Selected Topic: Application of text mining on customer satisfaction $($Nike Online Store$)$

Customer satisfaction is important in e$-$commerce in the modern digital landscape. For Nike, understanding consumer choice, and pain points so as to maintain a position in competition, protect their hard$-$earned positive image and enhance their products. A lot of consumers leave online reviews, providing Nike with substantial text data through reviews, social media comments, and customer feedback that reflect their experiences and expectations. Project focuses on applying text mining methods to analyze customer feedback from the Nike Online Store. Nike$$s reviewing system facilitates text mining, allowing the identification of trends, problems, and sentiment extremes in customer narratives. The goal is to use insights from online feedback to improve offerings and address pain points. By utilizing text mining, Nike can uncover actionable insights that create better customer experiences and solve product issues, ultimately enhancing online shopping. This enables Nike to detect certain variables necessitating enhancement, such as product quality, shipping speed, and consumer preferences, allowing for better decision$-$making.

Business Analytics Concepts:

Sentiment Analysis is a business analytics concept, that aids in determining the feeling behind text and classifying it as positive, negative, or neutral. This aids Nike in comprehending the overall tone of customer feedback and identifying areas needing immediate attention. Data Preparation entails cleaning and organizing text data through stemming, tokenization, and stop$-$word removal is also needed for text mining. Hence, Nike for efficient data preparation to uncover trends, improve clientele happiness with its e$-$commerce shopping experience, and acquire insightful insights from customer feedback. Topic Modeling uncovers underlying themes in customer feedback, allowing for the categorization of text data $.$ This enables Nike to promptly address recurrent issues through user feedback segmentation. A topic modeling technique known as Latent Dirichlet Allocation $($LDA$)$ can be utilized to identify significant text sections.

Objectives:

One objective is to obtain Customer Pain Points and Preferences. Analyzing textual feedback allows Nike to distinguish common problems related to its online store. Hence, allowing it to prioritize improvements and develop problem$-$cutting measures or strategies. Improving Clientele Experience is another goal. Nike will be able to get vital information through text mining on the likes and dislikes of its customers, resulting in tangible improvements to website user friendliness, customer support, and product options. The project aims to Measure and Improve Brand Sentiment to assess customer loyalty. Examining emotional intensity linked with certain items or services enables Nike to better target for enhancement. Monitoring emotions over period aids in maintaining and improving the reputation in the competitive e$-$commerce nature. Lastly, project seeks to Generate New Marketing and Product Strategies by directing investments in marketing and product development with client preferences. Comprehending client preferences enables Nike to tailor product recommendations and marketing efforts productively.

Text Mining:

Text mining is essential for achieving these objectives by extracting valuable information from unstructured text data. Parsing involves breaking down text and interpreting patterns using algorithms and statistical models. This implies analyzing numerous customer feedback to categorize them based on emotions and different subjects, offering a comprehensive summary of customer contentment for Nike. This method enables Nike to identify areas of dissatisfaction and preferences, prioritize enhancements, and create focused plans. N$-$gram analysis helps identify sequences of words within the text, highlighting popular phrases or ideas. This assists Nike in detecting frequently used terms or expressions in feedback, which provides hints regarding certain experiences that consumers are often referencing. It is crucial to enhance the clientele$$s experience by informing website user$-$friendliness and customer support. Named Entity Recognition $($NER$)$ identifies and categorizes proper nouns, aiding Nike in tracking particular products referenced in comments. By utilizing these insights, Nike is able to gauge and enhance brand sentiment$,$ evaluate customer loyalty, and develop new marketing and product strategies that meet the demands of the market.

Qs: Literature Review: Describe two $(2)$ references $($must be research articles from journal$/$conference$/$academic report$/$thesis$)$ that apply text mining in a way relevant to your selected topic. Include the general background of the study, the dataset used, the details of the text mining process applied, as well as relevant findings and conclusions. Discuss the implications of the references to the current project.