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Airport X Customer Experience Analysis and Recommendations

A Case Study submitted by

Sriya DXI LLC,

7975 Inverness Way, Duluth, GA, 30097

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Table of Contents Introduction: ........................................................................................................................................ 3

Key Objectives: ................................................................................................................................... 3

Customer Experience Analysis and Findings: ............................................................................. 3

Quantifying Customer Experience and Root Cause Analysis: .............................................. 4

Behavior Analysis of Cluster 0: ............................................................................................................ 7

Behavior Analysis of Cluster 1: ............................................................................................................ 8

Advanced ML Ensemble Algorithms .................................................................................................... 9

1.0 Boosting Algorithm Findings: ................................................................................................... 9

2.0 Stacking Algorithm Findings: ................................................................................................... 9

3.0 Simple Cart Algorithm: .......................................................................................................... 10

4.0 Farthest First Clustering:........................................................................................................ 11

Recommendation Summary: ............................................................................................................ 11

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Introduction:

SriyaDXI, LLC is an innovative company that provides cloud-based machine-learning (ML) software solutions to help consumer digital led companies maximize their Customer satisfaction. Through the adoption and implementation of our product (Digital Experience Index (DXi)), ML based predictions and recommendations; companies can improve their competitive advantage, market share and profitability.

Key Objectives:

- Compute a Customer Experience Index (CXi) based on the Feedback data - Identify the potential areas of improvement for enhanced Customer Experience

Customer Experience Analysis and Findings:

Customer Data is grouped under 4 different categories namely

- Visual - Pedestrian - Safety - Cleanliness

These are then mapped 2 key Business outcomes namely

- Customer Rating - Passenger Flow (Passengers)

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Quantifying Customer Experience and Root Cause Analysis:

Current Customer Experience Index (CXi) - 66.15 out of 100

1. Data Points = 236 (survey responses) 2. Visual was the biggest contributor to CXi @31.72 = ~45% of Cxi 3. Safety was lowest contributing category to CXi @8.95 =~14% of CXi

Top contributing Factors for Customer Experience Index:

The data was fed to the 3 top Feature selection algorithms namely Mutual Information, Support Vector Machines an PCA and they identified the following key parameters which contributed heavily to the Cxi score:

Mutual Information:

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Support Vector Machine:

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PCA:

Summary:

Cleanliness is identified as a key area where Airports should focus on. This is highlighted by priority given to both Restroom, Restaurant and Information Booth Cleanliness.

Pedestrian Category with the following Parameters were also ranked as the priority.

Ease of Personal Navigation

Signs and Directions

Escalators and Elevators.

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Advanced Machine Learning algorithms like clustering have identified 2 key groups of Data.

Behavior Analysis of Cluster 0:

This group of people clearly exhibited a leisure pattern within the Airport Area as they took enough time to enjoy the Art work, Do shopping and used the utilities like Rest Room.

Again this finding is corroborated by the fact that the following 2 attributes played a key role.

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They had enough time to utilize the Lockers and Ramps (instead of using elevators) and were more specific on the quality of these attributes.

Behavior Analysis of Cluster 1:

This group did not spend much time on Art work and were keener on moving faster. This is explained by their priority for the Escalators / Elevators and Ease of Personal Navigation both of which facilitates the faster movement of the Passengers within the Airport.

This group gave higher priority to Commute and Parking. Faster Parking access and proper and frequent commutation facilities are very critical to move faster within the Airport.

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Ensemble Algorithms: These algorithms have the highest intelligence as they are combination of individual algorithms. These ensemble alg. Do the job of learned committee of experts taking a decision rather than 1 expert taking a decision which is what regular algorithms do. 1.0 Boosting Algorithm Findings:

Here the regular ML algorithms get boosted by repeat runs to give more accurate results and hence the results of Boosting and Stacking algorithms have greater importance than the results of SVM or Clustering

This algorithm says that the airport should focus on 1 parameter, Security Screening and get its value to be > 4.5 and it is has a probability of 1 = 100% success of getting high CXi. This tells that most passengers give top importance to safety and then other parameters like convenience, cleanliness etc. Only SVM from the first list of 6 algorithms had chosen this parameter and that too as the 2nd most important. This explains why ensemble (committee) algorithms do better.

2.0 Stacking Algorithm Findings:

The optimal path to improve Customer Experience was again identified Security Screening > 4

The above resulted in 93 out of 93 Customers having a good Experience. As you can see this is 100% accuracy which is why this gains importance

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3.0 Simple Cart Algorithm:

Simple Cart alg also picked Security Screening with a value of 4.5 as the top parameter. So all the 3 ensemble alg were unanimous in picking security screening as the focal CXi parameter for this airport to gain high CXi

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Recommendation Summary:

Sl. No

Inference Recommendation

1 Cleanliness identified as a top factor contributing to good Customer Experience

Focus on Cleanliness: Information Booth Cleanliness Restaurant Cleanliness Restroom Cleanliness

2 Pedestrian Convenience was identified as second key Factor for Customer Experience.

Improve ease of personal navigation with Signs and Directions and improved Escalators and Elevators,

3 From the Facility perspective the key expectations differ between the leisure travelers Vs the travelers who are in the rush. Leisure Travelers: Ramps and Locker Travelers in Rush: Parking and Commute There is almost same number of Travelers in these 2 groups.

Improve the Facility / Convenience for below : Ramps, Locker, Parking, Commute

4 All the advanced ML Algorithms identified one critical factor contributing to the Customer Experience. Security Screening For Example by having a Securing Screening value above 4 results in 93 customers having a good Experience.

Focus on the quality of the “Security Screening”. As both groups of customers (leisure as well as the non-leisure) gave weightage to this factor by

- Reducing the wait time for Screen Screening

- Improve the security screening methods using advanced auto techniques instead of manual checks.