What is Natural Language Processing (NLP)?
Natural Language Processing, simply put, is the technology that allows digital shopping assistants to interact with consumers in a manner that is very close to human-like, if not identical.
This involves all parts of the conversation, from the ability to understand or gauge the intent of the consumer, brainstorming about what the best possible responses would be, and finally delivering answers that give the end consumer the feeling of interacting with a human agent.
The end goal and purpose of Natural Language Processing is to enable interactions where consumers cannot differentiate if they are talking to a human or a digital assistant.
Natural Language involves both processing power (NLP) as well as the ability to understand (NLU) and recognize intents or hidden demands within a consumer’s message. NLU or Natural Language Understanding plays as important a role as Processing does because it is what truly differentiates a humanized support experience, from a basic automated bot conversation.
Another important factor in Natural Language Processing is the ability to maintain conversational flow and ease, especially when dealing with an inflow of complex information or while conversing in different languages.
Most people have experienced Natural Language Processing, whether they know it, or not. From voice-operated map assistants, smart home assistants like Alexa, while giving commands to Siri, or even answering an automated question on a shopping website.
These are just a few applications of this technology, which will only keep expanding as research into bettering human-AI conversations continues.
How does NLP work?
What sets NLP apart as a technology is its ability to analyze unstructured conversational data, in the form of consumer questions, and extract the intents hidden inside the message. This quality of AI intelligence which is enriched through every new consumer conversation, allows digital shopping assistants to grow smarter and more capable of carrying on humanized conversations and catering to even the most complex demands.
NLP chatbots and retail, what’s the deal?
This refers to any communication from the consumer, whether logical or not, which is easily deciphered using NLP.
NLP chatbots can sift through unstructured conversational data to identify the true intent behind the consumer’s question or request.
This represents any entities that support the consumer’s request, for example, a date, location, or time.
Saves time as consumer profiles and preferences are saved to reduce repetition throughout the conversation.
NLP helps the chatbot in understanding human emotions through sentiment analysis.
Looking for an NLP-powered digital shopping assistant? Get in touch!
Business benefits of SID’s Natural Language Technology
Retail ontology at its core:
All of SID’s AI models are pre-trained on retail ontology and support the automated generation of tags, utterances, entities, and Q&As.
170+ retail intents:
AskSid’s NLP library is backed by 170+ unique retail intents that help provide exceptional conversational support from day one.
Minimal effort in data preparation:
AskSid’s customized NLP and NLU models help in the preparation and training of brand and product data, leading to an enriched centralized retail repository.
NLP in retail
The booming e-commerce business holds immense potential for the implementation of NLP-enabled digital shopping assistants, after all, consumers these days have high expectations in terms of brand support and the personalization of their shopping journeys. With almost every retailer having deployed an AI assistant on their website and other channels, they must be first and foremost backed by NLP to be able to understand consumer needs and provide accurate resolutions.
Accuracy is key when it comes to effective and sustainable NLP digital shopping assistant implementations and a deciding factor that determines if a consumer will go ahead with a purchase or abandon their cart. With consumer engagement and experience banking on NLP, precision in operations and immense domain data knowledge are highly necessary for successful implementations.
- Named entity recognition
- Intent and topic predictions
- Question answering
- Product recommendations
- Text generation
Learn more about SID’s digital shopping assistant that you can deploy for your retail brand in just 4-6 weeks.