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Name: Omkar Pradeep Gondhalekar Student ID: 14121972. Course: MSc in Data Analytics National College of Ireland. ANALYZING THE SERVICE QUALITY OF LONDON ‘TRAIN OPERATING COMPANY’ SURVEY TOWARDS THEIR FACILITIES.

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Name: Omkar Pradeep GondhalekarStudent ID: 14121972.

Course: MSc in Data AnalyticsNational College of Ireland.

ANALYZING THE SERVICE QUALITY OF LONDON ‘TRAIN OPERATING

COMPANY’ SURVEY TOWARDS THEIR FACILITIES.

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Overview• For the sustainable development and profitability of any transportation the

provision of high quality service to the passengers is the core competitive advantage.

• This project present a hybrid approach based on SERVQUAL for evaluating service quality of London train transportation systems.

• The participants of the survey provide linguistic assessments to rate the service quality criteria and their alternatives.

• The tools used to implement this are the SPSS statistical tool, Tableau Visualization tool and the powerful Excel for pivoting and cleaning the data

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INFORMATION OF DATASET

• This data set includes questions such as :

• 1) Is information about train times provided ?

• 2)Are the train stations clean?

• 3)Is there sufficient availability of seats?

• 4)Is there enough availability of places to eat?

• 5)How is the frequency of trains on the root?

• The data set has many missing values which are removed.

• The data set involves a survey which is done on different facilities of railways and the survey is carried out from 18th January to 29th March 2015.

• The survey involves different sectors and regions.

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Project Plan: Crisp-DM (Cross-industry Standard Process For Data Mining)

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Step 1: Business Understanding – Background• The transportation sector is ever-growing. However, the competition in this

sector is also increasing.• The train sector is also competitive and constant improvement is needed to

boost business.• Along with rival companies in train sector there is also a rivalry with other

sectors such as road transport and airline industry.• Hence, in this project we are analyzing a data set which involves a survey of

the rail customers. We will try and implement different techniques to obtain outputs which will help improve facilities. This in turn will boost business.

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Obtaining The Data Set-

• The data is obtained from http://data.gov.uk/ website.

• The data set is a National railway passenger survey.

• This is a survey for all the train operating companies in UK.

• This data set is taken to obtain outputs from the survey which would be helpful

for the betterment of facilities.

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Step 2: Cleaning The Data

Some of corrections include:

• Null values represented by ‘-’ symbol are represented by ‘0’ symbol.• Horizontal values are arranged in vertical values using transpose function.• The questions are paraphrased. Thus large sentences are reduced into smaller

formats.• 70% of the time is taken in cleaning.

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Step 3: Methods and Tools Include-• In this project the SERVQUAL method segregate the criteria into standard set

of dimensions.• Thus the dimension include Responsiveness, Reliability, Tangibles, Assurance,

Empathy.• Pearson’s Correlation Coefficient method is used to obtain the strength of

association.• This method is implemented using the tool ‘SPSS Statistical tool’.• The final reporting and analysis are understood using Tableau software.

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Step 4: Applying the techniques - SERVQUAL method• SERVQUAL method is used to segregate the questions in the survey.

• SERVQUAL is the most commonly used method by researchers for analyzing the survey and interview questionnaire.

• Responsiveness is prompt in providing service and also willingness of the service provider to be helpful.

• Reliability is to perform the promised service dependently and accurately by the service provider.

• Tangibles involve in Physical appearance of the service facilities that can be seen.

• Assurance refers to the knowledge and courtesy of service providers and their ability to inspire trust and confidence.

• Empathy includes caring and individualized attention to customer, for example: Old age customers who are seeking a help

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Step 4: Applying the techniques - SERVQUAL method• The questions/criteria of the survey which was cleaned is segregated as below

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Step 4: Applying the techniques Pearson's - Correlation Method• Pearson's correlation coefficient (r) is a measure of the strength of the association between the two

variables”

• Pearson’s coefficient if close to 1 ,shows strong correlation.• From the above figure is noticed that there are correlations of on a diagonal line across the table,

which is because each variable should correlate perfectly with itself. • From the result it is concluded that all values are significant at p<0.001. This proved that there are

statistically significant relationships between the perceived service quality of each of the five dimensions with 99% confidence level of overall service quality.

