Distribution Reference

Definitions, taxonomies, and inventories for distribution. Use this to look things up and categorize.

What Is Distribution?

A distribution channel is the pathway through which a product reaches its end consumer—the network of touchpoints, intermediaries, and mechanisms that move offerings from producer to user.

The core question: How do customers discover, evaluate, access, and begin using your product?

Distribution vs Marketing

	Creates awareness	Delivers product/user
Marketing	Yes	No
Distribution	Sometimes	Always

Marketing is one input to distribution, not a subset. Some distribution channels are marketing-heavy (paid ads); others have almost no marketing (viral loops, sales, integrations).

Distribution = complete pathway: unaware → aware → evaluating → using
Marketing = powers "unaware → aware" step

Other things that power awareness:

Viral loops (product creates own awareness)
Sales (human relationship)
Integrations (existing workflow)
Word of mouth (other users)

What IS vs IS NOT a distribution channel

What qualifies:

Direct pathways: e-commerce, websites, sales teams, retail, mobile apps
Intermediary pathways: wholesalers, resellers, marketplaces, app stores, affiliates
Network pathways: viral loops, referral programs, word-of-mouth, UGC
Content pathways: SEO, thought leadership, developer docs

What does NOT qualify:

Brand awareness without acquisition mechanism
Product features that don't drive discovery
Customer retention tactics (distinct from acquisition)
Pricing strategy alone

Network Effects vs Scaling

Many companies claim "network effects" when they have scale economies. The distinction matters:

	Network Effects	Scale Economies
What improves	Product VALUE	Product COST
Mechanism	More users → better for each user	More volume → cheaper per unit
Compounding	Accelerating (superlinear)	Decelerating (sublinear)
Defensibility	Very high	Medium (capital can replicate)

The test: Does each additional user make the product MORE VALUABLE to existing users?

WhatsApp: Yes — more contacts means more people to message
Waze: Yes — more drivers means better traffic data for everyone
Netflix: No — more subscribers doesn't improve your recommendations
AWS: No — more customers means cheaper for Amazon, not better for you

Examples of confusion:

Company	What they claim	What they actually have
Netflix	"Data flywheel"	Scale economics (afford more content)
DoorDash	"Network effects"	Mostly scale (more drivers = faster, but plateaus)
OpenAI	"Data moat"	Scale (compute, training) + brand

Distribution Edge Taxonomy

A distribution edge exists when a company possesses structural advantages in reaching customers that competitors cannot easily replicate.

Network effect advantages

Direct network effects: Each additional user makes product more valuable to existing users. WhatsApp's phone-book integration—useless alone, essential once contacts are there. Creates winner-take-all dynamics.

Indirect (cross-side) network effects: Drive marketplace and platform businesses. More Amazon sellers → more buyers → more sellers. More iOS apps → more iPhone buyers → more developers.

Data network effects: User-generated data improves product. Waze traffic accuracy improves with more drivers. New entrants can't match without comparable data.

Network effects are the strongest distribution edges because they make the product intrinsically better with scale.

Scale-based advantages

Economies of scale: Per-unit costs decrease as volume increases. Amazon's fulfillment gets cheaper per package. Creates cost advantages without making product better.

Demand aggregation: Massive audiences negotiate better supplier terms. Google and Facebook's "super-aggregator" status.

Key distinction: scale effects make distribution cheaper, network effects make it more valuable.

Switching cost advantages

Product-embedded: Lock-in through accumulated data, integrations, learned behaviors. Slack's 10,000-message history limit triggers at maximum lock-in. [Great example]

Ecosystem switching costs: Salesforce's AppExchange means switching CRM = abandoning entire integrated workflow.

Social switching costs: Leaving WhatsApp = losing family groups. Leaving LinkedIn = losing professional network. Psychological barriers beyond inconvenience.

Access and exclusivity advantages

Exclusive distribution rights: Legal barriers. Sole distributor for region/segment.

Regulatory moats: AngelList's SEC letter created multi-year head start. Healthcare, finance, telecom have massive regulatory barriers.

Relationship lock-in: ~90% of IT sales are partner-assisted. Deep channel partnerships take years to replicate.

Ecosystem Taxonomy

"Ecosystem" gets used loosely. Different structures have different moat mechanisms.

Full taxonomy

Transaction-based:
├── Marketplace (supply ↔ demand)
├── Content platform (creators ↔ consumers + algorithm)
├── Data network (contributors ↔ aggregated value)
└── Advertising platform (users as product ↔ advertisers)

Connection-based:
├── Social network (peer ↔ peer, direct network effects)
└── Payment/Financial network (acceptance ↔ usage)

Extension-based:
├── App platform (core + third-party apps)
├── API/Infrastructure (others build on you)
└── Hardware ecosystem (physical interoperability)

Relationship-based:
├── Product suite (your products reinforce each other)
├── Partner/Channel (third parties distribute for you)
└── Standard/Protocol (everyone conforms to your spec)

Compound:
└── Super-app (multiple ecosystem types combined)

Types explained

Type	Structure	Examples	Moat mechanism
Marketplace	Supply ↔ Demand	Uber, Airbnb, eBay	Cross-side network effects
App platform	Core + third-party extensions	Salesforce, Shopify, iOS	Ecosystem switching costs
Product suite	Bundled products from one company	Microsoft 365, Adobe CC	Integration + procurement convenience
API/Infrastructure	Others build ON you	Stripe, Twilio, AWS	Embedded, becomes plumbing
Standard/Protocol	Everyone conforms to your spec	USB, PDF, HTTP, Visa	Coordination lock-in
Partner/Channel	Distribution through third parties	VARs, resellers, SIs	Relationship + training investment
Content platform	Creators + consumers + algorithm	YouTube, TikTok, Spotify	Creator investment, algorithm
Data network	Value from aggregated contributions	Waze, credit bureaus	Data improves product for all
Hardware ecosystem	Physical interoperability	Apple devices, consoles	Peripheral investment + seamless UX
Social network	Peer ↔ peer connections	WhatsApp, LinkedIn	Direct network effects
Advertising platform	Attention + targeting	Google, Meta, TikTok	Data flywheel, auction efficiency
Super-app	Multiple types combined	WeChat	Layered lock-in

Key distinctions

Marketplace vs Content platform vs Social network:

Aspect	Marketplace	Content Platform	Social Network
Core relationship	Supply ↔ Demand	Creators ↔ Consumers	Peer ↔ Peer
Network effect type	Cross-side	Cross-side	Direct (same-side)
Why you stay	Liquidity	Content + algorithm	Your people are there

App platform vs API/Infrastructure:

App platform: Developers build apps FOR your platform (AppExchange, App Store)
API/Infrastructure: Developers build ON you for their own products (Stripe, Twilio)

Evaluating ecosystem moats

When someone claims "ecosystem moat," ask:

What's the cross-side dependency? Who needs whom?
How deep are the integrations? Cosmetic or workflow-critical?
Can participants multi-home? If yes, network effect weakens
What's the switching cost in practice? Hours? Days? Months?
Is it single or compound? Compound is much stronger

Channel Quality Assessment

Durability: which channels last?

Highly durable:

Network effects (extremely difficult to dislodge)
Owned audiences (email lists, direct relationships)
SEO authority (compounds over years)
Brand-driven word-of-mouth

Moderately durable:

Platform integrations (until platform changes rules)
Channel partnerships (while alignment persists)
Sales teams (requires ongoing investment)

Fleeting:

Paid advertising arbitrage (competitors bid up costs)
Platform algorithm exploitation (loopholes close)
PR/viral content (one-time spikes)

Andrew Chen's Law of Shitty Clickthroughs: Banner ads went from 78% CTR (1994) to 0.05% (2011). Every channel degrades.

Defensibility: which resist copying?

Highly defensible:

Network effects with high multi-homing costs
Proprietary data advantages
Deep integrations creating switching costs
Regulatory or legal barriers

Moderately defensible:

SEO authority (12-24 months to build)
Brand reputation (slow to build, slow to erode)
Exclusive partnerships (until contracts expire)

Easily copied:

Paid advertising strategies (weeks to replicate)
Referral program mechanics
Content formats and topics
Most "growth hacks" (months shelf life)

The question: If a well-funded competitor started today, how long until they match our distribution? Under 18 months = minimal moat.

