NLP and How It Improves Customer Experience

Get informed on NLP!

First off, let’s understand what NLP (Natural Language Processing) is. As defined by wikipedia:

Natural language processing (NLP) is a subfield of computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.

The insight on NLP

Conversational business applications enable long-running interactions with customers via different channels using the most intuitive interface available: NLP or natural language processing.

Conversational interactions are driven by words and intent, whether in full sentences or in a menu. And, unlike social media, they can support engaging, two-way interactions with private audiences. When combined with automation and artificial intelligence (AI), these interactions can connect humans and machines through virtual assistants and chatbots.

Letting your customers speak to you naturally dramatically enhances the results of your outcome with them:
• Enhancing brand equity and customer lifetime value.
– Up to 700% increase in Customer Lifetime Value.
•Improving service margins and reducing operating costs.
– Customer engagement cost reductions between 84% & 96%.
•Expanding up-sells, total sales, and sales per customer.
– Up to 400% increase in revenue per engagement.
•Augmenting customer experience through better conversations.
– Up to 80% increase in customer satisfaction per engagement.


View our features page to learn more about how we use NLP.

We can help your business engage and interact more effectively with your audience and customers.


Techemergence gives more insight on Using NLP for Customer Feedback in Automotive, Banking, and More.

Example 1: Improving Customer Experience in the Auto Industry

In the automobile industry, imagine that a car company receives incident reports on a daily basis car owners or car dealerships. These reports consist of a paragraph or two of text describing a problem that the customer has experienced with a particular car. These incident logs have unstructured information. They come in the form of text, diagrams or pictures. They can also include metadata such as the occasion and other incident details that are structured, Dr. Porter clarifies.

The question is: Can the car company mine the text reports to find patterns at the macrolevel and discover what is happening with the cars in a particular model and year? Can this information help diagnose important problems, or detect trends that might help the car company improve its products?

Data entry might vary widely across different parts of the automotive industry. Car incident reports could be typed if they are received by someone in the office. Technicians in the field could be using audio devices to report a problem. A business with the ability to find patterns and across all of these different data types would be better prepared to find and address problems and opportunities quickly.

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