Christmas can be the busiest time of year for contact centres, often requiring hiring temporary workers to cope with additional call volumes, while at the same time permanent staff want to take time away from the office. So how can businesses maximise efficiency and flexibility, while keeping customers and staff happy? As we look ahead to 2018 we expect Artificial Intelligence (AI) and machine learning technologies to have a significant impact. This time next year the Christmas contact centre could look significantly different...
Gaining insight into customer interactions is especially important during peak times of the year, such as Christmas and the New Year period - when there are generally higher than normal orders, returns or enquiries for example - because it allows a business to capture a broader set of data and better understand more unusual requests.
Where a business can record and understand a conversation or interaction and its outcome, machine learning can be used to help determine whether it was an effective communication or not, and provide ways to shape future interactions to make them more effective. To do this, the machine needs access to a large database of conversations and associated business data (such as order details, for example)so that it can start to learn and understand patterns and categorisations of communications, in the context of associated data to develop increasingly intelligent responses.
So, starting now, businesses should start building that database by logging, classifying and tagging all customer communications, including voice calls. Building up this valuable database of different sorts of conversations, interactions and outcomes will dramatically improve the value of machine learning when it is introduced because there are more opportunities for the machine to identify patterns using actionable insights. These thousands of data points will become the basis of automated systems in the future of your own unique business and context. This intelligence also empowers contact centre managers to make improvements to optimise efficiencies such as updating recorded information and adjusting agent prompts and training, so they can respond quicker with the right information.
Machines can also hook into this database to pre-empt and adapt to the most frequent customer service enquiries at any given time - supporting with automated responses for example about latest product availability, delivery times, opening hours, returns and so on. Using machine learning and AI to automatically answer enquiries frees up agents to focus on more complex calls, or those that need to be escalated.