Customer happiness should be stronger than ever. Businesses have almost unlimited scope to collect deep insights about individual needs, while three in ten customers are willing to pay more for products that come with better service. So, why are satisfaction levels stuck at their lowest rate since 2015?
It would be easy to say customer needs are still evolving faster than companies can adapt, but the reality is more complex.
According to recent studies, more than a third of firms have failed to pick up on real customer sentiment, including miscalculating how often individuals feel dissatisfied. At the same time, many are taking communication approaches that don’t necessarily match service needs — as illustrated by the rising use of conversational AI, even as a notable proportion of customers remain discontented with chatbot-led support.
Added together, these signs point to one likely conclusion: companies have fallen out of tune with their customers. If they want to restore harmony, there’s a pressing need to get back to the basics of great service – listening to customers and giving them what they actually want.
Switching into efficiency overdrive
When faced with the complex task of supporting customers across an ever-growing array of channels, it’s not hard to see why service leaders are keen to simplify what “good experience” looks like. For many, doing so often involves setting quick and cost-efficient service as a key priority and success marker – with nearly half (45%) globally citing first contact resolution (FCR) as the most important factor for their customers, closely followed by how long it takes to resolve issues (25%).
To an extent, focusing on swiftly tackling specific requests makes sense, especially given the top two reasons customers typically contact service teams: reporting product or service issues and asking for information. But this narrow remit only covers a small fraction of customer requirements. Beyond declared surface-level problems and queries, there are a host of deeper needs individuals want agents to meet, but rarely actively vocalise.
Research, for example, has shown customers often have tightly defined preferences around where and how they wish to be supported. This not only includes varied views about which communication modes should be used for issues, depending on how convoluted they are, but also an expectation that every person they encounter will know the full context of their case.
All of this illustrates a huge emotional intelligence (EQ) gap in standard service. Failing to consistently align communications and support with what works, and doesn’t, for customers means teams aren’t able to drive complete and unique satisfaction.
Upgrading listening systems
One prime solution to this challenge is enhancing data collection. Going further than basic profile building informed by purchase records, service history, and self-supplied personal information, companies need better listening processes for picking up on indicators of how customers are feeling and, critically, whether interactions are hitting the right mark or not.
However, considering that average monthly customer interactions can run into the hundreds, or even thousands, such monitoring isn’t a practical workload add-on for busy teams. To power the seamless delivery of quality service, what’s needed is a mechanism for gathering granular data about customer behaviours and emotions that can be quickly translated into accessible learnings. In short, teams need their own upgraded technical support.
Of course, using technology to measure customer service isn’t new. Over the past decade, forward-thinking businesses have increasingly leveraged unobtrusive tracking tools to assess live agent performance against multiple factors, including efficiency metrics such as FCR and average handle time. But this data doesn’t give teams the full picture of how to improve individual conversations – as shown by the fact 74% of agents feel having more data and tools at their disposal would open “more opportunities to personalise interactions”.
That is why it’s becoming necessary to introduce an extra layer of real-time and historical analysis that uses advances in smart tech to capture the subtle elements human teams miss across all points of customer contact.
Yes, unsurprisingly I’m talking about artificial intelligence (AI), but the goal here isn’t to automate communication; it’s about enabling agents to do their jobs more effectively.
Human delivery, powered by tech
Thanks to ongoing development in large language models, sentiment analysis is now highly precise, streamlined, and scalable. Using tools such as ChatGPT, live measurement systems can now follow conversations and note vital details, while also delving into the nuanced meaning of what each customer is saying — with the end result being an easily digestible summary containing both key highlights and a clear ranking of customer sentiment.
For service leaders and teams, the obvious benefit is oversight of multi-channel interactions. Arguably more importantly, however, an inflow of data about emotional response allows for greater development of EQ and the ability to put it into action. For instance, let’s explore a few of the key emerging use cases:
Swiftly personalising services
Using short, accurate summaries of previous interactions, agents can quickly extract contextual and sentiment insights that help them pinpoint what customers need and how to ensure services meet their individual preferences. As well as cutting down time once lost to trawling through records and manually collating customer stories, this instant accessibility ensures customers receive seamlessly tailored experiences every time they contact a business.
Facilitating collaborative resolution
Enabling real-time notifications of negative sentiment allows supervisors to immediately see where agents need support to get back on the right foot. For example, in addition to offering background guidance — such as suggesting switching to a different communication channel or style — supervisors can join interactions and actively participate in addressing complex issues. This maximises the chances of effective problem resolution and high satisfaction..
Spotting shared challenges
Unifying AI-produced data in a centralised repository that can be analysed collectively makes it easier and faster to identify common problems, in addition to which changes are required to improve service processes and satisfaction. For example, data may show that fielding questions about products via chatbots generally fuels negative sentiment; underlining a need to immediately answer queries with human support.
Making speedy and low-cost support the leading benchmark of customer service has created a disconnect that will only get bigger as customers become more and more dissatisfied with impersonal efficiency. To bridge the divide, it’s essential for teams to understand what is sending them so far out of tune and learn how they can reinforce crucial customer bonds. To achieve that while offering minimal friction and swift resolution, agents will need help from smart tools capable of detecting where sentiment is leaning and what’s required to dial up positive human connection.