Core skills and frameworks for analysts
Learn about vanity metrics - what they are, how to identify them, and how to replace them with more meaningful KPIs that drive business impact. Understand the difference between vanity metrics and actionable metrics, and get tips for choosing the right metrics to track.
Overview of key performance indicator (KPI) fundamentals, including definitions of leading vs. lagging, input vs. output, and actionable vs. vanity metrics. Discusses best practices for creating a consistent metric taxonomy and documentation.
This article explores the emotional experiences of users during the usage of three popular analytics platforms - Adobe Analytics, Google Analytics, and Piwik Pro. By examining the author's emotional state while using these tools, the article provides a unique perspective on the user experience and decision-making process when selecting an analytics solution.
Understand the difference between exploratory and explanatory data visualizations, and how to effectively use each type to support the data analysis workflow - from initial exploration to final presentation of insights.
Foundational concepts and best practices for defining, categorizing, and documenting key performance indicators (KPIs) and other business metrics. Covers the taxonomy of leading vs. lagging, input vs. output, and actionable vs. vanity metrics, as well as establishing naming conventions and a single source of truth for metric definitions.
This article discusses how BBC Studios utilizes geo holdouts and causal inference techniques like Propensity Score Analysis (PSA) to accurately measure the marketing impact of their campaigns. It provides a detailed overview of the methodologies and challenges involved in conducting rigorous lift tests to establish causality.
Dashboards are meant to guide decisions, not create confusion. This guide covers best practices for dashboard design, including layout, chart selection, cognitive load optimization, and narrative techniques to effectively communicate insights.
This article discusses how regression lines and Excel-computed trend lines are poor at detecting signals in KPIs with underlying trends. Instead, the author recommends using a trending XmR chart to better interpret signals in these types of KPIs.
A guide to using four causal analysis techniques to measure the contribution of product features and marketing initiatives to key business metrics and KPIs. Covers experimental design, quasi-experimental methods, propensity scoring, and causal graphs to establish causality and quantify feature impact.
Uncover the rare and highly sought-after data skills that set top analytics professionals apart. Dive into cutting-edge techniques and tools that enable deeper insights, predictive modeling, and data-driven decision-making – empowering data analysts, business analysts, and data scientists to drive transformative results for their organizations. Elevate your data mastery and unlock the keys to becoming an indispensable asset in today's data-driven business landscape.
Discover the hidden root cause of your analytics woes and unlock unprecedented insights with this insightful guide. Learn proven techniques to diagnose and address the underlying issues that plague your data analysis, empowering you to make data-driven decisions with confidence across marketing, product, and business domains. This essential resource equips analytics professionals with the skills and strategies needed to transform their data into actionable intelligence and drive meaningful impact within their organizations.
Designing effective metrics trees is a crucial skill for analytics professionals who need to extract meaningful insights from complex data. This blog post delves into the techniques and strategies for structuring your metrics in a hierarchical, intuitive manner, enabling you to uncover hidden patterns, identify key drivers, and present data-driven recommendations that resonate with stakeholders across your organization.
This blog post offers a valuable lesson for analytics professionals: embrace the natural phenomenon of regression to the mean and let it guide your data analysis. By understanding and leveraging this statistical principle, you can make more informed decisions, avoid common biases, and uncover meaningful insights that drive business impact. Whether you're a data analyst, business analyst, or product analyst, this post provides a thought-provoking perspective on how to approach your work with a deeper statistical understanding.
The blog post explores how AI-driven interactions can inadvertently undermine critical thinking, a key skill for analytics professionals. By understanding the "glazing effect" of AI, data analysts and product managers can learn to leverage AI responsibly and maintain a sharper, more discerning edge in their decision-making processes. This insight is particularly valuable for professionals tasked with extracting meaningful insights from data and driving impactful business strategies.