E-commerce is growing rapidly as many customers are changing their shopping habits. We more often prefer to lie on the sofa and scroll shopping websites to find suitable clothes or buy new console games. Our smartphones and laptops are becoming data warehouses gathering information about our characteristics, habits, and visited locations (including websites) and potentially are a source of information that may bring us more personalized products and services.
This data – if used properly and in line with specific legal and regulatory requirements – may be also a great opportunity for e-commerce providers or sellers. Natural language processing combined with a machine or deep learning may not only track users’ activity on the e-shop website or search engines but also recommend certain marketing techniques to get potential customer’s interest efficiently and ethically. It may also help to better understand how customers are navigating through the website when looking for specific content and in turn – give information on how to get their attention.
NLP is also helpful in ‘catching’ nuances and context of typed words and information that are not always clear and correct. The well-trained model may easily read users' intentions even though the information provided was not straightforward. Users will not be frustrated and will not have to undertake several steps to reach the content (product or service) he or she is looking for. Models may also generate content that is relevant for providers or sellers, including consolidated information and data about customers and their habits, desires, and sometimes problems.
With natural language processing eCommerce industry may also offer virtual assistants (sometimes called chatbots) that may help customers with less complicated cases, including complaints handling or finding products, for example by getting information from less clear descriptions. Of course, implementing effective and user-friendly assistants will require some time as people have their style and approach to communication. Therefore, it is important to ensure that the model is trained and ready for challenges that may emerge in the future. A frustrated customer may not be a customer anymore.
While implementing NLP and similar solutions in eCommerce it is also worthy to mention ethical issues that may come with ‘great wisdom’ about customers. It is important to ensure that customers and visitors are aware that they are interacting with automated tools (so-called transparency) and that any information we are gathering (if it is personal data) was subject to ‘target's consent in line with relevant personal data laws and regulations. This data should also be used for achieving specific goals that are explicitly indicated in the communication. Therefore, if you want to train your model and no anonymization techniques are used, don’t forget about the law!
With all ‘ingredients’ combined you will receive not only automated ‘selling machine but also more satisfaction from your (future and current) customers.
