The law of diminishing returns is an economic principle stating that as investment in a particular area increases, the rate of profit from that investment, after a certain point, cannot continue to increase if other variables remain at a constant. As investment continues past that point, the return diminishes progressively.
For example, the law of diminishing returns states that in a production process, adding more workers might initially increase output and eventually creates the optimal output per worker. After that optimal point, however, the efficiency of each worker decreases because other factors -- such as the production technique or the available resources -- remain the same (this is known, more specifically, as the law of diminishing marginal returns). This kind of problem might be addressed by modernizing the production technique using technology.Content Continues Below
The law of diminishing returns in the real world
While the law of diminishing returns originated in classic economic theory, it is one of the most widely recognized economic principles outside the economics classroom. Some of the most common examples relate to farming, but the law applies in many other real-world situations that extend beyond production and manufacturing into realms such as marketing and customer relationship management.
A good example is social media marketing endeavors. While it is tempting to think that doubling the budget on a social media marketing campaign will double the returns, the increase could easily lead to a glut on information on a single social media channel, causing the returns to decrease substantially. To address this problem, a marketing department should evaluate and adjust other variables, such as its chosen channels or its approach to social media monitoring and analytics.
To stay competitive in areas from campaign planning to enterprise resource planning, it's also important that organizations establish the point of diminishing returns -- which is when per-unit returns start to drop.
To do this, organizations can define the single resource they plan to increase: the number of agents in a call center, for example. Next, they define the total cost of the desired output. This formula becomes trickier, as the output runs the risk of moving from defined numbers to more amorphous metrics such as customer satisfaction. It's important to define metrics as clearly as possible here.
In this example, the metric might be the service level -- the number of calls an agent answers in an established time period. As you add another agent, the service level may improve because the agents aren't overwhelmed and they don't miss calls. At a certain point, though, the return will drop below its original level, and that last person who was added to the staff becomes the point of diminishing return.