concept profit curve in category AI
This is an excerpt from Manning's book Succeeding with AI: How to make AI work for your business.
A profit curve establishes the relationship between the business and technology metrics. It allows you to use a technical metric for your ML algorithms. It also lets you translate the threshold of business metrics (the minimum value the business metric project must achieve to be viable) into the corresponding value of a technical metric. This section shows you how to construct a profit curve.
The profit curve, in the context of data science projects, was originally proposed in the book Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking [81], although the general concept of establishing mathematical relationships between metrics predates it and was known before [1,3]. Figure 4.4 shows the process of constructing a profit curve.
Figure 4.4. A profit curve specifies the relationship between a technical metric and a business metric. It allows you to understand what the technical answer (in the form of a technical metric) means for the business terms.
When defining a profit curve, you’d combine business and technical metrics through a mathematical relationship. That joins technology and business by linking the research question with the business problem you’re trying to solve. You can think about it as a form of the exchange rate when answering the question of “How many US dollars is one unit of RMSE worth?” (if your business metric is measured in dollars, and your technology metric is RMSE).
Now that you understand what a profit curve is, we’ll construct one for the bike rental example from section 4.3. The construction of the profit curve requires the cooperation of business and engineering. If you don’t have an engineering background, here’s one area where you’ll be learning a little about the engineering side—see the sidebar for more information.
Figure 4.5 shows you how the profit curve we constructed for the bike rental project looks.
Figure 4.5. Profit curve for bike rentals. Note that in the case of a business metric being a cost, the goal is to minimize the business metric.
The construction of that curve is simple but comes with two caveats. The first is that the cost is the same for every peak usage hour’s RMSE between 0 and 1. The reason is that you can’t purchase half of a bike, so any error smaller than 1 has the same business implication. The second caveat is that any time you’re working with business metrics such as cost, less is better. When operating with this business metric, the goal of your AI team would be to minimize the business metric. If your business metric was something like profit, you’d want to maximize the profit curve.