When we hear the term, “statistics,” we might be taken aback a little bit because it conjures up highly sophisticated mathematics — something we’re not always so comfortable with. However, almost every day we come face to face with simple statistics such as:
- The average price of a grocery item
- The mean salary of the employees
- The range of weights and heights
- The percentages of growth
- The probability of rain
Generally speaking, statistics are the reflection of the data points we gather from our business activities and supports us in making informed decisions towards uncertain circumstances. When managers are able to collect and analyze the data, they can determine how to proceed in areas including credit and collections, auditing, financial analysis, manufacturing, and market research.
Basically, in any area where we can collect the data from an activity, that activity can be analyzed in ways to help us establish guidelines, identify when certain data falls outside of those guidelines, and help us to understand why they fall outside of the guidelines.
Naturally the more sophisticated we would like to know our business, the more statistical tools we could employ. For example, if you would like to analyze the billing data in the month of July from the basic to the more sophisticated, the following statistics could include:
- The mean (average) of monthly sales in July
- The meridian of monthly sales in July
- The mode of monthly sales in July
- The standard deviation of the sales which would tell you how dispersed the sales are spread out in relation to the mean.
- The Z-score, which can further be translated into a probability. For example, what is the probability that one sale for $50,000 or less billed on July 1st will be paid by July 31st? Subsequently, we could also compute the probability that all sales less than or equal to $50,000 would be paid by July 31st.
If for example we have computed the probability to be 92%, we can then multiply this number by the total number of sales that are less than and equal to $50,000. This becomes a very fast way to estimate the monthly cash flow.
Many collection agencies use an area of statistics called, Predictive Analytics, which makes predictions about future outcomes using historical data combined with statistical models, data mining techniques and machine learning. Companies employ predictive analytics to find patterns in their data to identify risks and opportunities.
If you haven’t delved into the area of statistics, there are countless resources that explain them very simply. Here are a two to start with:
