The recovery from the uncertainty, confusion and disruption that characterised 2020 and is now spilling over into 2021 will, when it comes, be swift and decisive. It will set the stage for the rapid emergence of a slew of new business models which will assist organisations to rebuild for a more optimistic future.
It is a future in which businesses of all sizes will adopt solutions geared to meet the challenges of the “new normal” central to which is the need to integrate online, cloud-based services into existing sales, marketing and communications strategies in support of the ever-evolving remote workforce.
As a remote workforce has fewer opportunities for personal interfaces with prospects and customers, issues regarding improved customer service will be swiftly elevated on the priority lists of many organisations.
In this light, companies can be expected to refine their business models in 2021 to become increasingly customer-centric, while reinforcing their remote approach.
Remotely keeping pace with customer expectations and demands will be challenging. The implementation of emerging technologies complemented by advanced, more sophisticated business processes will be required in order to better support customers and deliver seamless experiences for them.
According to the Gartner research and advisory firm, by 2022 the majority of customer interactions will involve technology.
Against this backdrop, the role of new technologies such as software-defined wide area networking (SD-WAN) will be paramount. SD-WAN technology unifies multiple management consoles and supports the collection and collation of data from network devices, traffic flows and network endpoints.
SD-WANs are paving the way for organisations to adopt the next-generation of cloud-based applications that will assist the remote workforce to deal effectively – and efficiently – with customers.
Companies can be expected to refine their business models in 2021 to become increasingly customer-centric, while reinforcing their remote approach.
SD-WAN technology is also forming the foundations on which real-time artificial intelligence (AI) will be able to operate across a variety of customer channels and touchpoints.
AI is essentially an umbrella term covering a number of technologies that imitate human functions, such as learning, problem-solving, reasoning and more. Expect AI to rapidly become a key aspect of customer service.
Together with advanced automation systems, AI will assist organisations to optimise customer interactions and analyse their usefulness, while devising solutions to obviate performance bottlenecks and minimise the potential for unforeseen glitches.
When humans are needed to handle more complex and perhaps high-value interactions with customers − where good measures of insight and contextual empathy may be essential − AI technology has an important role to play.
A subset of AI, Natural Language Processing (NLP), is able to automatically monitor a discussion between a sales/support agent and the customer, and extract keywords and phrases from the conversation. The intent of the exchange will thus be understood by AI, which may generate an appropriate response.
The response can then be immediately suggested to the agent (who most likely will be given permission to accept or reject it and thus retain control of the interaction).
Thanks to the machine learning or deep learning capabilities of NLP, every response and recommendation made by the agent over time will contribute to an improvement in the proficiencies of the AI solution as it learns how and when to replicate them.
While AI technology is able to broadly track customer satisfaction quality by analysing data that customers leave in digital interactions such as those on social media, there are additional important metrics that fall under AI’s aegis.
These include customer effort scores (CES), net promotor score (NPS) and customer satisfaction (CSAT) metrics.
CES measures how much effort a customer has to exert in order to resolve an issue, have a request fulfilled or get pertinent questions answered, while NPS measures long-term loyalty and is said to be a good indicator of potential company growth. CSAT is a versatile metric that allows organisations to analyse answers to a variety of questions concerning a single interaction or touchpoint.
The use of these and other AI tools to track the relative performances of sales, service and support agents will, for the first time, put the customer “in the driver’s seat” when it comes to providing evidentiary proof of less-than-optimal performances by organisations and their staff.
As AI becomes a major contributor to data-driven business success, so a cohesive data- and analytics-centric strategy must be developed by businesses in order to optimise feedback and spotlight aspects of customer service that might be improved.
According to research published in the book, “The Effortless Experience, Conquering the New Battleground for Customer Loyalty” by Matthew Dickson, Nick Toman and Rick Delisi, 96% of customers with a high-effort service interaction become more disloyal compared to just 9% who have a low-effort experience.
An organisation with a clear understanding of its customers − and whose sales and marketing teams are empowered with this information − will not only build loyalty within the customer base but will build a better business in the post-pandemic era.
With this in mind, organisations will look to employ “analytics officers”, who will be tasked with laying the groundwork for a data- and analytics-centric culture capable of giving the organisation a competitive-edge in the marketplace.
Looking ahead, there are clear indicators that AI will help progress the universal adoption of predictive analytics. This advanced form uses current and historical data to predict future behaviour patterns, such as demand cycles, while highlighting “over-the-horizon” marketplace opportunities and identifying hidden potential risk factors.
However, there is no one-size-fits-all solution. Organisations will need to navigate their own paths through the maze of AI and related technologies and solutions, while keeping stakeholders’ requirements and budget constraints firmly in mind.
A useful option is to begin with an AI-readiness audit to gain a greater understanding of how investments in new technologies will impact the organisation and influence its customer base now and in the future.
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