Call centres no longer handle only phone calls. Today’s contact centres have to manage emails, phone calls, and chat requests in the course of their day-to-day job.
These functions depend on AI to make sense of inflowing, unstructured data points at a speed and level of complexity that is too much for traditional, structured BI (Business Intelligence) approaches.
Real-time unstructured data
When we think about using data to understand our customers, we often prioritise structured information, such as call volumes and responses to questionnaires. While information like this is critical, it provides only a small part of the picture.
You need to access and leverage unstructured data to gain a handle on customers and their issues. This type of information can come from comments on surveys, emails, social media posts, and many other sources. How this data is obtained and where it comes from varies greatly.
Managing real-time unstructured data is possibly the most significant barrier preventing omnichannel contact centres from arriving at actionable insights. However, the benefits available to businesses cannot be understated. Companies that can mine the reams of data gathered can ensure employees receive more relevant and useful training and drive improvements in products and services, directly correlating to a business’ bottom line.
The fact that you have unstructured data is not enough. There needs to be a way of compiling the information to provide a cohesive picture. This is where Artificial Intelligence (AI) comes into the picture.
With AI tools like chatbots and voice agents, companies can acquire much more information on customer interactions. AI tools can clean data so that they retain only relevant and useful information. In this way, product support issues, for example, can be tracked differently to those related to product features, and businesses can make strategic improvement plans.
Before the conversation begins: Web and social data
Successful, omnichannel contact centres know that everything starts with the web. They understand that websites are a precursor to a sale and tools like social media can provide more insights into customers and their buying preferences. Overall they have realised a core truth – namely their presence online is a means of closing a sale.
Tools like web analytics tell us a little bit about who the customer is; potentially what they searched before landing on the site; which pages they have interacted with before reaching out; and might even provide their full customer journey.
Through AI and Machine Learning (ML), contact centres can automate this analysis and better identify which customer journeys are successful and which are not. Perhaps more importantly, it can help you understand why the transaction failed.
By understanding what happened, we can implement plans to alleviate the symptoms or issues. This could include specific incentives for customers at key points in the journey. These incentives could be tied to special offers or promotions to encourage them to continue or even provide information if the client is getting stuck.
Using data from social networks can be even more useful. Specific consumer information can help organisations understand how their products are being used and perceived in the real world. For many businesses, positive social interactions can be extremely useful in detecting and preventing damage to their brand.
An ideal starting point for improving contact centre productivity is AI-driven chatbots. Chat by itself helps improve response times, often allowing agents to field multiple conversations simultaneously.
For true efficiency, chat and AI should complement each other. While bots are not currently capable of resolving complex technical issues, they can complete the ‘grunt work’ and leave the complex decisions to a live agent.
Data shows that bots can field close to 80% of simple questions. From a business point of view, the advantages are obvious. Not only are the bulk of issues fielded 24/7 by an automated agent, but the overall response times across the board are also radically improved.
Newer technologies allow bots to engage with consumers more humanly with Natural Language Processing (NLP) and Machine Learning techniques. This lets bots understand client requirements and provides them with the capability to search for answers in knowledge bases and other repositories. Bots and AI will be instrumental in other ways, benefiting both the agent and the customer by removing tedious and time-consuming administrative tasks.
Consumers benefit from knowledge transfer and shortened wait times as AI-driven bots continue to evolve and grow in capabilities. Chatbots can help contact centres by responding to simple questions and issues. They can either respond directly or point customers to knowledge resources with more information.
In those instances where a bot cannot solve the initial enquiry, they can send it to a human agent for personalised attention. While training bots can be complicated, newer bots are now capable of teaching each other. This radically speeds up information flow, making them useful even sooner.
Speech Analytics coming to the fore
Voice data can give great insights into customer experience (CX) and how the customer reacts, in real-time, whilst on a call.
Speech-to-text data and true sentiment analysis that measure tone and volume provide fuel for analytics that contact centre management may use to identify and improve operations.
Speech analytics goes a long way towards fixing the challenge, and time-wasting practise of recording and listening to selected calls manually. The manual process often required teams of quality analysts focused only on call quality and making subjective decisions based on their interpretation of a call. This often meant that contact centre agents failed to get the knowledge they needed to improve.
Speech analytics goes a long way towards fixing the challenge, and time-wasting practise of recording and listening to selected calls manually. The manual process often required teams of quality analysts focused only on call quality and making subjective decisions based on their individual interpretation of a call. This often meant that contact centre agents failed to get the knowledge they needed to improve.
Automated speech analytics also provides businesses with the capability of listening to every call and interaction instead of only a small subset. This combination ensures that agents get fairly evaluated based on all of their calls.
A key point to realise is that speech analytics is not only restricted to quality. Tools like skill-based routing have been available with ACD and IVR systems for years, but when clients do not realise their problem or who they need to speak to, they do not suffice. With AI-powered voice technology, enterprises can build specific routes so that regardless of a client’s issue or concern, they can find the agent best suited to their needs
Build your intelligent contact centre with CCNA
To boost CX and stay ahead of your competition, your business needs an intelligent contact centre. We can help you implement AI to manage omnichannel communications, social media, and workforce optimisation.
CCNA takes a vendor-agnostic approach to technology, innovation, and design to help you build an intelligent contact centre that meets your business needs. To learn more about our capabilities, take a look at our CC Experience page.