(Tiểu luận) analyze airline passenger portrait and airline customer services satisfaction by using orange software

VIETNAM GENERAL CONFEDERATION OF LABOUR TON DUC THANG UNIVERSITY BUSINESS ADMINISTRATION FINAL PROJECT ANALYZE AIRLINE PASSENGER PORTRAIT AND AIRLINE CUSTOMER SERVICES SATISFACTION BY USING ORANGE SOFTWARE Subject: Big Data Lecture: Dr Ngô Tấn Vũ Khanh Group members: Trần Thị Lan Anh 718H0243 Nguyễn Cơng Đồn 719H0842 Phan Ngọc Dun 719H0025 Sín Gia Hân 719H0032 Ho Chi Minh City, 15/03/ 2023 Table of content Summary I Introduction II Data overview III Research method 3.1 3.1.1 Definition: 3.1.2 The idea of K-means++ algorithm 3.1.3 Details of the K-means++ algorithm 3.2 IV Clustering using K-means Linear Regression method 3.2.1 Definition 3.2.2 Evaluate Linear regression Data mining and analysis using Orange software 10 4.1 Data preprocessing 10 4.2 Data mining and analysis using Orange software 15 4.2.1 Model overview 15 4.2.2 The customer portrait of the airline industry 16 4.2.3 Describe customer portraits by using Box Plot 19 4.2.4 Relationship between Flight Distances, Annual Income and Type of Travel by using Scatter Plot 20 4.3 Customer clustering by K means 21 4.3.1 Process 21 4.3.2 Analysis 23 4.4 Analyze the factors affecting customer satisfaction 28 4.4.1 Process 28 4.4.2 Analysis 30 V Conclusion 36 Table of Figure Table Data information Figure Passenger age and gender 16 Figure Type of Customers 17 Figure Passenger’s annual income 18 Figure Customer’s profession 19 Figure Customer’s profession by Class 19 Figure Relationship between flight distance, income and type of travel 20 Figure Scatter chart of customer clusters by age 23 Figure Scatter chart of customer clusters by Annual Income 24 Figure Scatter chart of customer clusters according to Work Experiences 25 Figure 10 Scatter chart of customer clusters by Age 25 Figure 11 Scatter chart of customer clusters by Income 26 Figure 12 Scatter chart of customer clusters by Flight Distances 27 Figure 13 Scatter chart of customer clusters according to Work Experiences 27 Picture Data Preprocessing 10 Picture Adding data to Orange Data Mining by CSV File Import 11 Picture Evaluate the data by the Feature Statistics 11 Picture Missing Information 12 Picture Handling missing information 12 Picture Merging Data by Concatenate 13 Picture Table of Customer Data After Preprocessing and Merging 14 Picture Cluster analysis model and customer satisfaction level 15 Picture K-means clustering diagram using Orange 21 Picture 10 Select information 22 Picture 11 The result 23 Picture 12 The process of Linear Regression method 28 Picture 13 The table of variables 29 Picture 14 The result of independent variables 30 Summary In summary, we analyze and evaluate the data to come up with customer profiles as well as analyze customer satisfaction with airline services The data used in the article is taken from Kaggle and conducted using Orange software with K-means method to discover customer segments and portraits, and analyze the factors affecting customer satisfaction through Linear Regression I Introduction With the development of society as well as the economy, the need for non-stop movement of people is constantly increasing In particular, the aviation industry is one of the most favored means of transportation, especially for long flights or people who need to save commuting time The airlines not only provide their passengers with a safe and convenient flight, they also want their customers to experience quality flight service That is also one of the competitive advantages of airlines, especially when theỉr customer are often from middle-class above, who focus on their own experiences and customer services The Airline customer service is the activities or services of the Airline for their passengers at each stage of their journey to meet or improve customers' overall inairport and in-flight experience Specifically, these services include pre-flight services such as the ease of booking, check-in service to in-flight services such as seat comfort, entertainment and many other services Conducting an analysis of customer reviews and satisfaction of airline services is a method to help airlines understand their customers better as well as their satisfaction toward the airline services It is especially important to know whether the services provided by the airline are suitable and meet the needs of the customers Hence, airlines can assess customers' attitudes towards them as well as what services they need to change to be more suitable for the passengers The data on customer satisfaction ratings for flight services can be collected from many sources and the amount can be huge Therefore, in order to understand customer satisfaction, we had used the Orange tool to model data and analyze customer satisfaction of different services in a flight journey Specifically, we used K-means and Linear regression to determine the impact of factors on customer satisfaction Hence, make an assessment of customer satisfaction for the airline as well as orient the future strategy and reinforces customer satisfaction II Data overview The data set from Kaggle includes 4000 responses which is customer information as well as their evaluation of the