Step 2: Financial Estimates of Revenue or Cost of One Unit

“Show Me the Money”

In order to assess the financial implications of a statistical procedure, you usually need to translate a focal variable or relationship into either revenue or cost. Usually, the first step is to calculate the revenue or cost implications of one unit of a focal variable. For instance:
  1. Perhaps the dependent variable in a statistical analysis is employee turnover. If you knew the average cost of a single employee turnover, then you could calculate the overall cost of all turnovers this month (the level of the variable). Alternately, if you think you can reduce employee turnover by a certain percentage through management interventions (a change analysis probably estimated through regressions) the cost of a single turnover could give the value of the change brought about by the intervention.
  2. Perhaps the focal variable is new customer acquisition. Say that you know how much revenue the organization earns on average over the lifetime of one new customer. This information would allow you to estimate the revenue of all new customers acquired in, say, a year.
  3. And so on … Knowing the per-unit monetary implications of variables is the critical middle link.
Note that some variables or relationships can be measured only in revenue or cost terms, but some may be valued in both. In the case where both can be measured, you may be able to measure profitability or even investment potential. See Steps 5 and 6: Net Profitability Calculations below for more on this.
The following sections discuss various possibilities for valuing a variety of business variables in per-unit revenue or cost terms, arranged by business functions. This is only a subset of possibilities in the possible lists of variable costs and benefits. Perhaps the key thing here is to be creative and broad-minded in considering which core variables can be valued in terms of cost or revenue.

Costs and Revenues of Operational Variables

Value of Products or Services Made or Sold

Perhaps you have a focal variable that reflects products or services made or sold, such as:
  • Factory output (perhaps statistics where factory output is the dependent variable)
  • Per-employee average production (perhaps as the focus of a forecast model)
  • Employee productivity on a task measured in hours (perhaps just average and standard deviations over time)
  • Agricultural output per acre of land
Usually, the value of products would be achieved through calculations of the revenue per unit of production or service (sales value). For instance, if you are measuring factory output, translate the statistical outcomes measured in products to money by multiplying the number of products by the (average) sales value of each product.
For a more specific example, say that a forecast model predicts production of 3,102 units of Product A and 543 units of Product B in January. If Product A sells for $100 per unit and Product B sells for $1,000 per unit then the total sales value of the units involved are 3,102 * $100 and 543 * $1,000 = $310,200 + $543,000 = $853,200.

Costs of Inputs

Many variables in operations or production situations come with easy-to-reflect costs, such as the costs (wage bill) of labor, purchase costs of raw inputs, and the like.

Value of Operational Time

In many cases operational studies are interested in the time that a process takes, such as the average time taken to build a car in a factory. You can value time by estimating the costs per hour (or minute, day, etc.) to run the process, which would usually be made up of all inputs to the process such as the machinery, people’s wage bills, and the like. Then, savings in time can be expressed as cost savings.

Marketing Outcomes

Sales

Obviously, measuring actual sales of products or services is an easy example, as sales are valued immediately in revenue value based on the actual price.
If you are using sales, be careful to subtract out elements that may reduce sales values, such as returns, discounts, and the like.

Returns

Perhaps you can show statistically that you have lowered returns of products. Returns can also be valued, usually in the cost to the company of processing a return (in which case the gain in lowering returns is the gain of reducing costs).

Customer Lifetime Value

A popular modern marketing valuation tool is the notion that each customer has a customer lifetime value (CLV) to the organization, i.e. the expected lifetime net present value of the customer to the company. If you have estimates of the CLV of certain types of average customers (e.g. the CLV of different types of banking cardholders), then statistical results showing things like market share can be valued by using average value per customer. See, for instance, Bauer & Hammerschmidt, (2005) for more detail on CLV.
If you know customer lifetime value (or the like), then you can use such calculations to extend extrapolation to any variable that involves changes in the customer base. For instance, say that you have a customer base of 1 million customers, and that a statistical analysis forecasts a growth in this base of 3% in the next year. This is a change situation, so you must isolate what has altered. The change is the new 3% of customers, i.e. 3% of 1 million that have been added, which is 30,000 new customers. If the average customer lifetime value (CLV) is estimated at $10,000, the financial value of the new customer base is 30,000 new customers * $10,000 = $300,000,000.

