Data Driven Decision Making
Data Driven Decision Making
Describe a real-world business situation that could be addressed by collecting and analyzing a set of data.
|All businesses need to understand their customers. One way to achieve this is through customer segmentation to identify unique clusters. This helps firms customize services and products to suit each customer cluster. However, it is not commonly carried out in the hotel industry as most data collected goes to creating loyalty programs. Big data can help businesses achieve this vital function of grouping their customers. For example, pampering clients who demand spa, theatre and dining services, customers on business, customers on pilgrimage, and other particular activities such as sports, and conferences among others. Since consumers have unique wants and needs, they request unique hotel services depending on planned activity and time of year. This type of customer segmentation can be used to avail resources according to demand, hence, saving costs and increasing efficiency (Solis, 2010; Grotte).|
Summarize one question or decision relevant to the real-world business situation you will answer by collecting and analyzing a set of data.
|The question relevant to this case is what customer clusters exist among hotel visitors. This will be determined by the type of services demanded at the hotel, transportation needs and other services.|
Explain why the situation or question would benefit from a data analysis.
|Data analysis will be used to classify visitors depending on the services they demand from a hotel with the aim of predicting demand, reducing expenses and increasing efficiency.|
Identify data you will need to collect that is relevant to the situation or question.
|Age, sex, and length of stay, the size of the entourage, time of year, frequency and type of room service requests, and amenities and any extra services required. Also, flight records, social media reviews and ratings, car rental services and train reservation records will be required to enrich the hotel data.|
Describe the data gathering methodology you will use to collect data.
|The data related to the hotel stay will be collected as secondary data from various hotels located in different geographical and economic regions. The travel records will be sourced from travel agencies matching those hotel visits. Social media reviews and ratings will be collected from Yelp and Trip Advisor matching the locations and duration of the hotel stay identified earlier.|
Identify the appropriate data analysis technique you will use to analyze this data (e.g., linear programming, crossover analysis, t-test, regression).
|T-test, Analysis of Variance (ANOVA), correlation, and regression|
Explain why the data analysis technique you chose is an appropriate technique to analyze the data collected.
|ANOVA is necessary to identify the differences between customer groups and how they affect demand. Correlation will be used to determine the relationship between customers’ data such as length of stay, time of travel, frequency and type of room service requests, additional services requested, and means of transport. The time of the year, means of transport, and type of services requested will be subjected to regression analysis to predict demand for certain services and type of planned activity.|
Grotte, J. (n.d.). New Trends in the Hospitality Industry. Journal of Tourism Research. Retrieved from http://jotr.eu/index.php/hospitality-management/120-hospitality-trends
Kothari, C. (2004). Research Methodologies. New Delhi: New
Solis, M. (2010, Oct. 21). Hospitality Market Segmentation. Retrieved from http://www.hsmai.org/knowledge/summary.cfm?ItemNumber=4640:
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