flight service experience The information includes: Name Description Gender Gender of the passengers (Female, Male) Customer Type The customer type (Loyal customer, disloyal customer) Age The actual age of the passengers Profession The passengers current job Annual income The annual income of the passengers Spending Score The spending score of passenger (1-100) Purpose of the flight of the passengers (Personal Travel, Type of Travel Business Travel) Travel class in the plane of the passengers (Business, Class Eco, Eco Plus) Flight distance The flight distance of this journey Satisfaction level of the inflight wifi service (0:Not Inflight wifi service Applicable;1-5) Departure/Arrival time convenient Satisfaction level of Departure/Arrival time convenient Ease of Online booking Satisfaction level of online booking Gate location Satisfaction level of Gate location Food and drink Satisfaction level of Food and drink Online boarding Satisfaction level of online boarding Seat comfort Satisfaction level of Seat comfort Inflight entertainment Satisfaction level of inflight entertainment On-board service Satisfaction level of On-board service Leg room service Satisfaction level of Leg room service Baggage handling Satisfaction level of baggage handling Check-in service Satisfaction level of Check-in service Inflight service Satisfaction level of inflight service Cleanliness Satisfaction level of Cleanliness Departure Delay in Minutes Minutes delayed when departure Arrival Delay in Minutes Minutes delayed when Arrival Airline satisfaction level (Satisfaction, neutral or Satisfaction dissatisfaction) Table Data information III Research method 3.1 Clustering using K-means 3.1.1 Definition: The K-means++ algorithm is a combination approach to reduce the drawback of using the random centroid of K-means as the first focus selection technique By selecting statistical centers close to the actual centers, K-means++ gets over this problem This approach ensures more informed centroid initialization and raises the caliber of the clustering The remainder of the process will be the same as the common K-means approach, with the exception of the centroid initialization As a result, Kmeans++ can be thought of as the normal K-means method plus improved center of gravity initialization The K-means++ technique was found to successfully resolve some of the issues related to establishing the initial cluster centroids for K-means in a review by Shindler that included numerous clustering algorithms Document continues below Discover more from: Big Data Applied in Management 702075 Đại học Tôn Đức… 71 documents Go to course 48 Marketing VÀ TRUYỀN Thông CỦA IVY MODA Applied Big Data in… 100% (1) LUẬN CƯƠNG Chính TRỊ Applied Big Data in… 100% (1) Tôi chia sẻ Scan 08 Th11 22 095815 vớ… Applied Big Data in… None THAM KHẢO - BÁO CÁO CUỐI KÌ BIG DATA Applied Big Data in… None Outline big data cuối kì Applied Big Data in… None Unicorn BCCK - etcse K-Means is an unsupervised learning algorithm that partitions a given dataset into a Applied fixed number of clusters (K clusters) by defining K centroids, one for eachBig cluster To 39 Data in… ensure better results, the centroids are placed far away from each other 3.1.2 The idea of K-means++ algorithm The procedure for selecting the first centroid of k is to uniformly select the data points being clustered, ensuring that the closest centroid represents the group: Decide on a shared centroid for the data points Calculate D(x), the separation between each unselected data point x and the closest chosen centroid Using a weighted probability distribution, choose a new data point at a new centroid, where the chosen point x has a probability proportional to D(x)2 Up until the centroids are chosen, repeat steps and After choosing the first centroids, we continue to conduct clustering using the conventional K-means algorithm The K-means++ algorithm was created to get over the problem with picking the initial centroid at random Because the final clustering outcome is dependent on the original cluster centroids 3.1.3 Details of the K-means++ algorithm The K-means++ algorithm is a technique for displaying the shortest D(x) distance between a data point and the selected nearest centroid Initialization of Kmean++ steps: ● Step 1: For a center c1, the first centroid is evenly chosen at random from the collection of m points in the data ● Step 2: Determine how far each location in the dataset is from the chosen centroid You may figure out how far point xi is from the furthest center of gravity by using the formulas: 𝑑𝑖 = 𝑚𝑎𝑥(𝑗:1→𝑚) ||𝑥𝑖 −𝐶 𝑗 || In there: di: is the distance of the point xi to the farthest centroid k: number of centroids selected None ● Step 3: Take a new center xi , whose maximum probability is proportional to di ● Step 4: Repeat steps and 4, until we find k centroids After finding the centroid k, we continue to divide the cluster based on the standard K-means algorithm as in Section 3, Part III above 3.2 Linear Regression method 3.2.1 Definition Linear Regression is a statistical method for predicting the relationship between a dependent variable (also known as the response variable) and one or more independent variables (also known as predictor variables) Linear