Key Performance Indicators (KPIs) are measurable values used to evaluate how effectively an organization is achieving its objectives. Because KPIs are applied in many different contexts, researchers and practitioners have proposed several ways to classify them. These classifications help organizations choose the right indicators, understand what each KPI represents, and interpret performance correctly. Although many frameworks exist, several common dimensions are widely used to categorize KPIs. 

One of the simplest classifications is based on the industry in which KPIs are applied. Different industries focus on different performance aspects, so their KPIs vary accordingly. For example, agriculture KPIs may focus on crop yield or land productivity, education and training KPIs may measure student performance or training effectiveness, and finance KPIs may track profitability or liquidity. This classification highlights that KPIs must be aligned with the specific characteristics and goals of each industry. 

Another straightforward way to classify KPIs is by functional area within an organization. Each department or function has its own objectives and therefore requires different indicators. For instance, accounting KPIs might include cost control and budget variance, finance KPIs could involve return on investment or cash flow, and human resources KPIs might measure employee turnover or training hours. This approach ensures that performance measurement is tailored to the responsibilities of each organizational unit. 

KPIs can also be classified according to the time perspective they represent. In this dimension, there are leading, coincident, and lagging KPIs. Leading KPIs are predictive and reflect expectations about future performance, such as forecasted demand. Coincident KPIs measure what is happening at the present time, for example, the current number of orders. Lagging KPIs describe past performance, such as earnings before interest and taxes or historical customer satisfaction. This classification is important because organizations need a balance of predictive, current, and historical indicators to manage performance effectively. 

Another important dimension is whether a KPI measures the input or the output of a process. Input KPIs focus on the resources invested to achieve results. Examples include the number of employees (headcount) or cost per hour. Output KPIs, on the other hand, measure the results or impact of activities, such as customer retention or total sales. This distinction helps organizations evaluate both the efficiency of resource use and the effectiveness of outcomes. 

KPIs can also be categorized as qualitative or quantitative. Qualitative KPIs measure descriptive or subjective characteristics, often based on perceptions or opinions. Customer satisfaction collected through surveys is a typical example, since responses reflect personal judgments even if expressed in numeric form. Quantitative KPIs, in contrast, are based on measurable numerical values derived from mathematical calculations, such as units produced per labor hour. Quantitative KPIs are more common because they are easier to measure objectively. 

Another classification differentiates between strategic and operational KPIs. Strategic KPIs are aligned with long-term organizational goals and are usually reported to senior management. They are reviewed less frequently and have a medium- or long-term focus, such as market share growth. Operational KPIs support day-to-day management and are used at lower organizational levels. They are monitored more frequently and focus on short-term performance, such as daily production output. Both types are necessary, as operational performance drives strategic success. 

Finally, KPIs can be classified based on the type of issue they address. Process KPIs measure the efficiency or productivity of business processes, such as sales growth or shipping efficiency. Quality KPIs evaluate the quality of outputs, including measures like production defects or customer satisfaction. Context KPIs describe external conditions that influence the organization but are not directly controlled by it, such as market size or number of competitors. This classification helps organizations distinguish between internal performance and external environmental factors. 

Reference: 

Vaisman, A., & Zimányi, E. (2014). Data warehouse systems: Design and implementation. Springer.