Skip to content

Metric Overview

In data analysis and business intelligence, basic metrics, derived metrics, and composite metrics are three common metric types. Understanding their definitions, differences, and use cases is important for effective data-driven decision-making.

1. Basic Metrics

Definition and Characteristics

A basic metric is the most direct type of measurement. It comes from source data and does not require complex calculations or transformations. Basic metrics are the foundation for other metric types and provide the original measurement view of the data.

Use Cases and Examples

  • Sales analysis: total sales amount, order count, customer count.

  • Website analytics: page views (PV), unique visitors (UV).

  • Financial reporting: total revenue, total cost, net profit.

Examples

  • Total Sales: all sales revenue recorded within a specific time period.

  • Order Count: the total number of orders generated within a specific time period.

2. Derived Metrics

Definition and Characteristics

A derived metric is created from a basic metric by applying calculations or transformations. In Aloudata CAN, you can quickly define derived metrics in three ways:

  1. Business filters

  2. Time constraints

  3. Derivation methods, such as percentage share, year-over-year/month-over-month comparison, or ranking

1. Business Filters

Definition:
A business filter restricts and calculates a basic metric according to a specific business scenario or business rule.

Use cases and examples:

  • Sales: regional sales amount, calculated by region.

  • Customer management: active users, calculated as users with active behavior during a specified period.

  • Inventory management: safety stock, calculated from minimum stock levels defined by business rules.

2. Time Constraints

Definition:
A time constraint analyzes and calculates a basic metric by combining the date field in metric data with a specified time range and time granularity. Supported granularities include day, week, month, quarter, and year. Time ranges can include the current month, last year, the past number of days, and more. Statistical calculations such as maximum, average, and total can be applied on top of the selected range.

Main components:

  1. Time granularity:

    • Minute, hour, day, week, month, quarter, year

  2. Time range:

    • Current period: for example, current month, current quarter, or current year

    • Historical period: for example, last year, previous month, past 7 days, past 30 days, week to date, or year to date

    • Specific point in time: for example, the first day of last year, yesterday, the first day of the current quarter, or the last day of the current year

    • Custom period: a specific date range defined by the user

  3. Statistical calculation:

    • Maximum (Max): the maximum value within the specified time range

    • Average: the average value within the specified time range

    • Total/Sum: the total value within the specified time range

    • Minimum (Min): the minimum value within the specified time range

Use cases and examples:

  • Sales analysis:

    • Time granularity: month

    • Time range: January 2023 to December 2023

    • Statistical calculation: average monthly sales amount and maximum monthly sales amount

  • Website operations:

    • Time granularity: week

    • Time range: past 12 weeks

    • Statistical calculation: total weekly page views and maximum weekly unique visitors

  • Financial analysis:

    • Time granularity: quarter

    • Time range: past 4 quarters

    • Statistical calculation: year-over-year growth rate of quarterly net profit and average quarterly total cost

3. Derivation Methods

a. Percentage Share

Definition:
Percentage share represents the proportion of one part relative to a whole. It is commonly used to analyze composition and distribution.

Use cases and examples:

  • Market share analysis: the percentage of total sales contributed by one product.

  • Cost composition: each cost category as a percentage of total cost, such as labor cost share or material cost share.

  • Customer distribution: the percentage of different customer groups, such as by age group or region.

b. Year-over-Year and Period-over-Period Comparison

Definition:
Year-over-year and period-over-period comparisons compare basic metric values across different time periods to analyze trends.

Use cases and examples:

  • Sales analysis: compare this month’s sales with the same month last year (year-over-year), or with last month (period-over-period).

  • Website operations: analyze quarterly page-view growth year over year or period over period.

  • Financial management: analyze annual profit growth year over year or period over period.

c. Ranking

Definition:
Ranking sorts a metric by a specified dimension. It is commonly used to compare the performance of different entities, such as products, regions, or employees.

Use cases and examples:

  • Sales team: rank sales representatives by sales amount.

  • Product management: rank products by sales volume to identify best sellers.

  • Regional performance: rank regions by profit margin to identify high-performing regions.

Combined Example

In sales analysis, you can create the following derived metrics from a basic metric:

  • Business filter: sales amount by region.

  • Time constraint:

    • Time granularity: month

    • Time range: first quarter of this year

    • Statistical calculation: total first-quarter sales amount and average monthly sales amount

  • Derivation method:

    • Percentage share: sales amount of one product line as a percentage of total sales amount.

    • Ranking: top 10 products ranked by sales amount.

3. Composite Metrics

Definition and Characteristics

A composite metric is created by combining one or more basic metrics or derived metrics with arithmetic operations such as addition, subtraction, multiplication, and division. These operations can support ratio calculations, variance comparisons, and other combined measurements.

Use Cases and Examples

  • Performance evaluation: combine multiple performance-related metrics into an overall performance score through weighted averages.

  • Financial health: calculate a financial health index by weighting profit margin and cash flow.

  • User experience score: combine page load time, conversion rate, bounce rate, and other metrics through weighting or arithmetic operations.

Ways to Build Composite Metrics

Composite metrics are commonly built in the following ways:

  1. Ratio calculation: use division to calculate the ratio between two or more metrics.

    • Example: Profit Margin = Net Profit / Total Revenue

  2. Difference calculation: use subtraction to calculate the difference between metrics.

    • Example: Net Growth = Current Period Sales - Previous Period Sales

  3. Multiplicative combination: multiply multiple metrics to emphasize their combined effect.

    • Example: Composite Index = Conversion Rate x Customer Satisfaction

Examples

1. Sales Performance Index

Component metrics:

  • Basic metrics:

    • Total Sales

    • Order Count

  • Derived metrics:

    • Average Order Value (Total Sales / Order Count)

    • Year-over-Year Growth Rate (This Year’s Total Sales / Last Year’s Total Sales - 1)

    • Ranking (sales amount ranking)

  • Composite metric:

    • Sales Performance Index = (0.5 x Average Order Value) + (0.3 x Year-over-Year Growth Rate) + (0.2 x Ranking Score)

2. User Experience Score

Component metrics:

  • Basic metrics:

    • Page Load Time

    • Page Views

  • Derived metrics:

    • Conversion Rate (users who completed the goal / visitors)

    • Bounce Rate

  • Composite metric:

    • User Experience Score = (0.4 x (1 / Page Load Time)) + (0.3 x Conversion Rate) + (0.3 x (1 - Bounce Rate))