By Deon Scheepers, Customer Engagement Executive at Pivotal Data
In the modern digital economy, data has become the lifeblood of every enterprise. Today, access to rich consumer information is the driving force behind product design, service delivery and the evolution of customer experience and engagement strategies.
In an era dominated by smartphones, connected digital devices, always-on connectivity and online engagement via social media and e-commerce platforms, companies have numerous collection points from which to draw data.
The big data trend is so prolific that the IDC forecasts that by 2025 the global ‘datasphere’ will grow to 173 zettabytes – up from the relatively minuscule 33 ZB generated in 2018.
This data explosion is fuelling digital transformation across industry verticals. While the pace and scale of this evolution vary, every business understands the strategic importance of digitisation to their future sustainability.
But given the pervasiveness of consumer and connected device data, do big data capabilities still offer a competitive advantage? While there can be no doubt that rich consumer data will unlock business opportunities and craft unique user experiences, it’s no longer a case of “he who holds the biggest data sets, wins.”
Instead, data insights and applied intelligence have become the strategic differentiators that matter most within the context of digital transformation. More specifically, a subset of the digital transformation process, namely data transformation, will define a company’s ability to derive actionable insights and gain a clearer view of the customer.
Only then can they create the relevant and personalised products, services and interactions that grow market share and boost customer loyalty and retention. And nowhere within the enterprise are these capabilities more critical than in a business’s frontline customer engagement channel, the contact centre.
Beyond big data capabilities, contact centre operators also require data analytics and business intelligence solution that can apply artificial intelligence, Natural Language Processing (NLP) and algorithm-powered machine learning to analyse the massive data sets available and deliver real-time insights to automate actions or empower agents to shape customer engagements.
In terms of service delivery, connected devices can now feed information to contact centre staff and systems in real-time, but this information must be analysed and actioned to meet service delivery benchmarks and ensure continuity standards are met on the back-end.
Unlocking these capabilities can reshape customer experiences on many levels, be it predictive maintenance to mitigate or prevent system down-time, or deliver proactive services, like automatically processing orders via e-commerce platforms with a sensor detects that a consumer is running low on a specific product, for example.
Implementing intelligent technologies also empowers contact centres to accommodate shifting consumer engagement preferences, particularly among the mobile-first Millennial and Gen Z demographic cohorts.
Catering to the communication preferences of these digital natives not only requires omnichannel capabilities that allow consumers to engage via their preferred channel, with the ability to switch on-demand without a loss of context or relevance, but contact centres must also apply data-derived insights to effectively manage the customer engagement journey.
Intelligent customer lifecycle management plays an important role in shaping the customer experiences that keep this generation of consumers engaged and satisfied. The ability to identify customers on an individual basis and engage them in a proactive manner via any channel is, therefore, rapidly becoming a non-negotiable.
However, this is only possible when contact centres leverage customer profile data in real time through predictive analytics to intelligently route engagements to the most appropriate channel via their preferred medium. This may entail connecting consumers to virtual agents to expedite basic issue resolutions, or route customers to human agents to address more complex matters and realise the desired outcome.
Beyond real-time engagement enablement, digital contact centre solutions also play a pivotal role in quality assurance programmes. Most contact centres today still process a large volume of voice calls and, therefore, sit with massive content volumes from which to derive insights.
However, when quality analysts are tasked with conducting assessments, they are often only able to assess small samples due to the sheer volume and the cumulative duration of calls. These samples often represented just 1% of total call volumes, which makes it difficult to accurately rate enterprise-wide performance against quality benchmarks.
Today, speech analytics solutions can record and transcribe every interaction and apply assessment criteria in real time for 100% of all calls that pass through the contact centre. As such, these solutions automatically filter through and analyse the voice data to deliver actionable insights that operators can apply through better training to improve agent interactions.
It is clear that all modern operators today require the tools needed to deal with massive data volumes, with the ability to scale in future as levels grow exponentially over the next six years. However, only those contact centres that embrace data transformation through the application of intelligent solutions and advanced tools will gain the insights necessary to improve customer experience and engagement and deliver the levels of service that will enable them to survive and thrive in the modern digital economy.