Understanding the Consumer Voice using Natural Language Processing
In the past the way companies and consumers was simple, slow, and predictable. Brands would research their market through traditional surveys and focus groups. Once a new product had been developed, brands would advertise through traditional media such as TV, radio, print, billboards, and we, the consumer, would go out and buy them. A lot has changed in the past couple of decades. Consumers now research products in an instant via search engines, talk openly about the brands and product they like or dislike on social media and leave feedback immediately in the form of reviews on eCommerce sites. This means there is a huge swathe of data companies can use to better understand the digital consumer instantly. There are advantages in this new world, in that customer feedback is faster, companies can identify consumer trends quicker and create products and services to suit their needs. However, without the right tools and techniques, this mass of unstructured data can easily turn into a cacophony, which is difficult to decipher and derive useful insight for brands. Much of this consumer data, whether that be customer reviews, social media posts or search engine queries is in the form of natural language. Natural language processing (NLP) is a range of techniques for analysing and representing naturally occurring text.