The latest State of Service report reveals the importance of CX over time-to-resolution as a measure of success, and that AI is key to improving CX.
Not too long ago the top measure of success for a customer service team was its average handling time record, and the priority was closing the highest number of cases in the least amount of time possible. Top performing service-orientated teams are now moving away from this time-to-resolution measure to a customer-first mandate.
Customer service teams are acknowledging that customers don’t care how quickly you can process their request if they’re bounced around between departments or need to call back another three times to have their issues resolved. Your customers expect you to solve a problem well, not just quickly.
In the current consumer landscape, which is moving at an incredibly rapid pace, service is now a key differentiator that can set a company apart from its competition. When customers can choose between multiple suppliers, products and channels of communication to fulfil their needs, the extent and type of engagement they receive is so important.
Service teams are now embracing a broader set of customer channels, and phone and email interactions are merely the tip of the iceberg for service interactions. The latest Salesforce State of Service report indicates that more than half of customer service teams extensively deliver customer service on at least five channels. New and emerging service channels such as mobile apps, customer portals and video support are experiencing tremendous growth so it’s essential that customer service metrics also progress to keep pace with customer expectations.
Leading the growth is self service outlets that help customers find quick answers on their own terms, leaving agents to tackle the more complex issues. With this reality, the frontline needs to be made up of high-level problem solvers equipped with sales platforms and technologies to enable them to be forward-thinking in their service delivery.
Customer service teams need to take a step back and look at their delivery of service and desired outcomes holistically. Start by determining if your passion and processes are aligned. A genuine and authentic desire to support customers needs to be matched with the right tools and processes to give service teams a single view of their customers. This will enable them to create deeper relationships, provide a consistent experience across every channel, personalise their interactions and understand a customer’s full history with their company.
If your frontline team is provided with the right information and tools, it can excel at personalising customer service and ensure customers don’t feel they’re treated as a number.
With personalisation and consistency now at the centre of customer service strategies, how can customer service teams measure their performance?
We know that customer service is highly measurable. Many customer service teams are tracking customer activity and gathering mountains of data, and their goal is typically to improve customer satisfaction at each step of the customer journey. We measure it with team-level and individual metrics including call logs, interaction counts, resolution rates, chat times, net promoter scores (NPS) and many others to indicate performance.
Overall customer satisfaction is seldom a competitive differentiator; it’s more important to measure how your customers are feeling. Businesses should be looking at the emotions of their customers and their influence on this, rather than getting bogged down by their NPS score.
Do you want to give your customers a sense of belonging, a feeling of security? Wouldn’t it be great if your customers said ‘this company gets me’? This is the ultimate customer service benchmark.
When companies look beyond satisfying customers and instead connect with their emotions , the increase in customer value can be substantial. Emotional motivators drive consumer behaviour, and these should be measured and strategically targeted.
Take United Airlines, who now explicitly asks its customers what emotions they feel at the end of a flight – a great gauge of the service level customers believe they have received. There’s also a great example of Uber focusing on customer emotions when a customer left a $200 umbrella in a car. The obligation for Uber was to give the umbrella owner the driver’s number to get it back.
On this occasion, the customer service representative got hold of the umbrella, boxed it up with Uber sunglasses and a hat, and posted it over to the customer. None of this was part of Uber’s support logic process, but resulted in a superior customer experience, and a positive emotional connection with the brand.
Often you can’t script interactions like these, which is why the passion of the individual agent is also very important. Pick the right metrics that correlate with your company’s unique offering and desired customer emotion/s, and remember not to over rely on any single measure. Numbers on their own can be meaningless but measuring emotions can provide the context to allow individual agents to deliver outstanding service.
The answer is not passion or process in isolation; it’s a combination of this high-level human involvement and artificial intelligence (AI). It’s widely expected that predictive intelligence will have a transformational impact on customer service over the next five years.
The reality is that many teams still struggle to meet customer expectations and are inhibited by budgetary, resource and business alignment constraints. Personalised service seems so easy, but it’s also so easy to get it wrong. For instance, when I stay in a hotel the screen often greets me ‘Welcome Mr Newman’ – it may seem insignificant but it’s an interruption to my personal relationship with the brand. It reveals that they aren’t utilising the data they have on file to tailor my experience so it’s important to get these small interactions correct.
This is where technology and AI can support agents, rather than replace them. AI should be used to enhance experiences by adding human-like intelligence to interactions. The State of Service report gives the great example of machine learning analysing a caller’s word choice to understand emotions and recommending the next best thing for an agent to say. AI will be put to greater use by customer service teams, and better support agents to measure their customer’s emotions and anticipate their needs.
There is a significant appetite for it, especially with business-to-business services. Those playing in this field need to pay attention as 75% of business buyers expect that, by 2020, companies will anticipate their needs and make proactive suggestions before they even reach out. Now that’s service!
Download the State of Service Report to find out how your service team – your frontline – can provide exceptional customer experience.