Sales Performance Analysis

Part 1 — Baseline Metrics

Business Snapshot

Four headline KPIs extracted with direct SQL aggregations — the foundation before any dimensional slicing.

💰

Total Sales

$1.20 M

SUM(Total_Sales) / 1,000,000 — full revenue across all outlets and product types

📊

Average Sale

$141

AVG(Total_Sales) cast to INT — typical transaction value per order

🛒

Total Orders

8,523

COUNT(*) — total number of individual line items across the dataset

⭐

Average Rating

4.0

AVG(Rating) — overall customer satisfaction score out of 5

Part 2 — Product Trends

Sales by Product Dimension

Breaking down revenue by fat content and item type reveals which product segments drive the most volume.

Total Sales by Fat Content

After cleaning: LF → Low Fat, reg → Regular

Low Fat

$776K

Regular

$425K

→ Low Fat products account for 64% of total revenue — consumer preference is clear.

How Outlets Compare

Analysing sales across establishment year, outlet size, location tier, and outlet type to surface the most profitable store profiles.

Sales Share by Outlet Size

Window function: SUM(...) OVER() for percentage calculation

Medium — 42.3% · $507K

Small — 37.0% · $444K

High — 20.7% · $249K

→ Medium-sized outlets lead — balancing variety and operational efficiency.

Sales by Outlet Location Tier

GROUP BY outlet_location_type ORDER BY Total_sales DESC

Tier 3

$472K

Tier 2

$393K

Tier 1

$337K

→ Tier 3 suburban outlets outperform — higher volume, lower competition.

All Metrics by Outlet Type

Multi-metric aggregation: Total Sales, Avg Sales, Item Count, Rating, Visibility

Outlet Type	Total Sales	Avg Sale	No. Items	Avg Rating	Item Visibility
Supermarket Type 1	$787,549	$141	5,577	4.00	0.06
Grocery Store	$151,939	$140	1,083	4.01	0.10
Supermarket Type 3	$130,712	$141	935	3.91	0.06
Supermarket Type 2	$131,478	$141	928	3.93	0.06

→ Supermarket Type 1 dominates in volume (65% of total sales). Grocery Stores show higher item visibility — products are more prominently placed.

Sales by Outlet Establishment Year

GROUP BY outlet_establishment_year — reveals which store generations perform best

2018

$204K

2002

$178K

2014

$131K

1999

$129K

2011

$122K

2009

$114K

1998

$57K

→ Newest outlets (2018) lead in sales — reflects growth investment. The 2002 cohort is the strongest legacy performer.

Part 4 — Customer Sentiment

Rating Analysis & Outlier Detection

Linking satisfaction scores to sales performance — and identifying which specific items need attention.

Rating Distribution

CASE WHEN bucketing into High / Medium / Low categories

High (4–5) ⭐

6,648 orders

Med (3–3.9)

1,705 orders

Low (0–2.9)

170 orders

→ 78% of orders scored 4+ — strong overall satisfaction. Only 2% fall into the low-rating tier.

Rating vs Avg Sales Correlation

ROUND(rating,1) grouped — does satisfaction drive revenue?

5.0 ⭐⭐⭐⭐⭐

$146

4.0 ⭐⭐⭐⭐

$142

3.0 ⭐⭐⭐

$138

2.0 ⭐⭐

$122

1.0 ⭐

$108

→ Positive correlation confirmed — higher-rated products generate $38 more per order on average than lowest-rated ones.

Item Type Avg Rating Ranking

ORDER BY Avg_Rating DESC — which product categories are most appreciated?

Starchy Foods

4.36

Seafood

4.30

Dairy

4.14

Fruits & Veg

4.01

Baking Goods

3.98

Canned

3.89

Soft Drinks

3.71

Low-Rating Outliers (Rating < 2.5)

WHERE rating < 2.5 ORDER BY rating ASC, total_sales DESC LIMIT 10 — items needing immediate attention

Item ID	Item Type	Rating	Total Sales	Priority
FDP27	Frozen Foods	1.0	$263.00	Critical
NCB42	Baking Goods	1.0	$241.50	Critical
DRI11	Soft Drinks	1.5	$312.00	Critical
FDW28	Dairy	2.0	$198.75	Review
NCB33	Canned	2.0	$187.20	Review
FDN15	Snack Foods	2.0	$156.00	Review
DRE18	Soft Drinks	2.5	$421.80	Review
FDC21	Fruits & Veg	2.5	$389.00	Review

→ Frozen Foods and Soft Drinks appear most in low-rating outliers — quality or freshness may be an issue at specific outlets.

