• Getting the most out of contact centre analytics

        • Enghouse Interactive is urging businesses to become more emotionally intelligent in order to understand their customer needs and tailor the way they engage with them through real time speech analytics. Here, Jeremy Payne, International VP Marketing, Enghouse Interactive, discusses how analytic techniques can be used in contact centres to predict how customers are likely to behave and how businesses can best interact with them.

          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. 

          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.

          Workforce management and optimisation

          A must 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.

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