How Do You Identify Your Most Valuable Customer? Join Us and Learn How
Calling all BI and analytics leaders, revenue owners, data analysts, forecasters, and eCommerce experts! Do you know who your Most Valuable Customers (MVC) are? Do you know how to use your data to find them? We can help! In a live webinar on November 19th, Jen Stirrup, CEO at Data Relish and Dave Mariani, CSO at AtScale will show you how to identify your Most Valuable Customers in some surprising new ways by analyzing your own data.
WHAT WILL YOU LEARN?
In this live webinar, you will learn:
- What makes a customer most valuable and what that looks like for your business
- Common problems in calculating MVCs which include data consistency and granularity
- How to easily define customer metrics and attributes
- Where to find consistent data across data sources
And much more!
Register now and learn how to make your data a competitive advantage for your organization.
Date/Time: Thursday, November 19th, 2020 @ 8 AM PST/11 AM EST/4:00 PM GMT
ABOUT JEN STIRRUP:
Jen Stirrup is an award-winning, internationally recognized BI and data visualization expert, author, data strategist, and technical community advocate. She has been honored repeatedly, along with receiving peer recognition, as a Microsoft Most Valuable Professional (MVP). Jen has nearly 20 years of experience in delivering BI and data visualization projects for companies of various sizes across the world.
ABOUT DAVE MARIANI:
Dave Mariani is one of the co-founders of AtScale and is the Chief Strategy Officer. Prior to AtScale, he was VP of Engineering at Klout & at Yahoo! where he built the world’s largest multi-dimensional cube for BI on Hadoop. Mariani is a Big Data visionary and a serial entrepreneur.
AtScale powers the analysis used by the Global 2000 to make million dollar business decisions. The company’s Intelligent Data Virtualization™ platform provides Cloud OLAP, Autonomous Data Engineering™ and a Universal Semantic Layer™ for fast, accurate data-driven business intelligence and machine learning analysis at scale. For more information, visit www.atscale.com.
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