regression seeks the best-fit line that describes the relationship between variables Regression is also a method commonly used for two purposes The first purpose is to make predictions and forecasts, which is a task that overlaps with the field of machine learning The second purpose is to identify causal relationships between independent and dependent variables It's important to note that regression analysis only reveals relationships between a dependent variable and a specific collection of variables, and it does not provide conclusive evidence of causal relationships Simple Linear Regression is the most basic form of linear regression, and it involves finding the best straight line to fit the data The equation for a simple linear regression model is y = b0 + b1*x In this equation, y is the dependent variable, x is the independent variable, b0 is the intercept, and b1 is the line's slope Linear regression can be used to predict future values of the dependent variable, understand the relationship between the independent and dependent variables, and identify the most important predictors in a dataset, among other things It is commonly used in finance, economics, social sciences, and engineering 3.2.2 Evaluate Linear regression Linear regression analysis based on the coefficient (or regression coefficient) is used to evaluate the degree of influence of independent variables on the dependent variable in a linear regression model The coefficient indicates the average change in the dependent variable (the unit of measurement of the dependent variable) when an independent variable changes by one unit If the coefficient has a positive value, the changes in the independent and dependent variables are in the same direction, meaning that when the value of the independent variable increases, the value of the dependent variable also increases Conversely, if the coefficient has a negative value, the changes in the independent and dependent variables are opposite, meaning that when the value of the independent variable increases, the value of the dependent variable decreases The larger the absolute value of the coefficient, the stronger the impact of the independent variable on the dependent variable Therefore, the coefficient is essential in evaluating the effects of independent variables on the dependent variable and helps us to better understand the relationship between them From the chart, it can be seen that the C2 customer group mainly focuses on flights with a route length of 1,500 km or more Meanwhile, the C1 customer group will focus entirely on flights with a length of less than 1,500 km But both groups have a large number of customers between the ages of 20 and 60, so we will continue to find differences for these customers Figure Scatter chart of customer clusters by Annual Income About Annual Income, there is a difference that the income of group C1 will mostly focus on $50,000 - $190,000 and they will often choose flights with a distance of 1000 km In contrast, group C2 is a group of customers whose Annual Income is widely distributed, not concentrated at one point, but those who love flights about 2,500 km will have the most concentrated annual income and also range from $50,000 $190,000 Figure Scatter chart of customer clusters according to Work Experiences As for the C1 group, they are those whose Work Experiences focus mainly on the following: - year and - 10 years As for the remaining group of customers, they will have - year of experience mainly In addition, we apply another way of analysis using Bar Plot Figure 10 Scatter chart of customer clusters by Age By age, customer group C1 will focus on age groups including approximately 25 years old, approximately 39 years old, and approximately 45 years old As for group C2, customers will mainly be people around 42 years old, about 44 years old and almost 51 years old Figure 11 Scatter chart of customer clusters by Income The annual income of the groups has a clear difference when most of the C1 customer groups have almost twice the annual income of the C2 group In which, the highest level of customer group C1 is approximately $ 90,000 and frequency is approximately 120 times, while the lowest level is $ 20,000 with a frequency of 15 times In contrast, customer group C2 has the highest level at only $70,000 with a frequency of approximately 60 times and the lowest level of approximately $ 10,000 with a frequency of times Figure 12 Scatter chart of customer clusters by Flight Distances Based on the Flight Distance factor, C1 customers tend to prefer flights with a length of 1,000 km or less, of which more than 320 people love flight lengths of approximately 500 km For the C2 customer group, there is an opposite as they tend to prefer longer flights, namely the approximate length of 2,500 km is the most popular and the lowest is 1,100 km Figure 13 Scatter chart of customer clusters according to Work Experiences The last factor is Work Experiences, customer group C1 has times more experience than customer group C2 and the highest level of experience in customer groups is approximately years Combining the above analysis factors, the airline's customer data shows two customer clusters with the following meanings: ● Group C1: