From these tests, 4 users profiles were revealed after a card sort:

But this card sort made me realize using personas for this kind of mass audience project would lead to inaccurate results.

Indeed, I knew I hadn’t encounter every profile during my field study: using only these 3 axis, that is a total of 2 x 2 x 2 = 8 possible profiles.

People traveling with the metro come from various horizons, using personas presented the risk of being too reducting. I also didn’t want to fill the missing bits with hypothetical data, which would make them biased.

Rather, I chose to uncover their Jobs To Be Done: personas focus on who you are designing for, Jobs To Be Done focus on what your user’s goals are and put them in perspective.

This is particularly relevant here: every person using the metro is different but their Jobs To Be Done can be similar.

Here are the JTBD I identified using the Jobs Stories framework:

Jobs Story Framework

Metro traveler’s jobs stories

The translation of these needs into design decisions means providing shortcuts for experienced users while helping unfamiliar users finding the right ticket more easily.

Defining the problem

Context

1.

I set myself the constraints of keeping​ the existing​ machines and technology. Changing them would indeed offer new possibilities — I remember Hong Kong and Singapore’s machines offering a great experience by simply choosing your station on the metro map. Which is great but needs an improved technology.

We could also imagine totally new ways of buying your metro ticket, for instance choosing your ticket on your mobile phone and then getting it with a NFC payment or using directly your phone as the ticket.

However, for this first study, I chose to focus on what the RATP could improve quickly at a lower cost.

2.

These public ticket machines are hypothetically used by any type of people, which adds many constraints to the interface design: it should be simple enough so that everybody understands it — people from various cultures and age, with very low or high technology skills.

3.

Moreover, the machine isn’t for getting detailed information on ticket options. That would lead people to compare tickets options, resulting in longer waiting times.

Comparing ticket options would be the role of the RATP website. Instead, we could recall what the traveler had seen on the website during his research in his hotel room/flat by displaying a small caption which would help to confirm he’s making the right purchase.

Objectives

For a widely used machine like a ticket machine, addressing various personas needs can be necessary

The final objective would be to reduce the time to purchase in order to reduce waiting queues while giving satisfaction to the user. Therefore, mixing business and UX goals.

The most challenging scenario is when you want to buy a ticket for Paris region, as it requires you to type the station name (especially challenging if you are a tourist).

The most common scenario on the other hand, is simply buying a ticket t+: there are in average 600 million sold each year (stats here). This is the case for turquoise, rose and blue groups. This experience is quite efficient (success rate), though it could be more effective (time to success), especially when you hear from users that the overall feeling of using the machine is not satisfying. Shortcuts for experienced users would be welcome here.

Pain points to solve:

Pain point #1: Taking a metro ticket often gets repetitive and slow

Pain point #2: Taking a ticket for Paris Region is complex and confusing

Pain point #3: The overall offer is not clear and organized

For metro travelers, the ticket machine is most likely the first connection point with the metro.

Therefore, giving a good impression is crucial, as it will impact the entire Paris experience.

We have indeed several cognitive bias that lead us to confirm our first impression. If it was negative, our brain will be drawn to details that confirm our bad experience. For more information see confirmation bias, selective perception and subjective validation.