Like every other industry on the planet, banks and financial institutions are hungry to know more about the people using their products and services. And though they already store a ton of data – from credit scores to day-to-day transactions – they’re not too proud to look for it elsewhere.
Banking and Financial Services, they continue to purchase data from a host of retailers and service providers in an effort to create a 360-degree view of their customers.
This kind of customer segmentation allows them to:
• Offer customized product offerings and services
• Improve existing profitable relationships and avoid customer churn
• Create better marketing campaigns and more attractive product offerings
• Tailor product development to specific customer segments
By combining segmentation with predictive analytics, companies can also cut down on risk. For example, to decide whether certain customers are likely to pay off their credit cards, some major banks use technology developed by the company Sqrrl. This analysis takes into account the demographic characteristics of customers’ neighborhoods and makes calculated predictions.
Similar strides have been made in forecasting market behavior. Once upon a time (e.g., 2009), high-frequency trading – the speedy exchange of securities – was hugely lucrative. With competition came a drop in profits and the need for a new strategy.
HFT traders adapted by employing strategic sequential trading, using big data analytics to identify specific market participants and anticipate their future actions. In a field of breakneck speed, this gives HFT traders an unmistakable advantage.
Predictive analytics can also be used to issue early warnings on the market. In their paper, Quantifying Trading Behavior in Financial Markets Using Google Trends, Tobias Preis, Helen Susannah Moat and H. Eugene Stanley focused on the behavior of search engine users.
By studying search volume data provided by Google Trends, they were able to identify online precursors for stock market moves. Their results suggest that increases in search volume for financially relevant search terms usually precede big losses in financial markets.