Think of the top three activities you do during a working day. Chances are, emails, document creation and video/audio calls feature highly — and it wasn’t all that long ago that technology transformed these activities for the better.

Before email, important documents needed to be shared via fax, after having been produced on a typewriter or printed from a word processor. Collaborating on files meant transferring them on USBs or hard drives, to move them from one device to the next.

These are workplace realities some of us remember — and who knows where technology will take us before our careers come to a close.

That was the topic of our most recent Dare to Share webinar. In our talk and subsequent Q&A, we looked at how Machine Learning and Artificial Intelligence have shaped and will continue to shape, the world of Customer Experience design.

The first thing to note is that, at Hellon, we believe in the complementary power of humans and ‘machines’ working together. We use AI to augment and support our designers, not to replace them.

AI can help us achieve much more in less time, but it can’t come close to the nuance and cultural and societal understanding that humans are capable of, and something that the Hellon team prides itself on especially.

Based on data from the US, Canada, Sweden and Finland, Hellon has identified opportunity areas for traditional banks. Even though some challenger banks are making everyday banking easy, there’s still room to innovate.

1. Understanding text Qualitative data plays a significant role in CX research and design. There’s survey feedback, interview transcripts, and a whole host of other text-based inputs for the Customer Experience team to consume, analyze and take direction from.

Artificial Intelligence can substantially speed up the process of managing this data. Hellon’s AI tool, Aino, helps a designer read and organize thousands of customer sentences into clustered themes for insight gathering. It can even identify emotional sentiment and process multiple foreign languages, before translating it to the mother tongue of the team.

Neural networks are equally as impactful when it comes to text analysis and understanding. Imagine if a company could feed all of its written customer feedback into a machine learning system, mapping a neural network to visualize the most common pain points in the customer experience. Configuring the data in this way would identify opportunities for improvement at a scale that human analysis simply couldn’t achieve. Hellon’s system, Aino Reads, has been used to handle thousands of individual responses from large markets — delivering textual analysis, insights, and action points to improve product design and customer service.

2. Understanding speech The above application of machine learning relies on qualitative data being written down — and that’s not always the case within an organization. Call centers, for example, are a goldmine for CX insights and service agents are usually too busy serving customers to identify insights and opportunities at the same time. It’s here that machine learning tools can add value again. Seeing as all calls are recorded, AI can be used to analyze and measure what customers are saying and how they are saying it.

More than just fighting fires and solving complaints, the AI-driven insights can be fed straight into the sales and development teams too. What do customers say they want and where do the trends emerge? When measured at scale, Artificial Intelligence can be used to create a prioritized roadmap of new features and services — to be developed in direct response to the customers’ needs.

3. Neuroscience What customers say, however, isn’t always an accurate articulation of what they think. For example, customers can censor their thoughts to reflect the dominant norms in society — or to tell a researcher what they think you want to hear. So understanding the subconscious emotional response to a service becomes all the more crucial.

Can companies tap into subconscious opinions in the service development process? Absolutely, using AI. Neural tracking devices exist to monitor the activity taking place inside a customer’s brain as they read or observe a certain stimulus — this could be a storyboard for a new Customer Experience or any other audio/visual content. Using the neural tracking data, service designers can pinpoint the exact moments of positive emotional response and identify parts of the experience that deliver intended results.

4. Return on Customer Experience Let’s say your CX team and AI have worked together to suggest a list of optimizations and adaptations for your customer experience design. How do you know if your investment will pay off?

Artificial Intelligence can be used again to determine the most impactful areas of a service in relation to the organisation’s strategic targets.

We applied machine learning in this way for our work with a retail centre; testing five main hypotheses for RoCX (Return on Customer Experience). Once customers had filled in a multi-page questionnaire exploring the hypotheses, an algorithm was used for textual analysis and understanding, revealing that of all five opportunity areas, one stood out as being far more impactful than the rest. This insight gave the shopping centre a clear direction for improving the customer experience, making the retail journey more enjoyable for its audience.

Hellon utilises AI across the CX design process, end to end. But Artificial Intelligence and Machine Learning aren’t silver bullets; leaning on these technologies needlessly can result in wasted resources.

Here is an illustration of how AI could be used in a collaboration between Hellon and your company:

• Sales and proposal: If you’ve already got reams of unanalysed customer data, we can use AI at this early stage to shape our approach to your Customer Experience transformation.

• Defining the problem: Affinity mapping is a popular exercise here at Hellon. Customer data is written up onto sticky notes and then organised into contextual themes — but you need a big wall and a lot of time for the insights to emerge. AI can augment this process by scaling up the mapping process beyond what would be possible for human teams.

• Building a solution: What if AI could enable the creation of countless optimised ideas and strategies, allowing pre-prototyping by simulation? We mentioned strategic targets before and it’s at this stage that Artificial Intelligence can measure concepts against key objectives, defining the areas of biggest opportunity based on mass-scale feedback.

• Testing and validation: Once prototypes have been created, AI can then assess the impact of these ideas on the Customer Experience via a customer’s subconscious emotional response.

We’re incredibly excited about the possibilities of Artificial Intelligence in Customer Experience design. If you’d like to bounce some AI-driven ideas around with our expert team, then get in touch today.