young - middle-aged customers, have a higher number of years of working experience than group C1, high income and love short-haul flights ● Group C2: elderly customers, medium years of working experience, income is not as high as C2 and prefer long flights 4.4 Analyze the factors affecting customer satisfaction 4.4.1 Process Picture 12 The process of Linear Regression method Step 1: Based on the processed data, select the data for analysis, which is the survey data of factors affecting customer satisfaction measured on a Likert scale - Picture 13 The table of variables Here we see that the variables in the Features section are independent variables, and the variables in the Target section are dependent variables Step 2: Use the Linear Regression widget to perform Linear Regression Analysis Step 3: Read the results Picture 14 The result of independent variables 4.4.2 Analysis According to the findings of the linear regression analysis, various factors have a significant impact on customer satisfaction when using airline services The top three factors that have the greatest positive impact on customer satisfaction are gate location, leg room service, and baggage handling, followed by in-flight service and cleanliness In-flight entertainment, departure delay in minutes, and check-in service, on the other hand, have a negligible or negative impact on customer satisfaction Other factors have a smaller positive impact on customer satisfaction, but they still contribute to the overall customer experience A positive coefficient of number indicates that each factor has a positive correlation with customer satisfaction, implying that the higher the quality of the factor, the higher the level of customer satisfaction with air services The magnitude of the effect of this factor variable on customer satisfaction is also indicated by the value of factor, with a higher coefficient value indicating a stronger impact Example for a oneunit change in the corresponding factor A one-unit increase in gate location, for example, with a coefficient of 0.131702, is estimated to result in a 0.131702 increase in customer satisfaction Similarly, it is estimated that a one-unit increase in leg room service (with a coefficient of 0.122556) will result in a 0.122556 increase in customer satisfaction The results of the linear regression analysis indicate that the following factors have a positive impact on customer satisfaction when using airline services, listed in order of their coefficient values: Gate location (0.131702) Leg room service (0.122556) Baggage handling (0.119746) Inflight service (0.100483) Cleanliness (0.0912188) Check-in service (0.0689468) Seat comfort (0.0528193) Food and drink (0.0518326) Ease of online booking (0.0501179) 10 10.Online boarding (0.0387003) 11 On-board service (0.0383291) 12 Departure/arrival time convenience (0.0288492) On the other hand, the following factors have a negative impact on customer satisfaction or don’t have effect when using airline services: Inflight entertainment (-0.00609433) Departure delay in minutes (-0.000320877) Arrival delay in minutes (0.000693794) Inflight wifi service (0.00179923) It is important to note that, even if the coefficients show that these factors have negligible or negative effects on customer satisfaction, airlines should still consider them in their efforts to improve customer satisfaction Small improvements in these areas may still be appreciated by some customers and contribute to a positive customer experience overall More detail analysis about factor that strong affect on customer satisfaction: • Gate location: This factor has the highest coefficient and is associated with higher levels of customer satisfaction The gate location has the greatest positive impact on customer satisfaction Customers are more satisfied when the gate is conveniently located and easy to access Airlines should prioritize providing easy-to-find and navigate gate locations with clear signage and directions • Leg room service: This factor has a high coefficient value and is associated with higher levels of customer satisfaction The second most important factor influencing customer satisfaction is leg room service Customers are more satisfied when they have enough leg room on the flight, which can make a significant difference in their level of comfort during the flight Airlines can improve customer satisfaction by providing more leg room or charging a premium for seats with extra leg room • Baggage handling: The coefficient of 0.119746 suggests that efficient and reliable baggage handling is a key factor in customer satisfaction, as it reduces stress and inconvenience for travelers This factor has a positive relationship with customer satisfaction, indicating that customers who have their luggage handled well are more likely to be satisfied Customers are more satisfied when their luggage is handled properly and without problems, such as lost or delayed luggage Airlines should prioritize improving their baggage handling processes, such as investing in better tracking technology or hiring more baggage handlers • Inflight service: This factor is also positively correlated with customer satisfaction The quality of inflight service, such as meals and drinks, is