Classifying text and processing it for inference is very important in artificial intelligence based solutions. This article is about how artificial intelligence can be used in the contact centre and the benefits it brings.
Contact centre automation creates hassle-free customer support and helpdesk management. The application of artificial intelligence (AI) in a contact centre is still new and evolving. However, it can reduce operational costs, personalise the customer experience, increase agent efficiency, and provide more actionable analysis in the following ways.
Chatbots: Chatbots may be the most visible use of artificial intelligence (AI) in the customer service process. When customers choose to chat online with a business, chatbots greet them, collect some background information, and try to solve their issues. AI chatbots are good at solving very simple problems, but the more complicated issues still need an agent’s touch. The AI chatbot will pass along the information it collected to make the transition as seamless as possible and to boost agent efficiency.
More intelligent routing: AI makes the routing of the ACD (automatic call distributor) even smarter. Now contact centres can route enquiries based on additional criteria, such as customer personality and information gleaned from previous contacts. Matching a customer with the right agent at the right time can do a lot to improve the customer experience.
Insightful analysis: AI can also elevate analysis and provide business leaders with information they can act upon. For example, using AI powered analysis, businesses can analyse customer behaviour, identify ones who are at risk of churning, and reach out to them with a compelling, personalised offer.
AI can bring down costs in the contact centre due to increased self-service. It also helps to improve the customer experience, as customers can communicate using their channel of choice any hour of the day or night.
However, organisations must realise that the role of a contact centre agent will become more challenging with the introduction of AI. Automation remains most effective with interactions that involve simpler tasks and that do not pull in a lot of different pieces.
Here, the human touch is not that critical, and AI can provide straightforward information to customers overnight, during holidays and other times when it can be costly to staff a contact centre. This means that agents will receive a larger percentage of calls that are more complicated and challenging.
When customers need information about a specific problem, recovery from a mistake or a more detailed solution that is too complicated for automation, there is a significant need for a human agent. Here, human interaction is necessary to help relieve frustrations and provide correct responses with the appropriate empathy and emotions (which, at times, is more difficult than just navigating a system).
How AI can be used in a contact centre
Use of AI to assist agents: While an agent listens to a customer during a phone call, AI can run in parallel and do some of the heavy lifting of searching a knowledge base for answers while allowing the agent to focus on the customer interaction. Once the information is presented to the agents, they can review the results in real-time, decide what will work best in each situation, and communicate the response to the customer.
AI also works better if information is coming in from the customer via the written word and/or images in the form of an email or chat session. This removes accents, translations, and other questionable input so that automated processes can easily understand the data and then respond to those interactions. In either case, once AI is providing accurate information in a consistent manner, the technology can interact directly with customers via a bot.
Use of AI to run analytics in the background: First, predictive contact routing analyses key pieces of information such as whether the caller is a high spender, what kind of products they buy, their sales history, projected future behaviour, and other key factors to route calls to the agent who has the best set of skills to successfully work with the customer.
Second, contact centres can use speech analytics technology to analyse recorded calls with customers (post-call analytics). After listening to a call, AI can identify when a voice inflection goes above a specific threshold level, and discover when and why customers get angry and potentially churn.
Speech analytics technology can also be used on a real-time basis to analyse the tone and inflections of a caller to indicate when they appear frustrated, show anger, and need intervention from a higher skilled agent/supervisor. This is a more expensive use of AI in contact centres, but can provide an opportunity to avoid a negative interaction, making customers happier and increasing company loyalty.
There will always be a percentage of calls that will be fully automated using a form of AI. If an automated system works well, customers may even reach out more frequently to get their answers quickly and easily. This will reduce the time spent waiting in a queue, eliminate the time involved for a human response, and drive down overall costs.
However, on the flip side, agents will need to be better trained and require higher skills to deal with the more complicated and sensitive calls that can’t be handled through automation, which will offset some of the savings.
Businesses with large budgets can test various AI processes to see what works and what doesn’t. But smaller contact centres really need to look at the time and costs involved before implementing any kind of AI system. To take this step, it is important to ensure that effective training and coaching, workforce management, and other technologies are already in place. Customer satisfaction should be a top priority.
A number of AI services for contact centres are available in the market. Let’s take a look at some of the best ones.
AWS provides Amazon Connect as a fully managed cloud solution for contact centre automation.
Contact center AI is integrated with DialogFlow, which uses natural language processing (NLP) for voice based heuristic search for customer queries. It also helps to do contextual routing to the required support service, based on the query typed by the end user. A dashboard and analytics are on offer for query related metrics on a day-to-day basis based on ticket status like ‘active’, ‘closed’, ‘waiting’, to name a few. It is a hybrid ‘build once and deploy anywhere’ solution, and can work on smart devices including Alexa and Siri enabled devices.
AWS Connect helps to build a scalable, resilient IVR solution without any worry about infrastructure, deployment models and integration with other services for a public cloud environment. It is an omni-channel cloud contact centre service with unified machine learning (ML) capabilities to manage the customer and contact centre client (agent) experience seamlessly. It makes setting up a contact centre and chatbots very easy. This can be done without much technical experience as Amazon takes care of most of the technical configuration based on your inputs.
If you don’t have much experience in contact centre development, you can make use of the Contact Center Intelligence (CCI) support facility in Amazon, which can help in designing a strategy for the centre. You can choose any AWS CCI partner for setting up the contact centre, and integrate it with other AWS native services like data analytics, ML based data processing and data storage, to name a few.
