Online Sales Performance Analytics

Power BI for Sales Performance Analysis Boosting Sales with Power BI: A Real-Life Success Story   Scenario: Challenge: Our sales team struggled with tracking performance metrics across different regions and product lines. The data was scattered across various sources, making it difficult to get a unified view.   Solution: We implemented Power BI to consolidate sales data from CRM, ERP, and other systems into a single, interactive dashboard.   Steps: 1. Data Integration:    Used Power BI's built-in connectors to pull data from multiple sources.   Example Query:     let         SalesData = Sql.Database("ServerName", "DatabaseName", [Query="SELECT * FROM Sales"])     in         SalesData     2. Data Modeling:   Created relationships between tables to allow for comprehensive analysis.   Example: Linked sales data with regional data to analyze performance by region.   3. Interactive Dashboards:   Designed dashboards to track key metrics like total sales, sales growth, and regional performance.   Features: Drill-down capabilities, slicers for filtering by date, product, and region.   Impact: Improved Visibility: Sales managers now have a clear, real-time view of performance metrics. Faster Decisions: Quick access to data enabled faster decision-making and strategy adjustments. Increased Sales: Identified high-performing regions and focused efforts on underperforming areas, resulting in a 15% sales increase.     Include screenshots of the Power BI dashboard, before-and-after performance metrics, and user testimonials. Have you used Power BI to transform your sales performance? Share your story in the comments!   #PowerBI #Sales #DataVisualization #BusinessIntelligence #TechInnovation #DataDriven

We don't pay for any Amazon reporting. Amazon's Search Query Performance report tells us how customers discover our listings. This report tells sellers search volume, impressions, clicks and purchases for their top 1k keywords. Both in total and our brand's market share. To find this report: Brand Analytics ➔ Search Analytics ➔ Search Query Performance   At the top, toggle between 2 ways to view search data: ➔ "Brand View": 1k most important search terms to your brand. ➔ "ASIN View": 100 most important search terms by ASIN.   First: Organize & Label the Data.   Export the top 1k keywords by week as far back as possible. Merge into 1 spreadsheet. A free chrome extension makes this very easy. I'll share it at the end.   In a new tab, list each unique search term and add columns with fields you'd like to filter by. Match these fields into the main dataset. These are the fields I add:  • Keyword type: Branded, Generic or Competitor • Competitor: Yeti, Hydro Flask, etc • Product Type: Adult Bottle, Kid's Bottle, Backpack, etc. • License: Character or Sports Team   Now I can see our performance when customers search for Yeti, ice buckets, Paw Patrol, our branded keywords, etc.   Here are a few ways I look at the data:   1. Search Type   One of my favorite charts is the % of our clicks coming from branded, generic and competitor search terms.   Successfully brand building means more clicks from branded search terms over time.   Generic keywords drove 60% of clicks into our listings. Now branded keywords drive most of our clicks. Growing clicks from branded search is important, this is how we track it. (chart below)   2. How Are Customers Finding a Listing?   Pulling the "ASIN View" report for every ASIN in a listing shows exactly how customers are finding your listing.   For our kids listings, character specific keywords are a huge driver. They sum up to be about 40% of traffic.   "Spiderman Toys" has been a great keyword for us. We can know how we're doing YoY on keywords like this.   3. Amazon Ads Incrementality   Knowing if Amazon Ads are increasing total sales is one of life's great mysteries.   Match this report with Amazon Ads click data by keyword & date.   Test turning on and off campaigns and watch what happens to clicks in the SQP report.   The change in average clicks from a keyword is what ads are actually producing.   You can understand how much money you are lighting on fire with branded ads. Only 20% of branded ad clicks are incremental for us.   4. Simple Modern vs Competition's Search Volume   We compare total searches and clicks for our brand to competitors by week.   It shows relative brand health and who's trending up/down.   It shows us passing Hydro Flask over the last 2 years.   5. Flipping Competitor's Customers   With this data, you can see search volume for competitor keywords.   If successful, this is a great customer acquisition tactic. A great use for SP ads.   10% of our clicks come from competitor keywords.

