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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.
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.
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.
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.
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 Retention Rate: Your Leaky Bucket Metric
Customer Retention Rate measures how many customers you keep over a period, showing how "leaky" your business's bucket is. It's vital for subscription services and e-commerce to gauge loyalty and predict revenue. A high overall rate can hide dangerous churn.
Net Promoter Score (NPS): A Snapshot of Customer Loyalty
NPS distills customer loyalty into one number by asking, "How likely are you to recommend us?" It's a quick pulse check for product teams. The footgun is treating the score as a diagnosis; it tells you *what* customers feel, but not *why*.
Customer Acquisition Cost: What It Costs to Win a Customer
Customer Acquisition Cost (CAC) is the total price you pay in sales and marketing to get one new customer. Businesses use it to see if their model is viable by comparing it to customer lifetime value (LTV). The footgun is forgetting to include all costs.
Ensemble Forecasting: Predicting with a Crowd of Models
Instead of one 'best guess,' ensemble methods generate many forecasts to map the range of possibilities. This is crucial for complex systems like weather prediction, where a single model is misleadingly precise.
LSTMs: Giving Neural Networks a Long-Term Memory
LSTMs give neural networks a selective memory, letting them remember important information over long sequences. This is key for language translation or time-series forecasting where old context matters.
Granger Causality: Forecasting, Not Causing
Granger Causality tests if one time series can forecast another, not if it causes it. It's used in econometrics to see if money supply changes predict inflation. The footgun is the name itself: it only shows predictive power, not true cause-and-effect.
ARIMA: Forecasting by Modeling Autocorrelation
ARIMA models forecast a time series by learning its "memory"—how past values influence the next. It's used for forecasting sales or server load where patterns are driven by internal dynamics.
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.
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.
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.
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.
Exponential Smoothing: Weighting Recent Data More Heavily
Exponential smoothing creates forecasts from a weighted average of past data, where weights decay exponentially for older points. It's used for quick, reliable predictions like inventory demand. The footgun: simple versions ignore trends and seasonality.
Stationarity: A Time Series's Stable Personality
A time series is stationary if its statistical personality, like its average and spread, remains constant over time. Many forecasting models require this stability to work correctly.
Moving Average: Smoothing Out the Noise in Data
A moving average smooths out noisy data by replacing each point with the average of its neighbors, revealing the underlying trend. It's used to track stock prices or server metrics without overreacting to short spikes.
Propensity Score Matching: Mimicking an A/B Test
Propensity Score Matching (PSM) mimics a randomized trial with observational data by finding a "statistical twin" for each subject. It's used to estimate a feature's impact when a true A/B test isn't possible. The footgun is assuming it removes all bias.