In the current crisis, businesses are having to deal with unprecedented ways of working, customer emotions and volumes of interaction.
In such a fast changing and challenging environment many are understandably struggling to maintain the same levels of customer satisfaction.
Customers more likely to be anxious and vulnerable so the early warning and prioritisation of issues are key. Furthermore, agents themselves are likely to feel more anxious and there is a strong evidence of a correlation between agent satisfaction and customers.
If contact centres are dispersed or even working from home, with reduced support, then conversational analytics can play an even more important role in identifying opportunities for efficiencies and maintaining customer and staff morale.
Conversations with customers across all channels, including telephone, email, live chat and social media, are automatically analysed by machine learning models that can detect not only the topics in the queries themselves, but also emotionally-driven comments that indicate a propensity to churn as well as vulnerability from a basic lack of understanding, likelihood of a disability and circumstances. These potentially buying, churning or vulnerable customers can be triaged to the relevant members of staff for the next best action.
For example, when it comes to spotting vulnerable customers in difficult times, the analytics examines the specific language used against pre-defined phrases and sentiments that may suggest any element of vulnerability. You can then use that insight to trigger alerts for managers or nominated individuals, no matter where they are working.
Companies can also personalise and prioritise training for frontline staff, by focussing on the topics most common or difficult to deal with at that particular time. They can also identify failures in self-service platforms or other channels to help customers help themselves more.
And of course, it’s really important to look after agents during unprecedented times, maintaining their wellbeing and motivation. The machine learning makes every call, chat and email measurable in terms of how it went, as well as how well the agent did i.e. against the propensity of the particular issue and customer state at the beginning of the interaction.
Horizon2, who use omnichannel analytics from Warwick Analytics, said: "The advanced analytics from Warwick Analytics helps us to measure the CSat propensity of each interaction and therefore to evaluate how agents perform in detail without the need of post-interaction surveys. Credit can then be attributed to agents for handling hard calls as well as easy ones meaning more recognition, remuneration and motivation even when dealing with difficult circumstances. It made a dramatic difference to staff behaviour and their approach to these contacts resulting in a huge improvement in overall customer satisfaction and reduced staff attrition."
So whether you’re looking to prioritise, escalate and route specific interactions to key team members working remotely, trigger high risk alerts for vulnerable customers, keep agents motivated, automated omnichannel analytics could provide one central resource that not only maintains levels of customer satisfaction and service when a crisis hits but also monitors how your customers and agents are coping.
About Warwick Analytics:
Powerful, accurate, rapid and tunable machine learning for tagging conversations within contact centers. Used for accurate insight and automation as well as for supervising chatbots.
Published: Wednesday, April 15, 2020
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