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Full Description
Learn how to clean, organize and understand your data so your marketing and sales work smarter, not harder.
Optimizing Sales and Marketing Data is a clear, friendly guide for early-career professionals who want to build confidence with data and learn the essentials of fixing common data problems. If you're just starting out in sales or marketing and need simple steps to clean data, categorize customers and improve your results, this book shows you exactly how to get there.
Written by data expert Susan Walsh, it breaks data management into easy, practical tasks you can apply right away. Using real-world examples and beginner-friendly explanations, it helps you understand why clean data matters and how it can improve targeting, reduce wasted spend and make your campaigns more effective.
You'll learn how to:
- Understand the basics of data quality and spot issues quickly
- Clean and organize your data using simple, repeatable steps
- Build a clear taxonomy so you can segment customers with confidence
- Apply easy data governance tips to keep your data accurate over time
- Use real-world examples to see how better data leads to better results
With step-by-step how-to guides and practical tools, this book gives you the essentials to handle data with confidence and unlock stronger outcomes in your day-to-day role.
Themes include: data essentials, data cleaning, customer segmentation, beginner data skills, marketing fundamentals, practical how-to guides
Contents
Chapter - 00: Introduction;
Chapter - 01: The importance of clean and classified sales and marketing data;
Chapter - 02: What dirty sales and marketing data looks like;
Chapter - 03: A framework for effective data governance;
Chapter - 04: Why data must be cleaned for AI and Gen AI to work properly;
Chapter - 05: Normalizing company and brand names for more accurate analytics;
Chapter - 06: Categorization of sales and marketing data for better business decisions;
Chapter - 07: Cleaning key sales and marketing data points efficiently for accurate analytics;
Chapter - 08: How a customized taxonomy can leverage your analytics and improve decision-making;
Chapter - 09: How to maintain data to future-proof it;
Chapter - 10: Spot-checking data to avoid future errors;
Chapter - 11: Data disasters and how to fix them;
Chapter - 12: Real-world projects;
Chapter - 13: Summary