Value of Employee-Related Variables

Overview of Valuing People Variables

Generally, employee value arises from two sources.
  1. Employee stocks: The first is the productive value of employees, that is, the ability of a given group of employees to create value for the organization (which we call ”employee stocks”).
  2. Employee movement: The second way of creating value in the employee body of an organization is to affect the movement of staff, for instance to improve the productivity of new staff, slow down the turnover of productive staff, improve the process of promotions, and the like.
Also note that there are various ways to measure labor input to an organization, including:
  1. Number of employees affected by something
  2. Time (e.g. measuring average production time per unit in a factory)
  3. Wage bill of the workforce
When measuring such employee-related variables, there may be a variety of ways to measure per-unit revenue or cost, including the following.

Employee Performance

It is possible – although not necessarily easy – to value a change in general measures of employee performance. To crystalize what this means, consider again the Oracle SA vignette at the beginning of this chapter, in which part of the study considered the financial value of HR interventions on the general performance of employees. The challenge in extrapolating improvements in employee performance to financial value is the question “what is a one-unit improvement in employee performance worth to the organization?”
This question depends partly on the way that employee performance is measured in the first place (e.g. is it measured on a 1-3 or 1-7 point scale, or something else?) For the purposes of this book, it is most important to know that techniques exist to value changes in employee performance. The interested reader can consult texts such as Casio (1999) and Cascio and Boudreau (2008).

Changes in Workforce Numbers, Time or Wage Bill

Employee variables may involve measurement in staffing numbers, employee time or wage bill. All such variables may be open to financial measurement. The cost of each employee can be determined by their wage bill per annum or hour. Time spent by employees can be assessed in terms of the cost of a per-hour wage bill. (For instance, if you can show statistically that something improves the average time that employees take to do something, then you can value this savings in terms of revenue because of the cost savings implicit in reducing employee time.)

Reductions in Expensive Employee Behaviors

Various employee behaviors may be assigned a cost, usually through methods that are similar to cost-accounting, such as:
  • Employee turnover: Methods have been developed to estimate the average cost of an employee turnover (e.g. Hom & Griffeth, 1995). Therefore, such information can be linked with any statistical effect on employee turnover or retention levels to show the total financial values of the effect. For instance, say you can show statistically that a certain HR issue can improve employee retention among doctors in your clinics by 2%, which equates to 20 doctors per annum if you have 1000 doctors. If you have estimated the average cost of turnover of a doctor at $5000 in total, then the total gain in the retention effect would be 20 x $15,000 = $300,000.
  • Absenteeism: Likewise, employee absenteeism can also be estimated in cost terms, and therefore linked to any statistic involving absenteeism (e.g. Cascio & Boudreau, 2008).
  • Overtime: Obviously, overtime can be assigned a cost in terms of what you have to pay people to work that overtime.
  • Strikes: You may estimate the cost to the company of a strike day. Then, statistics using strikes as a variable could be extrapolated to financial value.
  • And many others.

Other Employee Variables

There are many other employee variables that could feasibly be valued in revenue or cost terms. Some may include innovation, survey results such as engagement, and many others.

Values of Financial Variables

While it may seem obvious that financial variables could be valued in financial terms, this is not always true. Say, for instance, that you do a behavioral financial study showing that an advertising campaign can lead to an increase in customers that renew insurance policies, or a reduction in customers that default on banking loans.
This study is by no means complete in terms of financial extrapolation. You would have to know the value of an insurance renewal (probably something similar to the customer lifetime value concept discussed above) or the cost of an average banking default. Only once these are known could you combine these per-unit financial results with the statistics to come to a complete financial extrapolation.
Last updated: April 18, 2017
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