NLP stands for Natural Language Processing. This technology helps to understand natural human language (in a spoken or written way), process it, and deliver a rewarding user experience.
That is why NLP plays an essential role in ecommerce business success. It is predicted to become the future of ecommerce search.
So, why should you focus on NLP technology in your ecommerce store? What are the advantages? How to exploit NLP to increase store revenue?
Where is NLP used?
NLP is used in many digital tools that require translating human language into a computable string of information. For instance, NLP is used in virtual assistants, chatbots, or other voice-generated systems.
In ecommerce, NLP plays a critical role in understanding users’ search intent. For instance, if a person searches for a green waterproof winter jacket, your site search should translate the query into computer-understandable information, apply relevant filters, and return only the products that match the query.
So, how does it work?
How does ecommerce search use NLP?
NLP is a critical part of a successful ecommerce business.
It converts complex user text into data. This allows the search engine to return only user-relevant results.
Eventually, this might increase your site conversion rates by at least two to six times, on average. LupaSearch provides even more promising results. This ecommerce site search has skyrocketed the conversion rates for their customers by up to 8 times.
So, how can you leverage NLP in your ecommerce store?
Use it as a part of a user-satisfying search solution:
NLP plays a critical part in an overall site search ecosystem. Powered by NLP, more basic search functions like autocomplete or search suggestions become hyper-relevant.
NLP can significantly enhance the relevance and efficiency of search results, improving user satisfaction and reducing bounce rates [1].
Once your site search engine understands the meaning of the user query, it can suggest user-relevant products in real time. This is especially beneficial for exploiting the user’s I-want-to-buy moment and increasing the conversion rates.
Analyze user queries to meet the demand:
By analyzing the words, phrases, and combinations used by your website visitors, you can understand what interests your potential buyers the most. Analyzing customer feedback through NLP could identify trends and improve product offerings [2].
For instance, as a clothing retailer, you might notice the increased demand for waterproof clothes during rainy weather periods. Plan your stocks accordingly to satisfy the demand. Over time, you can understand what products and categories are trending. You can even use the exact phrasings for advertising certain products.
Pay detailed attention to zero-results pages:
If users type in complex search queries, but your website fails to deliver matching results, analyze the data. By doing that, you can get to the core of the problem and prepare your action plan accordingly.
This can help identify issues with search relevance and guide adjustments to improve user experience [3].
LupaSearch - your business partner for exploiting site search
LupaSearch is an effective ecommerce business partner that can help you increase your revenue with a hyper-relevant site search.
LupaSearch is an ecommerce search solution tailored to each business. It uses your product database and custom rules to deliver a rewarding user experience on your website. (And that is shown to significantly enhance conversion rates [4].)
Powered by NLP, your site search can get to the core of each user intent and return only the products that match user requests.
LupaSearch is supported in many languages and can be easily integrated into the most popular ecommerce platforms.
All you need to do is contact a LupaSearch product consultant. We will provide you with a live demo (with your database), so you can immediately see how it works.
Contact us today, and let’s have a productive conversation, opening new opportunities for your ecommerce business growth.
References
Jammalamadaka, R., Chittar, N., & Ghatare, S. (2009). Mining product intention rules from transaction logs of an ecommerce portal. , 311-314. https://doi.org/10.1145/1620432.1620468.
Halder, K., Krapac, J., Goryunov, D., Brew, A., Lyra, M., Dizdari, A., Gillett, W., Renahy, A., & Tang, S. (2022). Enhancing Product Safety in E-Commerce with NLP. ArXiv, abs/2210.14363. https://doi.org/10.48550/arXiv.2210.14363.
Bhattacharya, S. (2023). MONITORING AND REMOVAL OF FAKE PRODUCT REVIEW USING MACHINE LEARNING (ML). International Journal of Engineering Applied Sciences and Technology. https://doi.org/10.33564/ijeast.2023.v07i12.007.
Sharma, A., Bajpai, B., Adhvaryu, R., Pankajkumar, S., Gordhanbhai, P., & Kumar, A. (2021). An Efficient Approach of Product Recommendation System using NLP Technique. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2021.07.371.