In many organizations there can be resistance to the implementation of a value or profitability measurement solution. First, people just don’t like to be measured. There can be a natural resistance to being held accountable for doing what is expected and profitability analytics does that.

There are also several cultural issues. An excellent book on the emotional aspects of performance management was written by Dean Spitzer PhD titled Transforming Performance Measurement. In that book Professor Spitzer states that 20% of organizations use measurement to improve while the other 80% use it to assign blame. This can make those being measured even more leery of measurement in general.

There can be several other reasons why there is resistance to profitability analytics in the Financial Services industry:


Bad Previous Experience: Many people have had poorly constructed value analytics systems forced on them. Those systems were commonly top-down solutions starting at the GL and then using “Black Box” algorithms to force income and expense to the target.

Here are some solutions to this issue. A value analysis system should be very transparent and easily explainable. Every result should be traceable to the inputs that drive the outcome.

Kohl addresses this concern with a highly transparent solution. Each number can be traced to its source. This includes, among many other things, tracing employee time/cost to an individual employee and outside costs to individual invoices.


Lack of data quality: Value analytics requires accurate and timely data to be effective. If the data is incomplete, inconsistent, or inaccurate, value analytics may not be trusted.

This is a transparency issue as well. The input data should have detailed extractions from existing systems that can be verified.

Not all data is available in a system. For example, the most important data is employee time/cost data that does not exist in the proper format or structure for value analysis. Data about how much time an employee spends performing specific activities is normally not available anywhere. However, this information drives approximately 75% of all non-interest expense. 

Kohl addresses this concern by extracting each individual loan and deposit plus their associated transactions as baseline data. Its collection of employee information is best practices according to many authoritative sources and verifiable at the lowest levels.


Complexity: Value analytics can be complex, requiring a deep understanding of the organization’s balance sheet, interest rate risk, and funding costs. If employees do not understand the methodology behind value analytics, they may not trust its results and be resistant to its use.

System trust is key. Transparency is a major key to building trust.

Kohl’s methodologies are actually very simple. Complexity normally comes from employing a myriad of complicated allocation rules. Most systems attempt to start at the GL level and use rules to disaggregate that GL data to a more granular level. This gets very confusing and is also very limiting as it’s almost impossible to fully understand.

The opposite approach is used by Kohl. We start at the transaction level and aggregate up which is the same method used to post to a GL account. The Kohl method does the same thing, only posts direct revenues and costs to associated products versus GL accounts. The result is a direct association of transactions to products versus an indirect allocation using convoluted rules based on an indirect driver.


Cultural resistance: Organizations may have a culture that favors revenue growth over value. In such a culture, there may be resistance to using value analytics if it suggests that certain business units or products are not as profitable as previously believed.

Value analytics can upset the power structure of an organization. It identifies where value is created and destroyed and that may not align with current perspectives. Those promoting themselves as big value creators may see their claims challenged by an unbiased analysis that all parties are subjected to equally.

Again, transparency of the Kohl solution is a key to overcoming this resistance. The traceable nature of results proves that the results are legitimate and most importantly defendable. In our experience, most resistance quickly goes away once this transparency is understood.

A note about credit unions. Many credit unions feel value analytics are inappropriate because they are not-for-profit entities. This view is universally incorrect. At a minimum, a credit union must make enough profit so that capital grows at least as fast as the balance sheet. The difference between a credit union and a bank is that credit unions should target an “optimized” ROA which maximizes the value of the membership. A bank strives to “maximize” ROA for their shareholders.

Maximizing the value of the membership means striving for an optimal ROA that permits the safe and sound operation of the credit union. Doing this returns any excess value back to the membership in the form of better rates and fees. A maximum ROA goal directs excess value to the shareholders, not the members. This is the major contribution of a value analytics solution for credit unions and Kohl’s solution addresses this issue universally.


Incentives: In some cases, employees may be incentivized based on revenue growth or other metrics that do not align with value-based goals. Value analytics may suggest that different metrics be used. There may be resistance to its use because it can impact compensation. Incentives should focus on creating overall value for the organization and not simply selling product volume that may not be contributing to value.

Many organizations encourage loan and deposit people to maximize volume over value. Once a value analytics solution is in place a clear picture of where value is being created emerges. Incentives may need to be adjusted to meet the value creation objectives of the organization. The first and most important step is identifying just where value is being created or destroyed and then promoting the good and fixing the bad.

Overall, resistance to value analytics can be driven by a variety of factors including data quality, complexity, cultural resistance, incentives, and lack of transparency. To overcome this resistance, organizations may need to invest in better data quality, provide education and training on the methodology behind value analytics. It also should align incentives with value goals and increase transparency around value calculations.