an important factor that affects customer satisfaction Customers are more satisfied when the quality and variety of inflight meals and drinks are good Airlines can improve customer satisfaction by offering more food and drink options or providing better quality meals • Cleanliness: The coefficient of 0.0912188 suggests that a clean and well-maintained cabin environment is an important factor in customer satisfaction, as it enhances overall comfort and reduces the risk of illness This factor is associated with medium levels of customer satisfaction Customer satisfaction is greatly influenced by cleanliness When the aircraft and cabin are clean and well-maintained, customers are more satisfied Airlines can improve customer satisfaction by investing in better cleaning equipment and processes, as well as training their employees to keep the environment clean and sanitary • Checkin Service: A positive check-in experience, including factors such as speed, efficiency, and friendliness of staff, can significantly impact customer satisfaction, as indicated by the coefficient of 0.0689468 Check-in service can refer to a number of aspects of the checkin process, including the check-in process's efficiency, the friendliness and helpfulness of the check-in staff, and the clarity of information provided during check-in Any of these improvements could lead to a more positive customer experience and higher satisfaction ratings • Seat comfort: This factor has a positive relationship with customer satisfaction, implying that customers who have comfortable seats are more likely to be satisfied Seat comfort has a significant impact on customer satisfaction Customers are happier when the seats are comfortable and provide adequate support Airlines can improve customer satisfaction by investing in more comfortable seats or providing passengers with seat cushions and pillows • Food and beverages: This factor is related to customer satisfaction Customer satisfaction is influenced by the quality and selection of food and beverages Customers are more satisfied when they have a variety of options to choose from and the food and drinks are of high quality Airlines can boost customer satisfaction by collaborating with high-quality food and beverage providers or investing in their own catering services • Ease of online booking: Customers who find it easy to book flights online are more likely to be satisfied with their experience, according to the coefficient of 0.0501179 The ease of online booking and check-in is a significant factor influencing customer satisfaction Customers are more satisfied when the online booking and check-in process is simple and straightforward Airlines can increase customer satisfaction by investing in easy-to-use online booking and check-in systems • Online boarding: As indicated by the coefficient of 0.038703, the ease and efficiency of the online boarding process can have a positive impact on customer satisfaction The online boarding process has a significant impact on customer satisfaction When the boarding process is well-organized and efficient, customers are more satisfied Airlines can improve customer satisfaction by investing in better boarding technology or staffing the boarding process more effectively • On-board service: As indicated by the coefficient of 0.0383291, the quality of on-board services such as duty-free shopping and entertainment options can have an impact on customer satisfaction The quality of on-board service, such as the crew and entertainment options, is an important factor influencing customer satisfaction Customers are more satisfied when the crew is friendly and helpful, and when there are plenty of options for entertainment Customers' satisfaction can be increased by investing in better crew training and providing more and better entertainment options • Convenient departure/arrival times: The coefficient of 0.0288492 indicates that convenient departure and arrival times for customers can positively impact their satisfaction The convenience of departure and arrival times has a significant impact on customer satisfaction Customers are happier when the departure and arrival times are convenient, such as arriving at a reasonable hour More flight options with convenient departure and arrival times can help airlines improve customer satisfaction The following factors have a negligible or negative effect on customer satisfaction: • Inflight wifi service: This coefficient suggests that improving the quality of in-flight wifi has a negligible impact on customer satisfaction It's possible that most customers don't care about inflight wifi, or that the quality of service is already satisfactory • Arrival Time Delay: According to this coefficient, an increase in arrival delay has a negligible impact on customer satisfaction Customers may not place a high value on arrival time because they understand that delays can occur for reasons beyond the airline's control • Departure Delay in Minutes: According to this coefficient, an increase in departure delay has a negligible negative impact on customer satisfaction Customers, like arrival delays, may not place a high value on departure time or may understand that delays can occur for reasons beyond the airline's