Artificial Intelligence (AI) is transforming how organizations utilize data. How can your contact centres adopt AI solutions to improve business workflows, boost revenue, and more?
Simply adopting an AI-driven solution and expecting immediate ROI is not enough. You need to know how to apply AI to target your contact centre’s pain points.
MiaRec has helped hundreds of contact centres across retail, financial service, and government sectors boost revenue and customer loyalty with its AI-driven Voice Analytics and Auto Quality Management solutions.
In this article, MiaRec’s Tatiana Poly will explore how AI is currently used in contact centres and why you should consider adopting AI for your organization.
By the end of this article, you will know how to best utilize AI for your contact centre’s needs and what best practices and next steps you should consider to guide your contact centre’s AI journey.
To help you navigate the AI market, we have compiled the most popular AI use cases in contact centres:
- Gaining Valuable Customer Insights
- Automating Post-Call Work
- Automating Compliance and Quality Management (QM) Processes
- Improving Customer Experiences
What Is An AI-Driven Contact Centre Solution?
At its core, AI enables machines to think. According to IBM, “Artificial Intelligence is a field, which combines computer science and robust datasets, to enable problem-solving.”
This broad definition means that AI-driven solutions cover a wide variety of use cases. Modern contact centres have adopted AI solutions to get customer insights, improve quality management processes, better utilize their data, and more.
Most AI-based contact centre solutions use a combination of Machine Learning (ML) and Natural Language Processing (NLP).
According to Columbia’s School of Engineering, Machine Learning is a subset of AI that “uses algorithms to automatically learn insights and recognize patterns from data, applying that learning to make increasingly better decisions.”
In other words, Machine Learning tools learn to recognize patterns and insights that can be used to drive business decisions, improve processes, and more.
Meanwhile, NLP is a branch of AI that helps machines understand text and speech similar to how a person would. NLP-based contact centre solutions can understand and analyze human speech.
Popular NLP-based applications include Speech-To-Text (STT) transcription, Sentiment Analysis, and chatbots.
As AI continues to evolve, its range of applications will only continue to grow. Contact centres are enthusiastic about the future of AI; Gartner predicts that Conversational AI tools could reduce agent labor costs by $80 billion in 2026.
Generative AI is currently generating a lot of buzz for its potential to improve text-based conversations and to better support agents during live calls. This makes it especially beneficial for real-time Agent Assist, automated call summaries, and chatbots.
Why Adopt an AI Solution?
You can reduce operational costs in the long run, personalize customer experiences while improving agent performances, and more by adopting AI solutions.
For example, you can use AI to automate repetitive processes by creating call summaries or automating call scoring.
By automating contact centre processes, your workers can have more for high-value tasks, you will gain a more comprehensive view of contact centre operations, and customers can enjoy a better experience.
It is important to emphasize that AI tools are meant to enhance agent interactions, not replace them. A majority of customers still prefer speaking to agents for more complicated inquiries. The future of AI is bright, but only if it is used properly.
Contact Centre AI Use Cases
Gaining Valuable Customer Insights
Contact centre AI solutions often offer Voice Analytics features to transcribe and analyze calls for meaningful insights that will improve contact centre processes.
In this section, you will learn how to use Voice Analytics to understand consumer behavior, measure agent performance, and improve customer experiences.
Turn Call Audio Into Accessible Transcripts
Most Voice Analytics solutions offer Speech-to-Text (STT) transcription. These solutions use AI to turn your audio into call transcripts.
Supervisors can then skim the call transcripts to quickly understand agent calls, rather than having to listen to the entire audio.
Gain a Deeper Understanding of Customer Sentiment
Most contact centres offering Sentiment Analysis will offer either rule-based or NLP-based Sentiment Analysis.
Unlike rule-based sentiment analysis, NLP-based Sentiment Analysis offers a more nuanced analysis by measuring context. By analyzing context, NLP-based Sentiment Analysis is able to better determine customer sentiment throughout the conversation.
With NLP-based Sentiment Analysis, you can understand how customers felt during their call with the agent. These insights can help you better understand how to meet customer expectations.
Organize Calls to Detect Trends, Prevent Problems From Escalating, and More
You can use Topic Analysis to organize calls by topics such as products, curse words, and more. It analyzes call transcripts for your desired keyword or key phrases. It is a great tool to organize your call transcripts and make it easier to review specific calls depending on your needs.
For example, Topic Analysis can be used to gather customer reviews and feedback. If you wanted to see what customers were saying about a specific product, you could use Topic Analysis to sort calls that only mention that product.
