Unleash powerful conversational AI-powered intelligence to transform customer experiences, business results

Did you know that 90% of the world’s data was generated just in the last two years? In fact, over the last 13 years the amount of data captured globally has increased by an estimated 7,400%!

As the front line for your customer experiences, contact centers generate an enormous amount of valuable data, yet few organizations have a firm understanding of how each customer interaction affects their KPIs. However, getting access to deeper insights with conversation intelligence doesn’t have to be a heavy lift.

Generative AI can do the majority of the work for you in a fraction of the time to help you make the most of your data. Plus, Aberdeen’s research shows companies that are investing in AI analytics generate much better results compared to companies that don’t invest in AI:^[1]

• 17.4% higher annual revenue
• 14.3% higher customer lifetime value
• 13.8% decreased service costs
• 12.2% higher cross sell/upsell revenue

Automatically analyzing 100% of customer interactions can help eliminate blind spots, identify opportunities for improvement and drive better business outcomes faster. NICE’s proprietary, powerful industry-specific AI models are based on over 30 years of labeled CX data. As a result, you can count on them to be effective right out of the box. Plus, embedded technology in NICE Interaction Analytics contains reports, dashboards and workflows which make insights actionable right away. With a unified view of the customer experience in a single interface for organizing, analyzing and operationalizing data, you can:

• Improve customer loyalty and retention
• Increase CSAT
• Drive efficiencies and reduce costs
• Increase quality and compliance
• Improve sales effectiveness

Conversation analytics can be used to power improvements across your contact center and tech stack. From enhancing your quality assurance programs to refining coaching strategies and adjusting self-service flows, the opportunities are limitless. Automatically categorizing your interactions and structuring your data can help you identify these opportunities quicker, and can often help you identify insights you would have missed otherwise.

Analytics without structured data is less effective and inefficient

With a massive amount of information about customers, interactions, agent performance and more, contact center leaders are often overwhelmed by the amount of data at their fingertips. Hidden in this information are meaningful insights that can be used to create differentiated customer experiences, pinpoint the root causes of issues and identify opportunities to reduce operational costs.

Yet, Aberdeen’s research shows only 35% of companies are fully satisfied with their ability to use data for CX programs. Standing out from your competition requires you to have a strong pulse on how all interactions flow through your contact center. To do this, organizations must be able to extract intelligence from unstructured interactions, but Forrester’s 2024 data shows that 30% of the data managed by analytics decision-makers is unstructured. This is why best in class contact centers are investing in AI for data insights and analytics and using it to structure their data:

With the rise of generative AI and large language models (LLMs), it is easier than ever to structure your data. Traditional analytics solutions use a rules-based approach to data analysis which is more limited in its ability to automatically group interaction topics into categories. It requires you to think of all the different ways someone might express something which makes it easy to miss synonyms and different word combinations to communicate the same intent. As a result, it doesn’t paint a complete picture of all your interactions. This approach is expensive and building these rules is labor intensive, often requiring specialized expertise. In fact, data analysts typically spend 80% of their time finding, cleaning and organizing data, according to the Pragmatic Institute. Top performing analytics teams find ways to do the opposite—spending the bulk of their time on higher level tasks.

Unstructured data also lacks flexibility which can make it hard to keep up with dynamic markets or changing customer expectations. On top of that, it makes it easier for analytics teams to miss subtle trends or unexpected patterns that could provide valuable insights, meaning crucial opportunities for improvement can easily go unnoticed.

Manually gathering and organizing large quantities of data can also be difficult to scale as contact centers and businesses grow. It is both costly and resource-intensive, placing added pressure on timelines, budgets, and staff. Analytics teams that adopt generative AI to structure their data can adapt to changing demands quicker without needing a lot of extra resources.

Unlocking deeper insights with generative AI

Using generative AI built with decades of CX data, you can upload your interaction data and an AI-powered data structure will automatically be created for you, which is a system for organizing data by defining the characteristics of entities and the relationships between them. It doesn’t require any expertise since the data is automatically classified. Imagine an e-commerce company trying to group its customer service inquiries together to identify patterns. Some customers call about returns, others about shipping delays, and some about product features. An AI-driven data structure allows the company to group these interactions under broader categories like order issues or sales opportunities, while also breaking them down into more detailed subtopics to capture nuances.

Generative AI helps you capture all of the ways topics are discussed and how often they come up so you don’t underestimate their impact on sentiment, churn, conversion rates and more. For example, there are many ways to ask for the status of an order and it may be easy to miss one of them if you are manually training a rules-based model. You may capture “order status,” “where is my shipment” and “when will it arrive” but miss something more obscure like “are my new shoes en route?” Some organizations try to build AI-powered data structures with publicly available LLMs like Chat GPT—but they are typically not effective because they are built on data that is available on the internet. NICE Interaction Analytics analyzes 100% of your interactions with purpose-built AI for CX, so your data will be grouped accurately and yield better insights.

Taking advantage of generative AI to automatically provide structure to your interaction data makes insights easier to access and allows the AI to perform the heavy lifting for you:

• Improve the accuracy of your data analysis: By structuring your data, you’ll have more confidence in the insights you gain from it, helping you create better strategies and uncover the root causes of issues.
• Increase coverage: Utilize AI to analyze 100% of your interactions and make sure you capture all the opportunities to improve CSAT, reduce customer churn, lower average handle times, increase sentiment scores, improve sales effectiveness and much more.
• Structure data around your business: Be more flexible by customizing your data structure around your processes and terminology.
• Act faster and be more agile: Having a sound data structure in place that is adaptable will help you react to changing dynamics in your contact center much faster.
• Spend less time analyzing data: Take advantage of automated, AI-driven data analysis so you can spend less time gathering information and more time extracting valuable insights from it.
• Democratize data analysis: Make it easier for stakeholders across your organization to take advantage of insights from your interaction data.

In a landscape where every customer interaction counts, having the right tools to automatically analyze and act on all of your data is a necessary competitive advantage. NICE makes it easier to surface key insights and contact drivers from your contact center by simplifying the complexities of structuring your data. Topic AI Editor, which is included with NICE Interaction Analytics, leverages LLMs combined with pre-built, industry-specific models based on 30+ years of labeled CX data. You can leverage these insights to get immediate value and make sure you aren’t missing any opportunities to improve. Plus, you’ll have out-of-the-box tools to proactively monitor data such as dashboards and reports so you can keep on top of the latest trends.

The result? Better business outcomes, happier customers, and a more agile organization ready to meet the demands of the future. Learn more about how to automatically structure your data using generative AI with NICE Interaction Analytics here.

_{[1] Aberdeen Strategy & Research: 2024 State of the Contact Center (2024)}