When we last left off, we went over the basics of natural language processing. The long and arduous history of NLP has little to do with retail, but with the Internet boom in the 1990s, it became an obvious way to make shoppers’ lives easier.
How it’s used in ecommerce
Natural language processing has permeated the ecommerce world, making it more accessible and more real than ever before. Not only has it made sites more user friendly, it has brought a new level of humanness into online shopping. Here are a few ways natural language processing improves the user’s shopping experience:
Natural language search engines
Because of NLP, computers understand colloquial phrases typed into the search box of an ecommerce site to help refine search queries and produce relevant results.
Foreign language translation
With the rise of the Internet, people from all over the world can shop at any store from the comfort of their homes. Thanks to natural language processing, it is now possible for machines to understand and translate different language for the user’s ease.
Virtual assistants and concierges
Ever spoken with a bot? With the new sophistication of machine learning, these AI bots are being used to help consumers shop online and on their mobile device.
Social media listening
It’s important to know what your customers are saying wherever they are: through an algorithm-based tool, businesses can index sites and monitor them. NLP used in these tools enables businesses to know what customers are saying on social media through identifying and categorizing words.
To improve the customer experience
Natural language processing has improved the user experience of shopping tremendously, but it has also helped businesses understand their customers better. Artificial intelligence and natural language processing has been the key to compiling information on consumers, analyzing that data, and using it help improve the overall experience.
Sentiment analysis is a popular way to do this. It is a natural language process that identifies and categorizes opinions from bodies of text to determine the writer’s attitude towards a product, service or topic.
Here at Welcome, we use this method, as well as created our own, to analyze conversations consumers are having with brands and retailers. From this data, we have been able to identify unique and detailed needs of consumers in a way never before possible.
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About the Author
Matt is the Director of Data Intelligence at Welcome. He has extensive experience working with large ecommerce retailers, growing overall business including topline revenue, channel specific revenue and contribution as well as offline revenue.