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Step 5: Evaluation And Results: Case 1: Which Dimension Of Criteria Has Higher Service Quality?• For “very satisfied” rating, the tangibles dimension has scored the least rating, then assurance is the second

least rating, reliability and responsiveness has the same rating which is in third place and Empathy has the highest rating. It can be suggested that the tangibles dimension has to be improved to reach service personal value of the passenger.

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Case2: Which Service Provider (TOC) Offering The Highest Quality Customer Service• From this line graph we can see that the non-franchised companies have highest customer

satisfaction rate.

• Non-franchise TOCs, have got the highest rating compared to the other three TOCs. To get deeper insight in this individual scale rating on all London service providers is made.

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Case3: Rating Scale Of Service Quality For The Four Different TOCs Using Bar Graph

• It is seen that the Non-Franchised service provider has received the highest rating, remaining all have equal value of rating.

London Train operating company

Very satisfied Fairly satisfiedNeither satisfied nor

dissatisfied Fairly dissatisfied Very dissatisfied

Franchised Train Operating

Company

Non-Franchised TrainOperating Company

Sector

Train Operating Company

'Building Block'

Franchised Train Operating

Company

Non-Franchised TrainOperating Company

Sector

Train Operating Company

'Building Block'

Franchised Train Operating

Company

Non-Franchised TrainOperating Company

Sector

Train Operating Company

'Building Block'

Franchised Train Operating

Company

Non-Franchised TrainOperating Company

Sector

Train Operating Company

'Building Block'

Franchised Train Operating

Company

Non-Franchised TrainOperating Company

Sector

Train Operating Company

'Building Block'

0.0

0.1

0.2

0.3

0.4

Value

Sheet 1

Measure NamesFranchised Train Operating Company

Non-Franchised Train Operating Company

Sector

Train Operating Company 'Building Block'

Franchised Train Operating Company, Non-Franchised Train Operating Company, Sector and Train Operating Company 'Building Block' for each London Trainoperating company. Color shows details about Franchised Train Operating Company, Non-Franchised Train Operating Company, Sector and Train Operating

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Case 4: Average Score Of All The TOCs (Box and Whisker plot)• The ‘Box and Whisker’ plot of TOCs, which calculates the median, upper and lower of both

whisker and quartile.

• The distribution of points has plotted between upper and lower whiskers to which the quartile is automated with the median.

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Case 5: The Horizontal Bar Graph For Criteria With Its Toc’s• From the graph it is clearer that empathy and tangibles are the dimensions with high service quality for the

increase in overall non-franchise TOC.

Responsiveness

Reliability

Tangibles

Assurance

Empathy

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

34%

34%

25%

30%

35%

33%

33%

24%

29%

35%

40%

42%

39%

45%

46%

34%

34%

25%

30%

35%

SectorsFranchised TOCNon-franchised TOCTOC 'building block'

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Case 5: Top Listed Criteria That Are Best In Service Quality• The tree map for very satisfied customers is seen below. We can observe that people are

fairly accustomed to particular stations.

Upkeep/repair of thestationbuildings/platforms

Up keep andrepair of thetrain

The value formoney for theprice of yourticket

Thespace forluggage

The provisionof informationduring thejourney

The length of time the journey wasscheduled to take (speed)

The facilitiesand services atthe station

The ease of being able toget on and off the train

The comfort of theseating area

The cleanliness of theoutside of the train

Thecleanlinessof the insideof the train

The choice ofshops/eating/drinkingfacilities available

The attitudes and helpfulness ofthe staff

Sufficient roomfor all thepassengers tosit/stand

Punctuality/reliability(trainarrival/departure time)

