Adman Analytics
Thoughts on marketing analytics
recent posts
- A Structured SQL Framework for Marketing Analytics: Building Scalable Query Architecture
- Did Google Just Take the Lead in AI? Yup.
- Practical Data Visualization with Seaborn: Analyzing Superstore Sales Data
- Introduction to Seaborn: Beautiful Statistical Visualizations in Python
- Google Search “Preferred Sources”
about
Tag: analysis
-
Marketing analytics teams often struggle with SQL query management as their data infrastructure grows. Without a systematic framework, queries become unwieldy, difficult to maintain, and prone to errors. This comprehensive guide presents a structured SQL framework specifically designed for marketing analytics applications, providing both the theoretical foundation and practical implementation strategies your team needs.
-
Understanding the distinction between Adobe Customer Journey Analytics (CJA) and Adobe Customer Journey Optimizer (CJO) – along with their powerful audience definition capabilities – is crucial for any organization building a comprehensive martech stack. While these platforms share the “Customer Journey” prefix and operate within the Adobe Experience Platform ecosystem, they serve fundamentally different purposes…
-
Panel research has quietly revolutionized how marketers understand consumer behavior, transitioning from mail-in surveys and diary studies to sophisticated digital ecosystems that track billions of transactions in real-time. As marketing measurement becomes increasingly complex and privacy regulations reshape data collection, panel research offers a unique window into actual consumer behavior that complements and often surpasses…
-
Every business faces the same fundamental question: How much is a customer really worth? While acquiring new customers often gets the spotlight, the real goldmine lies in understanding and maximizing the value of your existing customer base. This is where Customer Lifetime Value (LTV) becomes your strategic compass. Understanding LTV: Beyond the Buzzword Customer Lifetime…
-
Conversion lift studies represent a fundamental shift in how we measure marketing effectiveness—from correlation to causation, from assumptions to evidence
-
Data cleaning and wrangling may lack the glamour of machine learning or the visual appeal of dashboard design, but they represent the critical difference between data science that works and data science theater. Master these foundations, and everything you build on top will stand the test of time, scale, and scrutiny.
-
Those pesky data errors aren’t just problems to fix; they’re golden tickets to organizational enlightenment.
-
In today’s data-driven business landscape, poor data quality isn’t just an inconvenience—it’s a strategic liability. As we explored in our previous discussion on the true cost of poor data quality in digital marketing, organizations lose an average of $12.9 million annually due to data quality issues. The solution? A robust data quality framework that transforms…
-
In digital marketing, data drives every decision—from campaign targeting to budget allocation to performance measurement. Yet many organizations operate with a dangerous blind spot: the hidden costs of poor data quality. While marketers obsess over click-through rates and conversion optimization, bad data silently erodes millions in marketing spend, destroys customer trust, and creates cascading failures…
-
Introduction Customer Relationship Management (CRM) systems sit at the heart of modern business operations, housing invaluable data about customers, prospects, and business relationships. Yet for many organizations, CRM data remains siloed, underutilized, and disconnected from other critical data sources. This represents both a massive missed opportunity and a significant competitive disadvantage. CRM data engineering—the practice…