medium Retail Expansion Analysis

Problem Statement

Table: StorePerformance
Each row in this table represents a store, detailing its unique ID, revenue in 2020 and 2021, and its geographical coordinates.

+-------------+---------+
| Column Name | Type    |
+-------------+---------+
| store_id    | int     |
| rev_2020    | float   |
| rev_2021    | float   |
| lat         | float   |
| lon         | float   |
+-------------+---------+
store_id is the primary key for this table.
rev_2020 and rev_2021 represent the store's revenue in 2020 and 2021, respectively.
lat and lon represent the latitude and longitude of the store's location.

Develop a solution to calculate the total potential revenue for 2021 from stores that have the same revenue in 2020 as at least one other store and are located at unique geographical coordinates.

Example

Input:

StorePerformance table:
+----------+----------+----------+------+------+
| store_id | rev_2020 | rev_2021 | lat  | lon  |
+----------+----------+----------+------+------+
| 1        | 100      | 150      | 10.0 | 10.0 |
| 2        | 200      | 250      | 20.0 | 20.0 |
| 3        | 100      | 300      | 20.0 | 20.0 |
| 4        | 100      | 400      | 40.0 | 40.0 |
+----------+----------+----------+------+------+

Output:

+----------+
| rev_2021 |
+----------+
| 550.00   |
+----------+

In this example, stores 1 and 4 meet the criteria: they have the same rev_2020 as another store and are located at unique coordinates. Store 2 is unique in its revenue, and store 3 shares its location with store 2, disqualifying both.

Try It Yourself

-- TODO: Write your user queries here

Solution

We need to calculate the total potential revenue for 2021 (rev_2021) from stores that:

1. Have Duplicate Revenues in 2020 (rev_2020): The rev_2020 value is shared by at least two stores.

2. Are Located at Unique Geographical Coordinates (lat, lon): No other store shares the same (lat, lon) pair.

SQL Query

SELECT FORMAT(SUM(sp.rev_2021), 2) AS rev_2021
FROM StorePerformance sp
INNER JOIN (
    -- Identify rev_2020 values that are duplicated
    SELECT rev_2020
    FROM StorePerformance
    GROUP BY rev_2020
    HAVING COUNT(*) > 1
) dup_rev ON sp.rev_2020 = dup_rev.rev_2020
INNER JOIN (
    -- Identify unique (lat, lon) pairs across all stores
    SELECT lat, lon
    FROM StorePerformance
    GROUP BY lat, lon
    HAVING COUNT(*) = 1
) unique_loc ON sp.lat = unique_loc.lat AND sp.lon = unique_loc.lon;

Explanation of the Query

1. Subquery dup_rev: Identify Duplicate rev_2020 Values

   SELECT rev_2020
   FROM StorePerformance
   GROUP BY rev_2020
   HAVING COUNT(*) > 1

   • Purpose: Selects all rev_2020 values that appear more than once, indicating duplicate revenues.

   • Example Output:

     +----------+
     | rev_2020 |
     +----------+
     | 100      |
     +----------+

2. Subquery unique_loc: Identify Unique (lat, lon) Pairs

   SELECT lat, lon
   FROM StorePerformance
   GROUP BY lat, lon
   HAVING COUNT(*) = 1

   • Purpose: Selects all (lat, lon) pairs that are unique across the entire table.

   • Example Output:

     +------+------+
     | lat  | lon  |
     +------+------+
     |10.0 |10.0 |
     |40.0 |40.0 |
     +------+------+

3. Main Query: Join and Sum rev_2021

   SELECT FORMAT(SUM(sp.rev_2021), 2) AS rev_2021
   FROM StorePerformance sp
   INNER JOIN dup_rev ON sp.rev_2020 = dup_rev.rev_2020
   INNER JOIN unique_loc ON sp.lat = unique_loc.lat AND sp.lon = unique_loc.lon;

   • Purpose:

     • INNER JOIN dup_rev: Filters stores with duplicated rev_2020 values.
     • INNER JOIN unique_loc: Ensures that the stores are located at unique (lat, lon) coordinates.
     • SUM(sp.rev_2021): Aggregates the rev_2021 values of the eligible stores.
     • FORMAT(..., 2): Formats the sum to display two decimal places (550.00).

   • Processing with Example Input:

     • Eligible Stores:
       • Store 1: rev_2020 = 100, (lat, lon) = (10.0, 10.0) — Eligible.
       • Store 4: rev_2020 = 100, (lat, lon) = (40.0, 40.0) — Eligible.
     • Excluded Stores:
       • Store 2: rev_2020 = 200 (unique) — Excluded.
       • Store 3: rev_2020 = 100, (lat, lon) = (20.0, 20.0) — Shares location with Store 2, hence excluded as (20.0, 20.0) is not unique.

   • Final Calculation:

     SUM(rev_2021) = 150 (Store 1) + 400 (Store 4) = 550.00

   • Final Output:

     +----------+
     | rev_2021 |
     +----------+
     | 550.00   |
     +----------+

I'm working on the problem **Retail Expansion Analysis** (Databases). Give me a HINT LADDER: start with the tiniest nudge, then wait. Only reveal the next, stronger hint when I ask. Do NOT show the full solution unless I type 'show solution'. Keep me doing the thinking. If you're unsure or a claim isn't standard, say so and reason from first principles instead of guessing.

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