---
name: pricing-analytics-dashboard-builder
description: "Produces a complete pricing analytics dashboard specification: 18-25 metric definitions with exact formulas, chart specifications with axes and dimensions, a panel layout organized by decision layer, alert thresholds, and data source requirements. Use when designing a pricing performance dashboard, defining pricing KPIs and metric formulas, specifying charts for a pricing BI tool, or translating pricing strategy into a monitoring layout for marketplace, proptech, or e-commerce."
argument-hint: "[business-model] [pricing-model]"
allowed-tools: ["Read"]
---

# Pricing Analytics Dashboard Builder

## Purpose
Turn a business context into a build-ready pricing dashboard specification — metric formulas, chart specs, and panel layout — so a data scientist or BI analyst can implement in Looker, Tableau, Metabase, or a custom app without re-deriving definitions or debating what to track. Solves the gap between "we need a pricing dashboard" and an actual, decision-oriented spec.

## When to Use
Invoke this skill when the user asks to:
- Design or spec a pricing performance dashboard
- Define pricing KPIs and their exact formulas (elasticity, price realization, discount leakage, etc.)
- Decide which charts to build for pricing monitoring and how to structure them
- Translate a pricing strategy into a set of tracked metrics
- Set up alerting or thresholds for pricing anomalies
- Standardize pricing metric definitions across a data team

## Inputs Required
Before starting, collect:
- **Business model**: Marketplace / proptech / e-commerce (and whether transactional, subscription, or take-rate based)
- **Pricing model**: Fixed, dynamic, auction, tiered, take-rate/commission, surge, or usage-based
- **Primary pricing decision**: What the dashboard should help someone decide (e.g., set floor prices, adjust take rate, optimize discounts)
- **Available data**: Transaction-level data? Competitor prices? Cost/margin data? Inventory or supply data?
- **Audience**: Who reads it — pricing manager, exec, or analyst — and cadence (real-time / daily / weekly)
- **Key segments**: How pricing varies (category, geography, customer tier, listing type, supply cohort)
- **North-star pricing goal**: Revenue, margin, GMV, take rate, conversion, or fill rate

If pricing model or available data is unspecified, assume transaction-level data exists, dynamic pricing, and note which advanced metrics require additional data feeds.

## Instructions

### Step 1: Establish the Dashboard Decision Frame
Anchor the spec to the primary pricing decision. State in one line what a viewer should be able to answer within 30 seconds of opening the dashboard, and what action follows. All downstream metric and chart choices serve this frame. Explicitly note the north-star metric that headlines the dashboard.

### Step 2: Define 18-25 Metrics Across 5 Layers
For each metric, provide: **name**, **exact formula** (using named fields, not prose), **grain** (row-level, daily, weekly), **why it matters for this business**, and **good/watch/alert direction**.

**Layer 1 — Revenue & Volume (4-5 metrics)**
Headline outcomes. Include GMV/net revenue, order/transaction volume, average order value or average transaction price, and (marketplace) take rate = platform revenue / GMV. Use consistent revenue recognition (gross vs. net of refunds).

**Layer 2 — Price Realization & Leakage (4-5 metrics)**
Where intended price differs from captured price. Include price realization = actual price / list-or-target price, discount depth = (list − sold) / list, discount penetration = discounted units / total units, and discount leakage = sum(list − sold) over the period. For take-rate models, include effective take rate vs. posted take rate.

**Layer 3 — Elasticity & Conversion (4-5 metrics)**
How demand responds to price. Include price elasticity of demand = %Δquantity / %Δprice (state the estimation window and controls), conversion rate by price band, sell-through / fill rate, and price-to-conversion curve inputs. Flag elasticity as directional unless experimental or instrumented variation exists.

**Layer 4 — Competitive & Positioning (3-4 metrics)**
Only if competitor data is available; otherwise mark as "requires external feed." Include price index vs. market = own price / competitor median, win rate at price (auction/bid models), and price rank distribution. For proptech, include days-on-market by price-to-estimate ratio.

