5 Use Cases for Generative AI in Conversational Analytics

The rise of generative AI solutions, such as ChatGPT, has had a profound impact on virtually every business environment. This year, companies from all industries have begun rapidly adopting generative AI tools for everything from creating content to improving collaboration.

With generative AI solutions, companies can develop more advanced self-service experiences via creative and intuitive chatbots. They can empower workers with amazing virtual assistants, and even process and synergize business data more effectively.

But how does generative AI impact the growing world of conversational intelligence and analytics in the contact center space?

The Benefits of Generative AI in the Contact Center

Generative AI and conversational AI tools are beginning to work together in the customer experience landscape, empowering businesses to produce not only more valuable chatbots and virtual assistants, but also more engaged, productive teams.

Around 90% of contact centers say they handle calls faster and more efficiently after implementing cutting-edge AI tools. What’s more, generative AI solutions ensure companies can deliver more personalized, relevant experiences to consumers across multiple channels.

In the broader contact center space, generative AI can:

·????? Improve productivity: Generative AI solutions can automate repetitive tasks, such as summarizing text or collecting insights from consumers, giving agents more time to focus on delivering exceptional support and guidance.

·????? Enhance customer experience: Generative AI tools and conversational AI solutions can empower businesses to deliver intuitive self-service experiences, and more personalized interactions. They can also offer valuable insights into the customer journey, and consumer needs.

·????? Upgrade business insights: Generative AI solutions are excellent for collecting insights into brand strength, satisfaction rates, churn rates, and risks. Some solutions can even assist with market research and competitive analysis.

Use Cases for Generative AI in Conversational Analytics

Conversational analytics tools have become an essential component of the customer experience space in recent years. These solutions leverage natural language processing and understanding technologies, alongside AI and machine learning to assist businesses in unlocking valuable insights.

When combined with generative AI, conversational analysis tools can support a variety of use cases, five of the most significant opportunities include:

1. Automating Analysis to Improve Productivity

One of the biggest benefits of generative AI in the contact center is its ability to support employees in rapidly automating tasks, without the need for complex coding and workflow building. Generative AI can complete tasks with nothing but natural language input from team members.

This means employees can rapidly ask tools to take notes from meetings, upload information to a database, source information from a knowledgebase, and more. AI algorithms can use the data gathered from conversational analytics to create optimized schedules for teams and provide step-by-step coaching throughout customer calls.

2. Collecting Detailed Information from Transcripts

While many conversational analytics tools can automatically transcribe conversations for compliance, training, and business insights, not all solutions make it easy to assess transcriptions. If companies manage hundreds of calls per day, sorting through transcriptions to find trends and patterns can become a time-consuming and complex process.

Fortunately, generative AI and conversational AI tools can enhance the value of contact center transcriptions instantly. Companies can automatically transcribe audio and video speech using natural language processing, then leverage generative AI to transform highlights from transcriptions into valuable reports, training documents, and guides.

Innovative AI vendors even use generative AI to summarize conversations more effectively for agents, providing quick insights into the topics and action points of a discussion.

3. Building Improved Self-Service Solutions

Generative AI is already having a significant impact on the world of customer self-service. Apps and bots built with large language models can respond more creatively to customer queries and deliver a more human level of service. However, when these tools are combined with conversational analytics, the opportunities for building more advanced self-service flows are enhanced.

For instance, companies can use the data from their conversational analytics tools, such as insights into customer journeys, touchpoints, and preferences, to deliver more personalized service through chatbots. These bots can also draw information from CRM systems and databases, examine previous conversation histories, and ensure every user receives a unique experience.

Generative AI models can even synthesize large amounts of data from multiple environments, to help companies create guides and FAQs relevant to customer needs, develop more intuitive responses to common questions, and create scripts for contact center agents.

4. Transform Employee Training

Conversational analytics in the contact center doesn’t just offer companies a valuable insight into their customer’s journey, preferences, and pain points. It also provides an in-depth view of the best practices and actions that ensure employees can unlock greater customer satisfaction.

With the data taken from conversational analysis, companies can use generative AI to create realistic training simulations, used for a range of tasks, from fixing technical issues, to pitching products. Generative AI can even be used to build comprehensive training programs for each agent.

Additionally, companies can build generative AI bots and assistants capable of working alongside agents in the contact center. These bots can provide guidance and best-practice insights based on previous conversational data, improving satisfaction scores, and employee engagement.

5. Ensuring Compliance

While collecting data for conversational analysis is crucial for many businesses focused on enhancing their CX initiatives, any form of data collection has its risks. Companies need to ensure they’re curating the right information from conversations, without risking customer security.

Fortunately, generative AI solutions can help to improve compliance in contact center analytical strategies, with a range of tools. Companies can use PII redaction models to automatically detect and remove sensitive information from transcriptions and summaries.

There are even AI solution vendors on the market that offer enterprise-grade security and support, with GDPR compliance, SOC 2 type 1 and type 2 certifications, and intuitive tools to ensure customer and employee data is always protected.

The Evolving Use Cases of Generative AI

Generative AI and conversational AI are rapidly transforming the customer experience world, empowering businesses to better serve their customers, and support their agents. Not only do these tools help to boost productivity and workplace efficiency, but they can have an incredible impact on the value of conversational analytics strategies too.

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