NLP: Redefining the future of eCommerce
Investments in NLP are driving industry-wide innovations in eCommerce enabling enterprises to conduct semantic search, perform sentiment analysis, curate personalized product recommendations, understand customer intent, and drive intelligent search.
The ability of computers to understand human language has been growing for the past 70 years. Natural language processing began in the 1950’s as Machine Translation, focusing on automatic translation between English and Russian but it only came into its own with the advent of AI.
NLP is a subset of AI which has the ability to interpret and analyze human language using applications such as text-mining, sentiment analysis, machine translation among others. NLP divides text into smaller components so that the context and intent can be better understood.
NLP refers to the ability of AI systems to comprehend the structure and meaning of human language in order to facilitate interactions between humans and machines using natural languages. The possibilities for NLP applications is underlined by the studies that indicate that the global NLP market is projected to grow from $20.98 billion in 2021 to $127.26 billion by 2028.
NLP is a multi-disciplinary approach that lies on the intersection of machine learning, statistics and linguistics. It has two subsets,
- NLU which deals with the understanding of human language. For example: Discerning customer intent from the words typed into the search bar is a function of NLU
- NLG which deals with responding to humans using natural language. For example:Responding to customer queries through chatbots is a function of NLG
NLP in eCommerce
E-commerce was one of the early adopters of NLP; beginning with chatbots and conversational interfaces to automating business processes and enriching customers’ experiences through semantic search and sentiment analysis, some of the most promising applications for NLP are in the eCommerce industry.
NLP is the next frontier of eCommerce’ - Subrat Panda, Capillary Technologies
Using NLP, retailers can now cater to customers globally, providing curated shopping experiences for customers in the language of their choice. NLP has the potential to change the way customers shop while simultaneously presenting exciting opportunities for retailers. Retailers use NLP in search to lead customers to the exact product they are searching for. Retailers can use NLP to gather insights and identify gaps in a product offering.
NLP applications in eCommerce
Understanding Customer Intent
Understanding customer intent enables product discovery by driving customers to their desired products even when they use non-specific search queries. One major challenge in discerning customer intent is that broad search terms could display too many results. In order to deal with this, retailers can use domain-specific architecture to understand customer intent from an ever-growing dataset of customer search queries.
Personalized Product Recommendations
According to one study personalized product recommendations account for a third of eCommerce revenues. With enterprises coming to the realization that personalization is at the heart of brand loyalty, they are tailoring site interactions to suit their customers and provide personalized product recommendations.
While most recommender systems are keyword-based; NLP driven search usually considers other factors such as previous search data and context to drive personalized product recommendations which can lead to increased sales and reduce site abandonment.
Lexical and semantic ambiguity make languages hard to understand. Advances in NLP have led to AI models which can understand semantic context and discover customer intent from the typed text. Traditional rule-based keyword searches are no longer fit for purpose with NLP based search results yielding better more accurate search results by relying on semantics rather than keywords.
Semantic search functions by leveraging the meaning behind the keywords typed into the search bar, and then discovering intent behind the keywords by using tools like word categorization, or meaning databases. Semantic search yields results even when the exact search term is not typed into the search bar. eCommerce enterprises that have incorporated semantic search have had a demonstrably lower site abandonment rate than those that rely on text-based search.
Semantic search can grasp context and discover customer intent from search terms
Semantic search, powered by NLP allows for larger search queries, overlooking typos and even identifying synonyms. It also has the ability to distinguish between products and attributes, segmenting search terms and discerning which search terms carry more semantic weight. By analyzing search history, semantic search can also predict search terms through auto-completion, guiding customers to their desired products faster.
Intelligent Search Engines
Conventional search is so rudimentary that very often it cannot differentiate between the singular and plural forms of words. Poor search functionality and navigation are among the most important reasons why customers abandon eCommerce websites. With search being the primary navigation tool for customers, helping them move beyond cumbersome menu-driven navigation structures, NLP driven intelligent search engines are proving to be the need of the hour. Intelligent search functions by understanding human language.
NLP libraries to enable information extraction and conversational processing.
Enterprises are already deploying text-mining capabilities such as morphological analysis and Named-entity recognition to create NLP libraries through text-mining that enable information extraction and conversational processing. NLP libraries are organized datasets which contain large volumes of product data which retailers can then use to guide customers to their preferred products. The data mined from an NLP search engine can also be used to improve future search performance
Intelligent search engines can help cater to elderly people and those with disabilities by integrating voice commands in their search function. Using NLP in intelligent search engines can further incentivize additional spending. Semantic search aids intelligent search engines by providing synonyms, breaking down linguistic terms and relations in natural language.
Conversational AI-driven customer support
With research showing that dissatisfied customers are more likely to share their negative experience with other people, it is high time that enterprises optimize their support mechanism to ensure that negative experiences are minimized. Almost 55% of customer queries are product-related, the possibilities of automating these queries through Conversational AI are limitless. Advanced NLP models can even determine customer satisfaction from the tone of the customer.
eCommerce sites by their very nature lack the human touch when it comes to interacting with customers. NLP allows chatbots and virtual assistants to mimic human interaction. Intelligent chatbots powered by Conversational AI has the potential to offer 24/7/365 multi-lingual support to cater to customers round the clock. This can also free up time for human support agents to attend to more complex queries. Conversational agents can improve the site experience by determining customer satisfaction by gathering valuable feedback from customers.
Sentiment Analysis and Opinion Mining
AI can now understand the opinions and feelings of customers towards a specific product using sentiment analysis. NLP engines can interpret large volumes of data in emails, on social media, blogs, chats and so on to detect even subtle changes in customer behavior. It can recognize the emotions such as joy, sadness, frustration etc. and classify them as positive, negative or neutral outcomes opening up possibilities for personalized product recommendations.
Reviews offer more comprehensive insights than ratings and provide an understanding about user sentiment. Granular sentiment analysis can be used to figure out subjectivity and objectivity in reviews along with differentiating genuine reviews from the fake ones using topic modelling and latent semantic analysis. Using NLP, social media can be perused to analyse mentions to identify emotions of customers to understand market trends and buying behavior to predict future demand.
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