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Rail passenger segmentation - who uses the railway?


The Williams Rail Review is due to report later this year and will make recommendations to enable the delivery of a railway with improved reliability for passengers, whilst also offering good value fares.

As part of Which?'s evidence submission to the Williams review, we have undertaken a statistical segmentation of rail passengers using data from Which?’s annual Train Satisfaction Survey (TSS)1. Which? wanted to set aside the conventional passenger categorisation of commuters, leisure users and business users and allow a data-led grouping to be formed from our survey responses. This would allow for a better and more nuanced understanding of the passenger types that exist, and the different needs these groups may have, free from any preconceived ideas.

Our segmentation analysis identified rail passenger groups based on four reported behaviours: whether they take long or short journeys, how frequently they travel, how they purchase tickets and their use of connectivity on trains.

This article gives a detailed overview of the methodology of our analysis, summarises the key features of the segments we identified, and includes a further analysis of the demographics and attitudes of those segments.

1 The 2019 Which? Train Satisfaction Survey was conducted online between 11th and 30th October 2018, sampling 10,000 UK rail users.

What are the key identifiers of our five passenger segments?

The chart below provides an overview of the strength and direction of the relationship between each input variable and segment profile.

Savvy Leisure Users - this segment is made up of respondents who typically go on long train journeys and make use of railcards when purchasing their tickets. They have low levels of interest in connectivity, either for work or experience/enjoyment purposes. 86% of this segment uses the railway for leisure journeys only.

Rail Dependents  - these are typically highly frequent train users, but have short journey lengths, with 86% of this segment using the railway for commuting. Half (49%) travel to work by train at least 4 days per week. This figure is considerably higher than the other commuter segment (Clued-up Commuters), 31% of whom travel to work by train 4+ times a week. They are also more reliant on the train for their commute to work; 63% said that it would be difficult for them to find alternative transport if the trains were not available.

On-the-day Occasionals - this group is made up of infrequent rail users who typically take short journeys when they do use the railway. They tend to purchase their tickets at the station and do not engage in money-saving methods such as buying online in advance or season tickets. They place the lowest levels of priority on connectivity, whether for work or enjoyment purposes.  88% of this segment use the railway for leisure journeys only.

Web-ticket Tourers - these tend to be infrequent travellers but with the longest average travel time. They almost always purchase tickets through websites (with 71% purchasing tickets through a website more often than not or always) and place more importance than the other segments on connectivity for experience. 86% of this segment  use the railway for leisure journeys only.

Clued-up Commuters - this group is characterized by frequent travel (although not as frequent as Rail Dependents) and a high level of importance placed on connectivity for work purposes. In terms of purchase method, they also score highly on use of season tickets and buying on the train, but have the highest proportion of people using digital pay-as-you-go tickets, with 48% using these often or always. 87% of this segment use trains for commuting and is the most likely to be working full-time, at 59%.

Comparing our segment profiles - how do they differ in terms of demographics and attitudes?


The Savvy Leisure User group contains significantly more people over the age of 65 than any other segment (42%), at least double the proportion of any other segment. The Clued-up Commuter group is the youngest segment, with the highest proportion of 18-24 year olds (14%) and 25-34 year olds (38%). The Rail Dependents segment is also skewed towards younger ages, although not to the same extent as the Clued-up Commuters.

There were few notable differences between the segments in terms of region of residence. However, there was a greater proportion of London-based individuals in the commuter segments; for example, a quarter of the Clued-up Commuter group lived in London, and 29% of the Rail Dependents. This proportion was considerably lower in the leisure segments, at 10% for the Savvy Leisure Users and Web-ticket Tourers and 13% for the On-the-day Occasionals.

The Savvy Leisure User is the most likely segment to own a property (74%). Only 19% of this segment rent, compared to 30% and 31% in the Rail Dependents and Clued-up Commuter groups, respectively. The Savvy Leisure User was also the least likely to have children living with them in their household; 80% had no children living with them, compared to 35% of Clued-up Commuters, who were the most likely to be living with a child.  

Attitudes towards rail travel

The Savvy Leisure Users segment were the most likely to agree that they enjoy train travel, at 71%. This figure is fairly high for all segments, with the majority agreeing that they enjoy train travel in all but the Rail Dependents segment, just under half of whom agreed with this statement.

On the opposite end of the spectrum, the most likely segment to agree that train travel is stressful was the Clued-up Commuter group, at a little over half, followed by the Rail Dependents (37%). The Savvy Leisure Users and On-the-day Occasionals were the least likely to report stress.

The commuter segments were also more likely to agree with that statement that train travel significantly influenced where they chose to live. For example, 30% of the Rail Dependents segment agreed with this statement, as did over half of the Clued-up Commuters (53%). The proportion was less than a fifth for each of the leisure segments.

The Savvy Leisure Users and On-the-day Occasionals were the most likely to agree that travelling by train is easy (68% and 66% respectively), whilst the Rail Dependents were the least likely to agree (54%).

Compensation claim behaviour

Analysis of the compensation claims reported in the survey revealed that there were significant differences in the likelihood of each segment to claim compensation after experiencing a delay.

The two commuter segments were the most likely to claim compensation, although the proportion among the Clued-up Commuters was much higher, at just under half. The Savvy Leisure Users were the most likely among the leisure groups to claim for a delay, whilst the On-the-day Occasionals were the least likely to claim, at just 9%.

Among those who did not claim, 42% of the Rail Dependents group said that this was because they did not know how or where to complain, despite being the most frequent travellers (and the most reliant on train services to reach their destination). The proportion giving this reason was fairly high among all segments but was lowest in the Savvy Leisure Users, at around a quarter.