Support Agents During Customer Calls
AI can be used to support agents during calls. Some Voice Analytics solutions provide real-time Agent Assist services that can provide recommended next steps, suggested scripts, and more during the call. This can help agents provide better customer experiences while reducing call times.
Automating Post-Call Work
Traditionally, contact centre agents would take notes throughout the call. These notes would cover why the customer was calling, how the call was resolved, and any additional key information.
These insights would then be turned into a call summary. Supervisors, other agents, and your quality assurance team would then use the call summary to review the call, complete any necessary follow-up, and more.
An AI-based Automatic Call Summary tool can streamlines this process. Rather than taking notes throughout the call, your Auto Call Summary solution would use your call transcript to create a call summary for you. This allows agents to better focus on the customer.
Automating Compliance and Quality Management (QM) Processes
Your contact centre has a Quality Management (QM) process to make sure all contact centre conversations are up to your organization’s standards.
Here are a few AI tools you can use to get a more comprehensive view of how your contact centre is operating.
Ensure Your Agents Are Always Compliant
Without Automated Data Redaction, most contact centres require agents to manually pause and resume calls to prevent their customers’ sensitive information (SSIN, birth dates, etc.) from being recorded. Manually redacting data leaves room for human error.
An AI-based Automatic Data Redaction solution analyzes interactions for potentially sensitive information and redacts it from the call audio and transcript.
This ensures all of your calls meet compliance regulations and standards, allowing agents to focus better on the customer.
Automate Call Scoring for Faster and More Accurate Insights
Call scoring is when contact centre supervisors review agent calls to measure the agent’s performance and review script effectiveness. Most contact centres are only able to manually score less than 5% of their calls.
By automating call scoring with an AI-based tool, contact centres can grade 100% of their calls automatically.
This allows for a more accurate representation of their agent’s performance and allows supervisors to give agents more personalized and meaningful feedback.
Improving Customer Experiences
In this section, you will learn how AI can improve customer experiences while decreasing agent workloads. Discover AI-driven tools that will support agents before and during their customer calls.
Support Agents in Real-Time During Customer Calls
Some Voice Analytics solutions provide real-time Agent Assist services that can provide recommended next steps, suggested scripts, and more during the call.
This helps agents respond to customers confidently and quickly and provide customers with helpful resources.
Provide Customers With Self-Service Options
Conversational AI tools are AI-driven tools that interact with customers. It is typically associated with Chatbots and Interactive Virtual Assistants, both of which can answer repetitive and basic customer questions.
An Interactive Virtual Assistant (IVA) is a virtual assistant that automates call centre processes. It uses customer data to provide personalized, human-like interaction.
An IVA solution typically includes chatbots and text-to-speech recognition to route customers to the best channel that will answer their questions.
Chatbots are a valuable customer service tool. They give customers the option to interact with your business without having to face an agent. Customers can find answers to basic questions on their own, reducing agent workloads.
Improve Your Customers’ Self-Service Calling Experience
Standard Interactive Voice Response (IVR) systems have a set of predefined rules: greet customers at the beginning of inbound calls and then present a menu. Any time you have been on call and heard “Press 2 for Spanish”, that is an example of an IVR.
However, Conversational IVRs, or AI-based IVRs, provide a more personalized and helpful experience. With an NLP-based Conversational IVR solution, consumers could simply state their reason for calling and be directed to the appropriate self-service or agent channel.
It may decide on the best agent for the call based on expertise or personality, depending on how your contact centre decides on the determining metrics.
AI-powered Call Routing can also provide agents with insights into customer behavior and needs, so that agents can personalize calls and effectively address the customers’ issues.
Conclusion: What AI Solution Is Right for My Contact Centre?
Deciding on what AI tools your contact centre needs can be difficult, especially when different contact centre solutions offer different tools and services. The right AI-based contact centre solution should align with your business goals.
For example, a Conversation Intelligence platform that provides Voice Analytics and Automated Quality Management solutions.
It is a great choice if you want to analyze agent calls for customer insights, automate quality management processes, and ensure compliance workflows with AI.
It also helps automate post-call workflows with AI-based Automatic Call Summary. However, we do not provide chatbots or real-time Agent Assist, and would not be a good fit for your contact centre if you were looking for either service.This blog post has been re-published by kind permission of MiaRec – View the Original Article
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Call Centre Helper is not responsible for the content of these guest blog posts. The opinions expressed in this article are those of the author, and do not necessarily reflect those of Call Centre Helper.