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How Contact Centres Can Use AI to Optimise Performance

AI and machine learning are already a key strategic component for businesses of all sizes and types including call centres. Spending on the AI call centre is predicted to rise to $97B by 2023, according to IDC. This spending on AI is not just by large organisations, or just on customer engagement, but on solutions like predictive analytics and backend processes. A CMO report from 2019 indicates that more than half of businesses with revenue under $25M a year are using AI for predictive analytics. While Salesforce Research, says that 69% of businesses think AI and machine learning are transforming their business.

Over the coming few years, AI and machine learning will be a crucial tool in enabling you to provide better customer service, streamline work processes and even create happier employees. Also, with customer experience seen as one of the key battlegrounds to win and retain customers, AI can be used to understand customer intent and deliver more empathetic customer interactions.

Chatbot and Voicebots

Businesses have always sought to improve productivity and reduce costs by leveraging customer self-service: FAQs, knowledgebases, and product information sheets, among others. However, the trade-off of the experience being less personalized and interactive is clear and can have a negative impact on NPS and sales.

Chatbots and voicebots are a way to deliver that self-service – or automate your customer engagement - through rich text and voice conversational interaction. They use natural language processing to analyse customer questions and can, when trained properly, respond with sophisticated, rich answers that can solve up to 80% of these interactions. In other words, customers can get the product or service information they require in a form that simulates how they would interact with your live agents either in a live chat or on the phone.

The benefits of a bot are numerous. It reduces the burden on your customer service team as it takes responsibility for handling routine enquires, freeing your agents to deal with higher value, more complex conversations. As it can simultaneously handle unlimited numbers of conversations, it has the time to provide all your customers with personalised information. It reduces wait times, a major cause of unhappiness with customer service centres. A bot can remember previous conversations, your name, and search information much faster than a live agent. It can hand you over to a live agent with the full contents of the conversation. And it can offer support and answers 24/7, enabling you to attract customers who prefer to ask their questions and do research in the evenings and weekends.

It also provides a goldmine of data as interactions with your business, which would normally have just been searches of your knowledgebase, suddenly become rich conversations that can be analysed for customer intent.

Interaction Analysis

Interaction analytics is the tool that enables you to take advantage of that data from your chat and voicebot conversations. They use machine learning and natural language processing to analyse all your customer interactions across all your disparate communication channels – not just chatbot but live chat, calls and social. They are a game-changer because previously when a business wanted to get insight to improve coaching, a supervisor would have to choose a selection of calls or live chat conversations and then listen or read them, themselves - It would be prohibitively expensive to listen to all customer interactions. Machine learning and natural language processing make it possible to do exactly that - analyse 100% of conversations, providing much richer and accurate insight, compared to just a selection.

Not only is the time of your supervisors freed up to spend on other tasks, but they will be able to design much better training and support for agents. With interaction analytics your business will also be able to offer real-time support for your agents – calls can be monitored and if an agent is encountering problems suggestions can be feed to them immediately to get the conversation back on track. They can create better scripts, be more objective and accurate when measuring performance, provide a more consistent customer experience, brand image, and lowers attrition rates.

Backend Processes

A lot of time is spent on administrative tasks that AI can reduce. If interaction analytics are employed through your backend processes, not just to analyse conversations to improve customer engagement, they can be used to populate forms and workflows, reducing processing time.

Chatbots can also be used to speed up backend processes. For example, an agent may have to search for information across several software platforms. A chatbot can be set up to find information conversationally across those platforms and even give advice on the correct way to complete a task, ensuring information is found faster and processes are completed more consistently.

Skills Based Routing

A key feature of interaction analytics and natural language processing is the ability to identify intent and other emotions such as frustration or excitement. This can then be used to replace IVRs as a much more natural, and accurate, way of identifying caller needs and then routing to the right agent.

Workforce Management and Workforce Optimisation

AI can also be used to optimise employee scheduling and forecasting, and in doing so build up better goodwill with employees.

How can that be done? A contact centre manager has to balance employees’ demands for particular shifts, or days off, with his responsibility to ensure he has the optimism number of staff to handle inbound and outbound call volumes in a manner that fulfils KPIs around handle times, first-call resolution, and Net Promoter Score (NPS), among others.

In the past, the contact centre manager may have manually collected the data and created his own mathematical models to try and achieve this. AI and machine learning can automate the process of data collection, ensure it is formatted correctly and consistently, saving the manager a lot of time and create better scheduling.

Conclusion

AI and machine learning are going to profoundly change the contact centre over the next few years. It is not going to replace your agents or their supervisors, but will complement them, allowing your agents to spend more time on higher-value tasks and making the personal connections that win the hearts of your customers.



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