How can you leverage AI technology to improve call routing efficiency in your call center?

Routing a contact starts with making sure we have correct and complete info from the user, and we are learning that Large Language Models are great at categorizing customer contacts, identifying what additional info is needed and then following up with the user to get the missing info. Once we have complete info we can use historical data analysis to predict the correct resolver group (i.e. a machine learning model).

The use of AI is absolutely necessary , in all engagement platforms with customers as it helps organisations deal with huge , complex amounts of data so quickly which helps predicting the best way to handle the customer's enquiry based on all their previous interactions . This assists with connecting the customer with the best way to resolve their enquiry , either by connecting them to an agent or a bot which deals with repeated enquiries , like seasonal documentation.

When analysing data, it is never enough to look at performance, deflection and operational cost reduction. It is essential to check customer satisfaction on an ongoing basis and this must be done on all contact channels. When this point is achieved, then the indicators can be interpolated and the algorithms that deliver the solutions can be refined with reasonable certainty. Too often 'the operation was successful but the patient died'.

I've experienced how AI helps with active , in the call Agent Performance Analysis as it continuously monitor and analyse agent performance, identifying strengths and weaknesses. This data can be used to dynamically route calls to the best-suited agents, but also identifies repeat callers about separate issues AND helps identify issues and triggers root cause analysis thereby an opportunity for rapid recovery for members . Sentiment Analysis, one of my personal favourites , is the Implementation and access to real-time sentiment analysis to gauge customer emotions and prioritize calls that may require immediate attention.

Real time analysis promises to be a game changer. Imagine call comes in and we know it is a super simple task that can be performed by a bot, but we know we are dealing with a high value customer with a huge upsell potential - if we are doing real time analysis right, instead of sending this contact to the most efficient channel we would prioritize routing it to an experienced human being who could use the opportunity to strengthen customer relationship. Another example - if we are clustering incidents and identifying recurring incidents in real-time, we might observe that a new pattern is starting to emerge and we can prepare our team to deal with it before it becomes a crisis.

Personalisation at the end of the day is only possible if organisations truly understand and know their customers . AI helps with Customer Data Integration, as organisations can create detailed customer profiles using AI to integrate data from various sources (CRM systems, previous interactions, social media). Use this information to personalize routing and ensure customers are connected to agents who can provide the best assistance. Segmentation with be continuous and will also help in tracking changes in customer behaviour

Perhaps one of the most significant changes I have observed is the ease with which we can use AI in operations. And even people who are not deep experts in AI can make use of it much more easily. Where we would have needed weeks or even months to develop a model we are now doing it in minutes. So for teams who have continuous learning mindset, they are already seeing the opportunity and getting good at utilizing AI in various ways.

With the continuous focus on Digital , the demand for Contact centres ( especially in South Africa) remains real. Organisations continuously make the mistake of moving on a digital path and leaving some of their customers behind.