In an earlier article, we saw how AI can be used to deliver outstanding customer experience (CX). But AI can also be used to gather the right data to improve CX even further.

AI consumes vast amounts of data in a continuous loop: data in, analytical processing of the data, and intelligence out. This leads to more data for the system to digest… and the learning process continues.

Simply put, the more accurate and authentic data that goes into the model, the more valuable the intelligence that comes out of it.

Where does CX data come from?

Every interaction with a customer provides more data to feed the analysis and refine insights.

Your contact center is a kind of ground zero for customer data. Customers contact your business with all kinds of enquiries at every stage of their journey. Some may prefer to initiate contact online—carrying out research and comparing solutions via retail and market comparison sites—and then take their inquiry to a human level either through online chat or via a telephone call to your contact center.

These conversations are more personal and align to the individual caller’s needs and pain points. These interactions provide a rich stream of data for AI to mine.

Using automation and AI powered intelligence, a business can analyze customer data from both physical and digital experiences to provide a holistic, 360-degree view of the customer. Online interactions might include purchase history, browsing behavior, and social media activity. Data from direct contact experiences is more difficult to collate. That’s where AI comes in.

Using AI to get the right CX data

It’s been 18 years since the phrase ‘data is the new oil’ was coined. What its author, British mathematician Clive Humby, went on to say was, ”Like oil, data is valuable but if unrefined it cannot really be used.” In the intervening period, the value of data in business has exploded. This is largely down to the pinnacle of ‘refinement’ technologies like artificial intelligence (AI).

The customer data and artificial intelligence relationship

AI’s appetite for data is voracious. Where once customer surveys were the primary source of customer experience (CX) data, now we can gather vast amounts of accurate data from real-world customer interactions.

We now know that data is way more valuable than oil.

The more data AI has access to, the better it can handle the complexities of human language. Comprehensive information helps artificial intelligence understand slang, cultural references, and even the tone or sentiment behind a message.

For example, AI can be trained to distinguish between a user expressing frustration and one asking a simple question, tailoring its responses accordingly. This capability is crucial for making interactions with AI feel more human-like and less robotic.

Data allows conversational AI to improve over time. Each conversation provides new information to help refine its responses. This process, known as machine learning (ML), enables the AI to adapt to new language patterns and user preferences, and even changes in how people communicate.

Without data, a conversational AI would be limited to pre-programmed responses, unable to adapt or learn. It would struggle to engage in meaningful dialogue or provide accurate assistance, making it less effective and less valuable to users. Data is what allows the AI to evolve from a simple program into a sophisticated tool capable of dynamic, real-time interactions.

Customer behavior and personalized customer experiences

As a product manager focused on enhancing customer experience in contact centers and turning imagination into reality with super human, customer led conversational AI, the future is centered on delivering hyper-personalized interactions through effective data utilization.

In a contact center environment, this means creating AI-driven systems that don’t just respond to generic queries but truly understand each customer's unique context and needs.

Imagine a customer reaching out for support. Rather than offering standard responses, the virtual agent can analyze previous interactions, purchase history, and even the customer's current emotional state. If a customer has just had a frustrating experience, the virtual agent could offer a tailored solution with a tone of empathy, or escalate the issue to a human agent immediately with full context and offer next best action to the agent.

Alternatively, if a customer is seeking product information, the virtual agent could proactively recommend relevant services or upgrades based on their preferences and past behavior.

This level of hyper-personalization elevates every customer interaction from a mere transaction to a meaningful engagement. By leveraging data smartly, we're not just solving problems; we're creating experiences that surprise and delight, turning what could be a routine call into a positive, memorable moment. As I like to say “"Personalization and empathy turn customer service into genuine connection."

Building data sets for AI models to chew on

Personalization powers outstanding CX, but you can only offer superb personalization experiences if you have compiled personal data. This data fulfills two purposes: it gives the agent complete data on hand when someone calls your customer center, and it provides more meaningful, authentic data to build a model of your customer base and buying behaviors for use in marketing, customer support and business planning. To achieve this level of data-driven personalization, many businesses turn to AI development services to create and maintain the complex AI models that power these insights.

Of course, call center metrics are essential to keep tabs on CX. Metrics such as First Call Response rates, average handle times, customer satisfaction (CSAT), and customer sentiment enable businesses to make measurable improvements to ensure agents deliver the kind of positive customer service engagement that keeps customers returning. The two metrics that have proven to be valuable are scalability / volume and customer satisfaction.

How does AI feed the data demand?

Technologies like machine learning, Large Language Models (LLM), conversational and generative AI, and natural language processing (NLP) all draw on data generated through customer engagement from contact center interactions and wider data lakes.

Both generative and conversational AI tools are of enormous value in summarizing contact center conversations. Applying generative AI to natural language processing transcriptions of audio, text, and video conversations can transform insights into onward training and reports.

Generative AI can also analyze the content, tone and context of customer interactions within your contact center. Speech analytics tools employ AI, NLP and ML and can be set to identify specific keywords and phrases during conversations. These can direct agent responses and even be used to predict the caller’s behavior. Speech analytics can also analyze more emotional signals like pitch, tone, and intonation to identify satisfaction levels, stress, or frustration.

There’s a place for traditional data gathering

Data sources for CX improvements are not limited to conversations with customers. Valuable data is found in sources like call logs, agent performance reports, quality assurance feedback, and, our friend, the customer survey.

Old-school data-gathering exercises can be a springboard for AI analytics and insight. For example, gathering Customer Sentiment Scores by asking customers to label each interaction as either positive, neutral, or negative also helps to ‘train’ AI by reviewing call recordings in a given with the sentiment category. AI helps you see patterns in negative and positive interactions and come up with CX strategies to overcome negative sentiment.

Beyond the contact center

There’s a pleasing circularity to AI that is often overlooked. AI can be used to gather data from every different stage of the customer journey. From pre-purchase, when the customer is in research or enquiry mode, to their purchase experience and on to post-purchase service. In turn, AI turns the data signals from these interactions into data-driven CX improvements across the entire customer journey.

It’s all benefits

Businesses that harness the power of AI in the contact center can use intel from authentic interactions to improve CX elsewhere in the business. Applying AI to contact center exchanges provides data to inform other business applications that have a direct impact on CX. This includes use cases like accounting (no one wants an inaccurate bill), marketing, and aftermarket customer service.

  • More relevant conversations
  • Quicker responses
  • Enhances Satisfaction
  • Less Frustrations
  • Stronger brand relationships.

Who does not want that? "CX support that understands you—personalized, convenient, quick, and effortlessly rewarding."

Learn More…

To see successful AI tools in action, explore our case studies showcasing 8x8’s Intelligent Customer Assistant (ICA) or download the 8x8 State of Conversational AI in the Contact Center Report.

8x8 Intelligent Customer Assistant is a powerful, user-friendly Conversational AI solution that enables businesses to build advanced self-service experiences across any channel. It handles customer requests without human intervention and saves you time, effort, and the need for expertise because it’s easily modified using our graphical scripting tools.