Predictive Analytics: Think Big, Start Small … Just Start Now!
We are entering an era of connected experiences, where consumer banking interactions are increasing exponentially. Predictive analytics allows financial institutions to better understand consumer needs and to provide personalized and contextual experiences along the entire customer journey.
As mobile technology evolves and customer journeys are digitized, being able to gather data and conduct post-event analysis is no longer enough. Consumer expectations are being set by companies like Google, Apple, Facebook and Amazon, where real-time predictions about future needs and behaviors build enhanced experiences.
The marketplace is quickly moving from “mobile-first” to “AI-first”, evolving from a descriptive analytics model (rear view mirror view) to a predictive analytics model (insight GPS view). With predictive analytics, we are in a better position to ‘know the consumer’, ‘look out for the consumer’, and ‘reward the consumer’, learning from previous experiences and predicting future behavior.
Banks and credit unions that are not yet leveraging predictive analytics need to begin harnessing the power of this technology today to ensure competitiveness in an increasingly digital marketplace. This is the perspective advanced by the Predictive Analytics Working Group at Mobey Forum, in its report entitled ‘Predictive Analytics in the Financial Industry — The Art of What, How and Why‘.
“Banks have great data but if they want to compete in the digital age they need to get more strategic and more professional about how they use it,” comments Amir Tabakovic from BigML and Co-Chair of the Predictive Analytics Working Group at Mobey Forum. “The huge influx of new, specialist, data-centric players in digital financial services also means that (predictive analytics) is already becoming commonplace among the ‘new breed’; new services underpinned by predictive analytics are enabling the next generation service providers to extend their lead.”
Digital banking has reduced the cost of banking for the financial institution, but has made the relationship between the bank and the consumer more distant. There are fewer face-to-face interactions, but significantly more opportunities to interact with the consumer overall.
Analysis of these interactions, combined with already available customer insight, provides an opportunity to make banking more personalized, more real-time and more solution-focused than in the past. Advance, predictive analytics can improve the customer experience throughout the digital journey of a consumer, adding value for both the customer and the bank.
According to Mobey Forum, there are four reasons why now is the time for banks and credit unions to embrace predictive analytics:
According to Gartner, if an organization is using data to understand the past, the process is usually referred to as either ‘descriptive’ or ‘diagnostic’ analytics. These tools answer the questions, ‘what happened and why?’.
‘Predictive analytics’ moves the view from an historical orientation to a forward-looking perspective. The objective of predictive analytics is not just to describe and understand the past, but to explore ‘what is likely to happen’. Unlike descriptive or diagnostic analytics, predictive analytics is not as precise and is based on probabilities.
The fourth type of analytics is ‘prescriptive analytics’, which tries to answer the question ‘How can we make it happen?’ or ‘What should we do?’. According to Gartner, only 3% of companies are currently using prescriptive analytics. With each step up the ‘analytics ladder, the difficulty of analysis increases but the potential value of the insight also increases.
Many financial services organizations confuse descriptive analytics with predictive analytics.
Posted on 7wData.be.