In the present age, a number of companies are serving in the same industry which does not only increased competition among companies but also made customers highly demanding. Customers demand the best services and products meeting with their expectations and requirements. Customers require the best quality and best prices. In this way, faulty and defected products can directly impute to lower customer satisfaction level. In general, defects are the fault of manufacturing companies but they also influence the brand image and customer equity of the retailers.

Defective and faulty products are somehow the products which contain some imperfections in the manufacturing and designing process. Moreover, inadequate warnings and instructions also cause to make a product defective and faulty (Claudiu-Cătălin, Dorian-Laurenţiu, & Andreea, 2014). The products that unreasonably put a user in the endangering situation is also considered as a defective or faulty product. The main objective of the present work is to provide information about the solution of a real world problem through the use of predictive analytics approach (SRIVASTAVA, 2015). The present work is consist of information about the real world problem and its potential impact on businesses. Furthermore, modeling and testing related information are also presented to elaborate on the most suitable way to tackle this situation through data mining.

Practical Problem to be solved

The practical problem is related to the retail industry and e-commerce particularly emphasizing on Amazon e-commerce website. Faulty and defective products sold by the Amazon resulted in the decrease of customer churn as customers blame them for their bad experiences. Such situations happen because a customer buys the product from the retailers or e-retailers with a hope of getting something great but faulty or defective product make them disappointed (Assiouras, Ozgen, & Skourtis, 2013). Although another key preseason is that customer feel indifference between whether to complaint back to the retailer or not. They think that retailers would not take any positive action towards this fraud (as customers usually perceived it as fraud). Disappointed customers avoid buying the same products or other products from the same retailers because of their bad experience. Because of which delivery of faulty and defective products become a real problem for the retailers.

How I know about Problem

The section encompasses information about the experiences that I read just a couple of weeks ago. The bad experience of e-retailing turned my positive views towards negative about that particular e-retailing brand. For instance, I conducted research on this topic and I collected information about the defective products sold by the Amazon brand caused the death of infants. The case is about the defective Rock' n Play Sleepers that were sold at Amazon also. According to the statistics, Amazon sold 600 sleepers on the Canadian website in 2018. Records present that more than 32 babies have died because of this defective sleeper. Even Amazon recalled all the defective products but still, it caused negative reviews on its e-commerce website (Peachman, 2019).

Based on users experience and information collected from the secondary research data I know that faulty and defective products are problematic for the retailers and e-retailing websites. According to a research study conducted by Claudiu-Cătălin, Dorian-Laurenţiu, and Andreea in 2014, faulty and defective products are a real problem for the companies and retailers as it can directly draw impact on the brand reputation and social responsibilities of the business. Negative spillover affects the overall brand image and destroys customer's equity. The literature review also throws light on the potential negative effects of defective products sales on the e-retail industry as well as the retail industry. Considering the impact of faulty and defective products on brand sales, image, financial outcomes, and market reputation we can conclude that faulty and defective products should be considered as a highly influencing problem for the retail industry (Claudiu-Cătălin, Dorian-Laurenţiu, & Andreea, 2014).

Problem is Important and Problem Solving

As discussed in the earlier sections, faulty and defective products can cause serious business related issues for the retailers. Influence on market reputation is never acceptable for the retailing brands when most of the brands are spending a huge amount of budget on marketing to build a better market reputation and image. Although, faulty and defective products reduces sales of that brand as customers switch to the other brands and competitor brands because of a bad experience. Reduction in the sales correlates with the decline of profit in the fiscal year and upcoming financial performance of the brand unless the image is reconstructed. Furthermore, the problem is also important as it also relates to the safety of consumers. Defective and faulty products can be dangerous for users (Ni, B.Flynn, & Jacobs, 2014).

For instance, Toyota vehicles defective production caused several accidents. Such defective products not only destroy the image of manufacturing company but also causes negative consequences for the retailers. Excluding this, the solution to this problem can provide several benefits to the retail industry. Retailing brands can execute their business operations more appropriately and a strong image can be developed in the market through sorting out such issues with strategic action plans. The solution to this problem is also important to generate a significant return on investment for the stakeholders of the brands. Better service and the better image will encourage customers to visit that particular retailing brand each time for anything they need to buy. Thus sales would be enlarged and profit enhancement will result in the increase of return on investment for the investors. Additionally, it would also solve the pain points of the customers which cause disappointment and dissatisfaction.

Data Sources to Solve the Problem

The problem can be solved through identifying the key areas which cause such issues in the Amazon e-retailing sector. After the proper identification of the key reason, the eradication of root causes is possible. Furthermore, after identifying the reason Amazon would be able to develop a strategic action plan to deal with this issue. Development of action plan and identification of the key reasons all require data and information to the relevant areas (Waller & Fawcett, 2013).

The data sources are required to be used to obtain relevantly and desired data for the solution of this problem. For this purpose, potential data will be collected directly from the customers. The customer profiles can be used to contact the customers to collect their reviews about the recent operations of Amazon. Although, online portals and social media platforms can be used for access to qualitative and quantitative data sets.

Conducting research inside the supply chain and operational area of the brand can also benefit in identifying the key reason and developing a remedial plan for this issue. Quantitative data collected through data mining can provide statistics of negative experiences of customers. Additionally, data mining can also provide customer responses to these issues. In fact, specifically utilizing customer data bases managed by the Amazon brand would be the most suitable option to reach the main issue within minimum effort and time.

