Which Store Is Best? A Multi-Metric Performance Diagnosis

UrbanMart's monthly dashboard ranks 8 stores by revenue. Store A is #1 with ₹1.2 crore. But Store A has the lowest profit margin and the worst employee retention. Store E has the lowest revenue but the highest margin, best customer satisfaction, and happiest staff. The regional manager must determine which metric actually measures performance — and whether the dashboard is incentivizing the wrong behaviour.

# Aplly.xyz Case Study Submission

## Title
Which Store Is Best? A Multi-Metric Performance Diagnosis

## Type
Data Analytics

## Difficulty
Intermediate

## Estimated Time
60 minutes

## Overview
UrbanMart's monthly dashboard ranks 8 stores by revenue. Store A is #1 with ₹1.2 crore. But Store A has the lowest profit margin and the worst employee retention. Store E has the lowest revenue but the highest margin, best customer satisfaction, and happiest staff. The regional manager must determine which metric actually measures performance — and whether the dashboard is incentivizing the wrong behaviour.

## Case Details

Function Focus: Multi-criteria performance evaluation, metric validity critique, Goodhart's Law awareness, ranking methodology comparison

Scenario:
UrbanMart's regional dashboard ranks all 8 stores by monthly revenue. The top performer gets a bonus and is held up as the model for others to follow. Store A has held the #1 revenue spot for 6 consecutive months. But when the new analyst digs into the data, she finds Store A also has the lowest profit margin (5%), rock-bottom customer satisfaction (62%), and the highest staff turnover (54% annualized). Meanwhile, Store E — ranked last by revenue — has the highest profit margin (22%), best customer satisfaction (96%), and near-zero turnover. The regional manager wants a single, clean ranking for the board. The analyst must argue that the current metric (revenue) is misleading and propose a better approach — supported by manual analysis of the multi-metric data.

Dataset Structure:
- 8 stores across 5 metrics: Monthly Revenue, Profit Margin, Customer Satisfaction, Revenue per Sq Ft, Employee Retention
- No single composite score exists — the analyst must decide how to compare them fairly
- Store characteristics: location type (mall vs high street vs small town), size (sq ft), and years since opening

Tasks:
1. Rank the 8 stores by each of the 5 metrics individually — produce 5 separate rankings and identify how many different stores would claim "#1" depending on which metric you pick
2. Identify the pair of metrics that show the strongest inverse correlation (one goes up, the other goes down) — compute the rank-order difference manually for the most extreme case and explain why this tension exists
3. Propose a single composite score formula (describe your weighting logic and why those weights make sense for UrbanMart's stated goal of "sustainable profitable growth") — then manually compute your score for all 8 stores and produce a final ranking
4. Compare your composite ranking against the simple revenue ranking — state which stores move up or down the most and whether your ranking better reflects true performance
5. Only after submitting your manual analysis, implement your composite score in a spreadsheet or AI tool and check whether any calculation errors affected your ranking

Expected Output:
A performance evaluation memo containing: (a) a 5-metric ranking table showing how #1 changes per metric, (b) identification of the strongest metric tension with manual rank-difference calculation, (c) a proposed composite score with explicit weighting rationale, (d) the composite ranking vs revenue ranking comparison highlighting the stores that shift most, and (e) a round-trip error check.

Evaluation Criteria:
Completeness of all 5 individual rankings, correct identification of inverse metric pairs, clear and defensible weighting rationale for the composite score, correct arithmetic on the composite calculation, and meaningful discussion of why revenue alone is a misleading performance metric.

## Data Sources

Store Performance Data (August 2026):
| Store | Location Type | Size (sq ft) | Monthly Revenue (₹L) | Profit Margin (%) | Customer Satisfaction (%) | Revenue per Sq Ft (₹) | Employee Retention (%, annualized) |
|---|---|---|---|---|---|---|---|
| A | Mall | 2,800 | 120 | 5 | 62 | 4,286 | 46 |
| B | Mall | 3,000 | 115 | 8 | 71 | 3,833 | 58 |
| C | High Street | 1,800 | 95 | 18 | 92 | 5,278 | 92 |
| D | High Street | 2,000 | 88 | 16 | 88 | 4,400 | 85 |
| E | Small Town | 1,500 | 72 | 22 | 96 | 4,800 | 95 |
| F | Small Town | 1,200 | 58 | 20 | 94 | 4,833 | 93 |
| G | Mall | 3,200 | 110 | 9 | 74 | 3,438 | 65 |
| H | High Street | 2,500 | 105 | 13 | 80 | 4,200 | 78 |

Context:
- UrbanMart's stated goal: "Sustainable profitable growth"
- Current bonus structure: 100% based on revenue ranking
- Board requested: "a single clean ranking so we know who our best store manager is"
- Average industry profit margin: 12%
- Average retail employee retention: 70%

## Solution Frameworks
Multi-criteria decision analysis, metric validity auditing, Goodhart's Law, composite scoring, rank correlation

## Solver Guidance & Tutorials
Link to: "When the Metric Becomes the Target — Avoiding Goodhart's Law in Performance Dashboards" tutorial

## What You'll Learn
- Evaluating performance metrics for completeness and bias
- Detecting when a single-metric ranking rewards the wrong behaviour
- Designing a defensible composite score from multiple dimensions
- Communicating metric limitations to non-technical decision-makers

## Tags
performance metrics, retail analytics, Goodhart's law, multi-criteria ranking, metric design

## Registration Links
- Register as Solver
- Register as Evaluator

Store Performance Data (August 2026):
| Store | Location Type | Size (sq ft) | Monthly Revenue (₹L) | Profit Margin (%) | Customer Satisfaction (%) | Revenue per Sq Ft (₹) | Employee Retention (%, annualized) |
|---|---|---|---|---|---|---|---|
| A | Mall | 2,800 | 120 | 5 | 62 | 4,286 | 46 |
| B | Mall | 3,000 | 115 | 8 | 71 | 3,833 | 58 |
| C | High Street | 1,800 | 95 | 18 | 92 | 5,278 | 92 |
| D | High Street | 2,000 | 88 | 16 | 88 | 4,400 | 85 |
| E | Small Town | 1,500 | 72 | 22 | 96 | 4,800 | 95 |
| F | Small Town | 1,200 | 58 | 20 | 94 | 4,833 | 93 |
| G | Mall | 3,200 | 110 | 9 | 74 | 3,438 | 65 |
| H | High Street | 2,500 | 105 | 13 | 80 | 4,200 | 78 |

Context:
- UrbanMart's stated goal: "Sustainable profitable growth"
- Current bonus structure: 100% based on revenue ranking
- Board requested: "a single clean ranking so we know who our best store manager is"
- Average industry profit margin: 12%
- Average retail employee retention: 70%

Multi-criteria decision analysis, metric validity auditing, Goodhart's Law, composite scoring, rank correlation