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NLP in E-commerce: Top 5 Emerging Trends

nlp in ecommerce

One of the biggest challenge faced by e-commerce companies is to bridge the gap between human thought process and autonomous technology. Natural Language Processing or NLP is a big step in this direction. It makes use of the enormous data generated every day, and organizes it properly to generate relevant search results.

Nowadays, NLP in e-commerce platforms is used for extracting attributes and improving product search. Hence consumers get to see relevant products matching their requirements when they shop online. Advanced NLP-based sorting systems enhance the online shopping experience by understanding the behavior of target customers.

In this article, we define natural language processing and talk about the latest technological trends boosting NLP in e-commerce and transforming online shopping.

What is Natural Language Processing?

NLP or Natural Language Processing is an artificial intelligence-based technique that enables machines to read, understand, and manipulate human language. It finds applications in several industries, here we discuss a few emerging trends witnessed in the e-commerce industry.

Table of Contents

1. Product search

2. Customized product recommendations

a. Product Basis

b. Purchase Behavior

c. Browsing Pattern

3. Predict product success

Searching the right product is probably the most important feature of e-commerce platforms. Customers insert keywords to find products matching their interests. Here, NLP analyzes the kind of words used or how a sentence is structured for finding a particular item. NLP in e-commerce helps to extract meaning from unstructured data by detecting search patterns and building links. For example, if a user searches for ‘full sleeve flare dress’, NLP links full sleeve and flare dress and leads to results like below:

Full sleeves & flare dress - border

As search is the most significant touch point in a customer’s journey, there is no scope for going wrong. E-commerce companies use advanced machine learning and NLP technologies to analyze the most ambiguous and complex customer queries.

2. Customized product recommendations

Along with product search, e-retailers also use NLP and machine learning techniques to increase customer engagement. NLP in e-commerce is used to analyze customer’s browsing patterns, shopping trends, and provide customized recommendations for increasing upselling. By increasing recommendation accuracy, eCommerce companies build brand loyalty and expand their customer base.

Related: Download the checklist that lays out everything you should consider  before implementing a machine learning project or workflow →

Product personalization can be done using:

a. Product Basis

In the product basis, items are classified depending on brands, utility, and other common attributes. Below are the different categories of personalized products according to  product basis:

  • Similar Products
  • Premium Products
  • Trending Products
  • Who bought this also bought
  • Frugal Products
  • Best Sellers

b. Purchase Behavior

Below are the separate categories according to purchase behavior:

  • Top products
  • Best Sellers
  • Order History
  • Recently bought
  • Recommendation for you
  • Similar products

c. Browsing Pattern

NLP in e-commerce also tracks browsing pattern of customers to know their interests. Below is a visual representation of personalized recommendations offered to a person who searches for 'JK Rowling's books'.

3.  Predict product success

Many e-commerce companies have an automated NLP-powered system to estimate the success of a newly launched product through initial customer reviews. This analysis helps in predicting future demand of the product and the need to built up an inventory depending on the product’s market acceptance. This helps companies to make an informed decision on the product’s inventory strategy and sales promotions.

A leading European retailer harnessed the power of text mining and natural language processing for sentiment analysis of customer feedback. With the help of sentiment analysis, the retailer increased customer loyalty and achieved a decent return on marketing and sales investment. An NLP toolkit was used to understand the overall tone of customer feedback, vernacular expressions, and linguistic semantics. Hence, NLP is undoubtedly an effective tool to predict product success as well as return on investment.

4. Auto-generated product descriptions

Many companies are saving costs by automatically generating product descriptions using NLP in e-commerce. NLP algorithms use product data, like brand name, specifications, price to generate unique product descriptions in a jiffy. As writing product descriptions is a time-consuming process, NLP is used to generate consistent content for retail product catalog. It helps to create organic traffic and high conversion levels. The natural language processing tool interprets and transforms any structured data into human-level narratives by considering grammatical and contextual correctness.

Benefits of auto-generated product descriptions:

  • Increased conversion rate
  • Enhanced organic search ranking
  • Fewer instances of cart abandonment
  • Higher ROI

Voice search is an alternative for finding a product through text or image. Most consumers prefer using Google or Amazon voice search assistants to resolve their queries. This is a convenient hands-free way of ordering a product of your choice without spending too much time. Voice-empowered online shopping has been successful in getting user's attention and driving customer engagement. So most e-retailers resort to voice search for building customer loyalty and fostering online sales. No wonder, this is the most sought after trend in eCommerce business.

In this article, we discussed the emerging trends of using NLP in e-commerce and how the online retail industry can benefit from it. With an abundance of data, businesses need technologies for assessing customer sentiments. Using NLP, businesses are able to analyze consumer preferences, attitudes, and moods to stay ahead in the competition. Companies are able to deliver products matching the customer’s expectations, which is, in turn, helping them to move higher in the growth trajectory. Hence, NLP is expected to transform the way we communicate in the future.

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