Optimizing Sales and Marketing Data

Strategies for Accuracy and Efficiency
1. Auflage 2026
ISBN: 978-1-3986-2396-5
Verlag: Kogan Page

Buch, Englisch, 240 Seiten, Format (B × H): 156 mm x 234 mm

Data quality issues are an ever-growing problem for sales and marketing. Professionals in these sectors are expected to understand, clean and categorize data, though they may not always be data professionals.

Covering how to deal with and fix data disasters, this book is designed to be a complete guide for early-mid career sales and marketing professionals on how to clean and organise their data and create a framework for data governance to future-proof it. It explains how to build a taxonomy to allow for easy segmentation and targeting of customers and how this in turn reduces spend and maximises effectiveness whilst minimising unnecessary costs. As data is now the foundation of every company, making sure your data is clean, complete and correct has never been more important.

With practical examples and how-to guides throughout, Optimizing Sales and Marketing Data is immediately implementable, allowing readers to apply the knowledge to their own data right away. By being able to clean up and organize their data, sales and marketing professionals will be able to reduce their spend, increase their efficiency and profitability and become more confident handling and analyzing their customer data for further insights.

Autoren/Hrsg.

Walsh, Susan

Fachgebiete

• Wirtschaftswissenschaften Betriebswirtschaft Management Strategisches Management
• Wirtschaftswissenschaften Betriebswirtschaft Management Entscheidungsfindung
• Wirtschaftswissenschaften Betriebswirtschaft Bereichsspezifisches Management E-Commerce, E-Business, E-Marketing
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining

Inhaltsverzeichnis

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 Clean Data For AI, Machine Learning and Gen AI to Work Properly Chapter - 05: Cleaning Key Sales and Marketing Data Points Efficiently for Accurate Analytics Chapter - 06: Normalising Company and Brand Names for More Accurate Analytics Chapter - 07: Categorisation of Sales and Marketing Data for Better Business Decisions 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 Examples Chapter - 13: Summary

Über Autor(innen)

Walsh, Susan
Susan Walsh is the Founder of The Classification Guru, a company that focuses on helping clients solve dirty data problems to maximise profitability and impact. Based in London, UK, she is an industry thought leader, influencer and global speaker. She has been listed in the DataIQ100 for the last two years, as well as winning DataIQ Data Champion 2022 and 2023 and is a finalist for The Great British Businesswoman Awards in the Technology category.