Marketing Analytics & Retail Business Management using Excel

Post published:1 October, 2025
Post category:StudyBullet-22
Reading time:3 mins read

Retail analytics using MS Excel – Covering Forecasting, Market Basket, RFM, Customer Valuation & Price Bundling
⏱️ Length: 7.8 total hours
⭐ 4.49/5 rating
👥 108,061 students
🔄 September 2025 update

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Course Overview
- Transform raw retail data into powerful, actionable insights with Microsoft Excel. This course teaches strategic decision-making to boost sales, profitability, and customer engagement.
- Bridge the gap between data analytics and retail business management. Master quantitative methods to proactively respond to market dynamics and optimize every facet of your retail operations.
- Ideal for retail professionals and aspiring analysts, you’ll build and interpret sophisticated Excel-based models, fostering a robust, data-driven approach to complex business challenges.
Requirements / Prerequisites
- Basic Excel Proficiency: Comfort with fundamental spreadsheet navigation, data entry, and simple formulas is recommended. No advanced Excel knowledge required.
- Analytical Curiosity: A keen interest in data analysis and solving business problems quantitatively will maximize your learning experience.
- Retail/Marketing Interest: An existing passion for retail operations, consumer behavior, or marketing strategies will enrich course engagement.
- Microsoft Excel Access: Ensure you have Excel 2016 or a newer version (Office 365) for full feature compatibility.
Skills Covered / Tools Used
- Data Preparation in Excel: Master essential techniques for cleaning, structuring, and aggregating raw retail data for accurate analysis.
- Advanced Excel Functions: Utilize specialized Excel formulas for efficient data manipulation and sophisticated model construction.
- Strategic Product Placement: Optimize store layouts (physical/digital) using market basket analysis to maximize cross-selling and customer flow.
- Customer Segmentation: Employ RFM and CLV analysis to create targeted customer segments for highly personalized marketing campaigns.
- Dynamic Pricing: Set optimal price points for products and bundles, considering demand elasticity and competitive factors to boost revenue.
- Promotional Effectiveness: Design and evaluate marketing promotions by analyzing customer response, ensuring efficient budget allocation.
- Predictive Inventory: Use forecasting models to anticipate future demand, minimizing stockouts and reducing inventory holding costs.
- Interpreting Statistical Models: Translate complex statistical outputs (e.g., linear regression) into clear, actionable business recommendations.
- Revenue Management: Apply pricing psychology and value perception principles to craft compelling product bundles that maximize profitability.
Benefits / Outcomes
- Data-Driven Decisions: Shift from intuition to informed, analytical choices in retail merchandising, pricing, and promotions.
- Optimized Marketing ROI: Design highly targeted campaigns based on deep customer insights, boosting marketing effectiveness.
- Enhanced Operations: Implement data-backed strategies for inventory, store layouts, and pricing, directly impacting profitability.
- Stronger Customer Engagement: Develop effective acquisition and retention strategies through precise customer segmentation and CLV understanding.
- Career Advancement: Gain highly sought-after analytical skills valuable in roles like Retail Analyst, Marketing Manager, and BI Specialist.
- Competitive Advantage: Proactively identify market trends, new opportunities, and vulnerabilities through rigorous data analysis.
- Practical Model Portfolio: Build a collection of ready-to-use Excel models and analytical frameworks for immediate professional deployment.
PROS
- Highly Practical: Focuses on real-world applications and immediate business impact.
- Accessible Tool: Utilizes widely available and familiar Microsoft Excel, reducing the learning curve.
- Proven Value: High rating and large student base (108,061 students) attest to its effectiveness.
- Current Content: September 2025 update ensures the material is relevant and up-to-date.
- Comprehensive: Covers a wide range of critical retail analytics techniques in one course.
- Cost-Effective: Delivers advanced analytical skills without requiring expensive specialized software.
- Versatile Application: Applicable to both e-commerce and traditional brick-and-mortar retail environments.
CONS
- Excel-Exclusive Focus: Primarily uses Excel, which might not introduce learners to dedicated statistical programming languages (e.g., R, Python) or enterprise-level BI tools.