Martin Hill-Wilson, a prominent thought leader in the customer service and CX space, shares his views on how today’s generation of technology encourages service leaders to re-imagine a new North Star for customer contact.
From the perspective of four decades of involvement, I can confidently say that the world of contact centers has never been more interesting.
What a contact center does, how it does it and even why it does it are in a state of dynamic flux. Technology has never been so potentially game-changing. Key stakeholders such as customers and colleagues have never been as demanding or precise in their expectations. At the same time, organizations have never been so shaken to their core busy surviving a kaleidoscope of change in the world around them.
Added together, we are on a fast track to something new.
Here in the UK, we are in the middle of awards and conference season. As a judge and chair, I’ve been genuinely impressed with this year’s stories of innovation and transformation relative to previous years. The most adaptive organizations are not letting a good crisis go to waste. The back-to-back experience of COVID and the cost of living might have fatigued some but inspired others to step up and deliver.
So, what are they doing that we can learn from?
Start with user needs
The first point is that the businesses that adapted successfully are informed about relevant technologies, their use cases, and their potential impact. They are alert to these opportunities. But they also understand that any technology is an enabler, not a transformer.
In other words, they recognise that they need a plan before choosing their preferred vendor and version of CCaaS (Contact Center as a Service). Experience tells them that with a clear North Star and detailed understanding of how their future customer service model will operate, benefits will match any imagined ROI (Return on Investment).
It is one thing to recognize that there has been a step change in capability from AI-infused, cloud-powered solutions. But it is quite another to entirely re-imagine a customer service ecosystem and use it as a blueprint for re-organizing how today’s version will function.
So, let’s walk you through how to do this.
1. Everything starts with needs.
Use all the insight you can generate to map core customer, colleague, and organization priorities. What outcomes and experiences really matter?
“Your goal is to analyze the mix of service ingredients that inspire people to stay and become more engaged with your organization.”
Just as important is pinpointing the types of service failures that push people towards quitting in search of something better.
I discovered why this is important from IPSOS research, which showed how every service interaction influences the quality of a customer-brand relationship. It is either strengthened or weakened each time. In other words, there is no such thing as a neutral encounter.
Some service experiences have incremental impacts that only have consequences over time. For instance, it could be that a delayed response to an asynchronous message is OK until the customer notices it is not a reliable channel. Others are immediate. Contact centers must focus on these ‘immediate impact interactions’ from a service design, reporting, quality assurance and training standpoint. In other words, be focused on the factors that cause customer growth or attrition.
Once you have mapped this out, ask yourself if there are any core differences within customer cohorts. Is gender, generation, ethnicity, or other characteristic a cause for wanting something different? For instance, vulnerability has been a growing segmentation criterion in the UK since COVID. This could be an inability to pay household bills based on the increased cost of living. Or it could be comprehension challenges based on the effects of long COVID or dementia. Do your VIPs or advocates have distinct needs that you must cater to, given their greater value?
2. Understand what has influenced customer expectations and what is likely to do so.
For instance, lockdown disrupted daily life – customers went digital, and colleagues went hybrid. That much we know. But what’s next, and how will it play into service expectations?
Let’s pick the current global phenomena of Generative AI as an example. Is it reasonable to assume that Generative AI will become part of daily life for many people over the next three years? How will it influence their expectations of service innovation and quality? Will the mix between self-service and live assistance change significantly as a result? Will proactive service become an expected first response from brands? Will the need to contact customer service agents shrink as removing failure demand (the rise in contact volumes or demand caused by a failure to do something or do something right for the customer) becomes easier to manage at scale? In summary, what is a reasonable set of assumptions for what many accept is already a disruptive technology milestone?
These current and future needs and expectations will shape how you redesign your service ecosystem. This clarity will help you stay laser-focused on what you must operationally excel in. And equally, be highly responsive whenever you fall short.
What is your service gameplan?
It often helps to underpin this level of detail with some core service principles that capture the heart of what matters to stakeholders. Here is one I often use when assisting brands in shaping their service strategy. It summarises what a modern service strategy aims to achieve and the mindset that goes with it. In other words, it communicates the core of what matters to customers, colleagues, and brands.
Even though this looks obvious, it is not something most contact centers could realistically aim for or achieve until recently. In fact, most did the opposite.
Their default mode was reacting to customer demand most of the time. Routing and resolution might have used some historical customer data or knowledge but had little, if any, access to real-time data on intent, digital footprint, customer mood or any other influencing factors.
Any deeper insights into contact drivers or functional and emotive outcomes needed conversational analytics, which always happened after the event and was not commonly deployed. As a result, reporting and insight were historic. Only able to capture a skinny slice of what was happening between the customer and the contact center.
Without real-time, detailed insight, a ‘work harder’ rather than ‘work smarter’ culture prevailed. Decision-making was more a matter of instinct than evidence. Typically, high attrition levels amongst advisors tell the story of how this culture impacted the people taking enquiries.
