What is Textual analysis?

Textual analysis is the practice of reading the text in the form of data and analyzing it in order to generate insights. And in layman's terms, it simply means to understand human language, interpret it and perform further analysis of it. And text analysis is one of the most important applications of AI. 

The main idea behind performing text analysis is the mere fact that customers worldwide are encouraged to share their reviews. Now, this text is nothing but data, on this text operations, are performed in order to retrieve information. And nowadays companies are using text analysis to identify existing problems in order to build new solutions. 

Quantitative vs Qualitative Text Analysis

Decision-making and call to action depend a lot on exactly what information your data is telling you.

While quantitative text analysis will give you numerical or statistical analysis of text data, including word frequency counts, sentiment analysis etc. 
 

Qualitative analysis, on the other hand, will give you an interpretation and understanding of the meaning behind the text data. This type of analysis is used to understand the context and meaning behind text data and to gain insights into the experiences and perspectives of the individuals who generated the data. 

Types of Text Analysis

1. Sentiment analysis - It is the process of analyzing text by identifying the tone of the text and grouping it in the buckets of positive, negative, and neutral. The information can be used to gauge public opinion, understand customer feedback, or identify patterns in social media activity.
2. NLP - Natural Language Processing deals with the interaction between computers and human languages. It is a field of artificial intelligence and computer science.
3. Word Cloud- Word Cloud is a very interesting form of text analysis. And due to its uncomplicated features, it is very easy to use it. The benefit of the word cloud is that you can see what your customers are talking about you. And the higher the frequency of the usage of words, the bigger the size of the word. For example, if your word cloud shows the price
4. Topic Modeling: In this process, the AI identifies the main topics discussed in a text, and it is further used to understand the overall themes in a collection of documents. These topics are abstract "topics" that occur in a collection of documents.
5. Named Entity Recognition: It is a form of text analysis where the AI Identifies and extracts specific entities such as people, organizations, and locations from a given text.
6. Text Summarization: Text summarization is the process of condensing a piece of text into a shorter and more concise form while still retaining the important information. It can be used to quickly understand the main points of a longer document, article, or speech. There are two main types of text summarization: extractive and abstractive.
7. Text Classification: Text classification is the process of assigning predefined categories or labels to a piece of text. The goal of text classification is to accurately assign categories to the new text so that it can be processed and understood by computers.
8. Text Similarity:  Text Similarity is the method where the AI identifies the similarity between two pieces of text, used for tasks such as plagiarism detection. So whenever you are checking your blog or research paper or project for plagiarism, the software is basically performing text similarity operation.

How does e-commerce benefit from it?

In today’s day and age e-commerce stands in the front row of minting customer feedback, and it is not new to the digital world. What we miss to see is how this mere data could be beneficial to the business, reviews, feedbacks are not just form of data, but a gold mine to understanding user behaviour and pattern, and a source for businesses to get into the heart of the users and give them exactly what they are looking for. So, how o these reviews when put in action with product intelligence, artifical intelligence benefot businees ? lets take a deeper look into it - 

1. Recommendation system - Text analysis is an important function that should be included when building a recommendation system. And currently, almost 98% of e-commerce sites are also a recommendation system. In this system, the customer is suggested similar items which coincide with the items already bought, or searched for.
   Hence, in business terms, it is the simple “bread and egg placement” psychology, but automated. Hence, when you type anything on Google or your shopping site, that text is used to suggest you more products. This effect of complementary products not only helps in managing the inventory but also increases your average purchase order
    
2. Customer service - This is the core essence of the application of text analysis. Every review and every star that you give to any product on the e-commerce platform is stored and used to make a better product, provide better services and improve the customer experience. And it is not just limited to the e-commerce sites but even powerful tweets Instagram comments and Facebook likes, every engagement that you do online can be used to build a better customer experience.
    
3. Building a brand - If your e-commerce site talks to customers, resolve their issues and serves them better, they are more likely to come to your site than your competitor’s. And this will increase their loyalty to your brand. But for that, you need to have a very strong data analytics process.
    
4. Price Optimization- Analyzing your customers' reviews, opinions and stories will help you understand if your products are priced fair. In this case, Instagram comments are extremely helpful. For example, if you are a beauty brand and you have launched a new product, after use your customers can share their experience in the comments section, stating if the product is worth its price or not. Similarly, text analysis will also help you identify competitive prices of that particular portfolio.
    
5. Optimizing product description - Your customers' feedback will help you analyze what are they talking about your product. And you can use catchy phrases from these reviews. That will also help you update the product description and product listings in your e-commerce.
   For example, your e-commerce site offers clothes and through customer reviews you realized that the sizes are larger than normal which is confusing the customers as they buy clothes that don’t fit properly, hence you can change the sizing option to improve customer experience.
    
6. Inventory Management- Analyzing customer feedback will help you understand and predict customer behaviour, that is, which time period sees a shoot in purchasing particular products. For example, festivals increase purchases of related items. Understanding these patterns will help you stock more products when required and this way your inventory turnover ratio will be maintained.
    
7. Search Engine Optimization - When you know what customers are talking about, you can use it to your benefit by including the same in your website. For example, Black Friday Sale, and Festival Sale are some of the trending words when people search for online products. Using tools you can get the queries that users are currently searching for on google, and you can further use it in your website’s metadata for better ranking.
    
8. Increase in conversion rate - Text analysis can be used to understand customers' buyer intent , which can help increase conversion rates by tailoring marketing messages and website content to address specific concerns or objections that customers may have. This can also help increase conversion rates by addressing potential pain points that may be preventing customers from making a purchase.

Conclusion  

Textual analysis is a critical tool for the E-commerce industry as it provides valuable insights into customer sentiment and behaviour. By analyzing customer reviews, feedback can better understand their customers' needs and preferences, leading to improved products and services. 

Textual analysis can also help E-commerce companies to monitor and manage their brand reputation, identify emerging trends, and stay ahead of the competition. With the vast amounts of data available in the E-commerce industry, the textual analysis provides a powerful means to extract valuable insights and improve business outcomes. ReviewAnalytics is dedicated to providing advanced textual qualitative analysis tools that help E-commerce businesses harness the power of customer feedback to drive growth and success.

Some of the most famous e-commerce sites that are currently performing text analysis are - 

1. Amazon
2. Facebook
3. Myntra
4. Airbnb
5. Flipkart
6. Zepto
7. Zomato
8. Swiggy
9. Shopify
10. Walmart

Hence, if you are still avoiding text analysis on your e-commerce site, you are at a loss. And you don’t always need a big team to pursue this analysis, even third-party tools like 

• Review Analytics
• Chattermill.
• RapidMiner.
• Forsta.
• Relative Insight.
• IBM Watson Studio.
• Thematic.
• Kapiche
• SAS Visual Text Analytics can be used effectively to understand your customers better.