To design effective Data Governance and Business metrics, a structured approach is necessary. Simply counting numbers without a system leads to confusion. Imagine running a marketing or sales campaign without knowing what’s working and what isn’t—it would be impossible to track success or identify areas for improvement.
Metrics serve as a guide toward achieving business goals. This chapter explores how metrics are structured, particularly how they are divided into goal metrics and operational metrics, and how they are further categorized into Key Performance Indicators (KPI) and Key Performance Drivers (KPD). Using the case study from T Company and sales/marketing data, we will examine real-world applications.
1 The Basics of Metric Design: Establishing a Structure
A well-designed metric system must be structured. If metrics are scattered randomly, it becomes difficult to track progress. The foundation of metric design consists of two key categories:
- Goal Metrics: These define the ultimate business objectives, such as “increase revenue by 20%” or “achieve 90% customer satisfaction.”
- Operational Metrics: These measure daily progress toward goal metrics, such as “advertisement click-through rate” or “daily customer inquiries.”
The two must align to ensure that operational efforts contribute to overarching business objectives. In the case study from T Company, we observe a structured approach where goal metrics (e.g., revenue growth) and operational metrics (e.g., ad impressions) are managed separately but connected.
2 KPI and KPD: A Layered Approach to Metric Design
A structured metric system uses KPI and KPD to create a hierarchical relationship between business goals and daily operations.
- Key Performance Indicator (KPI): These are the main indicators of success, corresponding to goal metrics. They measure whether the company is achieving its strategic objectives. Examples include “monthly revenue,” “conversion rate,” and “customer retention rate.”
- Key Performance Driver (KPD): These are operational metrics that influence KPI. They track factors that drive performance. Examples include “ad copy quality,” “targeting accuracy,” and “landing page load speed.”
This hierarchical relationship ensures that improving KPD leads to better KPI results, which ultimately contributes to business objectives. In T Company’s analysis of “pain points → improvement measures → metric strategy,” KPI (e.g., revenue growth) is directly linked to KPD (e.g., ad effectiveness).
3 Lessons from T Company: A Practical Approach to Metrics
The case study from “T Company: Industry Benchmark Metrics” illustrates a structured approach to metric design by breaking down the process into multiple levels.
3.1 Step 1: Identifying Pain Points
T Company started by identifying operational issues, such as “customer data is inaccurate” or “advertising effectiveness is low.” These pain points represent areas where KPD can be improved.
3.2 Step 2: Establishing Improvement Plans and Metrics
The company defined improvement measures and linked them to measurable KPIs. For example, “increase customer data accuracy to 95%” (KPI) was supported by “automate data entry processes” (KPD).
3.3 Step 3: Execution and Monitoring
T Company tracked KPIs such as “monthly data quality score” and “ad click-through rate” while managing KPD metrics like “data entry error rate” and “frequency of A/B testing on ad copy.” This process ensured that operational metrics were aligned with business objectives.
4 Real-World Applications in Sales and Marketing
In T Company’s sales and marketing strategy, online campaigns were optimized using a structured metric system.
4.1 Goal Metrics (KPI)
- “Monthly Revenue” (final target in value chain)
- “Increase in Ad Impressions” (key KPI for FFP strategy)
4.2 Operational Metrics (KPD)
- “Frequency of Ad Copy Optimization” (affects ad effectiveness)
- “Landing Page Load Speed” (affects conversion rate)
For example, in the “Sales/Marketing” segment of T Company’s study, the goal was to “increase customer conversion rate by 10%.” To achieve this, operational metrics like “targeting accuracy improvement” and “email open rate optimization” were set as KPDs. With this layered structure, teams monitored KPD daily while tracking KPI progress over time.
5 Best Practices for Structuring Metrics
To build an effective metric system, follow these steps:
- Define Goal Metrics – Determine the ultimate business objective (e.g., “increase annual revenue by 20%”).
- Identify KPI – Establish core indicators that measure success (e.g., “monthly sales,” “number of new customers”).
- Determine KPD – Identify operational factors that drive KPI performance (e.g., “advertising budget allocation,” “frequency of customer feedback collection”).
- Link to Daily Operational Metrics – Set measurable daily indicators (e.g., “ad click-through rate,” “customer response time”).
- Monitor and Adjust – Regularly review KPI and KPD performance, adjusting as needed to stay on track.
6 Conclusion: The Power of a Well-Structured Metric System
Metric design is more than just counting numbers—it requires a structured approach where goal metrics and operational metrics are connected through a hierarchy of KPI and KPD. The case study from T Company illustrates how identifying pain points, establishing improvement plans, and executing structured monitoring leads to better business outcomes.
By implementing this structured system, teams stay focused, decisions become data-driven, and organizations move confidently toward their goals.