Companies and teams that are “non-data savvy” or “non-tech savvy” find it difficult to transform themselves. However, companies that do benefit from a broad competitive advantage by better understanding their customers and their demands. Verbatim analysis is part of this, as it is a process of examining and deriving insights from written comments to help companies make informed decisions.
In this article, we’ll look at how verbatim analysis can help companies move forward in a sustainable way and give them a competitive edge in their sector.
What is text analysis in Customer Experience?
Verbatim analysis is a technology-driven process that uses computer processing to read and understand the words written by users. It’s like having a high-speed reader that can analyze messages such as e-mails or online notes and explain their meaning to you. This method is very useful for companies that want to know what their customers are saying without having to decipher everything themselves.
There are two main approaches to machine learning. Firstly, machine learning helps technology to get better at what it does by learning from the words it reads. It’s a bit like getting better at a game as you play it. Secondly, there’s Natural Language Processing (NLP), which means that the computer is trained to understand the way people speak and write naturally.
So when a company uses verbatim analysis, the technology behind the process sorts through all those written words to find useful information. It helps the company understand what people are saying about it, whether it’s good or bad, and then suggest actions to take.
While automatic text processing helps to understand human language, machine learning (learning the algorithm with the help of data) improves the accuracy of textual data analysis and extracts useful information for the company.
Why is verbatim analysis important for business growth?
Text analysis makes it possible to analyze large volumes of data quickly and automatically. Indeed, trying to read thousands of customer e-mails or comments on social networks would be a considerable task. Text analysis, on the other hand, enables a technology like NLP to take on this task, scan through the data and highlight the most important elements. For example, if customers are unhappy with a product and express their dissatisfaction online, verbatim analysis will highlight this, helping companies to take swift action for improvement.
Sentiment analysis
This method of data analysis focuses on the feelings or opinions expressed in the text. Are people happy or upset? By examining reviews or social networks, sentiment analysis can help you find out what customers are feeling and why. This is very useful for solving problems before they escalate, or for finding out what people like about your products.
Managing your data
Text analysis can also be used to organize data and files. In a legal database, for example, verbatim analysis enables you to find the exact documents you’re looking for among millions of others. This is a real help for information management in establishments such as hospitals, law firms or insurance companies.
How does text analysis work?
Verbatim definition
Verbatim refers to raw customer feedback about a company, brand, product or service. These comments are generally obtained through satisfaction surveys with open-ended questions or free text spaces. They are used to support a rating or to express opinions on specific aspects.
Text verbatim analysis trains Language Modeling (LM) to understand the meaning and context of words, just as people learn the meaning of new words. It relies on two main technologies: Deep Learning and Natural Language Processing (NLP).
Here’s an example of Language Modelling to synthesize a large volume of text:
Deep Learning: This is a specialized type of machine learning, part of artificial intelligence. Deep learning uses what are known as neural networks to help computers understand texts almost as a human brain would. In this way, verbatim analysis becomes an excellent tool for reading and understanding words.
Natural Language Processing (NLP): is a way of teaching computers to understand human language. It uses various techniques to train models to process and make sense of written text, even if it’s handwritten. Functions such as optical character recognition (OCR) enable text images to be transformed into readable documents.
The two types of Language Model (LM)
The n-gram model
An n-gram language model is an approach to analyzing how words order themselves in a text. It assumes that the probability of the next word in a sequence depends solely on a group of preceding words of a specific size. For example, a bigram model takes only one preceding word into account, a trigram model uses two, and so on. In general, an n-gram model examines the preceding words in the sequence, up to a total of n-1 words, to predict the next word.
However, it is essential to note that n-gram models are no longer widely used in natural language processing research. They have been overtaken by more advanced deep learning methods, such as large language models, which have proved more successful.
LLM (Large Language Modeling)
A large language model, or LLM, is based on large collections of texts from different sources, such as books, newspaper articles, web pages, forums and social networks. Its aim is to predict the words and phrases that follow a given word in a text. These models are powerful tools used in a variety of fields. This model can be illustrated as a vast neural network.
LLMs are used for a multitude of tasks. For example, they are used to generate text, automatically translate from one language to another, classify text documents, and even answer questions. Among the most famous examples of LLMs are OpenAI’s GPT-4 and Google’s BERT.
Text analysis in Customer Experience
To improve the customer experience, it’s important to understand what customers are saying, and verbatim analysis is a key part of this process. Text analysis enables companies to quickly process large quantities of unstructured feedback data and make sense of it, transforming it into actionable information.
