F&B Sales Performance Dashboard
F&B Sales Intelligence Dashboard An interactive dashboard developed as part of my data analytics portfolio to explore sales performance across three F&B outlets in Indonesia. It uses the Anonymous Transactional Dataset from Mendeley Data, containing historical POS transactions and product metadata for January–September 2025. The dashboard covers sales KPIs, outlet performance, weekly trends, transaction intensity by day and operating hour, and product rankings. It also incorporates RFM-based transaction segmentation using K-Means, replicating a published methodology with the full dataset. Four transaction segments are summarized by volume, average spending, and total transaction value. The analysis covers 53,820 transactions with a total transaction value of approximately IDR 1.32 billion. Key findings include SHOP001 contributing nearly 49% of total transaction value and high-value transactions contributing a substantial share despite their smaller volume. Tools: Power BI and Google Colab (Python) Skills: Data modeling, DAX, KPI development, interactive visualization, RFM analysis, K-Means clustering, and insight communication. Dataset: https://data.mendeley.com/datasets/kcgf45y24m/2 Feedback on the dashboard’s design, analysis, and usability is welcome.
Olist E-Commerce Operations Dashboard: From Raw Reviews to Kano Model!
Hello everyone. I spent the last two weeks building a Power BI dashboard on a public e-commerce dataset (~100K orders). This started as a personal practice project but ended up covering more ground than I expected, so I'm sharing it here in case it's useful to others in the community. What the dashboard covers: Executive Overview 99K orders, 16M in revenue, 97% delivery rate. There's an interactive map that responds to filters so drilling into any state updates the whole dashboard. Delivery Performance A process flow breaking the order journey into stages, paired with a Delivery Delay Distribution chart. Review scores drop sharply once an order goes late and keep falling the longer the delay runs. On-time orders average 4.3 stars. Orders delayed +15 days average 1.7. Customer Satisfaction This is the page I put the most thought into. The original dataset is in Portuguese and includes customer scores but no complaint categorization. I used an LLM via API to classify +14K reviews into 9 categories in a single run, consistently and without manual labeling. It's like an Affinity Diagram, but instead of a team sitting in a room with sticky notes for hours, manual labeling, guesswork, an LLM did the classification consistently, at scale. From there, I built a Pareto analysis of Low Rating Orders (LRO) by complaint category, then combined it with ANOVA results to produce a Kano Model that maps each category by its impact on both satisfaction scores and fulfillment rate. Key findings: While most of the analysis provided by other analysts focused on Delivery Timeline, we can see from customers feedback that "Item Not Delivered" is a Must-Be requirement that is not being met consistently, followed by Missing Items. Early delivery was the only Delighter in the entire dataset. Six Sigma Quality Control Sigma Level 3.0, Cpk 0.4. The page includes a control chart, Cpk distribution, and DPMO trend over time, benchmarked against the e-commerce industry standard of 4 sigma as the next target. Tools used: Python + OpenAI API (review categorization), Microsoft Excel, Power BI. Dataset: Olist Brazilian E-Commerce, publicly available on Kaggle.
