Feb. 2, 2017 | InBrief

Numbers don't lie. Liars use numbers.

Numbers don't lie. Liars use numbers.

Numbers don't lie. Liars use numbers. I had a professor tell me this once and it has stuck with me ever since.

At first glance, it means people intentionally use numbers to misinform other people. In my opinion, I think he was implying that people have a tendency to misuse numbers unintentionally.

Data abounds. We hear about it and read about it all the time. The power of big data. It sounds powerful, but are we even making smart decisions given the simpler data sets that are readily available?

For example, take the following data sets (1-4), each with 5 data points (A-E). What do they have in common?

Data Points
Data Sets #1 15 15 15 15 100
#2 10 20 30 40 60
#3 10 15 15 60 60
#4 5 10 15 45 85


All the data points are in increments of 5? Yes

They are in ascending order? Yes

The data sets all average to 32? Really?

Yes, that’s right. The average is 32.

Why does it matter? Well, if we manage our operational metrics based on averages, it makes all the difference in the world.

For example, let's say we are in the business of making loans. We want to create a competitive edge in the market. We think if we can approve, close, and fund loans faster - while not compromising on quality - then we will create a competitive edge. Furthermore, we think that if we could average 32 days to fund a loan, then we would count it as a success.

So we undertake a project to measure our performance. Then, we implement a project or series of projects to achieve this goal. Let's assume the data points in the data sets above are the actual times to fund loans in the first four months after we implement our project.

We achieved our goal, but did it make a difference? Maybe. Keep in mind each of the 20 data points across the four data sets is representative of a customer experience. Seven of the 20 data points are over the 32-day average. Do you think those customers are happy, particularly those at 60, 85, or 100 days? Do you think they are coming back to do business with you? Do you think they are going to refer business to you?

This doesn't apply to lending only. For example, imagine this in the context of your wait time while on hold, or the amount of time it takes to see a medical specialist for the first time, or the amount of time you need to wait to get into the emergency room. Would you care if you were one of the customers or patients that experienced the response time or wait time of 60, 85, or 100, even though the average was 32?

It reminds me of the quote attributable to Mark Twain, “If a man has one foot in a bucket of ice and the other in a bucket of boiling water, he is, on the average, very comfortable."

Why do we rely so much on managing to averages? Well, for one, it's easy for us to mentally grasp. There are all sorts of measure for things such as central tendency but, they are harder to calculate and comprehend. So, what should we do?

Well, it's really quite simple. If we are aiming for 32 days to approve, close, and funds loans, that should be the threshold. We should focus on an experience that exceeds the threshold. Taken a step further, we should identify these instances early on and manage those that may exceed the 32-day threshold.

One simple way to quantify performance would be to count the number of deals that are past the 32-day average. However, that does not account for the magnitude of the effect on the customer. For example, the effect of a loan closing in 40 days (i.e. eight days past the average) is far different than one that closes in 100 days (i.e. 68 days past the average). Furthermore, such an approach does not account for the impact to the bottom line of our company. Not all deals are created equal. A $250,000, $500,000, and $1,000,000 loan do not all have the same contribution to the bottom line.

One approach worth noting is Eli Goldratt's concept of dollar days. The concept recognizes that customers will wait on delays for a period of time but not too long. It also accounts for the aforementioned considerations. In this example, it might be simplified as the value of potential loans, multiplied by the number of days past the 32-day average. For example, a $500,000 loan at 62 days in the process contributes $15,000,000 to the overall dollar days metric, i.e. $500,000*(62 days - 32 days).

This is not a financial metric by any means. However, if you track it over time, you would see how well you are meeting the goal, providing value to your customers, and improving your bottom line. It's not perfect, but it's much better than, well, average.

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