E-commerce & Retail: Optimize Customer Experience with Text Analysis

Abstract

E-commerce is an industry where businesses have to find new ways to understand and fulfill the needs of their customers. It has become more important after the worldwide COVID-19 pandemic. It forced brands and businesses to pursue online platforms to keep their existing customers and gain new ones. If they didn’t do it, they would eventually lose customers to their competitors. But moving to an online platform is not enough to keep the customers coming for more. Proving an exceptional user experience or service during and after purchase. To achieve this, businesses have to analyze the previous history of their customers by mining data. Text analysis/mining helps online businesses extract valuable information that can be used to further optimize and improve the customer experience.

In simple terms, text analysis is analyzing textual data to extract insights and identify patterns or trends. By identifying customer patterns and opinions, e-commerce retailers can target specific segments or groups with personalized recommendations, offers, and discounts.

Introduction

The digital revolution has given retailers access to a host of customer information. They can leverage this information to enhance their marketing efforts and services to distinguish themselves from their competitors. One such technology that can help you dissect consumer data and extract valuable information is text analysis.

Text analysis solutions are now being implemented in various industries, including e-commerce. Retail and E-Comm business owners use it to analyze consumer data mined from various sources such as search engines, blogs, consumer forums, and social media. It helps them identify various insights such as rising trends, purchase patterns, product preferences, areas of improvement, and more.

By penetrating deep into customer data with the help of text analysis, retailers can get an excellent understanding of the consumers’ needs. You can also analyze their sentiments towards your products or services.

How to Analyze Consumer Data?

Now that you know how important consumer data is, let’s discuss how to analyze it.

Customer Data Collection

The first is to gather relevant data from various sources. You can scrape data from various sources such as,

• Online Reviews and industry-specific review websites
• Customers Surveys
• Forums
• Social Media
• Business Listings
• Customers Purchase Histories

Unstructured Data Processing

As most of the complied data is unstructured, analyze the data becomes difficult. Using NLP (Natura Language Processing), you can break down and structure large volumes of textual data compiled from various sources.

1. Text Cleanup: Unstructured text data frequently contains errors, unwanted symbols, and colloquialisms. The resulting data is too noisy and unintelligible for computers. This step removes any information.

2. Tokenization: Tokenization is the process of converting data into tokens. Replace the data with a value generated at random of the same length. This is simply a placeholder for actual information.

3. Sentence Breaking: After the tokenization process, the sentence breaking process is applied to help identify the boundaries between sentences.

4. Parts of Speech Tagging: The part of speech tagging (PoS) process that decides and tags every token in a particular document with the right part of speech (noun, verb, adjective, etc).

5. Chunking: When we talk about breaking sentences, we talk about a variety of systems, such as a phrase of a noun and a verb phrase that divides a sentence into its constituent phrases.

6. Syntax Parsing: The syntax parsing sub-function includes a sentence structure checker or sentence diagramming. It is an important pre-processing step in sentiment analysis and other natural language processing features and is critical in general computational linguistic processing.

7. Sentence Chaining: Sentence chaining also known as sentence relation, is the process of finding relations between sentences.

Textual Data Analysis

Now that you know the basics of unstructured text data processing, let’s look at some of the commonly used text analysis models used to extract insights.

Keyword Extraction: Keyword extraction, also known as keyword detection or keyword analysis, is a text-mining technique that extracts from a text the words and expressions that are most frequently used and important to the reader.

Sentiment Analysis: Sentiment analysis is a machine learning technique that helps machines identify the sentiments expressed by the author in any piece of text.

Emotion Analysis: This text analysis technique helps in identifying the emotional tone expressed by the author in any text. It mainly identifies emotions such as fear, anger, happiness, sadness, and love.

Intent Detection: Intention detection is the process of identifying the intent expressed in the text. With customer feedback, it can help you identify customer support tickets or feedback that require your immediate attention.

Entity Extraction: The entity extraction technique allows you to extract named entities such as people, location, organization, products, monetary values, and so on. Businesses can use this to analyze feedback for specific products or for their competitor.

Applications of Text Analysis In E-commerce and Retail

Let’s look at some of the most useful applications of text analysis that can help optimize the user experience in e-commerce and retail.

Product Recommendations for one-on-one Personalization

The best way to entice customers to buy products on your e-commerce website is with a recommender system. Text analysis can help you analyze the past purchasing behavior of consumers, which can help develop an efficient product recommender system to assist customers in the future. You can then recommend products based on the most popular items, demographics, and past purchases to increase sales.

Automate Customer Support Tickets

Analyzing customer support tickets or queries can be an arduous task if done manually and can increase the response time. With text analysis, you can build an automated customer support ticket tagging solution. It can help you automate the classification of incoming customer support tickets based on the type of query, complaint, issue, intent, and more. You can then direct the query to the right customer support executive with ease.

Analyze Customer Feedback

Customer feedback is one of the most valuable sources of insights. It can help you identify the positives and negatives of your brand from the perspective of the end-user. You can then use the positive reviews to market your brand. As for the negative reviews, you can use them to identify where your product lacks and make improvements to fulfill the customers’ requirements.

Gain Competitive Intelligence

Analyzing the customer feedback of your customer on various platforms can help you gain valuable audience insights. By analyzing customer feedback, you can identify the features of your competitor’s product that the customers like, you can integrate them into your products as well. Similarly, you can also identify features that are disliked by the customers and avoid implementing the same.

Fighting Cyber-fraud

Retailers are becoming increasingly concerned as cybercriminals commit increasingly sophisticated and frequent acts of digital crime. Customers are also taking advantage of superior customer service to commit return fraud. They purchase something, enjoy it, and then return it to the retailer, claiming that the item was defective. Fraud can have a negative financial impact, but it can also harm the firm’s reputation. Retailers can keep a tight lid on fraud by using text mining and machine learning techniques.

Conclusion

Despite the significant advancements in online commerce, one area where online stores have yet to achieve an advantage over traditional brick-and-mortar retail is the lack of face-to-face interaction. Only by using data-driven decisions and actions to meet customer needs will a suitable counterpunch be given. Text mining solutions like BytesView can help you extract crucial insights from complex unstructured data for data-driven decision making. Do give it a try.