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Kpi Dashboard Design

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Design effective KPI dashboards with metrics selection, visualization best practices, and real-time monitoring patterns. Use this skill when building an executive SaaS metrics dashboard tracking MRR, churn, and LTV/CAC ratios; designing an operations center with live service health and request throughput; creating a cohort retention analysis view for a product team; or debugging a dashboard where metrics contradict each other due to inconsistent calculation methodology.

Originally by wshobson · MIT

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Design effective KPI dashboards with metrics selection, visualization best practices, and real-time monitoring patterns. Use this skill when building an executive SaaS metrics dashboard tracking MRR, churn, and LTV/CAC ratios; designing an operations center with live service health and request throughput; creating a cohort retention analysis view for a product team; or debugging a dashboard where metrics contradict each other due to inconsistent calculation methodology.

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kpi-dashboard-design/SKILL.md148 lines5.4 KBRawView on GitHub
Frontmatter — 2 properties
namekpi-dashboard-design
descriptionDesign effective KPI dashboards with metrics selection, visualization best practices, and real-time monitoring patterns. Use this skill when building an executive SaaS metrics dashboard tracking MRR, churn, and LTV/CAC ratios; designing an operations center with live service health and request throughput; creating a cohort retention analysis view for a product team; or debugging a dashboard where metrics contradict each other due to inconsistent calculation methodology.
1---
2name: kpi-dashboard-design
3description: Design effective KPI dashboards with metrics selection, visualization best practices, and real-time monitoring patterns. Use this skill when building an executive SaaS metrics dashboard tracking MRR, churn, and LTV/CAC ratios; designing an operations center with live service health and request throughput; creating a cohort retention analysis view for a product team; or debugging a dashboard where metrics contradict each other due to inconsistent calculation methodology.
4---A5 — No allowed-tools declared — no way to tell what this skill may touch
5 
6# KPI Dashboard Design
7 
8Comprehensive patterns for designing effective Key Performance Indicator (KPI) dashboards that drive business decisions.
9 
10## When to Use This Skill
11 
12- Designing executive dashboards
13- Selecting meaningful KPIs
14- Building real-time monitoring displays
15- Creating department-specific metrics views
16- Improving existing dashboard layouts
17- Establishing metric governance
18 
19## Core Concepts
20 
21### 1. KPI Framework
22 
23| Level | Focus | Update Frequency | Audience |
24| --------------- | ---------------- | ----------------- | ---------- |
25| **Strategic** | Long-term goals | Monthly/Quarterly | Executives |
26| **Tactical** | Department goals | Weekly/Monthly | Managers |
27| **Operational** | Day-to-day | Real-time/Daily | Teams |
28 
29### 2. SMART KPIs
30 
31```
32Specific: Clear definition
33Measurable: Quantifiable
34Achievable: Realistic targets
35Relevant: Aligned to goals
36Time-bound: Defined period
37```
38 
39### 3. Dashboard Hierarchy
40 
41```
42├── Executive Summary (1 page)
43│ ├── 4-6 headline KPIs
44│ ├── Trend indicators
45│ └── Key alerts
46├── Department Views
47│ ├── Sales Dashboard
48│ ├── Marketing Dashboard
49│ ├── Operations Dashboard
50│ └── Finance Dashboard
51└── Detailed Drilldowns
52 ├── Individual metrics
53 └── Root cause analysis
54```
55 
56## Detailed worked examples and patterns
57 
58Detailed sections (starting with `## Common KPIs by Department`) live in `references/details.md`. Read that file when the navigation summary above is insufficient.
59 
60## Best Practices
61 
62### Do's
63 
64- **Limit to 5-7 KPIs** - Focus on what matters
65- **Show context** - Comparisons, trends, targets
66- **Use consistent colors** - Red=bad, green=good
67- **Enable drilldown** - From summary to detail
68- **Update appropriately** - Match metric frequency
69 
70### Don'ts
71 
72- **Don't show vanity metrics** - Focus on actionable data
73- **Don't overcrowd** - White space aids comprehension
74- **Don't use 3D charts** - They distort perception
75- **Don't hide methodology** - Document calculations
76- **Don't ignore mobile** - Ensure responsive design
77 
78## Troubleshooting
79 
80### MRR shown on dashboard contradicts finance's number
81 
82The most common cause is inconsistent treatment of annual plans. Finance may prorate to a daily rate while the dashboard normalizes to monthly. Align on a single formula and document it directly on the dashboard card:
83 
84```sql
85-- Explicit formula shown in tooltip / data dictionary
86-- Annual plans: divide total contract value by 12
87-- Quarterly plans: divide by 3
88-- Monthly plans: use as-is
89CASE subscription_interval
90 WHEN 'monthly' THEN amount
91 WHEN 'quarterly' THEN amount / 3.0
92 WHEN 'yearly' THEN amount / 12.0
93END AS normalized_mrr
94```
95 
96### Dashboard shows green but product team reports users complaining
97 
98The dashboard likely tracks system uptime (a lagging indicator) but not user-facing quality metrics. Add customer-perceived metrics alongside infrastructure metrics:
99 
100| Infrastructure (green) | User-perceived (add these) |
101|---|---|
102| API uptime 99.9% | P95 page load time |
103| Error rate 0.1% | Task completion rate |
104| Queue depth normal | Support ticket volume |
105 
106### Retention cohort looks flat — no variation between cohorts
107 
108Check whether the cohort query is partitioning by signup month correctly. A common bug is using `created_at::date` instead of `DATE_TRUNC('month', created_at)`, which groups by day and produces cohorts too small to show trends:
109 
110```sql
111-- Wrong: too granular, cohorts are too small
112DATE_TRUNC('day', created_at) AS cohort_date
113 
114-- Correct: monthly cohorts
115DATE_TRUNC('month', created_at) AS cohort_month
116```
117 
118### Real-time dashboard hammers the database
119 
120A live dashboard refreshing every 10 seconds with complex cohort SQL will degrade production query performance. Separate OLAP workloads from OLTP by writing pre-aggregated metrics to a summary table via a scheduled job, and have the dashboard read from that:
121 
122```python
123# Scheduled every 5 minutes via cron/Celery
124def refresh_mrr_summary():
125 conn.execute("""
126 INSERT INTO kpi_snapshot (metric, value, snapshot_at)
127 SELECT 'mrr', SUM(...), NOW()
128 FROM subscriptions WHERE status = 'active'
129 ON CONFLICT (metric) DO UPDATE SET value = EXCLUDED.value
130 """)
131```
132 
133### Alert thresholds fire constantly, team ignores them
134 
135Static thresholds set once and never reviewed cause alert fatigue. Use dynamic thresholds based on rolling averages so alerts fire only when the metric deviates significantly from its own baseline:
136 
137```python
138# Alert if current value is > 2 standard deviations from 30-day rolling mean
139def is_anomalous(current: float, history: list[float]) -> bool:
140 mean = statistics.mean(history)
141 stdev = statistics.stdev(history)
142 return abs(current - mean) > 2 * stdev
143```
144 
145## Related Skills
146 
147- `data-storytelling` - Turn dashboard findings into narratives that drive executive decisions
148 

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