Helping Metro Trains Melbourne Have Better Passenger Conversations
Metro Trains Melbourne carries 450,000 passengers every day. Here’s how we helped those passengers get timely updates on their service.
"The team at Pattr have been amazing to work with from day one. Being able to utilise Pattr’s expertise in AI and real human language bots has changed the game for us."
A missed connection
Picture this. You’ve just finished a long, busy day at the office, and you’re mentally and physically spent. You leave the building and head down the street to the train station, ready for your commute back home. Trains usually come once every ten or fifteen minutes at peak hour on your line, so you don’t need to check the timetable.
When you get there, you can see a telltale sign of disaster: hundreds of commuters filling the platforms and spilling out into the concourse. Thanks to station announcers, you learn there has been a signal failure somewhere on your line, and trains are severely delayed.
How are you feeling? Probably not great. Delayed or cancelled trains already make for a less than ideal customer experience, but only finding out about them when you actually arrive at the station is worse still.
Your options are either to stick around on the platform until a packed train arrives, find something else to do until the problem is fixed and everything begins running more smoothly again, or find another way of getting home – and none of those options are ideal.
Wouldn’t it have been better if you had found out there was a delay before you headed to the station? If you had known the trains would be seriously late, you might have hung around at the office and kept working, or headed to the pub for a beer instead.
Put simply, if you’d received a timely notification as soon as the problem occurred, you would have been empowered to make better decisions and take control of your evening.
Riding the rails
This was exactly the problem faced by Metro Trains Melbourne, which operates the city’s suburban rail network and transports 450,000 passengers each and every day.
Delays and cancellations are a fact of life when you’re running a complex public transport system — but a cancelled train doesn’t necessarily have to end with a terrible customer experience. Metro Trains wanted to provide better and more timely information about its network to customers, keeping them updated on the status of their line and ensuring they were never caught off guard by problems and delays. That way, they could return the power of choice to passengers — and avoid the kind of unpleasant situation we discussed above.
There were serious barriers to making it happen. Metro Trains was relying on dated legacy tech, and was managing complicated customer conversations in inefficient ways. It provided information almost entirely via its website — an inflexible, one-way mode of communication that did not serve the evolving needs of its customer base.
In addition to these challenges, the Metro Trains social channels were being flooded with negative feedback and abuse, further worsening the passenger experience and making it difficult for its customer service team to address genuine problems as they arose.
Here’s where Pattr came in. Having already built a great experience for Transport for NSW, we saw a great opportunity to evolve the Metro Trains customer experience and start a real dialogue with its passengers using our AI-powered solutions. We planned to use the existing data Metro Trains was generating to provide better updates to customers via their preferred channels, rather than sending them scrambling to the website for up-to-date information.
Layering on the existing Metro Trains API, Pattr set up Conversational Bots on Twitter, giving customers transport alerts and information via their favoured social media platform. Instead of heading to the Metro Trains website, a passenger is instantly informed on disruptions to the schedule — and can query the relevant social channel for updates on their line and receive a prompt response.
By introducing prompt updates and two-way conversation to the Metro Trains customer experience, Pattr empowered passengers to make more informed decisions about their transport, and helped them become more deeply engaged with the brand.
Conversational Bots helped create an experience that made the Metro Trains brand not only feel more responsive to customers, but also more tech-driven and contemporary.
Bringing the power of AI to customer service
Pattr’s suite of AI-powered tools also assisted Metro Trains customer service staff deal with the huge volume of customer inquiries and feedback received through its social channels.
In the avalanche of general complaints and anger that are expected by anyone running a busy public transport network, it ’s inevitable that genuinely important feedback and information can be lost, no matter how large and active a customer service team is.
We had the perfect solution. Using sophisticated text and sentiment analysis, Pattr’s Conversation Health rapidly identifies and categorises important conversations across Metro Trains digital platforms, automatically sorting them into a hierarchy of importance to be addressed by its customer service team.
Conversation Health now instantly notifies the team of the most important messages across its network of digital channels. This could be anything from a pressing customer problem, to a serious health and safety threat to the rail network.
Thanks to Pattr, Metro Trains Melbourne has been able to improve its customer service experience for both passengers and their own team. Its customers are more reliably informed about its network, and its customer service team has been freed up to focus on the messages that really matter.
Want to learn how Conversation Health and Conversational Bots can help your brand drastically improve its customer experience? Book a demonstration today.
See Pattr in Action
Pattr brings you closer to your customers.
A conversational AI SaaS platform to power, enable, enrich and understand conversations between you and your customers, in real-time and at scale.