Over a third of non-claimants in the commuter groups also said that they thought claiming would be ‘too much effort’. The proportion giving this reason was considerably lower in the leisure segments, around a fifth for each. The commuter groups were also slightly more likely to say that they did not claim as they thought it would be too difficult or time consuming.

The Clued-up Commuters, of whom the largest proportion work full time, were the most likely to state that they were too busy to make a compensation claim (23%).


We identified five passenger groups in our segmentation analysis. Two of these groups are dominated by passengers who use trains to commute, and three were dominated by those who use trains for leisure journeys only.

Within the leisure-dominated segments, our groups ranged from those who turn up on the day and buy tickets at the station, to those who plan ahead with online tickets, or use railcards for discount fares. Among commuters, we identified a group who commute by train more often and are more reliant on trains to get to work, as compared to another group who take longer journeys and place a higher importance on connectivity to work on the train.

This provides a more nuanced picture of the groups of passengers who use the railway, which looks beyond the traditional classification of commuter and leisure user to explore the groups that exist within these categories, and have different needs and priorities to be met by the rail system.

Annex - Methodology

The Which? Train Satisfaction Survey (TSS)

The TSS is an annual survey commissioned by Which? covering many aspects of train travel, ranging from behavioural questions about how respondents use the railway to attitudes towards train travel as a whole, and satisfaction levels with specific Train Operating Companies. The questions are retrospective in nature, focusing on the experiences of consumers over the past year.

Online survey responses were obtained from a sample of 10,000 UK adults, which reduced to 9,145 for this analysis following data cleaning. Fieldwork took place between the 11th and 30th October 2018 and the sample was collected by SSI on behalf of Which?.

Respondents were recruited from the agency panel and the resulting sample is therefore not nationally representative. For example, compared to the sample of the National Rail Passenger Survey (NRPS) conducted for Transport Focus, passengers on longer journeys are over-represented in this sample. The segmentation aims to gain insights into the passenger groups that exist in the UK, but cannot determine the size of these groups in the general population, as the survey sample is not nationally representative. The NRPS is designed to be representative of all train journeys.

The variables we used

This segmentation aimed to identify potential types of rail passenger, identifying nuances in passenger types beyond the usual categorisation of commuters, business users and leisure users. To this end, a number of behavioural variables were inputted to the cluster analysis to draw out groups who use the railway in different ways. The variables included in the segmentation analysis were:

  • Ticket purchase modes - use of railcards, season tickets, digital methods etc.
  • Length of journeys
  • Frequency of travel
  • Uses for connectivity - for work, making calls, entertainment etc.

A number of steps were taken to prepare the variables before inputting to the cluster analysis. The journey length and frequency of travel variables are derived from questions about frequency and length of different journey types, and summarised into a single variable. The purchase mode and connectivity inputs come from a series of ratings questions across a number of items and were reduced to summary constructs using factor analysis.

Clustering analysis

We undertook a data-led statistical segmentation in two stages. The SPSS two-step clustering method was used to explore possible solutions and determine an appropriate number of clusters. This also provided us with initial cluster centres as a starting point for the next step - using k-means clustering to finally determine cluster membership. This led us to a five-segment solution based on the inputs described, with segment sizes ranging from 17% to 24% of the sample.  

Preparing our variables - additional detail

Length and frequency of travel:

Respondents were asked how frequently they travelled for each purpose (work, study, leisure, business and other). Options ranged from never and as infrequently as once a year, to 4 or 5+ times per week. The overall measure of frequency was taken as the maximum frequency rating given across all journey types.

Answers to the frequency of travel questions were also used to create a summary variable of the overall number of journeys taken in the past year per journey type, which was used in combination with the length of travel questions create an ‘average travel time’ summary variable.

Length of journey was collected per journey type as a categorical response from a series of options (e.g. less than half an hour, 30 minutes to 1 hour and up to 2+ hours). Mid-points from these options were taken as an estimated journey length (e.g. 30 minutes to 1 hour would be estimated as 45 minutes). This was then multiplied with the total number of journeys variable to calculate the overall length of time spent travelling by train. This figure was summed together for all journey types, and then divided by the overall number of journeys taken, to give an overall average travel time across all journey types.

Purchase mode:

Respondents were asked how frequently they used the following purchase methods:

  • Using digital pay-as-you-go methods such as electronic wallet or oyster card
  • Purchasing through a website
  • At the station (ticket machine, desk)
  • On the train
  • Railcard

We ran a principal components analysis to identify highly correlated items within this that could be combined into a single score. Season ticket users and those buying on the train were highly correlated into a factor determined as a regular/seasoned purchase mode. Station and website purchasing were negatively correlated, and are represented on opposite ends of a score.

  • PAYG/digital - using digital pay-as-you-go methods such as electronic wallet or oyster card
  • Railcard users
  • Regular/seasoned user purchase mode - Using a season ticket or buying on the train
  • Station to website purchasers - negatively correlated items with station purchases on one end of the spectrum and website purchasers on the other

The PAYG/digital factor was omitted from the cluster analysis on the basis of a couple of issues. Firstly, the option contains a number of different purchase modes that we are not able to un-pick, for example oyster cards vs. electronic tickets. Secondly, as oyster cards and digital tickets are not available on all networks in all regions, it may be more reflective of availability as opposed to a consumer choice, and is therefore not a useful variable with which to segment passengers.


Connectivity behaviours are derived from two ranking questions containing nine items which cover the importance of and use for connectivity on trains. Using a Principal Components analysis, this was reduced to three summary constructs:

  • Connectivity for work - importance of connectivity in order to work on the train using portable devices and being contactable by work
  • Connectivity for experience - level of agreement that connectivity allows making use of traveling time, making time go quicker, enhancing the enjoyment of the train journey, alleviating boredom
  • Connectivity for contact - low vs. high importance of having signal to contact people and of having access to the internet via wifi or data.
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