Pete Daykin is CEO and co-founder of Wordnerds. Social listening and business insight software has not been good. Platforms like Brandwatch and Crimson Hexagon are great at visualising quantitative data, but struggle with the nuance of topic and sentiment analysis. And who can blame them: language is complex, right?
When people talk about big data, they mean numbers. We like numbers. They’re unequivocal. We know precisely what they mean. Yet 80% of actionable data exists in the form of unstructured text: vast collections of words that are unpredictable, colloquial, fluid, sarcastic and entirely dependent on context.
Most organisations have millions of tweets, emails, webchats, online reviews, CRM-entries and survey results written by and about them. Big data that—because words are so hard to understand—remains totally invisible.
Wordnerds combines Artificial Intelligence (AI), Natural Language Processing (NLP) and Advanced Corpus Linguistics to provide automated understanding of unstructured text data. We deliver next-generation social listening, business insight software and automated text understanding to provide reliable and actionable insight.
Global and nationally recognised brands, including regulated industries and utilities like water companies use our Software-as-a-Service (SaaS) linguistics engine:
- To find specific things in large datasets (people complaining about problems with their service or product on Twitter, identifying leads, finding people who care about an issue etc.)
- To spot trends and see what issues people are talking about
- To understand how people feel about an issue, product or service, taking large quantities of text and summarising it in real-time (what learning can be gleaned from 15,000 Amazon reviews? What information is held in 3 million forum posts? What problems do you have that similarly sized competitors don’t? How can you make objective and quantifiable sense of the thousands of survey responses you have, each with long “any other comments” fields? How is sentiment changing over time?)
Pete is a bang average footballer, a dreadful drummer, world class at table football and not as young as he used to be. He has a puppy, a hamster, two pretty weird kids and, surprisingly, a wife.