Data Handling and Modeling of Problem Solving

        The data would be handled and appropriately prepared for modeling. First of all, data will be collected from the authentic sources and arrangements (e.g. grouping) will be made in the data for analysis. The descriptive analysis will be taken to primarily develop models on the basis of decision trees and logistic regression. In the handling and modeling process use of greedy algorithms would be avoided in order to prevent the subset of some features. Subsets can divert attention from the main point and result in the unauthentic results outcomes.

Additionally, advanced machine learning tools would be utilized in order to significantly reduce the task completion duration. Although in the initial stage of analysis and modeling descriptive analysis would be made having a focus on missing values. According to the plan, no missing values and big features would be ignored from all the collected data. In data handling treatment of data sets will be made specifically to deal with the problems related to missing values. The two simple steps that would be taken in the data treatment are presented below:

·                  Creation of dummy flags for all missing values in the data set.

·                  Imputing missing values with average and central tendency measures to make statistical analysis of collected data easy to understand for users.

Types of Models and Testing of Problem Solving

            Several database models can be utilized in finding the best solution for this problem. Some examples of database models are conceptual models, physical models, and logistic models. Flat file model, object-oriented model, two tables with the relationship, network model, and hierarchical model are also known as a collage of five database models. The selected approach and modeling style used in this project is the use of predictive analytics modeling. In the project around 100,000 observation cases are selected to conclude the key reasons for the problem. Considering the number of cases GBM can work effectively. Conclusively, predictive modeling will be used in the project for testing. Predictive modeling will be used because of its capability to deal with the categorically distributed information and data sets.

Potential Strengths and Weaknesses of Problem Solving

The potential strengths and weakness of the proposed approach are enlisted below: 

The proposed modeling technique will also run the analysis on the basis of segmentation. As the targeted audience of Amazon belongs to different geographical and demographical segments. Therefore while analyzing the problem and its solution for the customer there is need to specifically study segmented audience.     

Communicate Information with Stakeholders

            Communication with customers and proposed customers is also critical. Managerial staff needs to vibrantly communicate with the customer regarding the upgraded and advanced complain receiving system of Amazon. Managerial staff can directly do publicity of the new complaints receiving method to educate the targeted market that Amazon is interested in customer loyalty rather than frauds. Moreover, a positive response in communication is required by the complaint receiving staff to ensure customer satisfaction.

The best possible replies to the customers who come up with the complaints can be distinguished in the light of possible result and outcomes of this reply. Some sample of communication (replies) to the customers are presented below:

·         We are sorry to hear this (utilize emotional intelligence to empathize with customers)

·         Can we redelivered this order if it does not match your requirements? Or you would like me to make a refund (solution)

·         There is a possibility that the product got damaged in the shipping process (this message will show your customers that you did not sell the defective and faulty product intentionally).

·         We apologize to you for this inconvenience and bad experience.

Excluding customer information is required to be presented in front of the stakeholders such as investors. Discussing the findings and proposed solution to the delivery of the defective and faulty product with investors would show the positive attitude of the organization. Investors take interest in the organization which as a good reputation in the market because of the possibility for sustainable business performance. The selected information to be presented in front on the stakeholder encompasses the key reasons of this inconvenience in service delivery, outcomes of the problem on financial health and brand image, actions taken for the identification of the problem and proposed solution to the problem. These kinds of information would be presented to the stakeholders in organizational meetings.

Conclusion on Problem Solving

            The whole discussion concludes that delivery of the defective and faulty product is a problem for the retailing industry. A number of retailing and eCommerce related brands particularly Amazon is facing issues such as the decline in customer satisfaction rate, sales growth, financial profit, and bran image because of mistakenly supplied defective and faulty products. The problem can be handled by the support of data mining and the development of an appropriate model. Data mining and predicative analytics approach will support Amazon to eradicate this issue and ensure 10% customer satisfaction.

References of Problem Solving

Assiouras, Ioannis, Ozge Ozgen and George Skourtis. "The impact of corporate social responsibility in food industry in product-harm crises." British Food Journal 115.1 (2013): 108-123.

Claudiu-Cătălin, Munteanu, Florea Dorian-Laurenţiu and Pagalea Andreea. "The Effects of Faulty or Potentially Harmful Products on Brand Reputation and Social Responsibility of Business." Amfiteatru Economic Journal 16.35 (2014): 58-72.

Ni, John Z., Barbara B.Flynn and F. Robert Jacobs. "Impact of product recall announcements on retailers׳ financial value." International Journal of Production Economics 153 (2014): 309-322.

Peachman, Rachel Rabkin. Fisher-Price Rock 'n Play Sleeper Should Be Recalled, Consumer Reports Says. 2019. <https://www.consumerreports.org/recalls/fisher-price-rock-n-play-sleeper-should-be-recalled-consumer-reports-says/>.

SRIVASTAVA, TAVISH. Perfect way to build a Predictive Model in less than 10 minutes. 2015. <https://www.analyticsvidhya.com/blog/2015/09/perfect-build-predictive-model-10-minutes/>.

Waller, Matthew A. and Stanley E. Fawcett. "Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management." ournal of Business Logistics 34.2 (2013): 77-84.