CX maturity is at base camp

Unsurprisingly then, given the extent and depth of the organisational transformation required to adopt this kind of approach, Temkin rates the “CX movement” as currently being at “base camp” in terms of maturity – a finding that would appear to be backed up by technology services company NTT’s latest Global customer experience benchmarking report.

This study indicates that, according to most of the organisations questioned (57.8%), their CX strategy is still in development, with a further 13.8% acknowledging it is only at the planning stage and has yet to be defined. A mere 7.6% said they would describe their CX strategy as being optimised, while only 12.1% believe their customer base would give them a positive Net Promoter score.

This is despite the fact that 81.6% were convinced CX offered the business a competitive edge, with 58% considering the approach to be a primary differentiator – although only 14.4% stated that CX formed a crucial part of their overall organisational strategy today.

According to Rob Allman, NTT’s senior vice-president of customer experience, there are three key reasons that companies are failing to optimise their CX strategy effectively. These consist of “lack of leadership, a lack of inter-departmental cooperation, and a lack of empowered capability in implementing well-designed, intelligent processes and outcome-driven data fluency”.

As a result of this scenario, Temkin believes it will be another three to five years before CX progresses into the mainstream, not least because most enterprises are currently just moving through the first of three waves of adoption.

The first wave consists of deploying some form of CX or XM platform plus analytics and dashboard functionality to help organisations start collecting and distributing customer experience data, which can then be combined with operational information to create “signal data”.

“So experience data is what you collect, but signal data is what you take from that information,” Temkin says. “For example, if a telco sees that young consumers in the South East are reacting poorly to a particular service, that’s a signal.”

CX: the difference between success and failure

Other key technology initiatives that are currently being prioritised by CX teams, the NTT study indicates, include customer journey management and the introduction of artificial intelligence (AI) software. The latter is mainly taking the shape of virtual assistants, natural language processing and machine learning applications to undertake specific tasks rather than acting as a means of automating the entire business.

The second CX wave, meanwhile, is about introducing more intuitive analytics tools and integrating the CX system and underlying processes more tightly with organisational workflows. It is also about designing effective ways to put information and insights into the hands of employees who can take action on them.

Finally, the third wave will take place when a clear understanding exists of what data is required to operate effectively, Temkin says. In other words, companies’ CX system and processes will be able to provide staff with the right information to take immediate and appropriate action when dealing with customer issues, whether that means starting an action ticket or informing the customer support team of a particular problem.

But, he advises, it is important to bear in mind that CX is not just about how you treat an individual customer – instead, it is about “creating an entire system that continues to learn”.

“This system propagates insights and adapts rapidly in a repeating pattern that you have to make faster and better across the organisation. There’s no individual or team that owns CX – it affects everyone,” Temkin says.

But looking further into the future, he believes that AI software will also have an increasingly important role to play due to its ability to spot “signals” much earlier. This, in turn, will inevitably mean that companies are able to “reorient themselves at a much faster pace” – something that could make the difference between success and failure in the face of yet another crisis, whether local or global.

Case study: Marks & Spencer

Marks & Spencer’s decision to add a chatbot to its interactive voice response (IVR) system not only led to it generating incremental sales of several million pounds, but enhancing the customer experience at the same time.

The high street retailer’s IT team started working on the initiative about 18 months ago in response to a challenge from the senior leadership team to cut costs and adopt a digital-first strategy, with the aim of moving all sales online by 2026. This situation translated into a focus on simplification and automation, which included improving support for efficient processes and eliminating inefficient ones.

To this end, the choice was made to layer natural language processing (NLP) and customer intent analysis software on top of the company’s IVR system. The aim was to have the resultant chatbot act as a gateway, initially to deal with the high-volume, low-value and low-complexity customer calls coming into 12 of the company’s stores that still used legacy switchboards – although the software has since been rolled out at the company’s contact centre too.

When calling, most customers simply wanted to be put through to a specific store, department or individual, but rather rely on an expensive team of 135 switchboard operators – most of whom have since been redeployed – the aim of introducing the chatbot was to route people intelligently to the right place after establishing their intention.

As to how the system works, the NLP software, which was integrated with the telephone platform using Twilio’s speech recognition application programming interface (API), converts customer speech into text in real-time. Further integration with Google DialogFlow also enables the software to determine why the customer is calling. It then routes the call to the appropriate destination.

Much more flexibility

Chris McGrath, Marks & Spencer’s IT and digital programme manager, says the deployment has made a significant difference in customer service terms. “At the store level, it wasn’t a great service as between 60-70% of calls weren’t being answered, but that’s completely changed now,” he says.

As a result of this turnaround, the monetary gains have been high. Not only are customers happier with the service they receive, but stock enquiries are now handled by a centralised call centre rather than individual stores, which has resulted in a sales boost.

Reporting is also much quicker than previously as the retailer knows exactly how many customers are calling about what in real time. This means it is clear which products are in high demand and which are not, which helps improve forecasting.

In other words, says McGrath: “We’ve turned the system from not providing actionable insights into providing actionable insights.”

He also indicates that, although the expectation had been that 10% of calls would still need to be managed by humans, in reality 97% of customers are happy to communicate with the chatbot and have it route their call.

Such levels of acceptance are particularly important during the current Covid-19 lockdown when the number of contact centre agents required to work from home has expanded greatly.

“From a call centre point of view, it’s been really challenging as while supporting food-related enquiries is an essential service, we have a duty of care in not bringing people together. So, NLP technology has given us much more flexibility at the front end in redirecting calls, which has been really useful there,” McGrath says.