Improve the Customer Experience by Leveraging Text Analysis

Improving customer experience is essential to business success. Companies are increasingly using AI-powered text analysis to address this need, driving growth and customer retention.

The Decline of Brands’ Customer Experience

In June 2024, Forrester Research reported that the quality of customer experience (CX) among U.S. brands had plummeted to an all-time low, marking the third consecutive year of decline.

Several factors contribute to this downturn, including:

• brands’ failure to deliver seamless customer and employee experiences,
• lackluster digital interactions – particularly via chatbots,
• broader societal concerns, such as personal financial insecurity and economic instability.

Additionally, a report by Accenture revealed that 37% of consumers believe companies are prioritizing profit over customer experience. This perception reflects a growing mistrust, with customers feeling that businesses are not sufficiently focused on delivering high-quality CX.

The implications are significant. A McKinsey study found that organizations prioritizing customer experience enjoy twice the growth of those that do not. Clearly, improving CX is not just beneficial for customer satisfaction – it’s a strategic imperative that directly impacts business outcomes.

The Need to Listen to Customers

In a marketplace where it appears that brands are lowering their standards, truly innovative companies have the opportunity to adapt. One of the most effective ways to do this is by listening to customers and understanding their experiences at scale, especially through their own words.

The Challenge of Open-Text Feedback

Businesses regularly collect feedback through surveys, social media, and review sites. This feedback is typically divided into two categories:

1. Structured feedback: Made up of quantifiable responses, such as multiple-choice answers or ratings, which are easily analyzed but often fail to capture the full context of customer sentiments.
2. Unstructured feedback: Open-text responses that provide richer insights but are harder to analyze due to their unquantifiable nature.

While structured feedback can offer useful, easily digestible data points, it often lacks the depth necessary to understand the root cause of customer satisfaction or dissatisfaction. Without knowing why a customer assigns a certain rating, businesses miss critical information needed to implement changes that will ultimately improve CX.

Unstructured feedback, on the other hand, holds the potential to deliver powerful insights into customer sentiment. However, without advanced tools, analyzing this type of feedback can be time-consuming and resource-intensive. It means that many brands can’t meaningfully respond to customer feedback.

How can businesses unlock the value in their unstructured feedback?

The Role of AI in Text Analysis

AI-powered text analysis is revolutionizing how businesses approach customer feedback. By transforming qualitative data into measurable, actionable insights, text analysis allows companies to understand customer sentiment at scale.

Text analysis empowers companies to sift through vast amounts of unstructured feedback efficiently while providing key insights. The best solutions employ machine learning models that have been around for a decade or more to tag content with sentiment data. Those solutions are now paired with GenAI to generate summaries around the findings.

Essential Text Analysis Capabilities

When choosing a text analysis platform, organizations should look for the following capabilities:

• Conceptual themes and phrases: An AI-powered text analysis platform should employ both a “top-down” and “bottom-up” approach. The “top-down” approach continuously classifies feedback using predefined strategic themes. The “bottom-up” approach extracts and clusters the actual language used by customers. Equally important is the platform’s ability to accurately interpret the emotions conveyed in user feedback. By combining these methodologies, organizations gain a high-level view of the major positive and negative factors affecting customer experience, while also accessing the detailed insights necessary for informed action.
• Insight assistant highlights: Leveraging AI and advanced language models, the platform should summarize key points from user feedback, delivering concise, context-rich insights. This reduces the need for extensive manual analysis and accelerates decision-making by providing clear, actionable summaries of customer sentiment.
• Anomaly detection: When unexpected patterns arise, whether positive or negative, the AI platform should promptly detect and alert users to anomalies in the data. Early detection of these trends allows for swift investigation and response, preventing small issues from becoming significant problems.
• Translation: Advanced AI-driven translation is critical for understanding global customer feedback. Your platform should support real-time, multilingual translation, ensuring that feedback from customers in any region can be quickly understood and acted upon.
• Impact analysis: Your AI-powered text analysis platform should offer granular, in-depth analytics to assess the impact of customer sentiment on key Voice of the Customer (VoC) metrics, such as NPS or Net Sentiment. These insights should also link to critical business outcomes, like customer retention or product returns. Ensure your platform includes impact analysis to measure sentiment effectively and demonstrate its value to the business.

Why Text Analysis Matters

Prior to text analysis platforms, tagging feedback with sentiment data was manual and time-consuming. So, many businesses fell back to the traditional – and quantifiable – checkboxes and radio buttons found in so many customer surveys, introducing their own biases and capturing only a piece of the customer’s experience. Well-meaning survey builders struggled to determine how the check box and radio button questions really related to the answers they were trying to receive.

It doesn’t have to be this way. Now, instead of guessing and being wrong, you can ask and be right.

AI-powered text analysis empowers companies to collect unstructured feedback data to understand where they fail and succeed in terms of CX. By understanding customer themes and sentiment at scale, companies hone in on key insights – ways to improve the customer experience and see the kind of growth demonstrated in the McKinsey study referenced earlier.

For instance, retail companies using text analysis could easily hone in on why purchases are being returned – a major expense and hassle for retail companies. They can easily drill down into returns to determine what the problem is and how to fix it. Companies using Alchemer Pulse have quickly identified poor-fitting clothing or other complications using text analysis and leveraged those insights to fix the product and resolve future challenges for customers.

AI is no longer a “nice-to-have” technology; it’s quickly becoming foundational to how feedback programs will collect and analyze data to make better decisions, faster, and ensure brands have a path to stable growth.

Peter Zaidel is the Director of Product Management for Alchemer. He is an experienced product management and solution delivery leader with a demonstrated history of working in the software (SaaS) and services industries. He can be reached at [email protected].

Alchemer empowers you to do more with feedback. From a one-time survey to a powerful feedback program, Alchemer gives customer-obsessed teams the clarity to move from asking to action, driving your business forward. Expect more from feedback.