How eCommerce uses AI and Natural Language Processing (NLP)

To Drive Revenue and Customer Satisfaction

AI and NLP in eCommerce

Every day we face an enormous volume of text and voice data. How can we systemize this information and decide on the right response? Here is where we need NLP.

NLP can divide the text into components to understand the context and the person's intent. Then the machine can decide which command to execute based on the results of the NLP.

The eCommerce and Retail sectors adopted Natural Language Processing (NLP) among the first. It started from chatbots and conversational interfaces and continued to automating business processes and enhancing customer's experience.

Top three ways to drive customer loyalty and increase revenue for Retail and eCommerce with NLP:

Smart Product Recommendations

Product Recommendation Engines for eCommerce

Traditionally product recommendations were keyword-based. However, NLP can provide analyze many more factors, including previous search data and context. The combination of the NLP insights and keyword-based recommendations helps retailers keep visitors interested by getting the recommendations right.  

Pointing to customers products that fit their needs drives impulse purchases, reduces cart abandonment, and increases cart size.

 Amazon stated that purchases made through recommendations drive 35% of their revenue.

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Semantic-Based Search

Semantic Search for eCommerce

Over 80% of people use an online search to find and buy products. 60% of them use three words or more in search queries. So the query patterns resemble natural language.

The complexity of human language, typos, and context disorient textual search. It cannot understand natural language expressions and differentiate between product names and product descriptions. So it offers irrelevant or zero results and makes the user experience frustrating.

If someone searches "men's black jeans under $50", the result of textual will be a lengthy list. It will contain every product that has the keywords "men's, black, jeans, under, and $50".

Unlike textual search, semantic search can pinpoint the typos, long search terms, and recognize synonyms.

Semantic search uses Machine Learning and Natural Language Processing. So it learns to understand the customer's' buying behaviors and offer relevant products to the customers. Semantic search re-ranks the products and shows the most suitable products at the top of the results. Such personalization drives conversion and customer retention.

Semantic search analyzes the search history and predicts the terms that the user is typing. Auto-completion offered by semantic search saves time for the customers.

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Sentiment Analysis

Sentiment Analysis Social Media eCommerce

Sentiment analysis provides data on customers' opinions and feelings about a product or service and grow conversion rates.

Today sentiment analysis is one of the most popular use cases of NLP.

The global sentiment analysis market will increase from $123 million in 2017 to $3.8 billion in 2025.

E-commerce traditionally used social media monitoring, customer interviews, and analysis of reviews and ratings.
These tools cannot capture all the raw data that the online community produces every day. Over half of the customers expect a response from brands in less than an hour on social media. Hence quick response time is crucial to the image of the brand. Companies need a large workforce to handle this vast amount of unstructured data.

NLP-powered sentiment analysis can process enormous volumes of textual data found in emails, social media posts, text, chats, blogs, and more. It eliminates bias and spots the smallest changes in customer behavior.

It searches for words and phrases that convey emotions and analyzes a customer's opinion about a product. NLP identifies emotions such as happy, sad, angry, and then categorizes it by neutral, negative, and positive value.

Analyzing customer sentiment leads to a better understanding of market needs. So companies can improve their services and offer a more personalized experience.

By better knowing their customers and predicting market trends, brands can stay ahead of the competition.

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Conclusion:

With the actionable insights that it provides, NLP is getting more critical for online business. These insights help organizations make decisions that produce tangible outcomes. It increases business efficiency and drives growth by automating various processes.

Would you like to learn more about how NLP can help you grow your business?

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