Transformative NLP Use Cases in Retail

The retail industry has embraced AI in various forms, helping to create smarter customer interactions, more streamlined operations and helping to uncover purchasing insights from mountains of data. 

These developments are present in both e-commerce and in traditional brick and mortar retail stores which customers can visit to view items of interest. Here’s how the retail industry is embracing natural language processing to improve the customer experience.

Exploring NLP

Natural language processing, known as NLP, is the process that enables computers to understand, interpret and respond to human language. 

The technology powers everyday tech such as Alexa and Siri, as well as helping to personalize your Google searches. 

Not only can NLP solutions understand and respond to text, it enables machines to analyze the context and to detect emotion.

NLP within E-Commerce

1. Chatbots and virtual assistants

AI-powered virtual assistants are amongst the most prevalent and widely adopted usages of NLP technology. Custom trained chatbots provide e-commerce websites with the ability to provide round-the-clock customer service support, provide personalized recommendations and to handle customer queries. 

These NLP powered chatbots are helping to improve response times, reduce cart abandonment and to enhance customer satisfaction. Large brands such as Sephora have integrated an AI chatbot into their Facebook Messenger, providing personalized beauty advice, product recommendations and even virtual makeovers! You can read more about it here.

2. Customer feedback sentiment analysis

Sentiment analysis using the role of NLP categorizes written opinions into positive, negative or neutral categories. 

These can be used to provide actionable insights from large data, helping retailers to gauge customer opinions, spot common complaints, and to fine-tune their products and services to improve their offering. 

Amazon are industry leaders at utilizing sentiment analysis of their reviews to detect product trends, identify issues with their supply chain and to enhance their class-leading recommendation algorithm. 

For example, if a customer leaves a low review citing delivery issues, this will be flagged as a critical review that needs to be analysed and rectified, to ensure Amazon’s Prime delivery service doesn’t start to fall below the high standards expected of it.

3. Personalized recommendations

Everyone who has seen Pretty Woman will understand that having a personal shopper wasn’t always something available to all, but the advent of natural language processing has opened up the possibility for every e-commerce customer to receive tailored, personalized shopping advice. 

ASOS utilizes NLP, specifically Microsoft Azure AI, to match customers with products that they didn’t even know they wanted, using big data analysis from other customers' purchase history to suggest items that might be of interest. 

This hyper-personalization is helping e-commerce stores to increase conversion rates and customer retention, driving up revenue.

4. Search optimization

A common feature of e-commerce stores is the search bar, helping online shoppers to discover their favourite products easily. 

Whilst traditional search bars used keyword matching, this would often struggle to display all of the relevant products, especially if they were known by multiple synonyms. 

NLP search bars improve the understanding of the search intent, correct misspellings and many can even process voice searches! 

Check out eBay’s NLP-driven search engine to see for yourself how natural language processing is improving the relevancy of results displayed.

Brick and mortar NLP solutions

It isn’t just online stores that are embracing natural language processing, even conventional brick and mortar stores are using NLP to reinvent the in-store shopping experience.

Voice-powered shopping assistants

Larger stores are integrating NLP-powered voice assistants into their in-store experience, helping to guide customers around the store, checking stock availability and answering frequently asked questions. 

If you have been in a Walmart store recently, you may have already used their voice assistant, likely asking it to point you in the right direction towards a certain product. 

This reduces in-person cart abandonment, whilst reducing the staff workload, freeing the workforce up to focus on tasks such as restocking the shelves.

Feedback analysis

Stores and shops are regularly collecting customer feedback regarding their visit. 

There are many factors that contribute towards a positive shopping experience, from the way you are greeted to the overall cleanliness of the store. 

One of the most beneficial aspects of NLP review analysis is the ability to spot trends and changes over time. 

In the era of online shopping and increased strain on profit margins, shops are looking to maximise and perfect even the most minute of details; NLP analysis will help them to understand their data down to the last detail.

Inventory management

A lesser-known but equally important use of NLP within stores starts well before stock even reaches the shelves. 

Knowing what stock to buy is a key determinant of success for shops of all sizes. Large innovative retailers such as Zara are embracing NLP to analyse consumer trends, hoping to spot and predict changing consumer trends based on customer conversations, social media posts and online reviews. 

NLP enables users to ask questions of their data, supply chain data analytics is helping stores to identify bottlenecks within their inventory supply chain, proactively mitigating them before they become an issue.

Integrating NLP into your retail business

If you are looking to integrate an NLP solution into your business, NetGeist.ai is here to help. We create custom NLP solutions that help your business to approach data with confidence.

Our tools tackle textual challenges through automation, processing and summarization of large datasets. Contact us to discuss your retail NLP project.