Comprehensive Business Analysis for Lariat Car Rental Success

Comprehensive Business Analysis for Lariat Car Rental SuccessComprehensive Business Analysis for Lariat Car Rental Success

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Cherrie Cosby

• Jun 12

Business analysis for Lariat Car Rental — examining fleet utilization, revenue trends, and operational performance across locations. Full analysis includes data cleaning, exploratory analysis, and visualizations built in Excel. Published case study available online.

Business Analysis Microsoft Excel Data Analysis Data Modeling

Timilehin .O

• Jun 8

Most companies know their revenue. Very few know where their profit actually comes from. That gap is expensive. I recently analyzed a commercial & logistics dataset for a cement company and what I found underneath the surface numbers was the kind of insight that changes how a business makes decisions. Here's the headline: → ₦70.7M total revenue → ₦19.3M total profit → 27.3% average profit margin Clean numbers. But the real story started when I broke them down 👇 📍 Regional Profitability The South South region didn't just generate revenue, it led in profitability. Not every region performs equally and treating them like they do is a costly assumption. 🧑‍🤝‍🧑 Customer Segment Analysis Retail vs. Distributor customers showed clear differences in revenue contribution. Segment level visibility changes how you price, prioritize, and grow. 🚚 Delivery & Operational Efficiency Deals were closing but delivery gaps were quietly leaking value before products even reached customers. Operational blind spots don't show up in revenue reports. They show up in margins. 🏅 Sales Rep Performance A concentrated group of top performers was driving a disproportionate share of results. That's a talent insight, a succession risk, and a coaching opportunity all in one. Reporting revenue is easy. Understanding what's driving it and what's quietly eroding it is where real business intelligence lives. That's the difference between a dashboard and a decision. 🛠️ Built with Microsoft Excel, Pivot Tables, Data Modelling, Dashboard Design This is the kind of work I'm building my practice around: ✅ Sales & business performance analytics ✅ Financial analysis & margin optimisation ✅ Logistics, supply chain & operational data ✅ Risk analytics & data driven decision support If your business is sitting on sales, logistics, or financial data without a clear view of what it's telling you, that's exactly the problem I solve. 📩 Currently open to Data Analyst, Business Analyst, and Risk Analyst opportunities, full time, contract, or freelance. Drop a comment, send a DM, or follow along. More projects like this coming soon. #DataAnalytics #BusinessIntelligence #ExcelDashboard #SalesAnalytics #LogisticsData #FinancialAnalytics #ProfitabilityAnalysis #DataDriven #RiskAnalytics #OpenToWork #DataAnalyst #BusinessAnalyst #AnalyticsPortfolio #OperationalEfficiency #DataStorytelling

Data Analysis Data Modeling Product Data Analytics Microsoft Excel

Timilehin .O

• Jun 8

Fraud doesn’t always look suspicious, until you analyse the patterns. I recently worked on a Vehicle Insurance Claim Fraud Analysis case study where the goal was simple: 👉 identify hidden risk signals and process gaps that allow fraudulent claims to slip through. 🔍 The problem Insurance teams deal with thousands of claims, but: • Fraud is often buried inside normal-looking transactions • Manual checks miss systemic weaknesses, not just bad actors • High-risk signals are rarely quantified clearly So I approached this as a real-world fraud analyst, not just a dashboard exercise. 🛠️ What I did (end-to-end) Using Excel analysis, I: • Cleaned and structured claim-level data • Defined fraud-focused KPIs, not vanity metrics • Built an interactive dashboard with slicers to simulate decision-making 📊 Core KPIs tracked • Fraud Rate (%) • Total Fraud Cases • Average Driver Risk Rating (Fraud) • Police Report Gap (%) - fraud claims filed without police reports 🚨 Key insights discovered • Fraud concentration varied strongly by vehicle price band, not just volume • Certain vehicle categories consistently showed higher fraud exposure • An alarming 97%+ of fraudulent claims lacked police reports, highlighting a major verification gap • Driver risk ratings provided early signals that could support preventive controls These insights aren’t theoretical, they point directly to where insurers are leaking money. 💡 Business impact (how this helps) This type of analysis helps organisations: • Flag high-risk claims earlier • Strengthen fraud prevention rules • Improve compliance and documentation checks • Reduce payout losses without increasing manual workload 🧠 Tools & skills applied Excel (Advanced Pivot Analysis) Fraud & Risk Analytics Dashboard Storytelling Business Intelligence Thinking I’m building and sharing real fraud analytics case studies as I continue to grow in this space, focusing on credit card, insurance, and transaction fraud. If you’re a: • Fraud / Risk Manager • Insurance or FinTech team • Founder needing data-driven fraud insights I’m open to conversations and collaboration. #FraudAnalytics #FraudDetection #RiskAnalytics #BusinessIntelligence #ExcelDashboard #DataAnalytics #BuildInPublic

Data Analysis Data Modeling Data Visualization Microsoft Excel

Akorede Idowu

• Jun 9

wow this is very insightful

Lotte Frieda

• Jun 8

The client was an outreach-driven professional , either in sales, recruitment, or B2B consulting ... who had built up a substantial LinkedIn network over time but had no organized way to leverage it. Their connections existed only inside LinkedIn's ecosystem, making it impossible to run targeted email campaigns, segment contacts by industry or seniority, or feed prospects into a CRM or outreach tool. Every time they needed to reach out to a specific type of contact ... say, Managing Directors at accounting firms ... they had to manually scroll through their connections one by one. There was no master list, no exportable data, and no way to filter by job title, company, or sector. They were sitting on a goldmine of warm contacts with no infrastructure to mine it.

I extracted, cleaned, and organized the client's full LinkedIn connections data into a structured Google Sheets database with seven clearly labeled columns ... First Name, Surname, Job Title, Company, Email, Phone Number, and a direct LinkedIn Profile Link. Every row was verified and formatted consistently so the data was immediately usable for outreach, CRM import, or campaign segmentation. The spreadsheet covered a diverse range of decision-makers including Managing Directors, Chief Marketing Officers, Head of Sectors, Vice Presidents, Associate Directors, and Business Accountants ... spanning industries from accountancy and consulting to brand management and financial services. Each contact's LinkedIn profile link was embedded directly in the sheet for one-click access, making it easy to personalize outreach before sending. The final deliverable was a clean, filterable, ready-to-use lead database the client could plug straight into any email tool, CRM, or outreach sequence.

The delivered spreadsheet gave the client instant access to a fully organized, actionable contact database that had previously been locked inside LinkedIn's interface. What once required hours of manual searching and copy-pasting was now a single, filterable file the client could sort by job title, company, or industry in seconds. The structured format meant the data could be imported directly into tools like GoHighLevel, HubSpot, Apollo, or Instantly without any reformatting. The client was able to immediately launch targeted email and LinkedIn outreach campaigns to specific segments ... for example, filtering only Managing Directors in the accounting sector for a specific service pitch. By turning a passive network into an active, organized lead database, the client gained a concrete outreach asset that could generate pipeline on demand ... making every future campaign faster, more targeted, and significantly more effective.

Data Entry Microsoft Excel

Geethasree Naguboina

pro

• Jun 9

This is such a clean breakdown of a real client problem — the "goldmine with no infrastructure" framing is exactly right. So many professionals have hundreds of warm connections they've never been able to act on systematically.

I do similar work — extracting, cleaning and...