Marketing Analytics Dashboard | Customer Segmentation, Campaign & Channel Analysis
Project Overview This Power BI dashboard analyzes customer behavior and marketing performance using the iFood Customer Data dataset. The report consists of three interactive pages: an Analytics Overview dashboard, a Customer Segmentation Analysis dashboard, and a Campaign & Channel Analysis dashboard. 1. Analytics Overview The main dashboard provides a high-level summary of marketing performance and customer purchasing behavior. Features: Total customer spending and average spending metrics Campaign acceptance rate Web purchase ratio Spending distribution across product categories Purchase channel distribution Income vs. spending analysis Customer segment and education-level spending insights 2. Customer Segmentation Analysis Features: Spending analysis by age group Spending patterns by marital status and education level Customer demographic segmentation Channel preference distribution across customer groups 3. Campaign & Channel Analysis Features: Campaign acceptance rate comparison Purchase channel performance analysis Web purchases versus website visits by age group Campaign response analysis across customer segments 4. Other Features a) Customer Insights - Tooltip 1 Displays: Average Customer Spending Total Spending Campaign Acceptance Rate Displays: Total Accepted Campaigns Campaign Acceptance Rate Campaign-wise performance metrics An interactive slicer panel was added to the main dashboard for dynamic filtering. Included Filters: Age Education Level Marital Status Customer Segment Additional Features: Clear Filters button Open/Close slicer panel navigation Customer Spending Customers with incomes above $50K generally tend to spend more, regardless of age category. Among customers with incomes below $50K, young adults and seniors tend to spend less relative to their income. The $50K–$100K income range contains a large proportion of adults, middle-aged customers, and seniors. Wine is the highest-spending product category, with total spending exceeding $0.5 million. Customer Segments Spending across marital-status groups shows similar patterns across the different education levels, having a slight exception in masters education level, between 'single' and 'together' customers. Married customers have the highest total spending, while widowed customers have the lowest, across the education levels shown. Middle-aged and adult customers have the highest total spending among the age groups, while young adults have the lowest. Campaign Performance & Channel Preferance Four of the five campaigns have more than 125 accepted offers, while Campaign 2 has substantially fewer accepted offers than the other campaigns. Store purchases represent the largest share of customer purchases among the available channels. Although web is the second-most preferred purchasing channel, customers with lower numbers of monthly web visits account for the highest number of web purchases. These customers are predominantly middle-aged and senior customers. Customers with the highest numbers of monthly web visits have comparatively fewer web purchases and are predominantly adults. Business Implications & Areas for Further Analysis Campaign Performance How do campaign responses vary across higher-income customer segments, and which campaign warrant further investigation? Why did the Campaign 2 underperform relative to the other campaigns? What factors may have contributed to Campaign 2's lower acceptance, and what changes could be investigated to improve its performance? Product Spending Given the difference in spending across product categories, should the business investigate opportunities to further promote high-spending products such as wine, or focus on increasing demand for lower-spending categories such as gold, fish, sweet products, and fruits? What factors may be contributing to the lower spending on these products, and what strategies could be investigated to increase demand, such as expanding product variety, targeted promotions, discounts, or other customer benefits? Purchase Channels What factors may explain the higher preference for store purchases, and what opportunities exist to increase engagement through the other channels? Why do customers with fewer monthly web visits generate more web purchases? Why do customers with more monthly web visits generate fewer web purchases? Which products are viewed most frequently by customers with higher monthly web visits, and why do these visits not translate into higher web purchases? Tools & Technologies: Microsoft Power BI Power Query DAX Data Modeling Interactive Visualizations Links to the Dataset: ifood-marketing-campaigns Dataset on kaggle ifood-marketing-campaigns Dataset on GitHub (Link 1) ifood-marketing-campaigns Dataset on GitHub (Link 2) Find my GitHub repository: Dashboard_with_Power-BI_Marketing_AnalyticsHealthcare Analytics Dashboard - Power BI Template
