Cross-channel rail service Eurostar transports thousands of passengers between England, France and Belgium every day. It operates 31 trains daily, each carrying up to 750 passengers.
Until recently, its process for monitoring the satisfaction of those passengers was slow, with reports compiled quarterly making it hard for customer intelligence team to respond to issues in a meaningful way.
In 2010, Eurostar’s customer intelligence manager, Chris Haynes, decided to change that. “We wanted to get customer satisfaction information through much more quickly,” he explains. “Most people aren’t using our services every week, so if our customers don’t like their experience, that may be the last we see of them. If we only find out about bad scores every quarter, there’s not very much we can do about it.”
When considering their options, Haynes and his team trialled a system from Fizzback, a UK start-up that was acquired by Israeli software vendor NICE Systems last year, which allowed them to collect customer feedback via text message. Passengers are texted a short, two-question survey – how was the experience, and would they recommend it to a friend – and are invited to add any additional comments in the text.
After a three-month trial and five months of planning, Eurostar adopted the Fizzback system. Now, each morning, a long list of customers who shared their mobile phone numbers is sent to Fizzback, which randomly picks 1500 to survey. Haynes says he gets about 150 responses every day.
Their responses appear as a live feed on Fizzback’s web portal. This allows Eurostar employees to spot customer service issues in real time; however, it does not provide an accessible overview of current trends.
For that, Haynes feeds the Fizzback data, via an Excel spreadsheet, into a data visualisation application from Tableau Software. This allows him to identify emerging trends in the feedback data instantaneously.
For example, the Fizzback service performs sentiment analysis on the customers’ free- text survey responses, and splits the feedback into categories, such as ‘employee conduct’. Plugging this data into Tableau means that Haynes can spot an issue regarding employee conduct in the blink of an eye. The system colour codes for customer sentiment – green for happy, red for unhappy.
“The employee conduct report is usually just a big green bubble,” he says. “But this morning I looked at it and it’s a big red bubble,” indicating a management issue that needed to be addressed.
The new system means that customer service issues can be identified and addressed as they arise, not weeks afterwards. “It’s all about speed,” Haynes says. “and it was difficult for us to take action with quarterly results alone. It’s also important for me to have more regular information coming through and to be able to look at that in an interesting way.”
Now, Haynes is looking to feed more datasets into Tableau to give fresh perspectives on customer service. “I’m starting to use the data on whether the train was delayed, and if so by how many minutes,” Haynes explains. “That allows me to create a model that says that if a train is delayed by X minutes, we lose, on average, this many recommendations.”
This is leading to a much more empirical approach to customer satisfaction. “We normally measure punctuality by looking at what percentage of trains reach their destination within a certain amount of time,” he explains. “But that tells us nothing about the impact on customers. Now, instead of measuring our performance by our punctuality, I’m measuring how much ‘recommendation per month’ is being drained out of the business by disruption.”
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