Businesses operate in an unstable world, on the brink of constant change, especially when it comes to technology. At first, these technologies can be daunting. However, companies that seek them out and know how to exploit them, can achieve a significant competitive advantage by better understanding their customers and their demands. Text analytics is one such technology, which is further powered by Artificial Intelligence (AI).
In this article, we’ll look at how analyzing customer feedback can help companies take their business to the next level and achieve promising business objectives.
What is Customer Feedback analysis
Customer feedback analysis is the process by which companies evaluate and interpret feedback, reviews, comments and opinions from their customers regarding their products, services, or the buying experience in general. This process can involve several steps and methods, including :
- Data collection: This refers to the centralization of all feedback data.
- Enriching feedback: enriching feedback data with existing operational data.
- Analysis of this enriched data: Evaluation of scores and satisfaction rates, as well as interpretation of structuring data in order to extract a sentiment score, opinions, etc.
- Identifying areas for improvement: Based on the analysis, we identify negative points that could be improved.
- Taking action: Downstream of this analysis, the next step is to take action to apply the corrective measures and recommendations indicated.
What is text analysis?
Text analysis is a way to use computer programs to read and understand words that people write. It’s like having a really quick reader who can go through things like emails or posts online and tell you what they mean. This is really useful for businesses that want to know what their customers are saying without having to read everything themselves.
This works through two main approaches. First, Machine Learning helps the computer get better at its job by learning from the words it reads. It’s like how you get better at a game the more you play it. Second, there’s Natural Language Processing (NLP), which is just a fancy way of saying the computer is trained to understand the way people naturally speak and write.
So, when a business uses text analysis, the computer program sorts through all these written words to find useful information. It helps the business understand what people are saying about them, whether it’s good or bad, and then decide what action to take.
Why it’s important to analyze customer feedback
Customer feedback analysis is key for any company seeking to strengthen its relationship with its customers. Keeping the pulse of customer feedback enables a company to adjust its products and services to better meet market expectations. This active listening translates into continuous improvement, where every customer feedback is an opportunity for innovation and adjustment.
At a strategic level, the insights gleaned from analyzing customer feedback drive the company’s decisions, enabling it to allocate its resources more efficiently and adopt a proactive stance in its market. It also confers a competitive edge, as the company stands out for its ability to respond precisely to customer needs and deliver a quality user experience.
What text analysis does
Text analysis helps businesses make sense of a lot of information quickly. Imagine trying to read through thousands of customer emails or social media comments. That’s a lot! With text analysis, a computer program does that heavy lifting, scanning through all the data and highlighting what’s important. For example, if people are unhappy about a product and complaining online, text analysis will catch that, helping businesses take quick action.
Sentiment Analysis
This is a special use of text analysis that focuses on feelings or opinions in the text. Are people happy or upset? By looking at reviews or social media, sentiment analysis can help you know what customers are feeling and why. This is really useful for solving problems before they get worse or finding out what people like about your products.
Data Management
Text analysis can also organize records and documents. For instance, in a legal database, text analysis can find the exact documents you’re looking for among millions. It’s a big help for managing information in places like hospitals, law firms, and insurance companies.
How does text analysis work?
At its core, text analysis teaches computer programs to understand the meaning and context of words, just like people learn the meaning of new words. This is powered by two main technologies: deep learning and natural language processing (NLP).
Deep Learning This is a specialized type of machine learning, which is a part of artificial intelligence. Deep learning uses something called neural networks to help computers understand text almost like a human brain would. This makes the text analysis software really smart at reading and understanding words.
Natural Language Processing (NLP) is a way to teach computers to understand human language. It uses various techniques to train computers, so they can process and make sense of written text, even if it’s handwritten. Features like optical character recognition (OCR) help turn images of text into readable documents.
The importance of text analysis in customer feedback analysis
To improve the customer experience, it’s important to understand what customers say, and text analysis is a key part of this process. By using text analysis, companies can quickly process and make sense of large amounts of unstructured feedback data, turning it into insights that they can act on.
This lets companies find trends, figure out where they need to improve, and respond better to each customer’s concerns, which improves the overall customer experience.
Text analysis also lets businesses keep track of how customers feel 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 analysis is a strong tool for making customers happy and getting them to stay loyal for a long time.
2 methods that are used in feedback text 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 businesses find specific things that customers have said 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 customers feedback.
Benefits of text analysis in customer experience management
Text analysis has many benefits for businesses. It can help them learn important things and make good decisions. Some of the most important benefits are:
- Improved customer understanding: It lets companies pull out useful information from customer feedback, which gives them a better idea of their customers’ needs, preferences, and pain points.
- Efficient data processing: Businesses can save time and money by automating the study of large amounts of unstructured text data. This lets teams focus on more strategic tasks.
- Real-time insights: Text analysis lets businesses keep track of how customers feel and new trends in real time. This makes it easier to deal with problems quickly and take advantage of chances.
- Data-driven decision-making: It gives businesses the information they need to make smart, data-driven choices about product development, marketing strategies, and improving customer service.
- Enhanced customer experience: Text analysis helps businesses improve their offerings and give customers great experiences by pointing out places where they can do better and addressing customer complaints ahead of time.
- Competitive advantage: It gives companies a competitive edge because it helps them stay ahead of the competition by constantly changing to meet customer wants and expectations, which leads to long-term loyalty and growth.
Discover the text analysis Solution from Feedier
Maximizing the value of text analysis
To fully harness the power of feedback text analysis, businesses must focus on centralization and real-time reporting, utilizing robust tools such as those offered by Feedier.
