Barriers to Data Mindset

 It is very hard for a bank to develop a data mindset.  Traditionally, regulated banks have been trained to deliver accuracy as they are managing money. The focus is on getting it right without errors and making sure all compliance requirements are met. This culture does not reward any creative inputs. Legacy systems on which banks have spent decades managing are also inflexible. In addition the process of completing a transaction has to follow deeply structured processes.

This environment does not allow empowerment of any sort at the customer level. The demographics of the customers using the banking services have changed. They are younger, often more affluent and tech savvy . They are used to Amazon type services and Apple type user experiences. If a bank cant provide what they want, they will look to fintechs who can. Post pandemic this customer churn is hitting 15% .

So what can banks do? Its well established that a customer centric service paying attention to unique situation a customer is in their financial journey can make them happy campers. This level of service can be obtained by gathering and harvesting insights from the data the bank has about the customer. Banks have not found any meaningful ROI with their investments in data technologies. They still operate in silos so much so that a late 2017 study found they are valued at 54% below book value.

The answer lies in a top driven change management approach.

First, get the KPIs of the top level executives to include metrics on data initiatives, these should be tied to their bonuses. At the second tier of executives empower them resources on obtaining education on how data driven organizations work. At the third level get together small cross functional teams with clear risk and reward for targets identified to use data and improve operational processes, reduce time spent by customer on interaction with various channels to name a few. At the front line level make available the complete profile of the customer and recommendations on talking points when interacting with customer. Reward the frontline for usage and success stories making them stakeholders in the data mindset culture.

These are not impossible actions. Consider what a 5% customer retention means, a 15% uptick in new account openings, a 10% increase in loan book means to the bottom line of the bank and see the trade off between status quo and investing for a data mind culture in the bank

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Why did it happen? The Story of Diagnostic Analytics

 Diagnostic Analytics tries to answer the why something happened in the past  from our data sets- for example why did sales rise this month though we didn't have any marketing campaigns, why did patients in a sample have the same symptoms,why did our bank's customers choose the 5 year fixed mortgage product.

Frequently this side of data analytics looks at both internal and external data sets to draw inferences . The process of drawing out the inferences can lead to a significant benefit-- the creation of a data mindset in the company that will have force multiplier effects on the company's future performance . The process itself leads to exposure to tools for data drill down, statistical methods and confidence in future decision making --all leading to a virtuous cycle of creating a data mindset.

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What happened? The story of Descriptive Analytics

 The oldest know data analysis consists of two things-aggregating or summarizing data and  data mining.

The majority of a company's investment in data analytics is focused on aggregating data-- what was the sales, by month, by region for example.Comparing to previous time periods gives an understanding of where the company is going for instance the sales have increase 20% compared to previous quarter. It is an important tool for analysis of the performance of a company. Different types of data are then mixed and matched to provide further insights such as :

ROIC--Return on Invested Capital takes  three datasets--net income,dividends and capital .This allows us to compare performance of two or more companies.

On a similar vein, company annual reports are descriptive analytics in action . They tell the story of what happened in the past. These reports are mostly derived from spreadsheets and ancillary sources such as ERP tables .

Data mining techniques allow you run the group aggregated data and look for logical conclusions to understand customer behaviour. A grocery chain may use data from its loyalty card program group to analyze what are some of the fast moving items in the produce section.

Banks are one of the best examples of the descriptive analytics mindset. They are so focussed on the historical aspects which is important since they are custodians of our money. The problem is both legacy systems and the lack of new data mindset is making it very hard for them adopt the newer types of data analytics 

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Why are banks interested in your data?

 Why are banks interested in customer data? The answer is one word--Steve Jobs.

On 29 June 2007 the iPhone was launched. Fast forward 15 years  and the reverberations of iPhone are still being felt. The pandemic has only accelerated these changes.

Steve said:

 "A lot of times people don't know what they want until you show it to them"

What did Steve do that was so revolutionary?

He showed that a customer centric unified value proposition can be created by bridging gaps along the value chain, providing the customer with new experiences and whetting their appetite for more.

This results in an emotional connect for the customer. Apple had a $100 billion profit quarter. I rest my case.

The takeaway for banks is that a customer centric value proposition has to be created. This needs the capacity to collect a large amount of data. The data when translated to actionable business insight helps predict customer journey thereby creating the emotional connect. 

This is the reason why inspite of lack of a clear ROI , banks have been pouring billions in collecting and understanding  customer data. 

Now they have to solve for a seamless value chain-- a problem fraught with hubris, legacy systems, lack of a data mindset. New customer experiences and whetting the appetite will follow once these hurdles are overcome.

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