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Succeeding in Banking Analytics – Choosing the right business model

What is an appropriate business engagement model for succeeding in the analytics business?  This is a great question, but has no simple answer. This question seems to be on the minds of leadership in analytics service providers. What engagement model do you choose to build long term trusted relationships with your clients? What is unique in banking analytics space?  In fact, this question also came up recently, rather unexpectedly, when I was having dinner with two old friends in beautiful Los Angeles.

 Professional services provided by the vendors range from staff augmentation on one end of the spectrum to high-end solutions consulting that seeks to solve complex business problems.  All this emanates from the thought construct insiders refer to as the engagement maturity model.  This model tracks the morphing of provider-consumer relationship as the two embark on their journey over time. High-end consulting offers higher and obviously desirable ROI. Hence vendors strive to attain this utopia in each of their relationships. In an ideal world, if you can design solutions / offerings that will move clients up on the value chain - from staff augmentation to high end consulting, you have a winning recipe that will make you the top vendor with an enviable revenue stream.  All this is an ideal world. But, how do we work this magic in banking and financial services space in the real world?

Businesses that serve banking and financial services clients face hidden challenges.  It is well known that analytics is what differentiates successful banks. Large, well-run institutions have star-studded analytical teams that have the depth and skills to crunch through well-organized data and come up with insights to make the right decisions.  That is the upside. The down side is that, these teams may sometimes engage in dueling analytics in an effort to be one up on the other, an avoidable waste of resources and talent horsepower. Smaller institutions on the other hand, have smaller talent pool, less organized data and limited capability to undertake complex analytical projects on their own.  

Another key dynamic is that the outcomes of analytical projects impact the banks’ core decisions.  Hence banks often prefer to work with the crème-de-la crème in the business that they can trust. This provides the vendors a great window of opportunity to showcase their excellence in domain expertise, execution and delivery to win the trust of banks. Winning the trust of banks is a prerequisite for deeper, long-term engagements.  

In other words, the analytical ecosystems in these institutions are very different – ranging from the highly competitive and sometimes counterproductive to those with less sophisticated analytical infrastructure. Understanding the extant analytical ecosystem is critical in choosing the right business model for banking analytics providers.

But in the real world what engagement mix should we choose?  Seriously it depends on the prevailing analytical ecosystem of the banking customer. My personal view is that emerging and growing businesses tend to generate a significant proportion of their revenue via an on/off site staff augmentation model. Smaller boutique vendors have successfully demonstrated this as a key entry strategy in a very competitive business.  On the other hand, there seem to be fewer examples of providers choosing high end solutions as the dominant component of their mix. But I think an understanding of the nuances of this industry and the interplay of analytical ecosystem is fundamental to succeeding in banking analytics. This understanding helps discover the right mix.

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Tags: Advanced, Analytics, Banking, Business


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