In hospitals, delays and inefficiencies don’t just cost money, they risk lives. This Healthcare Analytics Dashboard (Power BI Template) turns complex health data into clear, actionable insights that empower teams to act quickly and precisely. Designed from real hospital use cases, this tested solution helps clinical and operational leaders improve care delivery, resource planning, and financial performance. Challenges This Dashboard Solves 1. Unpredictable Length of Stay (LOS) Hospitals often struggle with forecasting patient discharge times. This leads to bottlenecks in bed availability, overcrowded wards, and delayed admissions. Without visibility into LOS trends, resource allocation becomes guesswork. This dashboard provides historical and real-time LOS analysis by department, condition, and demographic, helping hospitals predict and plan bed usage accurately. 2. Hidden Billing Inefficiencies Revenue leakages are common when billing data is scattered or inconsistently tracked across departments. Costs tied to specific treatments, medications, or admission types often go unnoticed. The financial dashboard breaks down billing data by condition, hospital wing, and treatment category, helping analysts pinpoint cost spikes and optimize spend. 3. Readmissions Due to Missed Clinical Patterns Chronic conditions like hypertension can lead to repeated ER visits if treatment effectiveness isn’t monitored. Without automated alerts, early warning signs are often missed. This dashboard flags abnormal blood pressure cases, tracks medication response rates, and alerts clinical teams for timely interventions,reducing readmission risks. 4. Workforce Planning Gaps Admission surges during weekends or specific times of year catch teams unprepared, stretching staff thin. Weekend vs. Weekday admission trends allow administrators to align staffing with actual demand patterns, not assumptions. 5. Manual and Delayed Reporting Relying on Excel sheets and outdated reports can delay crucial decisions and lead to inconsistent care quality. Automated Power BI reports eliminate manual effort, providing stakeholders with real-time, exportable, and easy-to-understand insights. Key Benefits – Why This Dashboard Matters Live Monitoring of Admissions and Bed Utilization Stay on top of patient intake, identify overcrowded departments, and reallocate beds in real time to improve flow and reduce wait times. Length of Stay (LOS) & Discharge Trend Analysis Track average stay durations by department, gender, or diagnosis. Use historical trends to forecast bed demand, avoid overcrowding, and improve discharge efficiency. Hypertension Alerts & Medication Effectiveness Tracking Get automatic alerts for patients with abnormal BP, analyze which treatments work best, and proactively manage chronic conditions to reduce complications. Billing Insights by Condition, Department, and Admission Type Quickly uncover which procedures or admission types generate the highest costs. Identify revenue opportunities or overspending areas across service lines. Demographic-Based Performance Insights Segment KPIs by age group, gender, and medical condition. This helps tailor treatment plans and improve patient outcomes across different populations. Who Needs This? Hospital Admins – Optimize resources and costs Clinical Teams – Enhance treatment protocols Revenue Analysts – Fix billing inefficiencies Healthcare Analysts – Ditch manual reports for live data Data Source: Synthetic healthcare data for illustrative use cases. eyJrIjoiZTQzNDYzYjAtNWI4MC00MDJkLWI0NGEtNTFjMjAyYWU3ZGRiIiwidCI6ImE5NDUyNTg2LTJmNGMtNGNiMS04ZDJlLTI2ODkyODllZTcyNiIsImMiOjl9Unit lifecycle on the BIM model
Every unit in a building has a commercial life of its own: vacant, reserved, for sale, sold, rented, back under maintenance. That history usually lives in a spreadsheet, far away from the drawing everyone actually looks at. This report puts it back on the model. It covers a 28-unit mixed-use building — residential, offices, retail and a restaurant across five levels — and tracks every unit from January 2023 onwards. Five pages: Overview — the numbers first: 12 units on the sale side, 16 on the rent side, broken down by vacancy and status. Underneath, every status change plotted over time, one line per unit, split between commercial and residential. You can see a unit go For Rent → Rented → Vacant → For Rent and read how long each leg took. History 2D — the floor plans, with each unit coloured by its status on the date you pick. Move the slider and the plan repaints. History 3D — the same timeline on the 3D model, filtered by contract type and level, with the occupancy mix next to