Please Pick A Problem In Business Analytics

Please Pick A Problem Incorporated A Business Analytics Problem To Be

Please pick a problem incorporated a business analytics problem to be solved within a specific industry. Construct an essay specific to your industry and the potential problem to be solved that outlines your proposed exploratory data analytics approach. (a) Review the Kaggle website ( ) or use any public dataset. Choose a dataset that closely aligns with the problem you wish to solve. Add a link to the dataset. (b) Identify five types of data that would be useful in solving this problem. (c) Discuss your exploratory data approach. In your discussion also include mention of at least one alternative approach that you believe would be inappropriate. Minimum word count = 750 Essay formatted per APA specifications, including in-text and final references Minimum documented references = 3 NO plagarism pls follow the specific APA pattern -->mentioned in the attachment

Paper For Above instruction

Exploratory Data Analytics in the Hospitality Industry: A Problem-Solving Approach

The hospitality industry, encompassing hotels, resorts, and related services, faces intense competition and a constantly shifting consumer landscape. A critical challenge for managers within this sector is understanding customer preferences and optimizing operational efficiency to enhance customer satisfaction while maintaining profitability. Leveraging business analytics offers a promising pathway to solve these complex problems. This essay explores a specific analytics problem: predicting customer satisfaction in hotel stays, using publicly available data from Kaggle, and details an approach to exploratory data analysis (EDA) that can inform decision-making.

To initiate this exploration, I selected the “Hotel Reviews in New York City” dataset available on Kaggle, which contains rich information on customer reviews, including ratings, textual comments, and various hotel features. The dataset can be accessed via this link: Hotel Reviews in NYC Dataset. This dataset closely aligns with the problem of understanding and predicting customer satisfaction levels, which hotel managers can utilize to improve service quality and boost repeat bookings.

Identifying useful data types is crucial to developing a comprehensive analysis framework. Five key types of data that would aid in solving the customer satisfaction prediction problem include:

1. Customer Review Ratings: Quantitative scores (e.g., overall satisfaction, cleanliness, location) provide direct measures of customer perception and can serve as the dependent variable in predictive models.
2. Textual Review Content: Unstructured comments reveal detailed insights into customer experiences, pain points, and positive highlights, which can be analyzed using natural language processing (NLP) techniques.
3. Hotel Features and Amenities: Data on hotel attributes such as room type, price, availability of amenities (pool, gym, Wi-Fi), and star rating help understand what factors influence satisfaction.
4. Customer Demographics: Information like age, nationality, and stay frequency may affect perceptions and preferences, aiding personalized service offerings.
5. Temporal Data: Dates of stays, seasons, and booking lead times provide context for assessing trends and scheduling operational resources effectively.

The exploratory data analysis approach begins with data cleaning and preprocessing to address missing values, inconsistencies, and formatting issues. Initial descriptive statistics and data visualization—such as histograms, boxplots, and correlation heatmaps—offer insights into data distribution and relationships among variables. Investigating textual reviews involves NLP techniques, including sentiment analysis, to quantify customer sentiment levels and identify prevalent themes influencing satisfaction.

Furthermore, feature engineering entails creating composite variables—such as sentiment scores derived from review texts or aggregated ratings—and transforming categorical data into numerical forms suitable for modeling. Visualizations like scatter plots and geographic maps (if location data is available) highlight patterns and outliers, providing a foundation for model selection.

Economic and operational considerations suggest that a regression or classification model (e.g., decision trees, random forests) could predict satisfaction scores or classes (satisfied vs. dissatisfied). Cross-validation and hyperparameter tuning will ensure model robustness. Importantly, unsupervised learning techniques—such as clustering—can identify distinct customer segments and tailor marketing strategies.

An alternative approach that appears inappropriate in this context would be a purely descriptive analysis without predictive modeling or deeper data exploration, as this limits the application of insights in strategic decision-making. Relying solely on basic statistics neglects the richness of unstructured review data and the potential of advanced analytics in providing actionable insights.

In conclusion, applying exploratory data analytics to hotel review data can uncover vital drivers of customer satisfaction, enabling hotel managers to implement data-driven improvements. The combination of quantitative ratings, textual analysis, customer demographics, and hotel features facilitates a comprehensive understanding of customer perceptions. Employing predictive models, aligned with thorough EDA, offers sustainable advantages in competitive positioning within the hospitality industry.

References

1. Bieszczad, M., & Biezczad, A. (2020). Natural language processing for customer reviews: Sentiment analysis of hotel reviews. Journal of Business Research, 112, 176-185.
2. Huang, S. S., & Rust, R. T. (2021). Engaged to a Robot? The Role of AI in Service. Journal of Service Research, 24(1), 30–41.
3. Kumar, V., & Reinartz, W. (2016). Creating Enduring Customer Value. Journal of Marketing, 80(6), 36–68.
4. Marr, B. (2018). Data-Driven HR: How to Use Analytics and Metrics to Drive Performance. Kogan Page Publishers.
5. Yang, Z., Cai, X., & Zhou, Z. (2022). Big Data Analytics in Hospitality and Tourism: A Review and Future Scope. International Journal of Hospitality Management, 102, 103177.