Business Measures and Metrics


To ascertain the extent, dimensions, quantity, scope or capacity of. Measures can be used for a wide variety of purposes, including business analytics and performance tracking.

Measurement is a cornerstone of commerce, science, technology and quantitative research across many fields. Standards for measurement have evolved through historical agreements and laws.


A metric is a quantifiable measure used to evaluate or track the status or performance of a specific business function. They serve as benchmarks and enable comparisons over time or against established goals or industry standards.

Metrics are a critical tool for analyzing data-driven insights and making informed business decisions. They help companies identify and prioritize opportunities for improvement and monitor progress toward strategic initiatives.

For metrics to be useful, they should provide meaningful insights into a particular aspect of the process under study. This means that they should be relevant to the goals of your business or project, and they should depend on data that can be collected and tracked over time.

Additionally, the metrics you choose should be actionable, meaning they can provide information that leads to operational or strategic actions. For example, if the metrics show that your company is getting too top heavy, you can look to promote more laterally to counteract this trend.


In the context of business, benchmarks are used as a means to identify areas for improvement. To be meaningful, benchmarking should focus on best practice and identifying opportunities for improvement that will lead to sustainable success.

Benchmarking can be internal or external. Internal benchmarks involve comparing data within one organisation to itself, for example, year on year comparisons or by filtering the data by employment group (i.e. job grades compared to 2 Star organisations). External benchmarks are those which compare the organisation to a wider group, for example, a sector or stretch benchmark.

Before beginning a benchmarking process, it’s important to consider where the information will come from. Obtaining access to direct competitors’ data may pose challenges, so recording current procedures or having pertinent business metrics on hand will help to simplify comparisons to established benchmarks. Also, the results of a benchmarking exercise are only as good as the inputs put into it. It is not enough to just point out a problem, it must be followed up by action to improve performance.

Key Performance Indicators

A key performance indicator, or KPI, is a measurable and quantifiable metric used to track progress toward a specific goal or objective. KPIs set targets for teams to work towards and allow them to identify strengths and weaknesses, make data-driven decisions and optimize performance.

KPIs can be created at the organizational level, for specific teams and projects or at the individual employee level. They are normally aligned with strategic goals and monitored regularly to ensure they remain attainable. They can be a combination of leading and lagging indicators.

Identifying good KPI metrics involves evaluating the type of business function involved, the data source, frequency of collection and any benchmarks or targets that might be required. Then it is necessary to communicate the chosen metrics clearly to employees so they understand what is expected of them. The best KPIs should include both lagging and leading indicators and be focused on outputs, rather than inputs. For example, an employee whose KPI is to generate the most leads this month will be assessed on the number of new contacts generated.

Measures for Predictive Analysis

Predictive analytics looks at patterns in historical data and makes assumptions about the future. It can be conducted manually or using machine learning algorithms. Regression analysis, for example, examines how one independent variable affects several dependent variables over time and charts the results. It can then make predictions about what will happen if the independent variable changes.

Many predictive models also take into account the timing of events, such as predicting when to expect peak customer service needs or when specific sales will be made. These models can help organizations plan and budget accordingly.

In the energy industry, predictive models can determine when power-generating equipment will need maintenance or detect and prevent dangerous situations. For example, Salt River Project uses predictive models that monitor vibrations from machines to predict when they’ll need repairs and avoid costly breakdowns. These predictive models can also optimize operational efficiency by minimizing production and delivery schedules. For predictive model evaluation, sensitivity, specificity, positive and negative predictive values and accuracy are common measures.

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