Intermediate concepts in Analytics & Metrics, page 3
Holt-Winters Exponential Smoothing
Forecast time-series data by giving more weight to recent events. Holt-Winters smoothing is used to analyze metrics and make predictions by accounting for user-defined assumptions like seasonality.
ACF vs. PACF: A Time Series Signal and Its Echo
Autocorrelation (ACF) measures a time series's total correlation with its past, including indirect echoes. Partial Autocorrelation (PACF) isolates the direct correlation by removing intermediate effects. They help select forecasting model parameters.
Time Series Cross-Validation
Unlike standard cross-validation that shuffles data, time series cross-validation respects the arrow of time. It evaluates a forecasting model by training only on past data to predict a future point, then rolling forward. The footgun is using random k-folds.
Prophet: Automated Time Series Forecasting
Prophet treats forecasting like decomposing a signal, breaking a time series into trend, seasonality, and holiday effects. It excels at predicting business metrics with strong seasonal patterns, like web traffic.
DAU/MAU Ratio: Measuring Product Stickiness
The DAU/MAU ratio measures product stickiness by comparing daily to monthly users. Social media apps aim for high ratios (50%+), while e-commerce expects lower ones. The footgun is comparing ratios without context; a 'good' number varies by product type.
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.
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.
User Journey Analysis: Finding Friction and Opportunity
User journey analysis is like watching a film of your customer's experience to find plot holes. It helps spot where a product fails to meet expectations or has redundant steps. The biggest footgun is analyzing without a clear persona in mind.
User Engagement Score: A Health Check for Your Product
A User Engagement Score distills complex user behavior into a single number, showing if users find value or are at risk of churning. Product teams use it to gauge feature adoption, while success teams identify at-risk accounts.
The Aha! Moment: Finding Your Product's Core Value
The Aha! Moment is when a user first understands your product's core value, turning them from a trial user into a long-term customer. It's key for product teams improving activation and reducing churn.
The Power User Curve: Go Beyond DAU/MAU
The Power User Curve is a histogram showing user activity distribution, revealing what single metrics like DAU/MAU hide. It shows if you have a core of daily "power users" (a "smile" curve) or just casual visitors, guiding your product and monetization…
Product-Qualified Lead (PQL)
A product qualified lead is a user whose in product behavior, not a form fill or sales call, signals they are ready to buy. Product led companies use signals like inviting teammates or hitting a usage cap to route high intent users to sales.
Analyzing Feature Engagement: Breadth vs. Depth
Breadth vs. Depth analysis plots features on a 2x2 grid: how many people use a feature (breadth) vs. how often (depth). It helps decide where to invest, like improving a popular but seldom-used feature or boosting a niche power-user tool.
Data Democratization: Self-Service Analytics for Everyone
Data democratization means non-technical staff can access and use data without waiting for IT. It empowers sales to analyze their pipeline or marketing to track campaign ROI directly.
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