As we hosted a recent webinar series on “Getting to the Best Decision”, focused on testing and improving business rules, several questions came up on KPIs — Key Performance Indicators. It is true that we tend to use many different terms for the same thing. You may have heard us or others speak of business objectives, KPIs, metrics, calculations, characteristics, features or variables. Are they synonyms?
I’d like to suggest ‘my’ definitions. Feel free to comment on whether you agree or not, and share the nuances your recommend.
Business Objectives:
Business objectives are the high level goals for the initiative. The current project aims at increasing your profitability, reducing your costs, improving your customer satisfaction, adhering to compliance mandates, etc.
In addition to the ‘direction’ of the expected outcome (increase or decrease), The business objectives may state a tangible goal: obtaining 80% automation or more, decreasing fraud by 10% or more, etc.
Business objectives are often defined by the management team, before they invest in a project. Although the corporation or institution has captured those business objectives in the business plan, they are not often clearly documented in Decision Management initiatives.
Metrics:
Metrics are measurements that support your Business Objectives.
If you want to increase your profitability, the metric that matters here will be the formula that defines profitability. Will you measure ‘revenue – expenses’? Will you need to track profitability using a much more precise calculation?
If you want to improve customer satisfaction, the number of metrics involves could be much more than one… Will you measure the number of happy feedback on support calls? The number of support calls? The results of customer surveys? A sentiment analysis on your twitter hashtag?
There is no right or wrong as it relates to the metrics you decide to track. The most important recommendation in my opinion is to have SMART goals (not to be confused with SMARTS, our decision management system):
- S for Simple,
- M for Measurable,
- A for Attainable,
- R for Relevant,
- T for Time-sensitive.
Define the set of metrics that you can measure. Make sure that the supporting data will be available and reliable.
KPIs – Key Performance Indicators:
KPIs are a subset of the metrics that you track, used to monitor the health of your project, or your business performance. While you may want to have visibility into a multitude of metrics on your business, only a handful roughly will matter ultimately to measure how well you are doing on your business objectives.
In the mid-90’s, I used to build dashboards. We defined hundreds of KPIs. The executive dashboard only displayed a handful, maybe a dozen, KPIs on a summary view. The other metrics were used to investigate, to drill down, as a top indicator turned red.
Calculations or Variables:
Metrics are calculations, in the sense that a formula dictates how to come up with the number or label. It does not mean that all calculations are metrics!
Many calculations exist in Decision Management projects for the purpose of combining or preprocessing data elements:
- Mathematical formula: the ‘debt to income’ ratio for example
- Statistics: the average number of accidents per driver for example
- Binning: the age group or purchase category for example
These calculations are typically defined to support the business rules: business rules can refer to these calculations or variables in the same manner they also refer to the input fields.
Calculations may be tracked over time, like any other input field, to analyze whether the demographics are shifting. If the average age increases significantly as the population ages, you may want to rethink your decision logic that takes age into account. This is a useful data-point, but it would likely not make it into your KPIs.
Characteristics or Features:
Characteristics and features may be calculations or input fields that you take into account in your decision logic. This terminology if most often used by data scientists in charge of predictive modeling.
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