Scaling properties

Superlinear (returns accelerate):

Network effects
Content/SEO with compounding authority
Platform ecosystems with cross-side effects
Viral loops with K > 0.5

Linear (constant returns):

Paid advertising with stable unit economics
Direct sales with consistent rep productivity

Sublinear (diminishing returns):

Physical retail expansion
Geographic expansion into smaller markets
Channel saturation (best customers first)

Brian Balfour's insight: Companies with product-channel fit get 70%+ of growth from ONE channel. Double down on superlinear.

Cost structure

Low CAC, low marginal cost:

Viral/referral (Dropbox's $0 CAC vs $233-388 paid)
SEO/content (upfront investment, minimal marginal)
Network effects driving organic acquisition

High CAC, variable marginal:

Enterprise sales ($10K-50K+ per customer)
Account-based marketing
Channel partnerships (revenue share)

Benchmarks: LTV:CAC of 3:1 is healthy. CAC payback under 12 months.

Channel Inventory

Four scalable engines (can reach 100M+ users)

1. Paid acquisition (revenue funds CAC)

Meta, Google, TikTok, LinkedIn ads
Works when: LTV:CAC > 3:1, payback < 12 months

2. Virality (users invite users)

Referral programs, embedded virality, content sharing
Works when: Product has inherent shareability

3. SEO (unique content attracts search)

Editorial, UGC-SEO, programmatic/templated
Works when: Product generates indexable content at scale

4. Sales (revenue funds team expansion)

Outbound, PLG → sales, channel/partner
Works when: ACV supports CAC and sales cycles

Everything else (PR, events, community, influencers) = kickstarts or turbo boosts, not sustainable engines.

B2B-specific

Product-Led Growth:

Freemium, self-serve trials, bottom-up adoption
~12% freemium conversion, PQLs convert 3x vs MQLs
Most PLG companies need to add sales to maximize growth

Enterprise sales:

Outbound (SDR → AE), ABM, SI/VAR partnerships, DevRel
ABM boosts deal value by 171%, shortens cycles by 40%

Channel partnerships:

~90% of enterprise IT sales are partner-assisted

B2C-specific

App stores: 65-70% discovery through search. ASO matters.

Social: TikTok (younger, viral), Instagram (visual), YouTube (complex products)

Influencer/community: Micro-influencers (5K-100K), Discord/Reddit, ambassadors

Stage-specific

0-100 users: Do things that don't scale. Personal network, cold outreach, community engagement, manual onboarding, founder-led sales. YC benchmark: 5-7% WoW growth is good.

100-1,000: Test scalable channels while continuing unscalable. Build referral/viral mechanics, establish SEO foundations.

1,000-10,000: Double down on channel showing product-channel fit. 70%+ resources to primary channel.

10,000-100,000+: Maximize primary before diversifying. Layer secondary only when primary matures. Invest in brand.

Failure Patterns

Distribution advantages decay. Understanding how they fail helps avoid traps.

10 decay patterns

1. Platform dependency — Zynga/Facebook, iOS 14.5. Built on someone else's distribution; they change rules, you die.

2. Channel saturation — Banner ads: 78% → 0.05% CTR. Every channel degrades as competitors copy and users develop immunity.

3. CAC inflation — Facebook ads cheap in 2012, expensive now. Success attracts competitors; costs rise until unit economics break.

4. Audience exhaustion — Best customers acquired first. Diminishing returns within a channel.

5. Growth hack decay — Tactics get copied, platforms close loopholes, novelty fades. Months, not years.

6. Network effect reversal — MySpace, Tumblr. Exodus accelerates: fewer users → less value → more leave.

7. Technology/behavior shifts — Mobile killed desktop-first. AI may kill SEO-dependent businesses.

8. Regulatory changes — GDPR, CCPA, cookie deprecation. Government changes rules suddenly.

9. Single-channel dependency — 80% from SEO; algorithm update hits. Concentration risk.

10. Founder-led sales ceiling — Distribution "channel" was actually a person. Can't scale.

Decay speed

Pattern	What decays	Speed
Platform dependency	Access	Sudden
Channel saturation	Effectiveness	Years
CAC inflation	Unit economics	Months-years
Audience exhaustion	Conversion rate	As you scale
Growth hack decay	Tactic effectiveness	Months
Network effect reversal	User base	Accelerating
Tech/behavior shift	Channel relevance	Slow then sudden
Regulatory	Targeting/access	Sudden

Survival questions

For any distribution channel:

What could kill this entirely?
What will degrade it over time?
What's my backup when this decays?
Am I building owned assets while this works?

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