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The future of CXM, from digital transformation to digital conversation


The next step in Customer eXperience Management (CXM):

The future of CXM, from digital transformation to digital conversation

There has been a huge focus on the accelerated pace of digital transformation in recent years. An array of companies have invested in self-service and straight-through processing (fully automated customer request handling), using a digital portal as their main point of contact with their customers. The next step is getting closer and closer: from digital transformation to digital conversations. A digital conversation goes further than the ‘conversations’ we currently have with Siri, which are mainly a functional game of questions and answers. A digital conversation is a form of interaction that uses context about who you are and what the question behind your question is that forms the basis for a productive conversation. For digital conversations – especially when the customer isn’t talking directly to an employee, but an algorithm – it’s very interesting to look at the world of robotics and the developments happening there, as they will enable the ultimate digital conversation: natural conversations with machines.

“The continuous growth in the volume of customer contact, the consolidation of markets and the shortages on the labour market are making it increasingly difficult to take customer contact to a more personal level.”

Robots and human interaction: what’s the situation in the Netherlands?

You can now play with an artificial dog and get a robot to tie your shoelaces. But will robots soon be able to understand our feelings? Devices are getting smarter, but also more ‘sensitive’, with developers working hard to develop their sense of morality.

Back in January 2022, Underlined, together with MOA (the Expertise Centre for Marketing insights, Research and Analytics), ran the Data Science working group’s first session – a webinar with Professor Piek Vossen from VU University Amsterdam. We took attendees into the world of a communicating robot and artificial intelligence applications within the domain of customer experience (CX). We also met one such robot called Leolani, which means something like ‘voice of an angel’ in Hawaiian. Leolani experiences her own customer journey and has the ability to see, listen, talk, make connections based on what she already knows and eventually learn from this. Although she currently has an estimated 10% of a human’s brain capacity, the technology behind her is very interesting, especially for our field.

How does a modern data science architecture work for conversational and customer experience analytics?

To make Leolani work, and to make conversational analytics work in general, we need to start with a real-time data science solution. Conversational analytics is the data science we draw on to analyse customer voice and textual conversations, such as a chat, and potentially respond to them automatically using an algorithm. The solution is not a single algorithm, but an entire group of them – combining image, sound, text and context into one meaningful dialogue. These techniques are built on a real-time platform containing pieces of data that are picked up and enriched by a relevant algorithm. To bring this all together, we need a ‘brain’ that collects, merges and leverages all the enriched pieces of data. In the Netherlands, for example, KPN (an Underlined partner) has now developed exactly this kind of infrastructure, which is very suitable for this: it’s called ROCX’R. VU University has developed its own platform for its scientific applications for Leolani, and major players like Microsoft will certainly follow. Designing this architecture and how it interacts with data science algorithms requires a different line of thinking to a classic analytical approach. Classic analytics usually take a composite data set, rather than a stand-alone set of image-only or text-only data. In these new data science architectures, a specific algorithm can pick up and enrich these data files, and then put them back again. The ‘brain’ then brings all these components together again and displays them in the right context.
It is precisely this context that plays a major role in a digital conversation, because what does the artificial intelligence (AI) algorithm know about your services and your customers and their needs? In these modern architectures, each data science algorithm for image, sound, text or customer experience becomes self-contained. Each algorithm can also be optimised. So, you can see how a bot algorithm like Leolani learns much faster than an approach through classical analytics.

Data-driven customer experience management

Data-driven customer experience management (CXM) – our field of expertise – is the concept of improving the customer experience through the availability and development of data. More and more data science is being applied in this field, ranging from text and process mining to various machine learning algorithms, to identify drivers in the customer experience. This helps CX professionals spot and harness opportunities for a better and more personal experience through data visualisation.

The aim is to make customers happier with scalable operating concepts for a customised service dialogue, for example.

Figure: The key balance in CXM: value for the customer and value for the organisation.

The continuous growth in the volume of customer contact, the consolidation of markets and the shortages on the labour market are making it increasingly difficult to take customer contact to a more personal level. Here, Underlined sees a new form of interaction developing ever faster, which we too are investing in heavily: the personal digital conversation. This creates authentic conversations between consumers and bots. Conversations in which artificial intelligence plays a key role in making this possible, using knowledge that is continuously developing through data.
As robots become more prevalent in a range of customer support environments, it becomes even more important for them to understand the nuances of human communication and response. This will allow us to use them for a personal service dialogue. We will soon see robots in various roles, such as information guides in shopping centres, concierges in smart environments, helpdesk consultants, and waiters in restaurants. Robots don’t always need to take a physical form; many of us have been talking to chatbots for a while, but their current intelligence is questionable, not to mention the empathic capacity of current-generation chatbots.

Robots and machines still have a lot to learn about the world, but also about us…the people they serve. Through language communication, a robot can get feedback and learn from people efficiently, just as children learn not only through experience, but also through instructions.

Let’s take a step back for a moment: many projects for mapping customer journeys focus on the processes and touchpoints, but fail to capture the emotions customers feel during their journey. Mapping the customer journey has become one of the most important tools for customer-facing organisations today. In many cases, the journey maps themselves miss a vital element – something that can boost ROI to an even higher level. That element is emotion, an essential part of human decision-making. We make decisions based on emotions and only rationalise them later.

But let’s go back to the robots: they need to understand customers to know how to respond correctly, without negatively impacting the customer experience. An approach that reads multiple expressions will lead to better interactions between humans and robots, and can even create new opportunities for customer experiences, improving overall brand relationships.

Because product purchases are so closely tied to emotion, marketing and CX also need emotional AI technology to determine public feedback on commercials and advertisements, so they can understand the true responses to products and services.
Emotion analytics technology and robots promise to deliver new, exciting types of data that we will have to learn to use and refine over time.

This article was originally published in: MOA Topic of the Year 2018-2022

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