Concepts in Analytics & Metrics, page 7
Customer Lifetime Value (CLV): A Customer's Total Worth
Customer Lifetime Value (CLV) predicts the total net profit a customer will generate, not just a single sale's revenue. It's used to set acquisition budgets and guide retention efforts. The footgun is using revenue instead of profit, leading to overspending.
Conversion Rate Optimization (CRO): Get More Value from Existing Traffic
CRO is the science of getting more value from your existing users, not just acquiring new ones. It’s used to increase purchases on e-commerce sites or drive signups for a service.
Burn Rate: Your Startup's Financial Clock
Burn rate is your company's financial countdown timer, showing how fast you're spending cash before you run out. Startups use it to track monthly cash consumption and determine their runway.
Sales Velocity: How Fast Your Pipeline Makes Money
Think of sales velocity as the speedometer for your revenue engine, measuring how quickly your pipeline generates money. Sales leaders use it to forecast revenue and find bottlenecks.
Marketing Mix Modeling (MMM): Isolating Marketing's Impact on Sales
Marketing Mix Modeling (MMM) statistically links aggregate marketing efforts to sales outcomes over time. It's used to determine the ROI of past campaigns, like a TV ad blitz. The main footgun: the model is only as good as the historical data you feed it.
Business Analytics vs. Business Intelligence
Business Analytics predicts the future and prescribes actions, while Business Intelligence describes the past. BI reports last month's sales; BA forecasts next month's demand.
Workforce Analytics: Data-Driven People Decisions
Workforce Analytics applies systematic data analysis to people-related decisions, moving beyond gut feelings for hiring and promotions. It's used to predict turnover or measure training ROI.
Google Analytics 4
Google Analytics is a service for tracking user activity across websites and mobile apps. It helps measure marketing performance by tracking traffic and user 'events'. The footgun is focusing on raw traffic instead of the events that signal valuable actions.
Jupyter Notebooks: Interactive Code Sandboxes
Jupyter Notebooks are digital lab notebooks for running code, seeing output, and writing notes in one place. Data scientists use them for exploration, visualization, and prototyping.
Google BigQuery: A Serverless Data Warehouse
Think of BigQuery as a massive SQL database you don't have to manage. It's a serverless data warehouse for analyzing huge datasets, like terabytes of application logs. The footgun is treating it like a regular database for real-time transactions.
Amplitude: Analytics for Understanding User Behavior
Think of Amplitude as a DVR for user actions, not just a traffic counter. It tracks what users *do* inside your app, letting you build funnels and segment users by behavior.
dbt: Managing Data Transformations as Code
dbt treats your data transformations as a software project, letting you build, test, and version control your SQL. It's the 'T' in the modern ELT paradigm. Use it to create reliable data models in a warehouse. The footgun: dbt only transforms data.
Looker: Google's Data Analytics Platform
Looker is Google Cloud's data analytics platform that creates a single source of truth for metrics. It uses a modeling language, LookML, to define business logic on top of your database.
Segment: The Universal Translator for Customer Data
Segment is a universal translator for customer data. Track an event once in your app, and Segment forwards it to all your marketing and analytics tools, saving you from building dozens of separate integrations.
Apache Kafka: A Distributed Log for Data Streams
Think of Kafka as a durable, append-only log for events, not just a temporary message queue. It excels at handling high-throughput, real-time data feeds for analytics or log aggregation. The footgun is treating it like a simple broker, leading to data loss.
Snowplow: A Private Pipeline for Event Data
Think of Snowplow not as an analytics tool, but as a private pipeline you own for creating high-quality event data. It collects raw events, validates them against schemas, and loads them into your warehouse. The footgun is expecting turnkey dashboards.
Apache Spark: A Unified Engine for Big Data
Think of Apache Spark as a general-purpose engine for large-scale data analytics. It lets you program an entire cluster of machines as one, automatically handling data parallelism and fault tolerance so you can focus on the analysis itself.
Databricks: The Unified Platform for Data and AI
Databricks unifies your data warehouse and data lake into a single 'Lakehouse' platform. It's used for building ETL pipelines, training ML models, and running BI queries on the same data. The main footgun is cost: its power can lead to surprise bills.
Activation Rate: Measuring the 'Aha!' Moment
Activation rate measures the percentage of users who experience your product's core value, not just sign up. It's a key metric for diagnosing onboarding effectiveness. The common mistake is tracking 'completed onboarding' instead of the 'aha!'
Feature Adoption Rate: Measuring if New Features Deliver Value
Feature adoption rate measures if users actually use specific features, not just log in. It's vital for SaaS products to prove ongoing value for renewals. The footgun: a low rate means customers pay for unused bloat, which actively hurts perceived value and…
We are hiring for this. Every open role lists the topics its interview covers, so you can prepare for the real thing rather than guessing.
See open roles