The election of the Pope in 2005 and 2013.
According to Tony Potter, Regional VP, EMEA at DataSift, the digital universe is transparent, observable and measurable. Take the case of Twitter. A single tweet contains 120 unique items of data, such as the date it was published, where it was published and who published it. All these data points are potentially tiny nuggets of customer insight that go way beyond just the text of the tweet itself.
Understanding the world around us has come a long way from where companies started – just monitoring mentions of their brands online. In sheer volume terms, it is now impossible to eyeball every tweet, post & comment. Companies must move beyond just looking at and listening to online data towards actually understanding what this data is telling them. Why? Because this data, says Potter, has unlimited business potential.
3 examples in particular highlight this difference between listening and understanding:
1. Intent Measurement with Machine Learning
There is a lot of misguided interest in overall brand sentiment. Who is talking negatively about my business, who is talking positively about my business? But sentiment is not actionable, it doesn’t give us insight into what people really think. Datasift, for example, trains an algorithm to learn by example and categorise how people are actually talking. One company that really understands this difference is Dell. Dell maps social data to product and intent feedback. There are 38,000 social posts every day about Dell. Dell measures Net Promoter Score across 37 product lines. They then apply 150 metrics to give a social advocacy score that drives actionable insights that in turn have led to a 38% increase in product purchase loyalty.
2. Research Panels Applied to Social Data
Social has allowed access to polling data on a scale previously unimaginable. YouGov are known as a company leading the way in understanding this data through their UK social research panel. This allowed them to better understand voter preference, remove demographic bias and deliver real-time insights into the UK electorate.
3. Predictive Power of Social
What if we can predict what people are going to do by combining social data with internal company data? Predictive business intelligence that brings together transactions & conversations and gives us the ability to create predictive models based on social signals. For example, film studios that are now going beyond just listening to the sentiment around new film releases, and starting to predict relationships between box office revenue and social interest and intent in order to understand the ideal quantity of advertising spend to invest in a new film.