An excellent article from McKinsey (I may be biased, but analytics used right are very very powerful!).
More than 90 percent of the top 50 banks around the world are using advanced analytics. Most are having one-off successes but can’t scale up. Nonetheless, some leaders are emerging. Such banks invest in talent through graduate programs. They partner with firms that specialize in analytics and have committed themselves to making strategic investments to bolster their analytics capabilities. Within a couple of years, these leaders may be able develop a critical advantage. Where they go, others must follow—and the sooner the better because success will come, more than anything else, from real-world experience.
By establishing analytics as a true business discipline, banks can grasp the enormous potential. Consider three recent examples of the power of analytics in banking:
- To counter a shrinking customer base, a European bank tried a number of retention techniques focusing on inactive customers, but without significant results. Then it turned to machine-learning algorithms that predict which currently active customers are likely to reduce their business with the bank. This new understanding gave rise to a targeted campaign that reduced churn by 15 percent.
- A US bank used machine learning to study the discounts its private bankers were offering to customers. Bankers claimed that they offered them only to valuable ones and more than made up for them with other, high-margin business. The analytics showed something different: patterns of unnecessary discounts that could easily be corrected. After the unit adopted the changes, revenues rose by 8 percent within a few months.
- A top consumer bank in Asia enjoyed a large market share but lagged behind its competitors in products per customer. It used advanced analytics to explore several sets of big data: customer demographics and key characteristics, products held, credit-card statements, transaction and point-of-sale data, online and mobile transfers and payments, and credit-bureau data. The bank discovered unsuspected similarities that allowed it to define 15,000 microsegments in its customer base. It then built a next-product-to-buy model that increased the likelihood to buy three times over.
Results like these are the good news about analytics. But they are also the bad news. While many such projects generate eye-popping returns on investment, banks find it difficult to scale them up; the financial impact from even several great analytics efforts is often insignificant for the enterprise P&L. Some executives are even concluding that while analytics may be a welcome addition to certain activities, the difficulties in scaling it up mean that, at best, it will be only a sideline to the traditional businesses of financing, investments, and transactions and payments.
In our view, that’s shortsighted. Analytics can involve much more than just a set of discrete projects. If banks put their considerable strategic and organizational muscle into analytics, it can and should become a true business discipline. Business leaders today may only faintly remember what banking was like before marketing and sales, for example, became a business discipline, sometime in the 1970s. They can more easily recall the days when information technology was just six guys in the basement with an IBM mainframe. A look around banks today—at all the businesses and processes powered by extraordinary IT—is a strong reminder of the way a new discipline can radically reshape the old patterns of work. Analytics has that potential.
Tactically, we see banks making unforced errors such as these:
- not quantifying the potential of analytics at a detailed level
- not engaging business leaders early and to develop models that really solve their problems and that they trust and will use—not a “black box”
- falling into the “pilot trap”: continually trying new experiments but not following through by fully industrializing and adopting them
- investing too much up front in data infrastructure and data quality, without a clear view of the planned use or the expected returns
- not seeking cooperation from businesses that protect rather than share their data
- undershooting the potential—some banks just put a technical infrastructure in place and hire some data scientists, and then execute analytics on a project-by-project basis
- not asking the right questions, so algorithms don’t deliver actionable insights
