Customer experience training: Travelling on the train

The first time learners use Bodyswaps, an onboarding sequence familiarises them with the features of the app, takes them through an avatar selection and embodiment activity, and prepares them for the experience to come. 

In the onboarding, learners will: 

• Find out how this training is different from the rest
• Select their avatar
• Discover their virtual journal
• Learn how to navigate and use the tools and settings

After the onboarding activity, the learner meets Florence, a train attendant, in the train carriage. She introduces the impact delays can have on a customer, and the importance of bearing this in mind when choosing how best to deliver information and updates. Afterwards, she explains that this module allows the learner to practise this in a safe environment without real-world stakes.

Next, the learner is introduced to Jeremy, who is on his way home to take over childcare responsibilities. Unfortunately, his train has been stopped between stations and he’s panicking about the impact on his plans – not least as he’s lost his phone and can’t contact his partner to let them know about the delay.

The learner is then invited to answer a few Likert-type survey questions to gauge their confidence about the following key skills:

• Handling customers who are in a heightened emotional state
• Putting the customer in control of the solution
• Making the customer feel valued

These self-reflection questions will be repeated in the debrief at the end, to assess how the learner’s confidence levels have changed.

To demonstrate what customer service skills look like in practice, the learner watches Jeremy try to get information about his stopped train and how it will affect his plan from Florence, who is engaged in talking to another customer.

In this observation activity, the learner clicks or taps the ‘Good’ button whenever they witness Florence react appropriately and helpfully and the ‘Bad’ button whenever she reacts inappropriately or unhelpfully. Visual feedback is provided in real time to indicate whether the learner has identified each example.

As the conversation progresses, it becomes clear that Florence’s divided attention is being taken by Jeremy as deliberate unhelpfulness, as she can’t provide exact details about how to make his change at Birmingham new street. The situation goes from bad to worse when he realises he’s lost his phone, and his frustration tips over into a burst of anger that ends the conversation.

At the end, the learner receives a summary feedback panel showing buttons that indicate which events were identified. Clicking each one allows the learner to replay Florence’s good and bad reactions and see an explanation of how they helped or hindered the situation.

At this point, it’s time for the learner to try taking the reins and help Jeremy. Before they do, they’re given some advice about how best to proceed in a way that will calm Jeremy down.

In this freeform interaction, the learner is invited to speak to Jeremy in their own words, keeping a conciliatory tone and maintaining their professionalism. Behind the scenes, analytics tools pick up on the keywords and rate of speech that demonstrate that the learner is staying calm, apologising, and offering help in a way that will de-escalate the situation.

Once the learner has said their piece, they swap places with Jeremy to see how their speech looked from Jeremy’s point of view. After that, they receive the feedback from the analytics tools to help refine their performance, at which point they can either try again or move on to the next topic.

Now that Jeremy is feeling more at ease, the learner can help him regain control of his journey and feel like a welcome and respected customer.

In this scaffolded conversation, the learner chooses how to respond to Jeremy in a sequence of six multiple-choice exchanges. Once each response is chosen from the list of three, Jeremy answers, after which more detailed feedback is given to show the learner the benefits (and drawbacks, if applicable) of the answer they selected.

At each stage, the learner needs to select the response that encapsulates all three criteria that make up good customer service: good handling of customer emotion, putting the customer in control, and making the customer feel valued. These ideal responses calm Jeremy down and help him plot the next steps of his journey, showing the learner the positive impact of good service. Responses that meet only two criteria may help Jeremy find a way forward, but don’t leave him feeling valued, or vice versa – and responses that don’t meet any only antagonise him.

At the end of the conversation, the learner is shown some summary feedback on the direction of their responses as a whole. This also highlights which criteria the learner’s answers skewed towards, or whether they managed to hit all three.

Finally, the learner sees Jeremy call his partner on a borrowed phone to let them know about the delay and his plans as a result. Depending on how good a service the learner provided, his mood and prospects may have improved significantly despite the delay – or become much worse. If the learner provided a particularly bad service, Jeremy’s partner may end up angry too.

The Likert-type survey questions from the start of the course are repeated here, to gauge the change in the learner’s confidence after playing through the module.

Note: With soft skills training, it is not unusual to find that learners’ confidence levels actually fall upon completion. This is because people are often overconfident in their ability to communicate or empathise before the training begins. As the experience raises self-awareness, confidence levels may fall in response.