numbers don’t lie, liars use numbers

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

At first glance, you may interpret it to mean 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 – presumably because they don’t know better.

For example, take the following Data Sets (#1-4), each with 5 Data Points (A-E).

What do they have in common?

Each data set averages 32.

Why does it matter?

If we manage our operations 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 aim to create a competitive edge in the market to earn more business. We know that customers or borrowers value low interest rates and low fees. However, we also know that borrowers value service. The amount of time it takes a lender to approve, close, and fund a loan are indicators of its service quality. Borrowers value speed; the faster the better.

Let’s assume our current lead time to approve, close, and fund loans is 60 days on average. We undertake a project to decrease the average time to approve, close, and fund loans with a goal of achieving an average lead time of 32 days (note this number is arbitrary and for simplicity sake). How do we know we are successful in this project? Simple – we achieved our goal of an average of 32 days. We pat ourselves on the back.

However, let’s assume that the data in the above table represents a random sample of loans closed after we completed the project. Each of the 20 data points across the four data sets is representative of a customer experience – an opportunity to create an advocate or a critic. 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, e.g., refinance when rates are lower? Do you think they are going to refer business to you, e.g., family, friends, neighbors that are seeking to purchase or refinance?

This doesn’t apply to lending only. For example, imagine this in the context of the time you may wait on hold when calling your insurance company; or, the amount of time you may wait to see a medical specialist (e.g., surgeon) for the first time; or, the amount of time you may wait to get into an emergency room when critically ill. Would you care if you were one of the customers or patients that experienced the 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 measurements for things such as variability, but generally 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 target threshold – not the target average. We should focus on those customer experiences that are at-risk of or have exceeded the target threshold. For loans in the pipeline, we should identify the at-risk customer experiences early on and actively manage them to remove any constraints or potential constraints. Additionally, we should continuously review operations to understand and then eliminate the systemic constraints that delay loans from getting approved, closed, and funded within 32 days.

One simple method to quantify performance would be to count the number of deals or loans that are past the 32-day average. However, that method does not account for the magnitude of the effect based on the number of days past the target threshold. For example, the customer experience of a loan closing in 40 days (i.e., eight days past the 32-day average) is far better than that of one closing in 100 days (i.e., 68 days past the 32-day average). Furthermore, such an approach does not account for variances in the impact to the bottom line of the company. Not all loans are created equal. A $250,000 loan, a $500,000 loan, and a $1,000,000 loan have different size effects on profits – holding all else equal.

An alternative approach worth noting is Eli Goldratt’s concept of dollar days. This method recognizes that customers will wait on delays for a period of time but not too long. It also addresses the shortcomings of the aforementioned method.

In our lending example, for a given day, we would multiply each loan or principal amount ($) by the number of days that loan has been in the pipeline beyond the 32-day target threshold. For example, a loan with a principal amount of $500,000 at 62 days in the pipeline contributes $15,000,000 to the overall dollar days metric for that given day, i.e. $500,000*(62 days – 32 days). If it remained in the pipeline one more day, then the dollar day metric for that loan the following day would be $15,500,000. This can be done in isolation for one loan or in aggregate for the pipeline by adding the dollar day amounts for all loans that have been in the pipeline for greater than 32 days.

The target dollar day metric would be $0 each day, meaning the target sum of loans in the pipeline that are open and not approved, closed, or funded is $0. Higher sums would indicate worse operational effectiveness, undelivered value propositions, and poorer customer experiences – all of which lead to deteriorating profit – either today or in the future.

The dollar days metric is not a financial metric that would be reported in financials. It is an operational metric that would indicate how well you are delivering on your value proposition, i.e., speed and consistency of approving, closing, and funding loans. If tracked from day-today, it would provide insight into how well operations are functioning from one period to the next. It would give insight into constraints that could be actively managed or eliminated to make improvements in speed and consistency.

The dollar days metric is not perfect, but it’s much better than managing to the average. And that is the real takeaway. Averages are misleading. At a minimum, distrust averages. Even better, consider using alternate measurements that provide meaningful insight into what needs to change to improve operational effectiveness, deliver on value propositions, and delight customers – quickly and consistently.