Growth Planning
A quantified growth model that shows where growth actually comes from, and where the next dollar should go.
Most growth plans are targets with adjectives. Ours are models: we decompose historical growth into its arithmetic sources, measure the unit economics of each acquisition channel, and build a forward plan where every initiative has an expected contribution and a confidence level.
Who this is for
Founders and revenue leaders who need growth to be a system, not a hope, typically post-product-market-fit.
How the work is done
Growth accounting
We decompose net growth into new, expansion, contraction, and churn components, then build cohort retention curves. Where the data supports it, survival analysis (Kaplan–Meier estimates) separates genuine retention improvements from mix effects.
Unit economics
Contribution-margin-based LTV and fully loaded CAC by channel, with honest treatment of payback period and discount rates. We test LTV/CAC sensitivity to retention assumptions, because that is where such models usually flatter.
Model the engine
A forward model links funnel conversion, capacity constraints, and market saturation. For adoption dynamics we use S-curve and Bass-diffusion logic rather than straight-line extrapolation; for state transitions (trial→paid→expansion), simple Markov-chain structures keep the arithmetic honest.
Allocate and experiment
Budget moves toward marginal, not average, return. Where evidence is thin, we design experiments with explicit power calculations so you learn cheaply before you spend expensively.
Methods and models we draw on
- Growth accounting
- Cohort & survival analysis (Kaplan–Meier)
- LTV/CAC & payback modeling
- Bass diffusion & S-curves
- Markov-chain funnel modeling
- Marginal-return budget allocation
- Experiment design & power analysis
Methods are chosen for the problem, not the brochure, expect a subset of these, applied properly, plus whatever the evidence demands.
The decision this enables
A growth plan where every line item has an owner, an expected contribution, and a way to be proven wrong.