Zomato Sales & User Analytics Dashboard 1.Project Overview This project provides a comprehensive analysis of Zomato's operations, including sales, orders, customer behavior, and city-wise performance. The insights are derived from transaction and user data, helping identify growth opportunities, customer retention challenges, and high-performing regions. 2. Key Metrics  Overall Performance: • Total Sales Amount: ₹987M • Total Orders: 150.28K • Overall Rating: 148.45K • Total Users: 100K • Active Users: 77.93K  Category-wise Performance: Category Sales Ratings Veg 156K 731K Non-Veg 140K 316K Others 14K 1K  Yearly Performance: • Highest sales year: 2019 – ₹1002K • Top-performing city (by sales): Electronic City, Bangalore – ₹0.36M  User Insights • Age Group with Highest Users: 23 years old (18.8K users) • Gain Customers (Current Year vs Previous Year): 11.64K o Male: 6.56K o Female: 5.08K • Lost Customers (Current Year vs Previous Year): 33.04K o Male: 18.92K o Female: 14.12K  City-Level Insights • City Sales Amount: ₹368K • Sales Quantity: 367 • Orders: 0.10K • Overall Rating: 148.45K Example: Akola • Sales: ₹0.37K • Ratings: 0.10K • Users: 0.10K • Orders: 100 • Lost Orders: 100 3.📷 Screenshots

4. Data Model & Features 🔗 Data Sources o orders table – Transactional order data o users table – Customer demographic and behavior data o restaurant table – Restaurant ratings o Food table-Category o Menu table-Relatationship between Menu and Food ⚙️ Features Implemented • ✔️ Data Modeling (Relationships) • ✔️ DAX Measures & Calculations • ✔️ Dynamic KPIs • ✔️ Bookmark Navigation (City) 5.DAX Measures Sale_Value=SUM(orders[Value])

Year = VALUE( FORMAT(orders[order_date],"yyyy"))

Rating_count=COUNT(restaurant[rating])

Order_count=COUNT(orders[order_date])

Rank Table = DATATABLE( "Sort", INTEGER, "Type", STRING, "NO", INTEGER, { {0, "All sale", 0}, {1, "Top 5", 5}, {2, "Top 10", 10}, {3, "Top 20", 20}, {4, "Top 50", 50}, {5, "Top 100", 100} } )

Dynamic_TopN_Title = VAR selectRank = SELECTEDVALUE('Rank Table'[Type], "All sale") RETURN selectRank & " City by Sales"

Dynamic_Subheading = "Zomato Providing Services in"&COUNT(orders[City])&"and connected with"&DISTINCTCOUNT(users[user_id])&"where got"& COUNT(orders[user_id])& "orders."

TopN_sale = VAR SelectedRank = SELECTEDVALUE('Rank Table'[NO], 0) VAR RankValue = RANKX( ALLSELECTED(orders[City]), [Sale_Value], , DESC, DENSE ) RETURN IF( SelectedRank = 0, [Sale_Value], IF(RankValue <= SelectedRank, [Sale_Value], BLANK()) )

User_Count = DISTINCTCOUNT(users[user_id])

Active_Users = DISTINCTCOUNT(orders[user_id])

Curr_year = 2020

Prev_Year = [Curr_year]-1

Curr_Yr_Sale = VAR YR=[Curr_year] RETURN CALCULATE([Sale_Value],orders[Year]=YR)

PREV_Yr_Sale = VAR YR=[Prev_Year] RETURN CALCULATE([Sale_Value],orders[Year]=YR)

Gain_Customers = VAR Filters_users=FILTER(SUMMARIZE(users,users[user_id]),AND([PREV_Yr_Sale]<=0,[Curr_Yr_Sale]>0)) RETURN CALCULATE([User_Count],Filters_users)

Lost_Customers = VAR Filters_users=FILTER(SUMMARIZE(users,users[user_id]),AND([Curr_Yr_Sale]<=0,[PREV_Yr_Sale]>0)) RETURN CALCULATE([User_Count],Filters_users)

Rating Count by City = COUNT(orders[City])

Gain_Cust = VAR UserTable = SUMMARIZE( users, users[user_id], "PrevSale", [PREV_Yr_Sale], "CurrSale", [Curr_Yr_Sale] ) RETURN COUNTROWS( FILTER( UserTable, [PrevSale] <= 0 && [CurrSale] > 0 ) )

Lost_Cust = VAR UserTable = SUMMARIZE( users, users[user_id], "PrevSale", [PREV_Yr_Sale], "CurrSale", [Curr_Yr_Sale] ) RETURN COUNTROWS( FILTER( UserTable, [CurrSale] <= 0 && [PrevSale] > 0 ) ) 6.Business Insights & Recommendations

1. Customer Retention Focus: o High number of lost customers (33.04K) indicates a potential churn issue. o Action: Implement loyalty programs, personalized promotions, and targeted engagement campaigns to retain users.
2. City-Level Optimization: o Some cities like Akola show low sales and high lost orders, suggesting operational inefficiencies or low awareness. o Action: Strengthen local marketing efforts, optimize restaurant partnerships, and analyze user preferences in these regions.
3. Category Performance Optimization: o Veg category shows higher ratings than Non-Veg, suggesting user preference trends. o Action: Increase promotions and offerings for Veg dishes in high-demand areas.
4. High-Potential Demographics: o Young users (around 23 years old) form the largest segment of active users. o Action: Develop targeted campaigns, mobile-first features, and referral programs for this demographic.
5. Top Cities Strategy: o Focus on top-performing cities like Bangalore to maximize revenue growth. o Consider expanding delivery network and restaurant partnerships in these regions. 7.Conclusion The Zomato Sales & User Analytics Dashboard provides a comprehensive view of business performance across cities, categories, and customer segments. Key takeaways include: • Sales & Orders: The platform generated ₹987M in sales with 150.28K orders, indicating strong overall performance. • Customer Behavior: While 11.64K new customers were gained, 33.04K users were lost, highlighting a significant churn issue. • City & Category Insights: Certain cities and categories (like Veg dishes) are outperforming others, showing areas for strategic focus. • Recommendations: o Implement retention programs for churned customers. o Optimize city-level operations in underperforming regions. o Enhance promotions for high-potential categories and demographics. Overall, the dashboard enables data-driven decision-making, highlighting opportunities for growth, operational efficiency, and improved customer engagement.

Deepak Kumar Tekchandani Power BI|DAX (Data Analysis Expressions)|Data Modeling|Data Cleaning & Transformation|Interactive Data Visualization

📧 deepakkumartekchandani@gmail.com
🔗 LinkedIn: www.linkedin.com/in/deepak-kumar-tekchandani-b7200a146 💻 GitHub: https://github.com/DTekchandani1/Zomato-Sales-User-Analytics-Dashboard.git