Online retail shopping has become increasingly popular due to the convenience and the influence of the pandemic. One can see the transition between brick-and-mortar stores versus buying something online.
Natural Language Processing (NLP) is at the top of these adopted technologies. NLP has been headlined as one of the integral cogs of the online retail sphere. Putting into practice the uses of natural language processing and other AI-affinities technologies determines the difference between accomplishment and failure: AI in retail is a trend to watch out for!
There is merit in physical stores providing a personal touch with customers but e-commerce requires something extraordinary to build brand loyalty. NLP has evolved and reached a level where it can bridge the gap between physical store shopping and online shopping experiences and reduce costs, increase basket spend (the average value paid per customer per transaction in the store), and promote customer satisfaction. This is a monumental victory, especially for big shopping festivals like Black Friday and Cyber Monday, as examples.
NLP had a lot of tools under its belt to figure out the needs of a potential client. Combined with the power of AI, it behaves like a highly efficient human that never gets tired. Integrating that in the retail business would capture the elements of in-store shopping that many customers desire. Big companies in the online retailing industry are investing their effort into incorporating AI-enabled tech to revolutionize online experiences.
Let us peer into some of the most promising applications of AI and NLP in e-commerce.
If a brand is to be successful in this day and age, one must incorporate personalization to drive brand loyalty. This process can involve a couple of small tricks that lure the customer right back to shopping—for instance, Dispensing old-school coupons to offer customers discounts on their most sought out product or using Facebook ads that touch the consumer’s heart depending on age groups, or even tailoring the interactions with customers to make them feel important.
NLP introduces retailers with a potent analytical tool to create elegant representations of customers through social media listening. ‘Social intelligence’ in NLP refers to understanding your consumers' exact needs and wants to provide tailored communications that create a feeling of importance for the consumer. NLP delivers the ability to go through colossal towers of datasets to provide optimum customer service. Through social intelligence, NLP can continuously improve upon itself and update its conversation each time. Convenience is key here, as it’s human nature to root for the most straightforward option while buying a product.
Let’s take a look at how NLP techniques change the E-commence game:
A recent survey suggested that one dissatisfied customer speaks about their experience of at least nine to fifteen people on an average. At the same time, 13% of consumers will take the extra step and tell over 20 people. Brands simply cannot afford to provide lousy customer care if they wish to build a trustworthy reputation. Implementing NLP techniques is an effective way to reduce costs, remove delays, and improve the efficiency of your customer care department. Using rudimentary voice commands can bring the opposite of the desired effect and only frustrates the consumer. NLP technologies can create intelligent chatbots that provide 24/7/365 coverage as, unlike humans, it can never have a bad mood or get tired.
A considerable proportion of customer care deals with common queries about a particular product or service. NLP can create maximum efficiency and make more free time to deal with more complex queries.
Brands are constantly searching for ways to improve their website's search engine to help customers find their queries: NLP can achieve this. It analyzes semantic patterns in search bars to pinpoint the target. An excellent example of this is Yahoo Japan inspecting a combination of morphological analysis and named-entity recognition to further its text mining capabilities: This can build NLP libraries for information extraction and conversational processing.
Progress in Machine Translation (MT) creates opportunities for online retailers to seep into international markets and improve the customer experience across multiple languages (read this article to discover the challenges of language facing AI). One example of great NLP integration is Alibaba Cloud which has created NLP and deep learning technology alongside e-commerce data to produce accurate translation services that partners across the globe. Translating web content and advertisements with high accuracy is essential to enhance brand loyalty and make customers feel appreciated.
NLP in E-commerce is bringing improved customer care and is highly cost-effective. It clears up time for employees to work on more valuable or intricate projects rather than wasting time on mundane tasks. NLP is creating new propositions for business models and bringing a unique experience to consumers.
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