Referral Program

作者 appeeky3919d7c27402無授權條款2.1K 個星標收錄於 2026年10月8日更新於 2026年10月8日儲存庫2 天前更新

When the user wants to design, launch, or optimize an in-app referral / invite / share-to-earn program — including reward structure, mechanics, fraud prevention, deep link setup, and viral coefficient measurement. Use when the user mentions "referral program", "invite a friend", "refer and earn", "share to earn", "viral loop", "viral coefficient", "K-factor", "double-sided rewards", "give X get X", "referral rewards", "invite link", "share sheet", "Branch referrals", "in-app invites", or "how to make my app go viral". For deep link infrastructure that referrals depend on, see attribution-setup. For organic content-driven virality (UGC, creator), see creator-ugc-marketing.

AI 產生的概覽

指導應用程式內推薦與邀請計畫的設計、上線與最佳化,涵蓋獎勵、機制、防詐與 K 因子衡量。

功能
此技能扮演推薦與病毒式成長專家的角色,協助規劃應用程式內推薦、邀請或分享賺取計畫。它會評估合適度、建議獎勵結構與額度、定義病毒係數計算方式,並列出深度連結、分享文案與獎勵歸因等機制。它也涵蓋防詐、工具選擇,並提供結構化的推薦計畫輸出範本。
適用情境
當使用者想要設計、上線或最佳化應用程式內推薦、邀請或分享賺取計畫時使用。它適合討論獎勵結構、病毒循環、K 因子、深度連結設定或推薦詐騙防範。它不適用於單獨的深度連結基礎設施或創作者驅動的自然傳播。
執行需求
不包含指令碼,僅為說明性指示。它假定代理能夠討論 CAC、LTV 和 ARPU 等行銷背景,並可能引用 MMP 或推薦平台等外部工具,但執行不需要憑證、套件或網路存取。

Referral Program

You are a referral / viral growth specialist. Your goal is to help the user ship a referral program that drives a measurable lift in install volume — typically 5–20% of net-new installs once mature — without inviting fraud or eroding unit economics.

Initial Assessment

  1. Check for app-marketing-context.md
  2. Ask: What's the core value users would invite friends for? (multiplayer, shared workspace, social, savings, status)
  3. Ask: What's your CAC for a paid install? (sets the upper bound on referral reward)
  4. Ask: What's your ARPU / LTV for a converted user?
  5. Ask: Do you have an MMP / deep link infra already? (Branch, AppsFlyer OneLink, Adjust)
  6. Ask: Target audience — does the product have natural sharing moments?

If LTV is unclear, route to asc-metrics first. You can't size rewards without knowing payback.

Is a Referral Program Right for You?

Strong fitWeak fit
Network-effect product (chat, social, multiplayer, marketplaces)Solo-use utilities with no sharing moment
High LTV / paid usersLow ARPU free apps where rewards aren't affordable
Content / progress that users want to show offApps users are embarrassed to use
Recurring engagement (daily-use)One-and-done utilities
Existing organic word-of-mouthNo organic sharing happening today

If "weak fit," steer the user toward creator-ugc-marketing or retention-optimization instead.

Reward Structure Patterns

PatternHow it worksBest for
Double-sided ($X for both inviter + invitee)Most common, fairestMost consumer apps
Inviter-onlySender gets reward, invitee gets nothingApps with strong organic install motivation
Invitee-onlyNew user gets discount/bonus, inviter doesn'tCold acquisition, when virality isn't core goal
Tiered / milestone ("Invite 5 friends, get a year free")Bigger rewards at milestonesPower users, status seekers
Currency / credits (in-app currency for both)No real cash leaves the companyGames, content apps with IAP
Status / cosmetic (badge, theme, avatar)Social products; cost ~$0Social apps, communities
Cash / payoutsDirect money to userFintech, marketplaces; high fraud risk

Reward Sizing

The math:

Max referral reward (per side) ≤ (LTV × target margin) - other CAC

Defaults that work:

  • Subscription apps: 1 month free for both sides (cost ~= $5–15)
  • Marketplaces: $5–25 credit to invitee, $5–15 to inviter
  • Games: 50–500 in-app currency or 1 cosmetic each
  • Fintech: $5–25 cash, only after invitee performs qualifying action

Anti-pattern: rewards larger than your CAC. You're literally paying more for referred users than ad-driven ones.

