Top 7 Use Cases of NLP in Retail

Updated on Feb 6th, 2024

Top 7 Use Cases of NLP in Retail

Many retailers are utilizing cutting-edge technologies to improve online customer experiences and stand out from the competition as brick-and-mortar stores keep evolving in the quickly growing e-commerce industry. The use of NLP services in business is currently prevalent in around one-third of retail companies, and many more are following suit. But how can retail businesses benefit from NLP services? Let’s find out!  In this blog, we share powerful and practical NLP use cases in retail.  

NLP: Quick Overview! 

The artificial intelligence-based subsets known as “natural language processing” (NLP) enables computers to comprehend, decrypt, and alter human language. 

In 2021, the size of the worldwide NLP market was estimated at USD 20.80 billion. With a predicted CAGR of 18.1% throughout the projection period, the market is expected to increase from USD 26.42 billion in 2022 to USD 161.81 billion by 2029. 

NLP employs algorithms to convert varied, unscripted, spontaneous communication into something a machine can comprehend and respond to.  These algorithms enable NLP to extract meaning from the tangle of sentences, slang, jargon, and daily language we use. It filters through what we say and transforms it into a database, translating human speech into a language that computers can comprehend.  

NLP in Retail: Why Is It Important?

The exciting possibilities provided by NLP help close the crucial gap between in-person and online shopping experiences. It also lowers costs, raises basket sizes, and improves customer satisfaction. Retailers have an exciting opportunity to capture the characteristics of in-store purchasing that many customers demand by implementing NLP during retail software development to enhance interactions between humans and machines. 

NLP had a wide range of resources to assess a prospective client’s demands accurately. It acts like a highly effective person that never gets weary when combined with AI development services. By incorporating it, the retail industry could offer many customers what they want from in-store purchasing. Retailers are making progress in incorporating NLP services technology to transform the shopping experience. The newest behavioral trends, as well as competitors and their tactics, are constantly things that retail businesses need to be aware of. They can improve their marketing and sales tactics by using NLP to stay current with trends and other information. 

Let’s discuss some of the most exciting NLP use cases in the retail sector. 

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Use Cases of NLP in the Retail Industry!

The primary use cases of NLP in the retail industry are shown below, along with some additional information. So, have a look! 

Chatbots for customer support 

Though brick-and-mortar stores can employ in-store associates to address customer concerns and provide personalized purchasing guidance, most e-commerce websites lack the human touch many customers want. Making realistic touchpoints that resemble human interactions is excitingly possible with NLP services.  

Many customer problems can be resolved by NLP-powered chatbots, especially where prompt responses to straightforward inquiries are possible. By facilitating clients’ decision-making processes and altering lead generation while adopting a conversational approach, chatbots can also enhance conversions.  

An efficient strategy to reduce expenses, prevent delays, and increase the effectiveness of your customer success operations is to implement NLP technologies to substitute helpdesk agents. Modern NLP technologies are sophisticated enough to build smart chatbots that ensure service 24*7, rather than using crude voice instructions that could lead to further annoyance.  

Also Read: Retail Chatbot Development

Sentiment analysis 

Building a business plan requires an understanding of customer sentiment. To provide data insights, NLP-based software can evaluate social media information, product evaluations, and customer content. To examine the contexts of both positive and negative reviews addressed at a brand, sentiment analysis is performed. The algorithms create sentiment analysis models using comments in order to function. The most frequent words are analyzed to recognize classifiers, and so well, negative and positive words are examined.  

Then, a value is given to each bit of information, usually, a number designating whether a sentiment is good, negative, or neutral. With this information at their disposal, marketers can better plan their campaigns and predict consumer demand for products and services.  

Product search 

NLP can help brands uncover methods to make their ranking better on their platform so that users can locate the information they’re looking for. To find the target, it examines semantic patterns in search panels. 

Also, the ability of application programs to understand everyday human language is known as natural language processing (NLP). Traditional search panels have been inflexible; any consumer slang or typo produced no valuable results. 

The search panels will be assisted by NLP in understanding the context of user-inputted terms and returning relevant results. Without the requirement for human tagging, you can also leverage NLP for product catalog refinement to make your products more accessible at scale. This makes it easier for customers to find what they want faster—despite errors or jargon.  

Also, text and characters are recognized, converted to data, and then stored in the database. One of the language processing techniques that search panels have long employed is the capacity to read the text and turn it into machine-encoded text. Things are faster and more accurate with the aid of NLP than they were before. Instead of just providing search results, users now receive clear responses to their questions.   

Touchscreen assistants 

Touchscreen devices that utilize NLP are also available for use in retail environments. To help customers with the item on display, these gadgets can be placed every few meters or at specific product locations. 

Similarly, to robots, these assistants can be enhanced with other technologies to offer further support, such as computer vision. For instance, the robot can view the stock of the product that is on display with the aid of computer vision. It can then employ this data to reply to customers or notify store management to resupply the product. In order to provide better support, the tool can also be embedded with emotion and behavior recognition technologies.  

Smart product recommendations 

Keywords are typically used to underpin product suggestions. Users will only get the results of what they type. On the other hand, NLP has the capacity to incorporate more variables, such as context and historical search information. These elements may aid in making search results more targeted. 

Directing them toward the appropriate products, also helps the merchants keep the customers interested. Showing customers products that are suitable for their needs can decrease site abandonment and boost sales. With NLP, users won’t feel overburdened and will find browsing through products and items to be less of a hassle. Users will have a lot better and more pleasurable experience as a result. It’s not always possible to find the best information with these search engines. Extracting item features and recommending appropriate things to users is more difficult. Because of this, it’s necessary to have a recommender system that is effective and impartial and, in essence, creates a solid foundation. 

Competitive analysis 

When starting a business or entering a new market sector, a competitor study is typically carried out. A deeper understanding of the market, the potential customers, and the rivals can be gained by the analysis. 

Evaluating the competitive landscape can be significantly streamlined and automated with the help of NLP-powered solutions. Resources are available for keeping tabs on the competition. Typically, this involves searching the internet for publications about the industry and using that data to feed an NLP module that finds semantic relationships between businesses.  

Effective social media marketing 

For retailers and online store owners who wish to comprehend consumer buying behaviors, forecast product demand, or keep an eye on trends to tailor advertising messages, social media monitoring has emerged as a crucial resource. Given this information, advertisers who include holiday stress alleviation in their ads may find customers interested in them as Celebratory approaches. To target marketing communications, NLP helps to highlight the buzzwords. 

Looking to Develop NLP Solution for Your Retail Business


NLP has many advantages for the retail sector. These in-depth insights, managed by NLP services, would be beneficial to the retail business industry. It will contribute to raising customer satisfaction levels and enhancing the revenue curve. The operations of retail businesses will change in the future thanks to an increasing number of creative concepts centered around the idea of NLP. 

Also, natural language processing  in retail is improving customer service and is very economical. It frees up time for employees to work on more worthwhile or complicated tasks instead of squandering it on menial duties. NLP is generating fresh ideas for company models and giving customers a distinctive experience. 

In the upcoming years, NLP is anticipated to have a major and positive impact on the retail sector. It will alter and, in some ways, enhance how customers discover products.  

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