Excited to share my latest Data Analytics Portfolio Project—an interactive Swiggy Sales Dashboard. This dashboard transforms food delivery sales data into meaningful business insights through interactive reporting and KPI-driven analysis. Key Dashboard Features: - Sales & Order Performance Tracking - Total Sales, Orders, Customers & Average Order Value KPIs - City-wise Sales Analysis - Cuisine-wise Revenue Distribution - Peak Order Hour Heatmap - New vs Repeat Customer Analysis - Delivery Performance Monitoring - Payment Method Analysis - Top Performing Restaurants - Order Cancellation Insights - Weekly Performance Summary - Interactive Slicers & Filters Tools & Skills Used: - Microsoft Excel - Power Query - Power Pivot - Pivot Tables & Pivot Charts - Data Cleaning & Transformation - Data Visualization - Dashboard Design - KPI Reporting - Business Intelligence Business Insights Delivered: - Identify peak ordering hours to optimize operations - Track customer retention and ordering behavior - Compare city-wise sales performance - Monitor delivery efficiency and cancellations - Support faster, data-driven business decisions Building projects like this strengthens my expertise in Data Analytics, Business Intelligence, and Dashboard Development while solving real-world business problems.

I'm excited to share my latest data analytics project: a comprehensive Retail Performance Analysis Dashboard. Problem: The retail company struggled with a lack of clear insights, making it difficult to track overall performance, understand customer behavior, and manage inventory efficiently. Solution: I developed and deployed an interactive, end-to-end Power BI dashboard. By connecting directly to SQL databases, the solution provides a real-time, holistic view of the business, analyzing key KPIs like sales, profit margins, customer segmentation, supplier performance, and stock health. 📊 Tools Used: Power BI | SQL | Excel | DAX | Data Modeling 💡 Key Insights & Highlights: • Total Sales: ₹5.34M • Profit Margin: 28.77% • YoY Sales Growth: 23.48% • Top Performers: The North Region (₹1.52M) and the supplier "Boat" (₹1.1M) were the primary drivers of sales. • Operational Health: Maintained a 65% delivery rate against a 9.17% return rate. • Actionable Inventory: Identified 3 critical products as "Low Stock" (Stock = Reorder Level), flagging them for immediate re-purchasing. Dashboard Link: https://lnkd.in/gHTPaTce #PowerBI #SQL #DataAnalytics #BusinessIntelligence #Dashboard #DataVisualization #RetailAnalytics #DataInsights

Algorithmic merchandising was our catalyst for a 62% increase in revenue – with the same traffic. Here's our crazy experiment👇 We ran a crazy experiment over the last couple of weeks. While analyzing the data to find the next big growth lever for one of our longest-standing brands I’ve noticed something interesting. Over 32% of the site-wide traffic was hitting collection pages. Also, I identified some outperforming products (hidden champions) that were getting a lot of clicks even though they weren't in prime positions. On the other hand, some products that were getting the most impressions weren't performing as well. People stopped browsing more often when there were a lot of poor performing products in the visible space. So good products didn't even get a chance to be shown to many people. What if we could change the allocation of these products? – Give good products more visibility and bad products less. The challenge now was to find those outliers and position them accordingly. The real breakthrough came when I figured out how to use this data to improve product placement on collection pages. My approach went beyond just tracking clicks. I looked at several key metrics to get a full picture of how each product is doing: → CTR by position → Basket Rate → Purchase Rate: → 90-day Product LTV These 4 indicators were fed into RetentionX's machine learning process to generate a performance indicator that creates a score from 0-100. Products that weren’t performing as well in their current spots were moved to less prominent positions, freeing up space for the real stars — the products that were outperforming expectations. For the first time, our customer had a clear strategy for how to present their products, one that went beyond just gut feelings and good looks. They could now combine our automated insights with their own logic for sorting products—like aligning email campaigns with what customers would see on the site, push new arrivals and demote low stock items. The changes we made had a noticeable impact. Collection pages, which had been somewhat overlooked, suddenly became the go-to place to track what was happening with their customers and how their products were being perceived. The numbers told us we were on the right track, and remember this is a $40M+ brand: → 62% More Profit from the Same Traffic → 27% Additional Increase in Revenue → 23% Higher Conversion Rate → 12% Increase in AOV → 18% Increase in Basket Rates When we saw how well this approach worked, we knew we couldn't keep it to ourselves. So Merchandise Automation is now part of our RetentionX Core product. Read the full case study here: https://lnkd.in/dHh_Sbkp