it. Latest – Rent and Latest – Sale — the current picture on each side of the business: what is still available, what is gone, and where it sits in the building. The model comes into Power BI through Vcad, so geometry and unit properties arrive as tables you can relate to anything else — here, the status history. Selection works both ways: click a row and the unit lights up in the model, click the unit and the tables filter. The data is fictional. The workflow isn't. One question, since the report reads history in two different ways: do you get there faster through the status lines on the Overview, or through the slider repainting the plan? I keep changing my mind depending on who I'm showing it to.Occupancy monitoring, Vcad custom visual and Autodesk Forge
Power BI report that, using markers, monitors the occupancy of workstations in an office building. Click here https://www.bimservices.it/how-it-works/ to learn more about Vcad. eyJrIjoiZGJmMTE2YWQtZDM5MC00OTE5LTllZGEtNWViNjNlYTEyNDJmIiwidCI6IjkyNWJkNTViLTA1MjEtNDAwMy1iN2M5LWMzZTY4MzY3ZDcyNCIsImMiOjh9&pageName=ReportSection92c08b2c41e182ae4910Quantities and materials dashboard from BIM file - Vcad for Power BI
Dashboard highlighting how to use quantity and material data in a report. We then see how Vcad makes it easy to leverage the information contained in BIM files, transforming it and associating it with the rendering of the model. eyJrIjoiODQxOTkyMjYtYjgwNC00ZjY4LTg3MGEtMWJkZDg5NTA1YTZhIiwidCI6IjkyNWJkNTViLTA1MjEtNDAwMy1iN2M5LWMzZTY4MzY3ZDcyNCIsImMiOjh9Project Management Dashboard
This project management dashboard is essential because it provides a centralized, visual overview of project progress, making it easier to track tasks, deadlines, resources, and budgets, as well as identify potential issues early. This centralized view improves communication, streamlines decision-making, and ultimately helps keep projects on track and within budget. Why project management dashboards are so important: Real-time visibility: Dashboards offer a quick and clear view of a project's status, allowing teams and stakeholders to instantly see progress and identify potential issues. Improved communication: By providing a single source of truth for the project, dashboards reduce the need for constant updates and follow-ups, fostering better communication between team members and stakeholders. Improved decision-making: With clear and concise data readily available, project managers and stakeholders can make informed decisions based on real-time information. Efficient Resource Allocation: Dashboards help track resource utilization, ensuring they are allocated effectively and that no team member is overburdened. Early Risk Identification: By monitoring key metrics and trends, dashboards can help identify risks and potential issues before they escalate, enabling timely intervention. Increased Accountability: With clear visibility into who is responsible for each task, dashboards can help promote accountability and ensure deadlines are met. Improved Project Execution: Ultimately, the benefits of a project management dashboard contribute to better results, including on-time delivery, on-budget completion, and improved team performance. Visit Website: https://insight-analytics.com/dashboards/ Thank you eyJrIjoiY2MyZTRlZjgtMWM1MC00NWY0LWFlNmQtZWZiNDQ4OGZjNGQ0IiwidCI6IjBlMGNiMDYwLTA5YWQtNDlmNS1hMDA1LTY4YjliNDlhYTFmNiIsImMiOjR9Ecommerce Conversion Dashboard
This dashboard analyzes the ecommerce conversion funnel from Page Views to Adds to Cart to Purchases. It highlights conversion rates, period-over-period deltas, and performance breakdowns by department and product. eyJrIjoiOGVkNTI1MjctOTMyYi00NGQ2LWFmOWUtZTJmYzdiZDA2OWFiIiwidCI6ImE2MWFhYzQ5LWQxNTctNGU2ZS1iMzU3LTA4YmU5MmY4NDA5YiIsImMiOjEwfQ%3D%3DThe Finance Dashboard
The Finance Dashboard | Power BI Project 📊 Excited to share my latest Finance Dashboard built using Microsoft Power BI! This project focuses on Financial Data Analytics, Business Intelligence, and Interactive Data Visualization. Key Features: - Total Amount, Tax, Fees & Final Amount KPIs - Country-wise Financial Analysis - Company-wise Amount Analysis - Transaction Status Monitoring - Tax Analysis by Country & Company - Year & Account Type Filters - Interactive Dashboard Design This dashboard helps transform raw financial data into meaningful insights for better business reporting and data-driven decision-making. Tools: Microsoft Power BI | Data Analytics | Business Intelligence | Financial Reporting I am passionate about Data Analytics, Power BI, and turning data into actionable business insights. Your feedback is welcome! #PowerBI #PowerBIDashboard #DataAnalytics #BusinessIntelligence #FinancialAnalytics #DataVisualization #FinanceDashboard #PowerBIDeveloper #DataAnalyst #MicrosoftPowerBI
