Experience is everything: Connecting CX and MX

How machine connections shape customer experience

Each experience a customer has shapes their overall perception about a business and ultimately whether they’d use them again or recommend to a peer. Often people think of the human side of customer experience (CX); call wait times, trying to make payments, back and forth emails etc., which is why businesses are bringing emerging technologies, such as AI, VR, Blockchain and IoT, to address these challenges.

Today though, many of these initiatives won’t benefit the end customer unless another aspect is considered; machine experience (MX).

CX is a key differentiator; price and service will continue to evolve and always be important but customers are drawn to how you make them feel. For Colt, being a customer oriented business doesn’t mean we just focus one area on the customer, we focus the whole business.

Colt Technology Services
What is MX and how does it impact CX?

The quality of the machine experience (MX) is directly connected to the quality of the customer experience – you can’t have one without the other. “Fundamentally MX is about improving the experience between connected devices and machines, as well as how you communicate with them,” says Arvind Patel, Principle Consultant at Ciena. “If the new technology you bring in to enhance your CX isn’t connected in the right way, the experience the customer has will be poor.”

The role of connectivity

Digital services and technologies are continuously growing, but they’re only as good as the connectivity that links them. Connectivity to the branch location is, of course, vital, but now it’s crucial to consider the connections you need right across the digital ecosystem.

There are a combination of ways that connectivity plays a large part in the link between CX and MX:

  • Network: your network determines the overall quality of your services – whether you have low latency, reliability, security and connections to all your key locations between your business and your customer.
  • AI: enabling a smart network means spotting problems before they occur, analysing data and taking actions before customers notice there’s an issue. This will mean fewer faults in your services or the digital experience you provide. AI can also enable chatbots and virtual assistants to answer common questions, readily available for your customer 24/7.
  • APIs: application programming interfaces that are a bridge between two or more systems allowing for seamless communication. This is intelligent networking that works behind the scenes, automating processes for a faster, smoother experience for the customer.
  • Cloud Edge: the quickest experience, for IoT/5G/M2M applications depends on edge computing, which is why the next generation of applications will require a network that connects all the key edge compute locations.

“Running these complex workloads requires high-performance, robust systems that ensure seamless interoperation. To enable the success of these technologies, your network needs to be at the right standard. Machine experience can complement your customer experience if it’s done well. To do this properly and to ultimately deliver the best experience to end customers, you need to assess if your network infrastructure is built to support you.
Machines aren’t going to replace or automate all the processes you have but used correctly, and over time, they can help you save costs, improve efficiency and deliver a fantastic customer experience.”

Jim Crum – GM, Colt strategic alliances, ciena

How machine learning delivers an improved network experience

Delivering a great machine experience depends on a reliable, secure and predictable network. It also needs high bandwidth and low latency – very similar requirements to enterprise or telco customers.

The importance of a reliable and predictable experience is becoming more and more critical, particularly as cloud-based services become ever more tightly interwoven into modern business IT fabric. If the network underperforms then the applications and end users suffer. There are several ways in which businesses seek to mitigate the risk of a service interruption to these mission-critical links – such as adding route diversity and resiliency options. We help our customers by designing and delivering networks with extremely high levels of availability, but there’s even more we can do to reduce operational risk.

When it comes to network management, the more knowledge and insight we have into the performance of individual network components, the better. Monitoring isolated changes to how a network element is performing gives us a real-time indication of a potential problem – but what if we could go one step further and predict when an issue would occur – before it actually happens?

Whether that’s a subtle change in optical receive power on an interface or a minor fluctuation in the operating temperature of a card, we’re able to collect and assimilate millions of data points from across the network and use this to put together a picture of overall network health. Because of the high number of inputs, we can use supervised machine learning algorithms to start to accurately predict where an equipment failure might be likely to occur, and then take pre-emptive action to avoid that situation becoming a service-affecting fault condition.

The investment into the Colt IQ Network, particularly the recent upgrades with Ciena, have given us a flexible, state-of-the-art platform over which to deliver this market-leading experience – regardless of end user or application. Machine Learning takes this a step further and provides the tools to spot problems ahead of time, ultimately delivering a better, more reliable service for our customers.

Machine learning will increasingly play a role in shaping the customer experience and many of our customers already recognise the benefits of this approach. By starting with a solid network foundation, the addition of intelligently deployed machine learning can take the machine and customer experience to the next level.

The tools and technologies delivering on MX today

Machine experience brings automation throughout the entire customer journey. At each step we’re deploying new technologies to improve the machine and customer experience.

Service delivery
Traditionally, service providers use disparate, proprietary tools to manage delivery. These orchestrate end-to-end services on top of a very fragmented, multi-vendor network infrastructure. This complexity makes it hard to create a great machine experience.

