It seems everywhere we look, companies and analysts are talking about Natural Language Generation (NLG) software. For example, how to use Natural Language Generation to automate the writing of reports or product descriptions. Or how to use Natural Language Generation to increase operational efficiencies. But the question is, where should you start?
In January, Forbes magazine listed Natural Language Generation (NLG) as one of the hot topics for the year 2017. The announcement came as no surprise to those who follow the field. Gartner has been forecasting an increase in NLG adoption rates for the last 3 years, writing that by 2018, 20% of all content will be written by a computer.
Many industries have found different answers for how to use Natural Language Generation. For example, fund reporting in finance, campaign analytics reporting in marketing, and personalized client alerts for CRM dashboards in sales (to name a few). With NLG, all these reports can be generated on demand with the most up-to-date information available. Some NLG software available today can support bespoke report generation, incorporating your company’s best practices and relevant regulations. The end result? A personalized, non-robotic report that can explain not only what is happening in the data, but why and the next steps to take.
But what is missing from the current coverage is how do you choose the right use case? Obviously, you need to start somewhere to evaluate the software and to understand how it works in your business, but what criteria should you use?