Centralize feedback on a single platform
Feedback centralization streamlines the text analysis process by centralizing customer feedback from diverse sources onto a single platform:
- social media reviews
- online reviews website like google reviews or trustpilot
- email and SMS campaigns
- surveys campaigns
- customer support tickets
This comprehensive approach offers a holistic view of customer sentiment and experiences across multiple touchpoints. It makes the analysis more efficient and insightful.
Create and share real-time reports with your teams
Real-time reporting is a critical aspect of effectively leveraging text analysis. By generating automated reports and visualizations accessible to all team members, businesses can monitor customer sentiment and emerging trends. This enables companies to swiftly identify and address issues or capitalize on positive feedback.
Feedier empowers businesses to remain agile and responsive to customer needs, promoting data-driven decision-making. With the 360 Voice of Customer solution, organizations can strategically adjust products, services, and communication efforts based on real-time customer feedback insights. Thus, ultimately boosting customer satisfaction and driving long-term success.
Integrate with your existing business tools
Integrating with other business tools makes it more useful. It makes sure that every feedback is enriched by customer data and vice versa. By connecting text analysis solutions like feedier with tools like CRM systems, helpdesk software, and marketing automation platforms, companies can easily sync customer feedback data and insights.
This integration makes it easier to understand customer exchanges and feelings in a more unified way, which improves the analysis process. It also makes it easier for teams from different areas to work together and make smart decisions. They can access and use the information gained from it. In turn, this gives organizations the ability to make more targeted strategies, handle customer pain points, and improve their offerings. This leads to better customer experiences and growth for the business.
Difference between insightful data and non-insightful data
Insightful data is not the same as non-insightful data, and the difference lies in its value and relevance to decision-making, or to generating useful business or organizational intelligence. This is a key difference in the context of data analysis, in which the volume of information can be overwhelming, but only a portion of it can actually help inform actions and strategies.
What is insightful data
Insightful data provides a deep and actionable understanding of trends, patterns or behaviors. It is characterized by its ability to :
- Identify trends: They highlight patterns or evolutions within the data that are not always evident.
- Inform decisions: This data provides intelligence upon which strategic decisions can be made.
- Generate business value: Data has a direct impact on performance or success by providing insights that can be translated into concrete action.
- Drive change: Insights obtained can drive fine tuning or innovation in products, services, or strategies.
What is non-insightful data
At the opposite pole, non-insightful data is any information that, though it may be accurate or interesting, doesn’t add significant value in terms of understanding or decision-making. They are characterized by their :
- Lack of relevance: This data may be accurate, but not related to the strategic issues or objectives at stake.
- Redundancy: It may repeat information that is already known, without providing any new context or perspective.
- Low impact on decisions: Such data do not provide insights that significantly influence decisions or actions.
- Difficult to translate into action: Although they may be interesting, their practical usefulness is limited or not obvious.
Being able to differentiate between insightful and non-insightful data is key to effective data analysis. In a business environment where organizations are overwhelmed with massive amounts of data, identifying which data is truly useful enables resources to be allocated efficiently, decision-making processes to be optimized, and efforts to be focused on the intelligence that will really drive innovation and growth. This helps avoid the pitfall of analysis paralysis, where an excess of irrelevant information can blur decision-making and slow down action.
Which tools you can use to analyze customer feedback
To analyze customer feedback, operational teams can use different types of tools, each offering specific functionalities adapted to different types of data.
Customer Intelligence Management Platforms
Feedier delivers a platform that enables companies to harness the power of customer data to improve competitiveness, strengthen customer loyalty, and drive growth by enabling optimized and consistent customer experiences. Here’s how Feedier’s key features:
- Data centralization: The platform consolidates data from a wide range of sources to provide a 360° view of the customer journey.
- Data categorization and segmentation: Feedier processes, classifies and segments all data for easy access and analysis.
- Feedback data enrichment: Feedback data can be enriched within the platform by integrating with your business tools. This gives more context to your feedback and enables you to make consistent decisions based on your customers’ needs.
- Data analysis : Using advanced analysis tools (such as Artificial Intelligence, Machine Learning, etc.), the platform identifies trends and behaviors, and delivers precise, actionable recommendations for improvement.
- Sharing Intelligence: With Feedier, you can collaborate and share Customer Intelligence with your teams thanks to automated, real-time reports.
- Continuous improvement and decision-making: All these modules and data enable decisions to be made based on customer intelligence, and improvements to be implemented to optimize the customer journey.
Customer Experience Management Platforms
Provides a comprehensive platform for collecting, analyzing and acting on customer feedback across different channels.
Offers a robust solution for capturing feedback in real time and analyzing it to improve the customer experience.
Business Intelligence (BI) software
A data visualization tool that can be used to create interactive dashboards from customer feedback data.
Offers capabilities similar to Tableau, enabling companies to analyze feedback data and generate detailed reports.
Text and Sentiment Analysis Tools
IBM Watson Natural Language Understanding
Analyzes sentiment and emotions in customer feedback texts, offering deep insights into customer attitudes.Analyzes sentiment and emotions in customer feedback texts, offering deep insights into customer behavior.
A text analysis tool that uses machine learning to classify and extract useful information from customer feedback.
Survey Platforms
Typeform is a survey and form creation platform designed to maximize user engagement thanks to its simple interface and flexible integrations.
Create and send personalized surveys to collect customer feedback.
Wrapping up
Text analysis is a strong tool that uses machine learning and Natural Language Processing to get useful information from unstructured data. Companies can get the most out of it by centralizing feedback, setting up real-time reporting, and integrating it with other business tools. This method makes it easier to understand customers, handle data quickly, make decisions based on data, and improve customers’ experiences.
It helps companies stay ahead of the competition, which leads to long-term growth and customer loyalty. Text analysis is a good investment for any business that wants to improve its products and services and better serve its customers.