In an increasingly connected world, a homogeneous network infrastructure and a simplified and open IT environment is critical for enabling the automation of the customer journey. The Colt IQ Network provides a standard infrastructure layer with built-in software defined network (SDN) control capability as a foundation to enable and simplify the creation of a seamless machine experience. The adoption of open application programming interfaces (APIs) to communicate with the underlying network and the IT layer provides a common language that simplifies machine to machine (M2M) interactions.

Through API calls customers can locate services, obtain quotes, place orders and activate services with an on demand real time experience. Customers can establish seamless M2M communications between their IT environment and the Colt network and IT systems to cover the entire service delivery process. This makes the job quicker, reduces errors and ultimately makes it easier to work with us. Last year we announced our work on MEF’s set of standardised LSO Sonata APIs. These allow service providers and enterprises to adopt a common language and enable an end-to-end machine experience to deliver network services.

In-life management
Network services need to adapt to the changing requirements of the customer while in production. Applications may require changes to the network, for instance to increase bandwidth to connect to the public cloud or to apply an additional level of security through encryption. Handling service faults needs proactive and reactive ticketing. Notifications, performance management and billing require the collection and exchange of usage and service performance information. APIs can enable the dynamic modification of service parameters and the collection and exchange of real time information and events from the underlying network and IT systems.

We’re also using AI to better understand network data. A good example of this is looking for patterns across calls, analysing millions of call records to look for any sign of service degradation. Another is predicting the risk of network faults occurring in the future, applying machine learning techniques to the analysis of physical network parameters, for instance fluctuation in optical signal power.

AI recognises patterns in large volumes of data at a level where it would be impossible for a human, enabling us to take proactive resolution actions to keep the network running in good health. For this to work we need a seamless machine experience in collecting and analysing the data, to ultimately deliver far greater support and insight to customers.

Business administration
Delivering an improved experience here is all about making it easier to interact with us and to build trust as this is an area with little room for error. Blockchain technology adds a layer of trust and security that enables multiple parties to keep accurate and fully aligned records of business transactions. During the on-boarding process we can automate know-your-customer procedures such as identity verification, credit and compliance checks to enable new customers to do business with us in real time.

The ability of blockchain to maintain accurate service usage data enables payments to be settled automatically, freeing up customer and provider time, particularly where multiple parties are involved. We’re constantly looking at how we can make the experience better for our customers and at the machine layer. By making it easier to do business with us, remove manual tasks and analyse masses of data, we can improve the experience at every step of the customer and machine journey.

How Colt is utilising ML in customer experience

At Colt, we view customer experience through interactions at every touchpoint. We aim for every interaction to be easy, time-saving, consistent and personalised. This has been an ongoing focus for us, changing the way we imagine CX and how we measure it on a transactional basis, through programmes such as Net Easy.

Throughout the customer journey, our customers might not always interact with a human element. They might interact with our APIs, portals or chatbots. Regardless of the interface, those same principles still carry over and machine experience should be comparable to that of human interactions. In some ways, having a machine experience can be better for delivering aspects, such as speed and consistency, but a personal human experience can’t be matched and so we need to strike the balance between the two.

One of the most critical ways we utilise machines is through machine learning (ML). We created a bespoke algorithm to support our Customer Intelligence function. It helps better understand our customers and identify areas of improvement across our business. It also doesn’t work in isolation, our team review to make sure it’s still doing what we need it to. We see it as having a human hand on its shoulder.

In the future, some ML and MX processes will be automated – particularly for in-life activities such as network fault notifications or raising credit faults. In doing that there will be more time given to teams for higher-value tasks and to look at the bigger picture. When it comes to negotiating more complex transactions, like in Sales and with the delivery of services, automation will help complement the human experience, but not replace it. Across all sectors, we can expect automation to rise as businesses utilise this technology more and more.

For CX in the future, robotic process automation will improve and some processes will replace human interactions. Utilising machine experience in the right way will enable companies to offer more friction-free interactions. There is also an element of machines becoming our customers. Particularly for larger, wholesale-based customers who may order services in bulk, they may utilise machines to complete that process for them. In return, that buying process could be automated end-to-end, seeing their machines interacting with our machines on a transactional basis.

As a company, we’re moving to a more continuous improvement and development process for our customer experience strategy. Being industry leaders hasn’t caused us to rest. Our people are constantly re-thinking and re-imagining how we can deliver a fantastic customer experience. The machine experience we provide is a part of it, but not all of it. You can never underestimate or replace the human element of customer interactions. Like Maya Angelou said, “I’ve learned that people will forget what you said, people will forget what you did, but people will never forget how you made them feel.”