control • Inflight entertainment: According to this coefficient, an increase in the quality of in-flight entertainment has a minor negative impact on customer satisfaction Customers may have higher expectations for in-flight entertainment and may be more dissatisfied if the quality is not up to par We can see which factors have the greatest impact on customer satisfaction when using airline services based on the data provided Airlines can use this data to prioritize their efforts and investments in these key areas, resulting in higher levels of customer satisfaction and potentially increased revenue Furthermore, Linear Regression analysis offers a quantitative approach to understanding the relationships between these factors and customer satisfaction, allowing for more informed decision-making V Conclusion Based on the analysis results using K-Means and Linear Regression, we can conclude that there are two notable customer segments, including young and middleaged customers with high incomes who prefer short flights, and older customers with average incomes who prefer longer flights At the same time, we have also identified positive and negative factors that influence customer satisfaction Positive factors include entrance location, staff service, baggage handling, in-flight services, cleanliness, online ticketing and check-in convenience, comfort of seats, food and drinks on board, and convenience of departure/arrival time On the other hand, negative factors have little effect on customer satisfaction, such as in-flight entertainment services, delayed departure and arrival, and onboard WiFi services However, it should be noted that both K-Means and Linear Regression have limitations when used to analyze data For example, K-Means requires a predetermined number of clusters and can yield different results depending on the choice of initial centroids Meanwhile, Linear Regression assumes a linear relationship between input and output variables, which may not be suitable for some complex data models Therefore, to ensure accurate analysis results, we should use multiple methods and evaluate the results comprehensively K-Means has weaknesses when analyzing certain types of data, and Linear Regression also has limitations when analyzing certain types of data Here are some recommendation to focus on these customer segments and increase customer satisfaction: Offer tailored services and products: Based on the preferences and needs of these customer segments, airlines can provide tailored services and products For example, airlines can offer special deals and promotions for short-haul flights to attract young and middle-aged customers They can also offer comfortable seating arrangements and in-flight entertainment for long-haul flights to meet the needs of elderly customers Improve customer service: To increase customer satisfaction, airlines should focus on improving customer service This can include providing quality gate location, legroom service, baggage handling, inflight service, cleanliness, check-in service, seat comfort, food and drink, ease of online booking, online boarding, on-board service, and departure/arrival time convenience Use customer feedback: Airlines can use customer feedback to improve their services and products By collecting feedback from customers, airlines can identify areas for improvement and take necessary actions to enhance the overall customer experience Build customer loyalty: Airlines can build customer loyalty by providing frequent flyer programs, special deals and discounts, and personalized services This can help retain customers and attract new ones In summary, to focus on and develop these customer segments, airlines should offer tailored services and products, improve customer service, use customer feedback, and build customer loyalty By doing so, airlines can increase customer satisfaction and gain a competitive advantage in the airline industry References Airline Customer Service Fundamentals (e-learning) (n.d.) IATA Retrieved March 15, 2023, from https://www.iata.org/en/training/courses/customer-serviceairline/tall28/en/ Airline Passenger Satisfaction (n.d.) Kaggle Retrieved March 15, 2023, from https://www.kaggle.com/datasets/teejmahal20/airline-passenger-satisfaction Dịch vụ hàng không điều bạn cần biết (2023) Dịch vụ hàng không điều bạn cần biết https://vietjet.asia/ban-tin-du-lich-tong-hop/dich-vu-hang-khong-vanhung-dieu-ban-can-biet.html Linear Regression — Orange Visual Programming documentation (n.d.) Orange Data Mining Library Retrieved March 15, 2023, from https://orange3.readthedocs.io/projects/orange-visualprogramming/en/latest/widgets/model/linearregression.html David Arthur and Sergei Vassilvitskii, k-means++: the advantages of careful seeding, Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms Society for Industrial and Applied Mathematics Philadelphia, PA, USA pp 1027– 1035 Michael Shindler, Approximation Algorithms for the Metric k-Median Problem ML | K-means++ Algorithm, https://www.geeksforgeeks.org/ml-k-means-algorithm Satyam Kumar, Understanding K-Means, K-Means++ and, K-Medoids Clustering Algorithms (2020) https://towardsdatascience.com/understanding-k-means-k-meansand-k-medoids-clustering-algorithms-ad9c9fbf47ca