Top Uses for Contact Centre Analytics

Most contact centres know they need a more analytical approach to understand and manage the customer experience. The follow article explores some of the areas where contact centre analytics can play a role in delivering excellent customer service.

Measuring levels of stress and emotion in any interaction

The use of real-time speech analytics (RTSA) solutions to continuously monitor conversations between agents and customers enables businesses to effectively measure stress levels, script clarity, over-talking and raised voices. This effectively allows organisations to take the emotional temperature of any interaction and provide agents and supervisors with the information they need to quickly adjust the tone and calm down potentially difficult situations.

It is a clear illustration that for any contact centre, simply relying on quantitative metrics around the speed of interaction or the operational efficiency of the whole process, for example, will not be sufficient by itself. Businesses need to become more emotionally intelligent and they need to find ways of better understanding their customers and tailoring the way they engage with them accordingly.

In the long-term, sensitivity to this emotional element of every engagement will be key in ensuring that the organisation’s brand health and satisfaction remains high – in turn, driving transaction volume and revenue. The way the success of customer engagement is measured is changing fast and businesses need to quickly get up to speed with the new environment if they don’t want to get left behind.

Workforce management and optimisation

Predictive analytics is increasingly being used widely by contact centres today for workforce management and optimisation. In this context, the approach is primarily about mapping resource to demand. Businesses need to ensure that they are assigning the right level of resource to a given task to meet the predicted load at that time.

Indeed, a prerequisite for ensuring the right number of agents are available to match demand, is an accurate forecast that can be made for any interval of time; hours ahead or far into the future. Predictive analytics can have a key role to play here. In line with this, the best workforce management solutions take historical data and parameters, such as customer behaviour patterns, response targets, skills, opening hours and contact channels, and other long-range factors which are all taken into consideration. The result is an accurate calculation of the optimal number of agents required to meet customer demand at any given time.

Pinpointing the next best action

Analytic techniques are also widely used by contact centres to predict how customers are likely to behave in the future and therefore how businesses can best interact with them. With predictive analytics, organisations can use the kinds of channel customers are choosing, together with their age profile and the task they are focused on, for example, to assess what they are likely to do next. The business can then optimise the customer journey on the back of that knowledge.

Triangulating business activity monitoring (BAM) and business analytics functions with customer relationship management (CRM) and customer interaction management (CIM) allows the organisation to present customers with a range of service options, relevant to their past behaviour and preferences. The CRM platform will be registering, tracking and logging the last interaction with the customer involved and what the outcome was, giving the business a historical perspective.  In the meantime, the organisation’s analytics engine will tell it what certain types of customers have done in the past and predict what they are likely to do in the future.

Then, when the customer contacts the business, it can use techniques like caller line identification (CLI) or other mechanisms to pinpoint who they are, pull their records in from the CRM to look at their history and start deciding where to route them within the organisation.