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E-commerce Metrics Calculation

6 posts · Jul 2011 – Feb 2023

What this trail covers

Shopify has evolved its systems and methodologies for calculating core e-commerce metrics. Initial efforts focused on defining and implementing foundational metrics like customer count, churn rate, and lifetime value. This post details the refinement of churn rate calculation, moving from simplistic and problematic accounting-based definitions to a more robust, timely, and current approach that uses daily customer counts and cancellations, weighted by probability of being active. The BG/NBD model is being explored for more advanced predictions. The post also clarifies the fundamental concepts of states and events, and how they relate to intervals and points, providing a theoretical basis for accurate metric calculation. It emphasizes starting with events for metric definition due to their less abstract nature compared to states.

All Shopify trails
  1. ShopifyQL Notebooks: Simplifying Querying with Commerce Data Models - ShopifyThis post details the development of ShopifyQL Notebooks and the underlying commerce data models. It outlines the process of building these models, focusing on making them purpose-driven, explorable, and extensible. Key aspects include defining granularity, ensuring additive metrics, and the step-by-step data modeling process: starting with business questions, creating mock data, finding data sources, assessing data quality and consistency, and evaluating model freshness and performance. It also introduces the concept of ShopifyQL as a query language for these models.
  2. Double Entry Transition Tables: How We Track State Changes At Shopify - ShopifyIntroduced and detailed the implementation of double entry transition tables for tracking state changes of multiple entities simultaneously. This involves creating individual attribute tables for each entity's status, merging them, and then transforming them into a double entry format with a `net_change` column. This approach simplifies historical counting and analysis of product usage, such as tracking Shopify Balance account status alongside Shopify account status, and allows for easier scaling with additional attributes without rewriting existing SQL or PySpark jobs.
  3. Bound to Round: 8 Tips for Dealing with Hanging Pennies - ShopifyThis post details strategies for managing rounding precision in financial calculations, which is crucial for accurate e-commerce metrics. It introduces concepts like the rounding dilemma, banker's rounding, using high-precision data types, maintaining consistency in rounding methods, explicit code comments for clarity, referencing government rounding standards, minimizing rounding instances, and transparently communicating rounding rules to users. The author also shares anecdotal stories and emphasizes the importance of stakeholder awareness regarding precision loss in financial calculations.
  4. How to Make Dashboards Using a Product Thinking Approach - ShopifyThis post details a product thinking approach to building data dashboards, emphasizing understanding the problem and audience, user-centered design, and iterative improvement. It outlines when to use a dashboard, how to define goals and metrics, and best practices for layout, content, and context. It also touches on data freshness and the importance of marketing and follow-up. While it discusses data visualization and metrics, it does not introduce new technical systems or algorithms for metric calculation itself, but rather a methodology for presenting them.
  5. How Shopify Merchants can Measure Retention - ShopifyThis post introduces the BG/NBD (Beta Geometric / Negative Binomial Distribution) model as a probabilistic approach to customer churn and retention analysis, moving beyond arbitrary time-limit based definitions. It details the model's parameters (rate of purchasing \(\lambda\), probability of churn event \(p\)), its sufficient statistics (Age, Recency, Frequency), and how these are used to estimate customer behavior and probability of being active. The post also mentions the use of the 'lifetimes' Python package and Apache Spark for large-scale merchant data processing.
  7. Defining Churn Rate (no really, this actually requires an entire blog post) - ShopifyThis post details the evolution of churn rate calculation at Shopify, moving from problematic accounting-based definitions to a more robust, timely, and current approach. It explores various definitions, highlights their shortcomings (e.g., influence of new customers, assumption of even churn distribution, lack of timeliness and currency), and proposes a final definition that uses daily customer counts and cancellations, weighted by the probability of being active. This refined metric aims to be current, timely, comparable across period lengths, and reflective of actual churn behavior changes.
  8. Prognostication For Fun And Profit: States And Events - ShopifyThis post introduces the fundamental concepts of states and events, and their relationship to intervals and points, as a theoretical basis for calculating e-commerce metrics. It uses accounting as an analogy to explain the difference between balance sheets (counting states at a point in time) and income statements (counting events over a period). The post advocates for starting metric definitions with events due to their less abstract nature compared to states, and discusses how event-based counting offers greater flexibility for adapting to changing business definitions and for building predictive metrics.