**Layer 5 — Margin & Efficiency (3-4 metrics)**
Profit quality. Include contribution margin = (price − variable cost) / price, margin per transaction, and (if cost data missing) contribution margin proxy = net revenue after platform costs. Include revenue per available unit/listing where supply is constrained.

### Step 3: Specify Charts for Priority Metrics
For each priority metric (aim for 10-14 chart specs), provide a build-ready spec:
- **Chart type**: line, bar, heatmap, scatter, cohort grid, funnel, distribution/histogram, or KPI tile
- **X-axis / Y-axis**: exact fields and units
- **Series / breakdown**: what splits the data (segment, price band, time)
- **Aggregation**: sum, avg, median, weighted avg — specify weighting field
- **Time grain & window**: e.g., daily, trailing 90 days
- **Reference lines**: target, prior period, or threshold overlays

Choose chart types deliberately: elasticity and price-to-conversion → scatter with fitted curve; discount leakage → stacked bar over time; price index → line vs. reference line at 1.0; sell-through by price band → heatmap; conversion funnel by price tier → funnel.

### Step 4: Design the Layout by Decision Layer
Specify a top-to-bottom panel layout, grouped so the eye moves from "what happened" to "why" to "what to do":
- **Row 1 — KPI header tiles**: 4-6 north-star and outcome metrics with period-over-period deltas
- **Row 2 — Trend row**: primary outcome and price realization over time
- **Row 3 — Diagnostic row**: elasticity/conversion and leakage charts
- **Row 4 — Segment breakdown**: the layer that matters most for this business (category, geo, or supply cohort)
- **Row 5 — Competitive/margin detail**: deeper analysis for analysts
State default filters (date range, segment) and which panels are cross-filtered.

### Step 5: Define Alert Thresholds
For 5-8 metrics, specify a monitoring rule: metric, condition (e.g., discount leakage > 8% of GMV, price index drifts below 0.95, elasticity estimate crosses −1.0), comparison basis (prior period / rolling baseline), and the pricing action the alert should trigger.

### Step 6: List Data Requirements
Enumerate the source tables/fields the spec assumes (transactions, list prices, costs, competitor feeds, inventory). Flag every metric that cannot be computed with the stated available data and name the missing input.

## Output Format
- **Structure**: Six sections following the steps; metrics in a table (name / formula / grain / why / direction); chart specs as structured blocks; layout as a labeled row-by-row diagram in text
- **Length**: 1,300-1,800 words
- **Format**: Mixed — metric and threshold tables, structured chart-spec blocks, ordered layout description
- **Sections**: Decision Frame → Metric Definitions (5 layers) → Chart Specs → Layout → Alert Thresholds → Data Requirements
- **Tone**: Analytics specialist — precise, formula-driven, implementation-ready; no marketing language

## Quality Checklist
Before delivering output, verify:
- [ ] Metric count is 18-25 and distributed across all 5 layers (count them)
- [ ] Every metric has an exact formula using named fields — not a prose description
- [ ] 10-14 chart specs each include chart type, both axes, breakdown, and aggregation
- [ ] Layout assigns every priority metric to a specific row and states default filters
- [ ] 5-8 alert thresholds each name a condition AND the pricing action it triggers
- [ ] Every metric requiring data the user didn't confirm is explicitly flagged with the missing source

## Notes
- Formulas must match the pricing model: take rate and effective take rate are central for marketplaces; days-on-market and price-to-estimate ratio for proptech; discount leakage and margin for e-commerce.
- Elasticity from observational data is directional — recommend an A/B or geo test before treating it as a control lever, and label it as such in the spec.
- Weighted averages beat simple averages for price metrics; always specify the weighting field (units or revenue) to avoid Simpson's paradox across segments.
- If the user names a specific BI tool, adapt chart-type vocabulary to that tool's chart library, but keep the metric formulas tool-agnostic.

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