Every contact centre generates a huge amount of data, and equally, every contact centre uses data to monitor metrics such as customer satisfaction, response times, and more.
However, most companies are also sitting on a veritable gold mine of dark data, the unused and hidden information that is stored throughout the contact centre. Could accessing and analysing this dark data be the difference between companies that thrive, and those that survive?
The contact centre industry increasingly needs to excel in offering customers a premium service experience that provides high levels of customer satisfaction, which in turn leads to increased brand loyalty.
There is good reason to pursue this outcome – a study by Replicant found that 76% of consumers said a poor customer service experience negatively impacts their perception of a brand, with one in three saying it affects loyalty. With customer service at the front line for a company’s brand and reputation, it’s crucial to deliver excellent customer experiences.
What is dark data?
Dark data is defined by Gartner as the information assets that organisations collect, process, and store during regular business activities and generally fail to use for other purposes. It’s often collected for compliance purposes only or is used once and then filed. Contact centres generate a massive volume of this data. It comes from a wide range of sources, including call transcripts, survey responses, text messages, reviews, emails, live chats, chats and documents, and for many companies it has historically been largely or wholly untapped.
Dark data can reveal a rich story about demographic, geographic and other purchasing patterns, customer pain points and trends that can help businesses better understand their customers and tailor personalised offerings for them. It can also help businesses streamline their operations, increase revenue, reduce costs, improve efficiencies, and drive overall optimisation.
Where dark data comes into its own is in its context. It starts out as unstructured data, which isn’t inherently valuable, but when it’s sorted, labelled and categorised, it can offer valuable insights. Sorting dark data is crucial to unlocking its value, but how the information is labelled and categorised is key. Here, AI and machine learning can aid in processing dark data and uncover customer patterns, trends and signals.
Where are the opportunities?
Many companies are only now realising the true value of their dark data – that when processed correctly, this massive data lake represents enormous business potential. However, it’s important to be cognisant of the POPI Act and in Europe, the GDPR legislation, that mandates how data must be stored and managed. One way of extracting value is through pseudonymisation. This process replaces identifying information with reference numbers, which prevents the data from specifically pinpointing the individual. This allows companies to analyse this data and continue providing services to customers, without compromising their right to privacy, with the intent of improving the customer experience and services that are relevant to a particular customer segment or type.
Customer interactions are a repository of dark data and can be used to gain insights into their preferences, their pain points and brand expectations. Analysing call centre telephony data, chat transcripts and customer support tickets is useful to help identify recurring customer issues, understand sentiment and gain customer insights. These data points can be used collectively to drive operational excellence and enhanced customer experiences by correlating separate pieces of dark data. This correlation can reveal unique insights and patterns that offer the opportunity to provide customers with bespoke offerings that fit their needs and offer a seamless customer experience. For example, two geographically nearby, yet non-competitive retailers could offer a cross-selling experience to customers from both stores to add value to the customer experience while also adding to profitability. Or a contact centre team could upsell or cross-sell certain products based on buying patterns revealed through dark data analysis.
Contact centres can find operational value in the data
For contact centres, there is value in dark data that extends beyond improving customer experience and satisfaction levels. Workforce management can leverage dark data correlations to ensure that staffing is designed to produce the best possible outcome for the customer who is connecting with the contact centre.
Absence data, recruitment profile data and geographical data could all be deployed to drive performance and help contact centre employers maximise customer experience. Here, the data can provide insights into trends that might inform how contact centre managers design work shifts that utilise their employee resources in the most productive way, for example, tracking which set of employees is best suited to night shift work, or tailor shift start and end times to coincide with public transport availability to ensure punctuality and less absenteeism, which in turn decreases call wait times.
Whichever way you look at it, dark data, once sorted, labelled, categorised, and analysed, has the potential to offer significant value to organisations, whether it’s utilised for business intelligence strategy, customer experience or marketing strategy. Companies that can successfully leverage all their data, including their dark data, are more likely to uncover untapped or hidden opportunities, drive operational excellence, and gain a competitive edge in the marketplace.
Singh is the chief services officer at CCI South Africa