This enables companies to detect trends, identify areas for improvement and better address the concerns of individual customers, thus improving the overall customer experience.
Text analysis also enables companies to track changes in customer sentiment in real time. This is useful information that can be used to improve products, services or communication strategies to better meet customer needs and expectations. In this situation, text analytics is a powerful tool for keeping customers happy and loyal over the long term.
The 2 methods used in text feedback analysis
Entity detection
Entity detection is the process of recognizing and classifying key elements in a text, such as names, companies or places. This helps companies find specific elements that customers have mentioned about their products or services.
Here’s an example of how to classify key elements:
Entity detection is the process of recognizing and classifying key elements in a text, such as names, companies or places. This helps companies find specific elements that customers have mentioned about their products or services.
Sentiment analysis
On the other hand, sentiment analysis evaluates the emotional tone of the text to determine whether the feedback is positive, negative or neutral. By using these text analysis methods, companies can learn more about what customers are saying. This will help them deal with problems and make the most of customer feedback.
The benefits of text analysis in customer experience management
Text analysis has many advantages for companies. It can help them learn important things and make good decisions. Here are some of the most important benefits:
- Improving customer understanding : It enables companies to draw useful information from customer feedback, giving them a better idea of their needs, preferences and hot and cold irritants.
- Efficient data processing : Companies can save time and money by automating the study of large quantities of unstructured textual data. Teams can then concentrate on more strategic tasks.
- Real-time information: Verbatim analysis enables companies to track customer sentiments and new trends in real time. This makes it easier to deal quickly with problems and seize opportunities.
- Data-driven decision-making: It gives companies the information they need to make intelligent, data-driven choices in product development, marketing strategies and customer service improvement.
- Improving the customer experience: Text analysis helps companies improve their offerings and deliver an exceptional customer experience by pointing out areas for improvement and addressing customer complaints upstream.
- Competitive advantage : text analysis gives companies a competitive edge, helping them to stay ahead of the competition by constantly evolving to meet customer wishes and expectations, resulting in long-term loyalty and growth.
Optimizing the value of text analysis
There are several stages in the process of optimizing verbatim analysis. To fully exploit its power, it’s important to focus on centralizing feedback and reporting in real time, using solutions such as those offered on Feedier.
Step 1: Centralize all your feedback data
Centralized feedback streamlines the text analysis process by centralizing customer feedback from various sources on a single platform:
- Comments on social networks
- Online review websites such as Google Reviews or Trustpilot
- Email and SMS campaigns
- Satisfaction surveys
- Customer support tickets
This holistic approach provides a comprehensive view of customer sentiment and experience across multiple touchpoints. It makes analysis more effective and, above all, far more insightful and accurate.
Step 2: Generate and share real-time reports
Real-time reporting is a key element in making the most of text-based data processing. By generating automated reports and graphs accessible to all employees, companies can monitor customer behavior and emerging trends. This enables them to quickly identify and resolve any incidents or capitalize on positive feedback.
Feedier strengthens the ability of companies to remain agile and flexible in response to customer needs, by promoting data-driven decision-making. With Feedier’s 360 Customer Intelligne platfom, companies can strategically adapt their products, services and communication efforts based on real-time customer feedback. As a result, customer satisfaction is boosted and long-term results are improved.
Step 3: Enrich your feedback data with your business data
Integration with other business tools makes the process even more useful. It ensures that every piece of feedback is enriched by customer data, and vice versa. By connecting a textual data analysis solution like Feedier to tools such as CRM systems, support software and marketing automation platforms, companies can easily synchronize customer feedback data and information.
This integration makes it easier to understand customer exchanges and feelings in a more homogeneous way, improving the analysis process. It also makes it easier for teams from different departments to work together and make informed decisions. They can access and exploit the information obtained. As a result, companies can implement more targeted strategies, address customer friction points and improve their offerings. The result is a better customer experience and business growth.
To sum up
Text Analytics is a powerful device that leverages Machine Learning and Natural Language Processing to obtain actionable intelligence from unstructured data. Companies can make the most of it by centralizing feedback, setting up real-time reporting and integrating it with other business tools. This helps to better understand customers, process data quickly, make data-driven decisions and improve the customer experience.
It helps companies stay one step ahead of the competition, resulting in long-term growth and customer loyalty. Text analysis is a good investment for any company wishing to improve its products and services and better serve its customers.
