Uncover Hidden Gems in Your CX Data with AI by NICE

The power of artificial intelligence (AI) and machine learning in the customer experience (CX) world is undisputed. In data analysis, AI gives CX teams valuable insight into patterns of customer behavior and sentiment trends. Without AI tools, it would be impossible to truly capture and analyze the wealth of data available across a wide variety of customer touchpoints. Yet, even AI-mature CX teams are likely overlooking valuable gems hiding in their data.

Beyond the usual demographic info — names, emails, addresses, age, buying history — hides in-depth stories that can give new life to customer experience strategies. Unstructured data and feedback (qualitative details) craft a more holistic picture of individual customers and their experience. For example, consider two “princes” who appear very similar on paper: Prince Charles and the “Prince of Darkness,” Ozzy Osbourne. Both born in 1948, raised in the UK and married twice, these two men also both live in castles and could be considered wealthy and famous. On demographics alone, the data might sort them into the same categories. Yet, they likely have very different customer expectations and purchasing behaviors. AI can help eliminate those blind spots, digging out the hidden data gems to reveal insights about customer complaints, possible product issues, and even uncover insights about how effectively, or not, your agents are selling.

In the current era, customers expect the brands they interact with to be transparent, empathetic, proactive and able to personalize individual experiences. When those expectations are not met, it creates friction and frustration, sometimes leading to complaints, possible compliance issues for regulated industries, and the risk of negative customer sentiment. Similar to demographics, stats about the agent response alone won’t provide a full picture; data about both sides of an interaction are key to illuminating the full story. AI capabilities are critical in creating better CX and ultimately reducing and friction.

Measure and Manage Agent Behaviors Through AI to Improve CX

The agent’s side of an interaction has a major impact on the customer experience and overall customer satisfaction (CSAT). Organizations know how critical agents are to providing CX and driving customer satisfaction, but they are often not properly equipped to execute a holistic analysis of customer sentiment. There are three primary reasons for this:

• Deficient performance measures. Soft skills are critical in improving customer sentiment, yet they are often not measured. These skills can also be subjective and difficult to define, and what does get measured is frequently not tied to CX during analysis. Additionally, voice and digital data are still siloed at many organizations, and data analysis focuses on cost and compliance primarily instead of CX.
• Outdated quality programs. Many quality programs leverage manual sampling and analysis which is highly subjective. This results in expensive approaches that are difficult to scale.
• Agents not engaged. Lacking performance measures and outdated quality programs frequently lead to unengaged agents. It’s hard to provide meaningful, objective feedback in this environment, and agents who are not engaged in performance improvement, run the risk of leaving the company.

AI analysis that incorporates unstructured data shines a light on the agent’s side of customer interactions. Without the need for manual analysis, AI can tackle volumes of soft skill data and free up time for agents and CX leaders to collaborate and focus on strategy.

AI driven analytics can analyze both unstructured voice and digital interaction data and scale quality programs by assessing 100% of agent interaction. As a result, CX teams have more capacity to improve customer satisfaction and even effectively handle complaints when they inevitably arise. Both of those results are achieved by applying AI to CX data.

Turn Complaints into Business Success

Interaction history and customer behavior — for each individual customer — will inform an overall experience with your brand. The amount of data involved, even for a single customer, can be overwhelming and difficult to digest. Organizations often invest significant resources into identifying consumer complaints, but programs that rely on manual processes and subjective analyses can be error-prone and costly. This is where AI comes in, analyzing unstructured data to find and present actionable insights. Applying AI to your CX data to manage complaints, involves three main steps:

Identify. With machine-learning processes, AI can consistently identify customers and situations that may trigger a complaint by analyzing unstructured data. Demographics alone might show a pattern by region or classification of customer that more frequently result in complaints, but data by itself cannot explain the “why.” The information is often hiding in unstructured
data and is important for step two.

Remediate. Remediation includes complaints and situations that are already in crisis as well as situations flagged by AI as a potential issue. Understanding why a customer is upset — those “hidden gems” beyond the base-level complaint — is how brands ensure personalization, transparency and empathy.

Prevent. Finally, AI can support complaint prevention strategies, ultimately helping brands exceed customer expectations by avoiding problems before they happen. Pairing customer sentiment analysis with agent behavior creates a more holistic view.

With deep insight from your CX data combined with engaged agents, your team can focus on transforming what previously seemed to be negative feedback into powerful business growth.

Exceed Customer Expectations by Applying AI to CX Data

With the ability to mine unstructured data, any industry can drive improvement on any customer experience metric. It’s proven and impactful, and can help solve a variety of needs:

• Protect your reputation. Healthy customer sentiment means a strong reputation.
• Compliance. Reduce regulatory risk and hone escalation procedures.
• Identify product/business issues. Organizations can uncover areas to improve operational efficiency.
• Stronger agent engagement. Engaged agents have less performance variability.
• Increased customer sentiment and retention. With better CX personalization, organizations see less customer churn.

CX is tied directly to business success, and organizations need the right AI tools to uncover the hidden gems in interaction data. AI is needed to understand the vast volumes of CX data, as human manual analysis can’t handle the volume and complexity. AI creates actionable insights from unstructured data, helping CX leaders better understand the “why” behind customer behavior and equipping agents with the data they need to create a frictionless experience for every customer.

In addition to delivering and continually improving upon the overall customer experience, brands today are using AI-driven insights to empower their agents as true representatives of the business. Armed with CX data, agents can strengthen their skill sets and be trained on effective selling techniques, such as identifying needs, value selling, handling objections, and more.

One key tool is NICE’s Enlighten AI, the CX industry’s only comprehensive AI and machine learning framework for customer engagement. Enlighten AI is the core AI brain of all NICE applications, helping teams make smart decisions throughout the customer journey. It is purpose-built to provide an objective analysis of every interaction, guiding CX teams in quality programs, agent coaching and effective selling, complaint management, as well as identifying process or product improvements and ultimately improved CSAT. Learn more about how Enlighten AI powers perfect CX and makes your entire contact center smarter.

You can also learn more about the benefits of CX Data and AI by checking out this webinar: “Uncover CX Hidden Gems in your Data with AI.”