Impact of Travel Services

Overview

A global Energy, Resource and Marine client required an impact analysis on the implications of changing policy from business class to economy/premium economy on medium haul routes (flights between 4-6 hours) from a cost and traveler experience standpoint.

Frame 737
Frame 737

Impact of Travel Services

Overview

A global Energy, Resource and Marine client required an impact analysis on the implications of changing policy from business class to economy/ premium economy on medium haul routes (flights between 4-6 hours) from a cost and traveler experience standpoint.

Business Need

The organization wanted to ensure they remained competitive in the market to attract and retain top talent among key travelers. They were looking for a complete study on the traveler experience from booking to trip completion with an aim to balance cost with experience and wellbeing.

Scope

New Service

Industries

Energy and Utilities

Services

Strategy & Consulting

Technologies

Solution Overview

01

AARCHIK identified the key routes impacted by the proposed change to policy. With a majority of APAC routes falling between 4-6 hours, finding the balance between cost saving and employee experience was crucial.

02

The client’s current travel policy and booking behaviors were both benchmarked against peer group to evaluate alignment within industry.

03

A custom 12-point model to consider the complete door-to-door journey of a traveler was developed.
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Solution Overview

01

AARCHIK identified the key routes impacted by the proposed change to policy. With a majority of APAC routes falling between 4-6 hours, finding the balance between cost saving and employee experience was crucial.

02

The client’s current travel policy and booking behaviors were both benchmarked against peer group to evaluate alignment within industry.

03

A custom 12-point model to consider the complete door-to-door journey of a traveler was developed.
MAPPING THE DOOR-TO-DOOR TRAVEL EXPERIENCE
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Results

01

The travel journey is far more intensive than flight time alone. A typical flight between 4-6 hours has an average end-to-end trip time of 9.5 hours, which is important to evaluate.

02

A shift from business to economy/ premium economy-maintained scores averaging 7-out-of-10 or higher on our travel experience index. Ensuring a continued positive travel experience.

03

Further analysis suggested an opportunity to segment policy allowance by either job hierarchy or travel frequency to maintain savings with an even smaller impact to experience, which is common practice in APAC Region.

04

The same shift scenario projected a 7% cost savings on total APAC spend. Showing clear benefit from a financial perspective.

Results

01

The travel journey is far more intensive than flight time alone. A typical flight between 4-6 hours has an average end-to-end trip time of 9.5 hours, which is important to evaluate.

02

A shift from business to economy/ premium economy-maintained scores averaging 7-out-of-10 or higher on our travel experience index. Ensuring a continued positive travel experience.

03

Further analysis suggested an opportunity to segment policy allowance by either job hierarchy or travel frequency to maintain savings with an even smaller impact to experience, which is common practice in APAC Region.

04

The same shift scenario projected a 7% cost savings on total APAC spend. Showing clear benefit from a financial perspective.

Benefits

01

The study provided important Insights into the policy changes on customer mindset and buying patterns besides also providing insights on market factors which can drive the policy changes

02

The cost benefit ratios provided by our model provided a complete framework by which the different factors can be adjusted to study the overall figures and their impact

03

Past trends were analysed using Machine Learning scripts to generate the trends which have generated positive results in the past

04

Causal Models were used as a base for the Machine Learning scripts to provide insights as to how the historical data can be used for future prediction models
Frame 740

Benefits

01

The study provided important Insights into the policy changes on customer mindset and buying patterns besides also providing insights on market factors which can drive the policy changes

02

The cost benefit ratios provided by our model provided a complete framework by which the different factors can be adjusted to study the overall figures and their impact

03

Past trends were analysed using Machine Learning scripts to generate the trends which have generated positive results in the past

04

Causal Models were used as a base for the Machine Learning scripts to provide insights as to how the historical data can be used for future prediction models

Have A Query?

Do you need a detailed consultation or feasibility study for this topic? Or it maybe you just want to exchange views and thoughts on this topic. Do get in touch with us and we would be glad to share a cup of coffee together and discuss this topic together.

homw page

Have A Query?

Do you need a detailed consultation or feasibility study for this topic? Or it maybe you just want to exchange views and thoughts on this topic. Do get in touch with us and we would be glad to share a cup of coffee together and discuss this topic together.

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