Big data, big insights, big opportunity. Data has risen in the ranks of banking business priorities as customer intelligence promises to puts budgets to better use. The undeniable advantage of data capture, analysis and application has retail banks scrambling for real-time insights.
Big data is a big asset. Statisticians can mine for customer insight that translates to better segmentation strategy, targeted interactions and customer-centric product development. The data scientist can literally mine for money, which is why this sought after and scarce role is being propelled to the forefront of resourcing in retail banking.
So why is data so powerful?
Because it creates a smarter business. One that can deliver personalized experiences, optimize marketing spend and innovate services. The data scientist can create customer storyboards with analytics, predicting future purchases and banking behaviour. It is this logic that is forcing retail banks to recognize research as the enabler of innovation not the killer of creativity, as data underpins all innovations in pricing, promotions, products and experience.
Big data, big innovations, better banking
Banks now need to turn bytes into business advantage as mining becomes a must-master skill. Volume, variety & velocity – The 3 V's that are challenging the data scientist to find focus in a seemingly endless pool of data. Predictive analytics techniques can assist in anticipating needs where micro-segmentation & customer profiling puts marketing dollars to better use. Tackling unstructured data will be the next big hurdle for retail banks looking to learn more about their most valuable asset – their customer.
Crunching data will also create a stand-out banking experience. Yet real-time analytics creates real-time expectations. Retail banks need to focus on developing a data driven retention strategy, where interactions are personalized and data enables a proactive and informed customer service team.
Data analysis and application can also find fraudsters in disparate databases with optimized risk models. Retail banks need to quantify and predict liquidity risk and data allows for a higher number of variables to be integrated into risk analytics; in other words, analytics is providing answers on activities not just transactions. Real-time data feeds can thus strengthen compliance.
Data can also identify future banking services by turning insights into inspiration for customer-centric product design. By weaving analytics into the product development process, retail banks can generate art through data science. With customers increasingly driving the development of new banking services, data now need to be positioned at the forefront of the innovation pipeline.
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