Azure Cognitive Services
Microsoft’s Azure Cognitive Services can help you realise partial or full automation of telephony based customer interactions, and provide accessibility across multiple channels. With its language and speech services, you can analyse call centre transcriptions, extract and redact personally identifiable information (PII), summarise the transcription, and detect the sentiments.
Azure Cognitive Services can be implemented in call and contact centres in various ways.
Virtual agents: Conversational AI based telephony-integrated voice bots and voice-enabled chatbots.
Agent assistance: Real-time transcription and analysis of a call to improve the customer experience by providing insights and suggesting actions to agents.
Post-call analytics: Post-call analysis for creating insights into customer conversations to improve understanding and support continuous improvement of call handling.
Google Contact Center AI
Google Cloud Platform (GCP) provides loads of AI based solutions, which are integrated with Google Contact Center AI services for virtual assistance. GCP’s Contact Center AI is a hybrid solution suitable for on-premises private as well as the public cloud environment.
Some of its important components, which are now in general availablility (GA), are listed below.
Dialogflow CX: This helps with handling complex customer queries and supplemental questions by looking for the required responses from a knowledge base. It supports human agents by providing the relevant details required to respond to a query. Dialogflow CX agents build on Google’s natural language understanding (NLU) algorithms to handle these search capabilities.
Agent Assistant: This is built with AI assisted conversational services to prepare the recommended answer choices for human agents to choose from, for replying to customer queries. It uses a centralised knowledge base to prepare ready-to-use responses. It transcribes live calls in different languages and helps train AI models to analyse frequently asked questions, answer patterns and customer groups based on interest.
CCAI Insights: These services include text-to-speech and speech-to-text conversion, sentiment analysis, ranking of responses, integrated IVR services, and natural language processing capabilities to provide unified customer support powered by Google Cloud AI services.
|Amazon Transcribe Medical
|In a crisis like Covid-19, remote medical consultation is a real need. Amazon Transcribe Medical is a machine learning based service, which can convert audio instructions given by doctors into text, and can interpret the technical medical terms used in the audio. It uses machine learning based automatic speech recognition (ASR) to capture the conversation between the doctor and the patient for making an electronic medical/health record (EHR). This helps doctors to attend to patients quickly.
As can be seen in Figure 2, a user connects to Amazon Connect for interactive chats, which is a virtual assistant service that works with a given knowledge base for answering queries (e.g., the remedy for a dry eye problem). Lex (chatbot service) is used to fetch the follow-up questions that the user is asked. This is done through a Lambda function invocation, which searches the knowledge base. If the required response is not found, it moves into the next level of search through Rekognition, which helps identify the face of the user, and connects him or her to their preferred doctor’s advice. The advice for various medical problems is stored as an audio file and can be searched through Transcribe Medical service to get the solution for the given query.
This is then given to Comprehend to convert as a response to the user in Amazon Connect or through email.
Contact centre AI: The trends
Contact centres are undergoing a massive transformation. During the Covid-19 pandemic, call volumes increased but the number of customer service agents on duty fell. Calls increased by nearly 300 per cent during the first few months of the pandemic, resulting in longer hold times and more escalations to human agents or other support representatives.
With about 90 per cent of contact centres migrating to remote and hybrid workplace models since the beginning of the pandemic, companies are investing in new technologies that allow them to provide high quality and personalised customer service experiences.
In response to the changing landscape, many companies are turning to AI to power customer service operations. Listed below are six trends with respect to the use of AI in contact centres that promise to transform customer service and experience.
Alleviation of shortages in staff: Labour shortages were a side effect of the pandemic, and many industries found it difficult to manage high call volumes with low volumes of agents. Many businesses are turning to AI to account for the gaps within their workforces.
AI is helping to fill skills shortages of the existing workforce through career transition support tools. It is also helping employees do their jobs better and faster using digital assistants and in-house AI-driven training programmes.
Understanding the nuances of human conversations: AI solutions that are perceptive to caller intent, language, and sentiment are vital. Contact centre AI helps to understand the nuances of human conversation because it can pick up on the tone of voice, inflection, etc, to detect mood and modify behaviour accordingly. Understanding voice nuances and serving customers on that basis improves the overall experience.
Self-service: Self-service helps to deliver customer support quickly and efficiently since customers don’t have to wait for a human agent. Companies continue to provide human backup, but prioritise AI support for their contact centres. One way businesses can use AI to deliver round-the-clock support is through chatbots and interactive voice response (IVR) systems.
Chatbots automate a personalised experience, connect meaningfully with customers and deliver engaging content. IVRs are like ‘virtual receptionists’. Callers interact with them through a series of menus, which direct them to the most appropriate help for their needs.
24/7 customer service: Contact centre AI enables customer service during every hour of the day, as it does not need to sleep or take breaks.
Companies can strategise staff shifts for 24/7 service, taking the weight off of individuals expected to be on call at all hours of the night. Utilising AI for 24-hour contact service helps companies to allow fewer irregular work hours for employees, while making sure customers receive help whenever they need.
Enabling cost-effective pricing: Like any investment in new technology, businesses are wary of contact centre AI pricing too. However, research shows that investing in quality AI for contact centres will save businesses money and increase the quality of service provided over time. Contact centre AI also helps generate revenue by allowing allocation of employee hours to other key business functions.
The competitive advantage: Since customers like to solve issues at any time and on their own, the quality of a contact centre’s AI self-service adds hugely to the value of a contact centre team.
Contact centre AI is intuitive and accurate, and helps businesses deliver experiences that meet customers’ demands. Contact center AI technology also helps mitigate stressors like long hold times and repetitive questions. Businesses that use AI for their contact centre achieve higher year-on-year revenue and profit margins.