Unstructured Data is the Key to Improving the Customer Experience: Here’s Why

Any successful business knows that understanding their customers is key to success. The best way to do that is by being able to understand the vast amounts of unstructured data that come with customer interactions.

What is Unstructured Data?

Unstructured data refers to information that doesn’t have a predefined data model or isn’t organized in a structured manner like traditional databases. Unlike structured data, which fits neatly into rows and columns, unstructured data lacks a clear format, making it more challenging to analyze using traditional data processing techniques.

What Are the Characteristics of Unstructured Data?

Unstructured data is characterized by its lack of organization. It doesn’t adhere to a predefined schema or format, which makes it difficult to organize and categorize. Unstructured data often comprises a significant portion of the total data generated by organizations and individuals. Analyzing unstructured data requires more advanced techniques than standard data analysis. 

Where Does Unstructured Data Come From?

Unstructured data can come from various sources. Anytime data is qualitative, like how different customers felt they were treated by your business, it is most likely unstructured data. Other examples of unstructured data sources include social media posts, call transcriptions, and customer reviews. 

Why Is Unstructured Data Important?

To put it simply, it is estimated that close to 90% of all data is unstructured. Unstructured data is so important because it represents such a large portion of the total amount of data you will interact with. If you do not have ways of dealing with this data, you will fall behind your competitors. 

Furthermore, the most important customer data is unstructured. Normal data analysis won’t be able to tell you about a customer’s feelings related to your brand, and how those feelings will affect their interactions with your brand in the future. 

Structured Data vs Unstructured Data

Structured data and unstructured data differ primarily in their organization, format, and ease of analysis. Structured data is organized neatly into rows and columns within a database or spreadsheet, following a predefined schema. Unstructured data doesn’t adhere to a specific format or structure, which makes it more challenging to categorize and organize.

Similarly, structured data typically exists in a structured format such as databases (SQL, NoSQL), spreadsheets (Excel), or other tabular formats. Unstructured data doesn’t follow a standardized structure and can exist in forms from audio files to customer reviews. 

Overall, structured data typically represents a smaller portion of the overall data compared to unstructured data, and is relatively easier to analyze using traditional data analysis techniques. 

Examples of Unstructured Data

The best example of unstructured data is customer reviews. Online reviews don’t usually hold much quantitative value, but that doesn’t mean their impact is any less significant. Customer reviews can either elevate your brand by increasing consumer trust and brand reputation, or they can deter potential customers away from your business.

Another example of unstructured data is a call transcript. Customers who speak with contact center agents often provide key pain points that they need to be able to identify. Analyzing these transcripts with solutions such as conversation intelligence can reveal valuable insights into customer preferences, concerns, and issues, which can inform business strategies and improve customer service.

How is Unstructured Data Used?

Unstructured data, despite its inherent complexity, holds immense potential for various applications across industries. By leveraging advanced unstructured data analytics techniques, organizations can extract valuable insights and derive actionable intelligence from unstructured data. 

When customer data comes in the form of social media posts, reviews, or survey responses, it can be analyzed to gauge public sentiment toward products, services, brands, or events. Sentiment analysis algorithms classify text data as positive, negative, or neutral, which provides valuable feedback for businesses to understand customer perceptions and sentiment trends.

Consider a retail company that monitors social media platforms to analyze customer feedback about its new product release. By conducting sentiment analysis on tweets and comments, the company identifies areas of improvement, addresses customer concerns promptly, and adjusts its marketing strategies to enhance customer satisfaction down the road.

Advantages and Disadvantages of Unstructured Data

Unstructured data offers organizations rich insights and real-time feedback from diverse sources like social media and customer interactions, driving innovation and flexibility in decision-making. However, its inherent complexity, large volume, and potential quality and security challenges can pose significant hurdles in analysis, storage, and privacy protection. Here is an overview of the advantages and disadvantages of unstructured data:

Advantages of Unstructured Data:

  • Rich Insights: Unstructured data often contains rich, diverse information that can provide valuable insights into customer behavior, market trends, and business operations. By analyzing unstructured data, organizations can uncover hidden patterns, correlations, and opportunities that may not be apparent from structured data alone.
  • Real-Time Feedback: Unstructured data sources such as social media, customer reviews, and online forums provide real-time feedback and insights into customer sentiment, preferences, and opinions. This enables organizations to respond quickly to customer needs, address concerns promptly, and adapt their strategies in real-time to meet changing market demands.
  • Flexibility: Unstructured data is inherently flexible and adaptable, allowing organizations to capture and analyze a wide range of data types and formats, including text, images, videos, and audio recordings. This flexibility enables businesses to gain a comprehensive understanding of their customers and operations, driving innovation and competitive advantage.
  • Innovation: Unstructured data fuels innovation by providing new sources of inspiration, creativity, and discovery. By exploring unstructured data sets, organizations can uncover novel insights, ideas, and solutions that lead to breakthrough innovations, product enhancements, and business opportunities.

Disadvantages of Unstructured Data:

  • Complexity: Unstructured data is inherently complex and challenging to manage, analyze, and interpret. Unlike structured data, which follows a predefined schema and format, unstructured data lacks organization and consistency, making it difficult to extract meaningful insights without advanced analytics tools and techniques.
  • Volume: Unstructured data often constitutes a significant portion of the total data generated by organizations, resulting in data overload and scalability issues. Managing and storing large volumes of unstructured data can strain IT infrastructure, increase storage costs, and impact performance.
  • Quality: Unstructured data may vary widely in quality, accuracy, and reliability, leading to potential inaccuracies and biases in analysis and decision-making. Cleaning, preprocessing, and validating unstructured data can be time-consuming and resource-intensive, requiring careful attention to ensure data quality and integrity.
  • Privacy and Security Risks: Unstructured data may contain sensitive or confidential information, such as personal data, intellectual property, or trade secrets, which pose privacy and security risks if not adequately protected. Unauthorized access, data breaches, and regulatory compliance issues are significant concerns associated with unstructured data, requiring robust security measures and data governance frameworks to mitigate risks.

Overall, there are various pros and cons to the use of unstructured data. But, if businesses are diligent in setting up the proper unstructured data analysis processes, it can provide a wealth of useful information to your business. 

How Unstructured Data Relates to the Customer Experience

Harnessing the power of unstructured data will allow you to create the best customer experience for your business. By properly analyzing unstructured data, you will not only be able to identify what your customers are currently liking or disliking, you’ll be able to predict their expectations in the future utilizing predictive customer analytics. Here are some ways that unstructured data can help you improve the customer experience:

Understanding Customer Sentiment

Unstructured data, such as social media posts, customer reviews, and feedback emails, contains valuable insights into customer sentiment. By analyzing the language, tone, and context of customer interactions, you can gain a deeper understanding of customer attitudes towards your products, services, and brand. This knowledge enables organizations like yours to identify areas for improvement, address customer concerns proactively, and enhance overall satisfaction.

Personalizing Customer Interactions

Unstructured data allows businesses to personalize customer interactions and tailor their offerings to individual preferences. By analyzing customer data from various sources, such as call transcripts and purchase histories, organizations can identify patterns and trends that inform personalized marketing campaigns, product recommendations, and customer service interactions. This personalized approach can also be a part of larger AI customer experience initiatives that enhance the customer experience, foster loyalty, and drive customer engagement and retention.

Monitoring Brand Reputation

Unstructured data allows businesses to monitor and focus on their brand reputation management in real-time. By tracking mentions, reviews, and conversations about their brand on social media, news sites, and online forums, organizations can quickly identify and address potential reputation issues or crises. This proactive approach helps safeguard brand integrity, maintain customer trust, and mitigate the impact of negative publicity on the customer experience.

Harness Your Unstructured Data with InMoment

Ready to unlock the full potential of your unstructured data with InMoment? Schedule a demo today and discover how our platform can drive actionable insights and elevate your customer experience strategy!


Research World. Possibilities and limitations, of unstructured data. (https://researchworld.com/articles/possibilities-and-limitations-of-unstructured-data) Accessed 2/29/24.

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About Author

Mike Henry CX Writer

Mike is a passionate professional dedicated to uncovering and reporting on the latest trends and best practices in the Customer Experience (CX) and Reputation Management industries. With a keen eye for innovation and a commitment to excellence, Mike strives to deliver insightful content that empowers CX practitioners to enhance their businesses. His work is driven by a genuine interest in exploring the dynamic landscape of CX and reputation management and providing valuable insights to help businesses thrive in the ever-evolving market.

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