Conversational AI and Analytics: Contextualizing Communication

Conversational AI enables machines to understand and respond to written and spoken language. This technology is used in various interactive applications, including customer service chatbots, digital assistants, and more. But for many organizations, the true value of this technology lies in the insights it generates. By analyzing conversations on websites, social media channels, apps and more, businesses can glean valuable insights into the wants and needs of customers. In this article, we’ll look at how organizations use conversational AI data to accelerate innovation and growth.

How does conversational AI work?

A sophisticated combination of natural language processing (NLP) and machine learning (ML) powers conversational AI-enabled tools. During the NLP phase, unstructured text or speech input is converted into a format the machine can understand. That data is then analyzed and used to generate a response. Large language models (LLMs) are essential to natural language processing. They possess an extensive understanding of general language patterns and knowledge based on massive data sets. This enables them to achieve superior results on various tasks, such as question answering, information retrieval, sentiment analysis and more. Fundamentally, they enable any business user to query their data sets through their natural language, serving as a catalyst for data-driven marketing.

Machine learning processes enable conversational AI algorithms to grow more intelligent over time, using each interaction as an opportunity to learn, adapt and improve the quality of responses. Unlike traditional chatbots, conversational AI tools aren’t simply human-scripted or rules-based programs. This new technology can handle complex dialogue and provide intelligent, contextually aware responses. 

Conversational AI tools generate massive amounts of valuable data, both written and spoken. Conversational analytics extracts, processes and analyzes this data, giving businesses insight into customer sentiment and how their needs and wants influence behavior. 

Benefits of using conversational AI

Conversational AI is reshaping how brands interact with their customers, helping them extend their reach to provide highly personalized interactions. Here are several benefits it offers. 

Rich source of first-party data 

Conversational AI-enabled tools generate a steady stream of actionable first-party data. Businesses can analyze this data to better understand their customers' views of the brand and how products and services may be customized to reduce churn and better personalize offerings to meet customer’s desires and needs. 

Easy to scale

Human customer service agents have limitations that don’t apply to conversational AI tools. Unlike human agents, conversational AI can help multiple customers simultaneously, answering questions, offering resolutions, providing order updates and more. This enables businesses to scale up their customer support and reduce wait times.

Cost-effective

By automating many routine customer support inquiries, businesses can reduce operational costs and expenses associated with hiring and training customer service personnel. With its ability to provide basic customer support, conversational AI technologies free human customer service agents to focus on resolving more complex customer requests.

Opportunity to increase revenue

Conversational AI helps businesses become more profitable through increased customer engagement. Conversational AI chatbots can initiate conversations with customers, providing information on new product releases and upcoming promotions. They can also offer post-sale follow-up, gathering feedback from customers after they’ve made a purchase. This proactive approach increases the chances customers remain engaged with the brand. It also provides a valuable source of data businesses can use to develop a more comprehensive understanding of customer preferences and pain points, allowing them to fine-tune their product offerings and promotional strategies.

Conversational AI and data analytics 

By gathering and analyzing data from customer interactions, conversational analytics is paving the way for a new era of data-driven insights and enabling companies to provide exceptional customer experiences.

More effective offers and promotions 

Conversational analytics enhances customer engagement by gathering data from interactions and previous buying behaviors and using it to generate highly personalized product or service recommendations. With a deeper understanding of customers' preferences and needs, businesses can create more relevant offers and promotions that increase the likelihood that customers will respond by making a purchase.

Evaluating and supporting customer service agents

Conversational analytics can be applied to develop and grow the skills of customer support agents. Managers can identify and reward top talent and offer additional support for agents requiring additional training.

Product feedback and improvement

Analyzing conversations can yield essential feedback on products and services. Businesses can gather suggestions, identify issues and make necessary improvements based on customer input. With this information, businesses can make more informed decisions on product design changes, new product development and new markets to target.

Lead generation and conversion

Conversational AI chatbots can identify new prospects, introduce them to relevant products or services and collect their contact details for future follow-up. AI-led conversations with potential leads can be analyzed to assemble a more detailed picture of the prospect’s preferences, pain points and buying behavior. Armed with this data, sales representatives can adjust their approach to maximize the chance the lead will convert.

Competitive analysis

Monitoring and analyzing conversations can offer insights into what customers think, not only about a company’s products and services, but also about those of competitors. Information gleaned from these interactions can help businesses increase their competitive edge by identifying potential gaps in competitors’ offerings and unmet needs in the market.

Realize the full potential of conversational AI with Snowflake

Conversational analytics unlocks new opportunities for businesses to extract value from customer-generated data. The Snowflake Data Cloud’s scalability, flexibility and performance provide a powerful foundation for LLM-enabled machine learning applications for conversational AI. Snowflake paves the way for unlocking the capabilities of large language models, including enhanced language understanding, text generation and advanced analytics at scale. 

At the Data Cloud Summit 2024, Snowflake's Apps and AI Summit, learn how Warner Bros. Discovery leverages conversational analytics powered by Snowflake and Gen AI to translate natural language into SQL for databases in Snowflake, enabling dynamic data retrieval.

Learn more: Using Snowflake and Generative AI to Rapidly Build Features