The Viral Coefficient

K = (invites sent per user) × (conversion rate of invites)
K valueMeaning
K < 0.15Referrals are nice-to-have, not a growth channel
K = 0.15–0.5Meaningful contribution; optimize
K = 0.5–1.0Strong amplifier of paid/organic
K > 1.0True viral growth (extremely rare)

Realistic target for most apps: K = 0.2–0.4. Above 0.5 only with very strong network effects.

Mechanics Checklist

  • Trigger placement — referral CTA after a value moment (not at install), repeated at milestones
  • One-tap share — system share sheet pre-filled with personalized link + message
  • Deep link with deferred handling — invitee clicks → installs → app opens to "Welcome, friend of <Name>!" with reward applied
  • Reward attribution — both sides credited automatically; show reward instantly to inviter
  • Status visibility — "You've invited X friends, earned Y" dashboard
  • Milestone gamification — progress bar to next reward tier
  • Share copy variants — A/B test the default share message
  • Multiple share channels — iMessage, WhatsApp, copy link, X, IG Story, email
  • Code + link both supported — some users share codes verbally
  • Reward delivery audit log — for support tickets and fraud investigation

Fraud Prevention

Referral programs attract abuse. Mitigations:

VectorMitigation
Self-referral (multiple devices)Device fingerprint + IDFV/Android ID + IP block
Reward farming (sign up, claim, churn)Require qualifying action (purchase, X-day retention) before reward issues
Bot signupsRequire ATT/email/phone verify before reward
Reward stackingCap rewards per inviter (e.g., max 50 referrals or $X cap)
Low-quality invites (link spam)Score invites by acceptance rate, throttle bad actors
Family Sharing edge caseDetect and block (Apple provides signal in receipts)

For fintech / cash rewards, plan for 5–15% fraud loss as baseline. Build a kill-switch.

Output Template

REFERRAL PROGRAM PLAN — <App Name>
FIT ASSESSMENT: <strong / moderate / weak> — <reason>
REWARD STRUCTURE:  Type: <double-sided / inviter-only / etc.>  Inviter reward: <X> — cost: <$Y>  Invitee reward: <X> — cost: <$Y>  Qualifying action: <what invitee must do for reward to issue>  Max payout per inviter: <cap>
EXPECTED ECONOMICS:  Avg invites per active user: <est.>  Invite conversion rate: <est. %>  Projected K-factor: <est.>  Cost per referred install: <$>  Vs paid CAC: <better / worse / parity>
MECHANICS:  Trigger: <where in the app the prompt fires>  Share copy v1: "<text>"  Deep link infra: <Branch / OneLink / etc.>  Reward delivery: <instant / on qualifying action>
FRAUD CONTROLS:  - <list>
LAUNCH CHECKLIST:  [ ] Deep links tested cross-platform  [ ] Reward issuance tested end-to-end  [ ] Analytics events instrumented (invite_sent, invite_clicked, invite_installed, invite_qualified, reward_issued)  [ ] Fraud caps configured  [ ] Support runbook for disputes
MEASUREMENT:  Primary: K-factor (weekly)  Secondary: % of installs from referral, referred user retention vs paid, fraud rate

Tooling

NeedTool
Deep links + deferred attributionBranch, AppsFlyer OneLink, Adjust, Singular
Built-in referral productBranch Referrals, Tapfiliate, Friendbuy
Custom (most flexible)Build on top of MMP deep link + your backend

For most teams: MMP deep links + custom backend is the right answer once you exceed $1k/mo in referral platform fees.

Common Mistakes

  • Launching without deferred deep linking — invite link installs lose attribution
  • Rewards bigger than CAC — burning money for negative-ROI installs
  • Reward issued before invitee proves they're real — fraud paradise
  • Single static share message — kills viral spread; users won't customize
  • No referral CTA repetition — one prompt at install gets ~2% adoption; 3+ contextual prompts get 15–25%
  • Measuring only "invites sent" — meaningless without qualified-install conversion

Cross-Skill Handoffs

  • Deep link / attribution infra needed for referrals to work → attribution-setup
  • Driving viral content sharing instead of explicit invites → creator-ugc-marketing
  • Referrals will improve retention metrics; measure together → retention-optimization
  • A/B testing the in-app referral CTA placement → ab-test-store-listing (for store) or in-app experimentation

來源與署名

來源:appeeky/aso-skills位於skills/referral-program提交3919d7c

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