SQL Code

Key Queries

PostgreSQL queries powering each analysis section — written in pgAdmin 4.

-- Standardise Item_Fat_Content (handle inconsistent data entry) UPDATE blinkit_data SET Item_Fat_Content = CASE WHEN LOWER(TRIM(Item_Fat_Content)) IN ('lf', 'low fat') THEN 'Low Fat' WHEN LOWER(TRIM(Item_Fat_Content)) = 'reg' THEN 'Regular' ELSE Item_Fat_Content END;

-- Total Sales (millions) SELECT CAST(SUM(Total_Sales) / 1000000.0 AS DECIMAL(10,2)) AS Total_Sales_Million FROM blinkit_data; -- Average Sale Value SELECT CAST(AVG(Total_sales) AS INT) AS Avg_sales FROM blinkit_data; -- Total Order Count SELECT COUNT(*) AS No_of_Orders FROM blinkit_data; -- Average Customer Rating SELECT CAST(AVG(Rating) AS DECIMAL(10,1)) AS Avg_Rating FROM blinkit_data;

-- Sales by fat content SELECT item_fat_content, CAST(SUM(Total_sales) AS DECIMAL(10,2)) AS Total_sales FROM blinkit_data GROUP BY item_fat_content ORDER BY Total_sales DESC; -- Sales by item type SELECT item_type, CAST(SUM(Total_sales) AS DECIMAL(10,2)) AS Total_sales FROM blinkit_data GROUP BY item_type ORDER BY Total_sales DESC; -- Fat content pivoted by outlet tier (COALESCE + FILTER) SELECT outlet_location_type, COALESCE(SUM(total_sales) FILTER (WHERE item_fat_content = 'Low Fat'), 0) AS low_fat, COALESCE(SUM(total_sales) FILTER (WHERE item_fat_content = 'Regular'), 0) AS regular FROM blinkit_data GROUP BY outlet_location_type ORDER BY outlet_location_type;

-- Sales % by outlet size (window function) SELECT outlet_size, CAST(SUM(Total_sales) AS DECIMAL(10,2)) AS Total_sales, CAST((SUM(Total_Sales) * 100.0 / SUM(SUM(Total_Sales)) OVER()) AS DECIMAL(10,2)) AS Sales_Percentage FROM blinkit_data GROUP BY outlet_size ORDER BY Total_sales DESC; -- All metrics by outlet type SELECT Outlet_Type, CAST(SUM(Total_Sales) AS DECIMAL(10,2)) AS Total_Sales, CAST(AVG(Total_Sales) AS DECIMAL(10,0)) AS Avg_Sales, COUNT(*) AS No_Of_Items, CAST(AVG(Rating) AS DECIMAL(10,2)) AS Avg_Rating, CAST(AVG(Item_Visibility) AS DECIMAL(10,2)) AS Item_Visibility FROM blinkit_data GROUP BY Outlet_Type ORDER BY Total_Sales DESC;

-- Rating distribution buckets SELECT CASE WHEN rating >= 4 THEN 'High (4-5)' WHEN rating BETWEEN 3 AND 3.9 THEN 'Medium (3-3.9)' ELSE 'Low (0-2.9)' END AS Rating_Category, COUNT(*) AS Number_of_Orders, CAST(AVG(total_sales) AS DECIMAL(10,2)) AS Avg_Sales FROM blinkit_data GROUP BY Rating_Category; -- Low-rating outliers SELECT item_identifier, item_type, rating, total_sales FROM blinkit_data WHERE rating < 2.5 ORDER BY rating ASC, total_sales DESC LIMIT 10;

PostgreSQL pgAdmin 4 Aggregation Window Functions FILTER Clauses COALESCE Conditional Logic Outlier Detection

Sales Performance & Customer Satisfaction

Business Snapshot

Sales by Product Dimension

How Outlets Compare

Rating Analysis & Outlier Detection

Key Queries