Provision of shelterfacilities

Provision of information abouttrain times/platforms

Personalsecurity whilstusing thatstation

Personal security whilston board the train

Overall stationenvironment

Overall satisfaction withyour journey

overall satisfaction ofthe journey afterboarding

Frequency of the trains onthat route

Familiarity with this particularstation

Ease of using ticketgates

Connections with othertrain services

Connectionswith other formsof publictransport

Cleanliness ofthe station

Cleanlinessin the train

Availability ofstaff at thestation

Availability of seating

Sheet 4

0.1700 0.5700

Very Satisfied

Questions. Color shows sum of Very Satisfied. Size shows sum of Very Satisfied. The marks are labeled by Questions. The view is filtered on

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Case 7: Top Listed Criteria That Are Least In Service Quality• This is a tree map showing customers who are dissatisfied. This shows that people

are highly dissatisfied with choice of sources of refreshments.

Up keep andrepair of thetrain

The value for money for the price ofyour ticket

The space for luggage

The provision ofinformation during thejourney

The lengthof time thejourneywasscheduledto take

The facilities and services at thestation

The easeof beingable to geton and offthe train

The comfort of theseating area

Thecleanlinessof theoutside ofthe train

The cleanliness ofthe inside of thetrain

The choice of shops/eating/drinkingfacilities available

The attitudes andhelpfulness of the staff

Sufficient room for all thepassengers to sit/stand

Provision ofshelterfacilities

Provision ofinformation abouttrain

Personalsecurity

Personalsecurity

Overall stationenvironment

Overallsatisfaction withyour journey

overall

Frequency of the trainson that route

Ease of using ticket gates

Connections with othertrain services

Connectionswith otherforms of publictransport

Availability of staffat the station

Availability of seating

Sheet 5

0.0000 0.1200

Very Dissatisfied

Questions. Color shows sum of Very Dissatisfied. Size shows sum of Very Dissatisfied. The marks are labeled by Questions. The view is filtered

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Conclusion• SERVQUAL is one of logically applied method to segregate the criteria of survey

on dimensions like Responsiveness, Reliability, Tangibles, Assurance and Empathy.• Pearson’s correlation calculates strength of relation using SPSS tools.• Graphs are found out from SPSS and Tableau.• We get one key conclusion that the non franchised sector is doing well in terms of

customer satisfaction. This means that others should follow a similar way of handling customers if not exactly the same as there are certain differences in functioning of each sector.

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References• A. Awasthi, S. S. Chauhan, H. Omrani, and A. Panahi, “A hybrid approach based on SERVQUAL and fuzzy TOPSIS for evaluating

transportation service quality,” Comput. Ind. Eng., vol. 61, no. 3, pp. 637–646, Oct. 2011.Availablefrom: http://www.sciencedirect.com/science/article/pii/S0360835211001203 [Accessed: 29-Jul-2015].

• J. Khan, R. W. Belk, and M. Craig-Lees, “Measuring consumer perceptions of payment mode,” J. Econ. Psychol., vol. 47, pp. 34–49, Apr. 2015. Available from: http://www.sciencedirect.com/science/article/pii/S0167487015000070 [Accessed: 29-Jul-2015].

• Developing zones of tolerance for managing passenger rail service quality - 02656710710720303.” [Online]. Available: http://www.emeraldinsight.com/doi/pdfplus/10.1108/02656710710720303. [Accessed: 29-Jul-2015].

• Shearer, C. et al., (2000). ‘The CRIS-DM model: The New Blueprint for Data Mining.’ Journal of Data Warehousing14, 5(4), pp.13–22. Available from: https://mineracaodedados.files.wordpress.com/2012/04/the-crisp-dm-model-the-new-blueprint-for-data-mining-shearer-colin.pdf [Accessed June 2, 2015].

• “ETL (Extract-Transform-Load) | Data Integration Info.” [Online]. Available: http://www.dataintegration.info/etl. [Accessed: 29-Jul-2015

• “Three Steps in ETL Processing.” [Online]. Available: http://bi-insider.com/data-warehousing/three-steps-in-etl-processing/. [Accessed: 29-Jul-2015].

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Questions?