An interactive, multi-page Tableau dashboard built on a comprehensive client dataset covering sales, marketing, customer behavior, delivery, risk, and AI-driven performance metrics. The workbook uses button-based navigation to move seamlessly between an executive overview, trend pages, and analytical deep-dives.
| Tool | Tableau Desktop (.twb) |
| Data Source | ClientDataset.csv |
| Dashboards | 6 (Overview, Month wise Sales, Month wise Profit, RCA, S.W.O.T, Order risk by delivery) |
| Key Metrics | Sales, Profit, Profit Margin, Avg. Delivery Delay Days, Avg. Customer Satisfaction Score, Avg. Churn Probability, Return Rate % |
| Navigation | Custom button/tab-based navigation between all dashboards |
Every page in the workbook is reachable from a shared tab bar, with Overview acting as the home page:
Order risk by delivery · Month wise sales · Month wise profit · S.W.O.T · RCA · Overview
Each analytical page (Sales, Profit, S.W.O.T, RCA) also has a dedicated Overview button to jump straight back to the home dashboard.
The main landing dashboard, combining top-line KPIs with drill-down visuals.
- KPIs: Sales (44.63B), Profit (8.92B), Profit Margin (20%), Avg. Delivery Delay Days (3), Avg. Customer Satisfaction Score (3), Avg. Churn Probability (50), Return Rate (50.04%)
- Filters/Slicers: Financial Year, Subscription Plan, State, Region, City
- Visuals included:
- Sales by state, shown on an interactive map of India
- State-wise Sales (bar chart)
- Category-wise Profit Margin
- Delivery Status by Risk Level (Cancelled, Delayed, Delivered, In Transit)
- Navigation: Links out to Month wise Sales, S.W.O.T, RCA, and Order risk by delivery
Trend view of monthly sales from late 2022 through 2026, with an Actual vs. Estimate forecast indicator distinguishing historical values from projected ones.
- Navigation: Jump to Month wise Profit or back to Overview
Trend view of monthly profit over the same period, also using the Actual vs. Estimate forecast legend.
- Navigation: Back to Overview
A strategic SWOT summary built directly from the dataset:
- Strengths: Complete data coverage, strong customer insight tracking, KPI/ROI performance tracking, AI-driven forecasting, wide coverage across regions and products
- Weaknesses: High data complexity, data quality issues, high operational costs, customer churn, uneven inventory availability
- Opportunities: Regional expansion, promoting top products, better-targeted marketing, improving delivery experience, greater use of AI
- Threats: Strong competition, shifting market trends, high returns/refunds, fraud risk, supply chain disruption, negative customer feedback
A root-cause (fishbone) diagnostic built around the problem statement "Sales are Decreasing," breaking potential causes into six branches:
- Customers — low satisfaction, high churn, low NPS, negative feedback, fewer repeat purchases
- Marketing — low spend, poor campaign ROI, weak targeting, low conversion
- Product — low demand, limited variety, high returns, quality issues
- Pricing — pricing above competitors, excessive discounting, retention-affecting price changes
- Delivery / Inventory — delivery delays, stockouts, poor demand forecasting
- Competition / External Factors — aggressive competitor pricing, new competitor launches, economic slowdown, regulatory changes
Key takeaway: sales decline stems from multiple, interacting factors — the recommended next steps are to investigate top root causes, track KPIs closely, take data-driven action, and monitor continuously.
The underlying ClientDataset.csv is a rich, wide dataset spanning multiple business domains:
Sales & Financials
Sales, Profit, Profit margin, Quantity, Discount Percent, Forecasted Revenue, Forecast Accuracy, Refund Amount
Customer
Customer Id, Customer Name, Customer Age, Gender, Customer Type, Customer Satisfaction Score, Customer Lifetime Value, Churn Probability, NPS Score, Sentiment Score, Retention Score
Product & Inventory
Product Id, Product Name, Category, Sub Category, Brand, Inventory Level, Supplier Name, Supplier Rating, Warehouse Id, Warehouse Capacity
Marketing & Engagement
Marketing Channel, Marketing Spend, Campaign ROI, App Sessions, Session Duration Minutes, Bounce Rate, Conversion Rate, Feature Adoption Rate, Social Media Mentions, Cart Abandonment Rate
Operations & Risk
Order Date, Delivery Date, Delivery Status, Delivery Delay Days, Return Status, Support Ticket Count, Bug Reports, Risk Level, Fraud Risk Score
Strategic / Executive
Business Unit, Strategic Priority, Board Meeting Flag, Executive Review Flag, Decision Recommendation, KPI Achievement Percent, Operational Efficiency Score, ESG Score, Carbon Emission Kg
AI-Driven Metrics
Ai Prediction Confidence, Ai Usage Score, Forecast Indicator
Geography
City, State, Region, Pincode
This dashboard was built to give both executives and analysts a single source of truth for client performance — combining a quick-glance overview with dedicated pages for trend analysis, root-cause investigation, and strategic (SWOT) review, all connected through intuitive one-click navigation.
- Open the
.twbworkbook in Tableau Desktop (or Tableau Public/Reader). - Start on the Overview dashboard — use the filters (Financial Year, Subscription Plan, State, Region, City) to slice the whole view.
- Use the tab bar or on-screen buttons to move between Overview, Month wise Sales, Month wise Profit, S.W.O.T, RCA, and Order risk by delivery.
- Click Overview from any analytical page to return to the home screen.
- Tableau Desktop — dashboard design & interactivity
- CSV — flat-file client dataset as the data source
Client_Dataset_analytics/
├── Dashboard/ # Tableau workbook (.twb)
├── Data_Source/ # ClientDataset.csv
├── images/ # Dashboard screenshots used in this README
│ ├── overview.png
│ ├── month-wise-sales.png
│ ├── month-wise-profit.png
│ ├── swot.png
│ └── rca.png
├── .gitattributes
└── README.md
Manoj Kushwaha — GitHub