Revolutionize Your Contact Centre with AI: Top 5 Reasons Why You Need It Now
AI can boost Call Centre Efficiency and Customer Satisfaction.
Call centres are a vital part of many businesses, providing customer support and assistance. In recent years, the use of artificial intelligence (AI) has become increasingly prevalent in the call centre industry, and for good reason. By leveraging AI, call centres can increase revenue and improve their overall performance.
Throughout the rest of this blog post we will use “Contact Centres” instead of “Call Centres” as it is a more appropriate description of what these organisations do. They do not just respond to calls but also to chat messages, emails and sometimes even social media posts.
Here is a list of the top 5 reasons why a contact centre should use AI to “listen” to all customer interactions:
- AI can be used to monitor and analyse customer feedback and sentiment. By using natural language processing (NLP) algorithms to analyse not only the calls and chats but also customer reviews, comments, and social media posts, call centres can gain valuable insights into customer satisfaction levels and identify areas for improvement. This data can be used to make tactical and strategic business decisions and improve the customer experience, ultimately leading to increased revenue.
Our research shows a revenue uptick of 5% in a year just by calling back customers that end a call expressing negative sentiment
- Another way in which contact centres can leverage AI is by using predictive analytics to identify and respond to customer needs before they become big problems. By analysing data from previous customer interactions, AI can help CX organisations using contact centres to identify emerging pain points early on.
Unlock the Potential of Your Contact Centre: AI-Powered Predictive Analytics
- By using machine learning algorithms to analyse past interactions, AI can rank customer pain points based on frequency of mention daily. This ranking can be the compass that directs the CX department’s activity in producing proactive and reactive solutions to customer problems.
Ranking Customer Pain Points with AI: The Key to Proactive Solutions
- Contact centres can leverage AI to reduce cost using chatbots. Chatbots are AI-powered virtual assistants that can answer simple customer queries and direct them to the right department or representative. By automating the initial stages of customer interactions, contact centres can reduce their operating costs and handle a higher volume of calls. The problem is: it is not so clear how customers feel about talking to a chatbot rather than a human.
AI is Boosting Contact Centre Efficiency
- Last but not least, AI can automatically populate an agent performance evaluation scorecard for all the calls or chats in which they are involved. This means cost savings from not having to employ supervisors to listen to 1-3% of all the calls, which is the norm. An even bigger advantage, however, is that all calls are evaluated, and supervisors can focus on training agents whose performance is below par.
Say Goodbye to Costly Supervisors: How AI is Transforming Agent Performance Evaluation in Contact Centres
Call centres can leverage AI in a variety of ways to reduce cost and even increase their revenue. By automating customer interactions, using predictive analytics, improving call routing, and analysing customer feedback, call centres can improve their efficiency, reduce costs, and provide better customer service. As AI technology continues to advance, the opportunities for call centres to use it to increase revenue will only grow.
DMR uses proprietary machine learning models on its listening247 platform that are customised for each subject or product category, achieving a minimum accuracy of over 80% each time, in any language. Often, the accuracy is over 90%, depending on the amount of training data used for the custom models.
While there are ML models available for anyone to use (e.g. open source, Google, AWS and Microsoft), free or paid, the problem with those is that they are generic to a language, which means not specific to a product category. Thus, they can never reach acceptable accuracies without custom training data as top-up. Typically, their accuracies linger below 70% at best and usually around 50%-60%.
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