# Network effects assessment · [Product] · [Date] **Owner:** [PM] **Evidence sources:** [analytics, CRM, partner portal, marketplace data] ## Claimed effects | # | Claimed effect (as stated in our materials) | NFX type | Sides involved | Mechanism: who gets more value when whom joins? | Evidence today (metric, value, source) | Density and clustering | Limits (asymptote, multi-homing, disintermediation) | Verdict | |---|---|---|---|---|---|---|---|---| | 1 | | | | | | | | | Verdict options: real network effect / data-enabled learning / scale effect / switching cost / aspiration (no evidence yet). ## Data moat test (for any data claim) | Question | Answer | Evidence | |---|---|---| | How much value does customer data add compared with the product without it? | | | | Does the marginal value of more data stay high, or plateau? Where? | | | | How fast does the data lose relevance? | | | | Is the data proprietary, or available to competitors? | | | | Are the improvements hard to imitate? | | | | Does one customer's data improve the product for other customers? Are we allowed to use it that way? | | | ## Cold start position - Stage (Cold Start Problem / Tipping Point / Escape Velocity / Hitting the Ceiling / The Moat): - Atomic network (smallest stable network that grows on its own): - Hard side (participants who create disproportionate value and are hardest to attract): - Hard problem we solve for the hard side: ## Platform health | Metric | Definition for our platform | Current value | Trend | |---|---|---|---| | Interaction failure | | | | | Engagement | | | | | Match quality | | | | | Negative network effects (congestion, spam, misbehavior) | | | | ## What changes in our strategy and board story [One paragraph: which effects we will build on, which we will stop claiming, what we will measure.]