Let's say you are running a global online store. Your website's inventory and sales transactions are managed in a high-performance database (Spanner), while all your marketing and customer analytics are stored in a data warehouse (BigQuery). You want to run a flash sale and need to see in real-time which products are selling best in which regions, so you can adjust your advertising spend instantly. Previously, combining this live sales data with your marketing analytics was a slow process. By the time your report was ready, the crucial moment to make a decision might have passed. With BigQuery's new materialized views over Spanner, this all changes. The system automatically creates and regularly updates a cached, pre-computed summary of your sales data inside your data warehouse. Now, when you open your dashboard, the results are instantaneous. You can see that your new line of sneakers is flying off the shelves in Europe thanks to a specific social media campaign. Without waiting, you can immediately shift more of your advertising budget to that campaign, maximizing your sales and ensuring you don't run out of stock. The focus is on the speed of insight, allowing you to make smarter, faster business decisions when it matters most. Read more here: https://lnkd.in/gZBuvUXV #BigQuery #Spanner #DataAnalytics #CloudComputing #Performance #RealTimeData

🚀 Excited to share my latest Power BI project! 🚀 📈 Sales Analysis - Electronic Store 📊 Problem Statement: The organization faced challenges in effectively analyzing their sales records, resulting in limited visibility into product performance, sales trends, and inventory management. A comprehensive solution was required to visualize key sales metrics, enabling data-driven decision-making to enhance profitability and optimize regional and product-specific strategies. 🔍 Key Metrics and Insights: Total Quantity Ordered: 209K 📦 Total Revenue: $34.49M 💰 Profit Margin: 58.83% 📈 Identified peak sales periods in October and December 🎯 Top-performing cities like New York and San Francisco 📍 🛠️ Key Features: Sales trends by month 📊 Product performance analysis 🛒 Regional sales heat map 🌍 Weekly sales distribution 📆 💡 Impact: This analysis helped optimize inventory management and drive targeted marketing strategies, providing crucial insights for data-driven decision-making. 🎓 What I Learned: How to design and structure dynamic dashboards that effectively communicate data insights. Leveraged DAX functions to calculate advanced KPIs and metrics. Improved understanding of sales trends and how visual analytics can influence business strategy. Gained experience in real-time data analysis using Power BI to create actionable insights. Live dashboard - https://bit.ly/47ucnM3 Github Project link - https://lnkd.in/grvS2bTy #PowerBI #DataVisualization #DataAnalysis #SalesAnalysis #Dashboard #Analytics #BusinessIntelligence #DataDriven #DecisionMaking #LearningJourney

𝐒𝐚𝐥𝐞𝐬 𝐀𝐫𝐞 𝐃𝐨𝐰𝐧. 𝐍𝐨𝐰 𝐖𝐡𝐚𝐭? 𝐓𝐡𝐞 4 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐓𝐡𝐚𝐭 𝐆𝐢𝐯𝐞 𝐀𝐧𝐬𝐰𝐞𝐫𝐬 Last week, my team was puzzled. Sales numbers for the quarter were down. Someone asked the big question: 👉 “We have all this data, but how do we actually use it to make better decisions?” Instead of jumping into complex models, We broke it down into 4 types of analytics each one answering a different business question. 1. 𝐃𝐞𝐬𝐜𝐫𝐢𝐩𝐭𝐢𝐯𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 (What happened?) We pulled sales reports from the last 3 months. That showed us the drop was real and quantified it. 2. 𝐃𝐢𝐚𝐠𝐧𝐨𝐬𝐭𝐢𝐜 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 (Why did it happen?) Digging deeper, we compared product categories. Turns out, one competitor launched heavy discounts in the same period, explaining the decline. 3. 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 (What’s likely to happen?) Using historical sales + seasonal trends, we forecasted that if the competitor continues their campaign, our sales might dip another 8% next quarter. 4. 𝐏𝐫𝐞𝐬𝐜𝐫𝐢𝐩𝐭𝐢𝐯𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 (What should we do about it?) Finally, we simulated scenarios: adjusting pricing, offering bundled deals, and launching targeted marketing. This gave leadership clear recommendations. ✨ By moving through these 4 stages, we turned confusion into clarity and data into decisions. 💡 𝐏𝐫𝐨 𝐭𝐢𝐩: Don’t try to jump straight to predictive or prescriptive analytics. Always master descriptive and diagnostic first strong foundations make advanced analytics more accurate and reliable. Learning is better together, follow for more Data Analytics insights, Lasya Nandini👋 #AnalyticsForBusiness #DataAnalytics #BusinessIntelligence #DecisionMaking #SQL