More in Growth & Experimentation — page 10
Forecasting with Monte Carlo Simulation
A Monte Carlo simulation forecasts outcomes by running thousands of 'what if' scenarios with random inputs. Use it to model complex systems like user growth with variable conversion rates.
Survival Analysis: Predicting When, Not Just If
Survival analysis predicts *when* an event like user churn will happen, not just *if*. It's used to model customer lifetime or hardware failure rates. The key mistake is using simple averages, which are skewed by users who haven't churned yet (censored data).
Prophet: Time Series Forecasting for Seasonal Data
Prophet treats a time series as a sum of its parts: a long-term trend, seasonal cycles, and holidays. It's used for business forecasting, like predicting sales, when you have strong seasonal data. The footgun is using it for non-seasonal data.
The Bass Diffusion Model: Innovators vs. Imitators
The Bass Diffusion Model splits product adoption into innovators who buy first and imitators who follow the crowd, creating the classic S-curve of growth. It's used to forecast sales for new products by modeling how word-of-mouth drives adoption.
Market Sizing: TAM, SAM, and SOM for Realistic Planning
Market sizing is a funnel, not a single number. TAM is the total universe of customers, SAM is the segment you can reach, and SOM is who you can realistically win. It's crucial for business plans and investor pitches.
Linear Regression: Forecasting with a Straight Line
Linear regression forecasts the future by drawing a straight line through past data. It's used to predict outcomes like sales based on ad spend or energy use based on temperature.
Growth Accounting: What's Really Driving Your Growth?
Growth accounting splits your growth into two parts: adding more resources (like ad spend) and getting better with what you have. Use it to see if growth came from a bigger budget or a better product.
Entitlements: Use Feature Flags for Permanent Access Control
Entitlements use permanent feature flags to control long-term access, like a bouncer for your app's VIP section. This is how you manage premium tiers or special user permissions, ensuring the right customers always see the right features.
The Hybrid GTM Model: PLG Meets Enterprise Sales
A hybrid go-to-market model blends a self-serve product with a sales team, letting users start on their own and bringing in sales for big deals. B2B SaaS uses this for efficiency, but the footgun is creating friction if the handoff isn't seamless.
In-Product Discovery: Finding Growth Inside Your App
In-product discovery is finding your next growth lever by observing users within your live product, not just in pre-launch research. It's used to scale existing products via in-app experiments.
Product Bumps: Testing Prices with Temporary Increases
A product bump is a temporary price increase for an in-app purchase to test user price sensitivity. It helps you find the optimal price without permanently changing it for everyone. The footgun is misinterpreting statistical noise from small test groups.
Reverse Trial Model: Freemium Reach, Free Trial Urgency
A reverse trial gives new users a time-limited taste of paid features before downgrading them to a free plan. It aims for the best of both worlds: freemium's user acquisition with a free trial's conversion urgency.
Freemium Model: Free Forever, Pay for More
The Freemium model offers a basic product for free, charging only for premium features, services, or goods. This pricing strategy is common in software and video games (as "free-to-play").
The Self-Serve Funnel: Let Your Product Do the Selling
A self-serve funnel lets users discover, try, and buy your product without talking to a human, making the product the main sales driver. It powers rapid growth for companies like Slack. The main footgun is poor onboarding; if users get stuck, they churn.
Conjoint Analysis: What Features Do Users *Really* Value?
Stop asking users what they want; make them choose. Conjoint analysis reveals true priorities by forcing trade-offs between product features, like price vs. battery life. It's used for pricing and roadmapping.
Price Elasticity: How Price Changes Affect Demand
Price elasticity measures how sensitive sales are to price changes. An elasticity of -2 means a 1% price increase causes a 2% drop in quantity sold. It's key for forecasting revenue from price tests, but the biggest footgun is assuming this ratio is constant.
Value-Based Pricing: Charge for Impact, Not Cost
Value-based pricing anchors your price to the customer's perceived benefit, not your production costs. It's used for unique goods like art or software where value is high. The main footgun is assuming value instead of researching customer willingness to pay.
Usage-Based Pricing: Pay for Value, Not Seats
Usage-based pricing links cost directly to consumption, letting customers pay for what they use instead of a flat fee per user. It's common for cloud services (AWS) and APIs where value isn't tied to seats.
Tiered Pricing: One Product, Many Prices
Tiered pricing sells one product at multiple prices by packaging features for different customer needs. It's common in SaaS (Basic, Pro, Enterprise plans) and telecoms.
Average Revenue Per User (ARPU)
ARPU is the average revenue each user generates, a pulse check for a subscription business's health. It's used by media and SaaS companies to track if users are becoming more valuable, but it can be skewed by a few high-paying "whales."