Many agree it was a poor use of the value that people-powered service should be delivering. In my book, live assistance is optimally used when conversations are complex, emotive or matter to the customer-brand relationship. Simple queries should be resolved through self-service and proactive communication.
“In summary, reactive service has been traditionally hard work and often a source of mutual frustration for all involved.”
Here’s what the future looks like
The North Star of what is now possible in reactive service is quite different. Let’s imagine how a typical service journey would now flow.
Multi-modal, natural language-enabled conversation delivers automated, early discovery of customer needs. The customer need is then contextualized with a synthesis of real-time and historical data (orchestrated from multiple sources across the customer lifecycle) to influence triaging and routing decisions. In other words, we can direct the customer to the best resource based on a detailed understanding of their needs and identity: first time, every time.
Advisors also benefit. Relevant knowledge base articles, workflow and on-screen prompts are surfaced to help them deliver more personalized interactions and outcomes. As the conversation progresses, a host of AI services continue working in the background. Topics and sentiment are tagged and then analyzed for new insights ready to be communicated via dynamic dashboards or real-time alerts. Automation helps task completion during and after the conversation, saving time and ensuring greater consistency.
This transformed state of engagement is the fruition of many converging technologies. Cloud-based single platforms facilitate tight collaboration between workflow, data, and interaction channels. The ease of API connectivity and no/low code automation brings them together. They are then made intelligent by infusing AI into every aspect of the customer service ecosystem.
All this combines as an exciting new opportunity in how customer service is able to react to customer demand. We can now aim for a vastly improved experience and value exchange between customer and brand by re-imagining how service outcomes are designed and experienced.
From the customer’s perspective, it’s all about making things simple.
If a choice of channels is on offer, are customers signposted to the best option given their needs? For instance, each modality has unique capabilities. Voice is a naturally empathetic mode of communication. Text-based communication has the convenience of being asynchronous and is more accessible than voice for some people. Video is perfect when issues are best explored visually.
Do all channels lead to the customers’ outcome? If not, have you designed things to avoid repetition when a channel needs swapping or a conversation stretches over different sessions with multiple colleagues? How tight is the handover between virtual and human advisors?
If self-service suits the preferences and needs of a customer, is discovery easy, are the services on offer functionally useful? Are the information and knowledge detailed enough to satisfy needs, and do they generate trust without provoking further escalation? Are bots or avatars behaving in a way that creates confidence in their use? And referring to an earlier point, is self-service working well enough to enable live assistance to concentrate on higher-value engagement?
Customer service technology can enable everything I’ve just mentioned. But you must architect the service regarding design, people behaviour, policy, and process to realize this chapter of your North Star.
Using the power of analytics for Smarter Service
You will recall that reacting to customer needs was last in line within my proposed service strategy. Standing in front were two other interventions.
Anticipating customer needs and
Eliminating the need to make contact.
Both are win-win strategies. Less effort for the customer. Less cost to the brand. Both rely on the pattern recognition capabilities of machine learning algorithms. These are now commonplace in conversational analytics, which is one of the ways how customer demand can be analyzed.
In the use case of anticipating customer needs, the goal is to identify frequently asked enquiries at common points in the timeline of a customer journey. Proactive notification of deliveries, service visits, appointment reminders, and annual renewals are well-established examples.
Others I’ve come across include a new product launch that triggers a rise in customer queries. Or notable change events such as weather, interest rates, and supply chain disruptions that cause customers to make contact.
Beyond these examples are more subtle correlations that can and will be discovered using pattern recognition to help anticipate customer needs hidden in the vast datasets generated from daily customer service interactions.
Eliminating the need to make contact (also referred to as failure demand management) uses a similar methodology. This time, the focus is on identifying and grouping conversation topics and then pinpointing those that could be avoided altogether with upstream redesign, asking why the customer needed to make contact and how we can remove that need. I’ve witnessed significant volumes of inbound demand disappear through this type of initiative.
Insights and Decisions
So, let’s draw this exploration to a close with a final summary.
Every time a new generation of technology emerges, it is worth asking this question. What can customer service now become as a result? It should consider the needs and expectations of customers, colleagues, and the organization. It’s a North Star answer you want to aim for. Untainted by consideration. Based purely on the potential embedded in the technology.
This will help you see something new and quite different from today’s version. Acting fast enough will make you noticeably better than others, which always helps retention and growth.
The secret sauce this time is the availability of insight for real-time decision-making. This drives relevance, effectiveness and empathy in customer and employee experience management.
Secondly, things now work together more efficiently and can be flexed in ways we could only dream of in previous eras. We can scale on demand and run customer service wherever we choose securely.
And at last, those tired, old phrases such as ‘seamless,’ frictionless,’ and ‘hyper personalized’ are now transformed into tangible benefits.
So, what’s your North Star going to be?
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