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Article 4 min read

How CX will evolve in 2023 and beyond

By Matthias Göhler, EMEA CTO, Zendesk

Last updated January 4, 2023

We couldn’t possibly have predicted the changes that have taken place over the past couple of years. In 2020, companies had to pivot without warning, and in 2021, many firms embraced the agility they were able to unlock. But what about 2022? Could it finally be time to revert to the business goals we had in place prior to Covid? One thing we know for sure is that further changes will be needed in this digital-first world, particularly to the way we present products and services to customers – and how we interact with them.

Put CX first

Customer experience (CX) used to be a concept that was intangible and difficult to pin down. Now, with products increasingly becoming commoditised and subscription-based, the CX is what sets companies apart. Firms are therefore spending more time considering ways to improve it. For those who haven’t set out on this journey yet, now is a great time to hit the CX reset button.

Preferences and behaviours have changed radically over the past couple of years, so the things that might have ticked all the right boxes in 2020, won’t necessarily do so now. A great starting point for businesses would be to think about the customer journey. How would your customers want that journey to take place? Everything from first contact and purchasing, through to issue resolution and after-sales care can be included in this part of your assessment. Once you’ve appraised this, consider how new or improved technology and processes can add value to that customer.

New-found opportunities are springing up all the time. For example, instead of immediately closing a ticket once a problem has been solved, engagement teams are having ongoing conversations with customers. This move toward conversational service deepens engagement and forms the basis of a more personalised experience – one that customers really appreciate.

Changing models for changing times

We’re also seeing business models changing in line with customer behaviours. For example, automotive retailers are pivoting to rental models and are even becoming platform providers. This opens up new opportunities for a car sharing account to become a central point for other commercial activity through platform integrations. For example, drivers could be automatically charged for parking by linking number plates to online platforms where their bank details are already registered. Drivers could also better interact with brands from inside their cars in a more seamless way, thanks to rudimentary voice controls that are commonplace today.

Other examples exist too, where a service can act as a connector to new business models. For instance, utilities firms could connect a household’s energy usage to the power they generate from solar panels that they can sell back to the company. Or travel firms could bundle event tickets with core products like flights, to add value and create new product lines that allow them to diversify. With these changes also comes the need for companies to adapt to how they interact with their customers. As these changes create more opportunities for companies to better understand and build relationships with their customers, taking advantage of these opportunities will depend on having the agile systems to quickly adapt.

Faster and better resolutions using AI

Customer service is changing all the time, but one thing will always stay the same: we will always need service agents. Why? Because, that human touch that agents provide is difficult to replicate and it will always be desired by customers in certain situations. A major source of innovation then, is automating the simple, repetitive customer queries, so that service agents can do what they do best – solve more complex issues in a personable way. Natural language processing (NLP) is improving in a way that’s able to get the best help or resolution for customers, whatever form that help might come in.

Here’s an example I experienced recently. I rented a car and went to pick it up at the airport, but it wasn’t there. I had to ring the switchboard who didn’t know my current situation, so the process ended up taking much longer than it needed to be. A better solution would have been giving me the ability to send a text message to say: ‘my car isn’t here’ and AI could identify my profile based on my phone number. I could then get a response to provide an alternative means of transport or be put straight through to an agent who already knew the situation and could help immediately. This would reduce customer service hassle and provide a better solution, powered by AI. And while it may require some complex integrations to achieve, it’s not as far out of reach as you might think.

Recent memory has taught us that a crystal ball to predict the next 12 months might not be easy to read. However, what we can do is take stock and learn from the last two years, to make sure we’re ready for what’s next.

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