Getting a look at real world natural language processing examples helps build the case for utilizing new technology to improve the customer experience. It’s the social proof teams need to convince decision makers that the natural language processing (NLP) is worth the money and has the potential to bring in considerable returns. By seeing the power of the technology through the eyes of real users, anyone can make a compelling case for its use.
Natural Language Processing Examples: Confirming What Works
A great use of NLP software is when a company wants to confirm the effectiveness of a new or ongoing strategy. Getting immediate feedback through online reviews helps to confirm or deny an apparent trend in the customer experience.
Let’s take farm supply brand Rural King as an example of this practice in action. The company offers free popcorn at its locations as part of the shopping experience. The investment in the snack is paying off with the “popcorn” keyword used in a positive sentiment in more than 2,400 reviews. The company uses the customer experience analytics software to make note of other positive keyword sentiments, such as the brand’s overall product selection and variety.
Likewise, evo, an outdoor goods store with locations in Seattle, Portland, and Denver, utilize their NLP reviews to gauge the current state of the customer experience against benchmarks. By monitoring multiple aspects of the customer’s experience, such as atmosphere, product variety, and even the parking situation at each location, the company can make the insight-based changes needed to exceed consumer expectations and attract even more customers.
Natural Language Processing Examples: Addressing Issues
Another powerful natural language processing example works in tandem with your review response strategy. Specifically, companies can use NLP to address online reviews that have specific keywords with negative sentiments. Not only does this help dictate changes in the experience; it’s also a way to address issues and maintain a strong online reputation.
A hospitality brand, with over 400 properties in the U.S. and Canada, uses NLP in this exact way. When a positive or negative trend becomes apparent for a specific keyword, the customer experience analytics program creates a category around it, which notifies the team in charge of reputation management. With this data, the team can triage the reviews with that specific keyword and create response templates that addresses the issues while maintaining a uniform brand tone.
As your team sees these trends, it would be worth learning how to respond to negative reviews and look at positive review response examples to get an idea of how to properly respond to reviews of any type.
Conclusion
These natural language processing examples are only the tip of the iceberg when it comes to the possibilities of what can be done with NLP software. As more companies use the software and gain more insights, the technology can be used to anticipate consumer expectations, which further enhances the shopping experience and delights customers to the point where they become loyal consumers.
