Data scientist is a business intelligence role that has seen its profile skyrocket – and businesses serious about content strategy are capitalising on their insights to predict and profit.
It’s been here all along
Finance companies, particularly those with quant teams and a heavy investment focus, have long been data pioneers whether they realise it or not. The analytics involved in day-to-day assessment and strategy already drive content efforts. Companies now increasingly employ data scientists to take that raw traditional data, discern patterns and draw further insights to predict what content will work best in a particular scenario.
Heads or tails?
The best definition of data science is from GE Digital: “Data science is the art of looking at data and applying scientific principles to figure out how to make heads or tails of that data.” And data is in overabundance. Geolocation technology tells you where customers live and purchase history can predict future shopping. A data scientist will take this information and tease out insights to engage and entice customers.
Brands use data science to develop new products and services. Consider LinkedIn’s ‘People You May Know’ feature, iTunes’ suggestions that predict what films or songs you’d like based on your buying history. Facebook is brilliant at analysing what you’ve liked, shared, and responded to – and giving you more of it (at the possible peril of users living an online echo chamber). Of course, Wall Street, and political prognosticators, through advanced algorithmic trading and polling, rely on data science for their very existence.
Companies now increasingly employ data scientists to take that raw traditional data, discern patterns and draw further insights to predict what content will work best in a particular scenario
Data science in finance
The onslaught of big data in the financial sector promises consistent new avenues to break down and benefit from. New insights and corresponding application can revolutionise user experience: from mobile app interactions to social media shares, market feeds, mock-ups, livestream simulations, and much more.
Streaming analytics is a key development the financial industry should leverage. This lets you manage and monitor performance and customer experience inside of streaming data by way of real-time query alerts. For example, setting an alert each time users spend more than two minutes on a transaction or receiving an alert if an IP address tries to sign in incorrectly more than five times. In this way, financial institutions are able to track and exploit opportunities, while targeting trust concerns such as possible fraud or malfeasance. Streaming analytics is the future of data science.
The crystal ball
By cannily ascertaining what content will work for (and expand) their client base, strategies become more targeted. Predictive analytics combine content data with powerful statistical models. Finance companies can work out the factors that best help sales or engagement. For example, what age demographic most responds to a marketing post; what is the most used (or most avoided) tool on an app; what exactly has people clicking to sign up most of the time? Data science streamlines the experience – and can make assumptions and adjustments without the need for a costly, old school focus group or customer questionnaire.
Watch this space.
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