducted using BI and one of these is the predictive analysis and modelling.

E-commerce Industry Overview
E-commerce is the industry that includes the conduction of the business activities and transactions over the Internet. In the present times, the customers prefer the online modes of shopping and gaining access to the services provided by an organization. These online business firms come under the e-commerce industry (Ojha, 2019).

Drivers to Adopt Predictive Modelling
There are a number of drivers associated with the e-commerce industry that has led to the need to adopt predictive modelling.

  • Increased Competition: There is a lot of competition in the e-commerce industry due to the increase in the demand among the customers. The customers nowadays prefer the online modes which have led to the establishment of the e-commerce portals for the desktop and the mobile devices. Every business firm wishes to stay ahead of its competitors and wishes to determine the factors that may assist in the same. The same can be determined using predictive modelling (Dhavale, 2019).
  • Enhanced Use of Data: There is a lot of data that is involved in the e-commerce industry. The volume and variety of the data is increasing with every passing day. Due to the increase in the generation and usage of the data sets, it has become necessary to make proper use of the same. It is another driver that has led to the increased need to adopt predictive modelling.
  • Supply Chain Regulations: The regulation of the supply chains is essential for the e-commerce industry. It will only be possible when the understanding of the consumer demands and preferences is ensured. The use of predictive modelling can be helpful in ensuring the demand forecasting is done so that the supply chain regulation can be carried out (Earley, 2014).

Perceived Benefits
There are several benefits that are perceived with the utilization of predictive modelling in the e-commerce industry.

  • The e-commerce organization will be able to offer improved customer experience and engagement levels. The consumer data will be analysed using the predictive modelling techniques and the determination of the customer preferences and requirements will be done. This will enable the organization to offer the products and services matching with the demands and expectations.
  • The market shares, revenues and profits will improve for the e-commerce industry with the use of predictive modelling. This is because of the ability to offer the customers with their expected products and services resulting in the expansion of the customer base and the customer retention rate (Fu et al., 2019).
  • The e-commerce organization will be able to gain competitive edge in the market. The organization will succeed in determining the consumer requirements and will also be able to model and predict the activities of the competitor firms. It will then be able to develop the plans and strategies so that the competitive edge is obtained.

Strategy Employed
The strategy that is currently employed by the e-commerce industry is to make use of the predictive modelling technique primarily in the area of consumer analytics. There are several e-commerce organizations that are present (Chi-Hsien and Nagasawa, 2019). For instance, in the online retail industry, Amazon has emerged as one of the leading online retailers across the globe. There are massive clusters of data that Amazon has that is associated with its customers. The use of the predictive modelling technique is done by Amazon to present the customers with the product recommendations on the basis of their previous purchases. There are discounts and offers offered to the customers on the basis of their purchase patterns. The promotions, marketing, and notifications are also designed as per the consumer interests.

Amazon uses predictive modelling to regulate its supply chains. The demand and supply cycle is predicted for the product categories and the strategies are accordingly developed. The allocation of the resources is also done as per the predicted demand-supply cycle. For example, in the festive months, the demand is usually predicted to increase and the regulation of the supply chains is accordingly done.

The similar strategy is deployed at the other e-commerce business organizations to ensure that the enhanced usage of the predictive modelling can be done (Boire, 2015).

Challenges & Issues
There are a few challenges and issues that may be associated with the use of predictive modelling in e-commerce.

  • There are certain uncertainties that are always associated with the business and the market factors. The predictive models may not consider these uncertainties and there may be a few discrepancies in the actual outcomes.
  • There are information security and privacy issues that may be associated. The access to the datasets is available to the data engineers, analysts, scientists, and others. These issues may lead to the loss of the information properties, such as integrity, confidentiality, and others.
  • There can be implementation errors and issues that may be observed and it may be challenging to resolve the same (Lainjo, 2019).

Actual Benefits Achieved
E-commerce industry has benefitted from the strategy applied in terms of predictive modelling and analytics. There are various e-commerce organizations that are now regulating their supply chains on the basis of the predictive models and outcomes.

There are also e-commerce organizations that have immensely benefitted with the consumer patterns and trends that are determined using the predictive modelling. The development of the customer relationship strategies and the sales strategies are developed keeping these models in consideration. The sales benefits and the enhancement of the market shares have been observed for a number of these organizations (Ojha, 2019).

Competitive edge is another benefit that the e-commerce firms have achieved with the aid of the predictive modelling techniques. The activities carried out by the other business organizations are determined and analysed using the predictive models. This assists in the understanding of the competitive gains that can be achieved. The implementation of the distinguished strategies is done which provides the market benefits and enhancement in the shares.

Drawback of the Selected Strategy
The selected strategy has a few drawbacks. It has been seen that the regulation of the access to the datasets is not properly done. This has led to the violation of the privacy and security norms and principles. As a result, the consumer data and the other information associated with the e-commerce businesses are often put at stake. There are confidentiality violations and integrity issues that are observed as a result (Sreedhar, 2018).

Conclusion & Recommendations
Predictive modelling and analysis is one of the suitable techniques that the e-commerce industry can adopt for the execution of the business operations and management of the data sets. There are several benefits that the use of predictive modelling can provide to the industry with some of the issues and challenges in terms of the security and privacy risks, implementation issues, and errors.

There are a few recommendations that can be followed to avoid the issues. The access control shall be effectively done with regular reviews and updates. The involvement of the technical experts shall be ensured at the time of the implementation. The data validation and verification shall be made a practice while using the predictive modelling and analytics.

Boire, R. (2015). Is predictive analytics for marketers really that accurate? Journal of Marketing Analytics, 1(2), pp.118–123.

Chi-Hsien, K. and Nagasawa, S. (2019). Applying machine learning to market analysis: Knowing your luxury consumer. Journal of Management Analytics, 6(4), pp.404–419.

Dhavale, Y. (2019). Business Intelligence in Retail Industry and E-commerce. International Journal for Research in Applied Science and Engineering Technology, 7(5), pp.58–61.

Earley, S. (2014). Big Data and Predictive Analytics: What’s New? IT Professional, 16(1), pp.13–15.

Fu, M., Chen, Q., Lin, W., Wang, P. and Zhang, W. (2019). Constructing a Scene-Based Knowledge System for E-Commerce Industries: Business Analysis and Challenges. Business intelligence assignment Data Intelligence, 1(3), pp.224–237.

Lainjo, B. (2019). Enhancing Program Management with Predictive Analytics Algorithms (PAAs). International Journal of Machine Learning and Computing, 9(5), pp.539–553.

Ojha, S.C. (2019). Transpiring journey of innovative e-commerce. International Journal of Business Forecasting and Marketing Intelligence, 5(4), p.385.

Sreedhar, G. (2018). Improving e-commerce web applications through business intelligence techniques. Hershey, Pa: Igi Global, Disseminator Of Knowledge.


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