Calculators, playbooks, and AI skills, free for operators shipping referral programs.
By Kate Syuma. Incentive types, UX mechanics, reward structures: all in one Miro board.
A ready-to-install Claude skill: a go/no-go read on partner-led growth, scored partner segments, 25 named leads with contact routes and hooks, outreach copy and a launch plan.
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Lookups pulled out of the chapters, ordered by chapter. Some open the chapter they came from, others open a full reference table further down this page.
Referenced from Chapter 01: the KPIs worth tracking for each of the three referral program types. Fuller benchmark detail lives in Cello's B2B referral metrics guide.
| Program type | KPIs to measure |
|---|---|
| User Referral Programs | Activation rate, sharing rate, number of new users, purchase rate from user referrals, total user referral ARR. |
| Affiliate Referral Programs | Click-through rate (CTR), conversion rate, affiliate earnings per click (EPC), total affiliate referral ARR. |
| Influencer Referral Programs | Engagement rate on sponsored content, conversion rates from influencer referrals, brand awareness metrics (# of impressions, # of shares, etc.), total influencer referral ARR. |
Referenced from Chapter 01: the numbers to set your reward against before you commit to a payout.
| Input | Where it should land |
|---|---|
| Target LTV/CAC | 3:1 or higher. A referral program that cannot clear this is paying too much per referral or converting too few of them. Note that referral CAC is usually counted as rewards only: platform fees, engineering time, program management and the margin given away in the referee discount are rarely in the number. |
| Reward cap | Start from ACV and work backwards to the headline number users actually see, for example a percentage of MRR up to a limit such as $10,000. |
| Reward basis | Tie rewards to generated revenue wherever possible. It carries built-in fraud protection and removes the payback period entirely. |
| Sharing rate | 5 to 15% of active users sharing a link is the healthy band. |
| Invites per referrer | 2 to 5 invites on average from each user who shares. This is a mean over a heavily skewed distribution: a small group of advocates sends most of the invites, so plan against the shape rather than the average. |
| Invite to paid conversion | 25% and above for free-trial products. Referred free users converting to paid at over 30% is a free-trial number; against a freemium base it would be extraordinary. In Cello platform data (n = 4 million B2B SaaS referral users) referred traffic converts up to 5x better than paid and high-intent SEO in the same companies. Growth Unhinged publishes a separate 5x, comparing card-required trials with trials without a card: different comparison, different claim. |
| Referred user quality | Wharton research: referred users churn 18% less and carry 16% higher lifetime value. They also carry a contribution-margin advantage early on, but it decays and is gone by roughly month 29, so do not model it as permanent. |
Referenced from Chapter 03. Everything you need with a spreadsheet open: what each funnel metric means, what best in class looks like across Cello platform data covering 4 million B2B SaaS referral users, and the quantity and quality benchmarks worth tracking alongside them.
| Metric | Definition |
|---|---|
| Sharing rate | Percentage of users who share a link with potential new users. |
| Unique clicks per shared link | Number of potential new users who click the referral link. |
| Sign-up rate | Percentage of potential new users who click through to sign up for the referred tool. |
| Purchase rate | Percentage of new users who convert to a paid plan. |
| Annualized user to customer conversion (AUCC) | New paying customers generated via referrals per 1,000 existing users over a year, expressed as a percentage of that user base. AUCC is Cello's own term rather than an industry standard: searching it returns only cello.so. Best in class is 49%, meaning paid users grow by roughly half again over a year through referrals alone. Companies such as tl;dv, Blockpit, Typeform and Fellow see 10 to 15% additional monthly revenue growth from referrals. |
The funnel metrics tell you what happened. These three tell you whether the program is attracting the right users in the right volume.
| Benchmark | What it tells you |
|---|---|
| Participation rate | Percentage of active users who have shared at least one referral. This is the read on how well you recruit referrers out of the user base you already have. |
| Invites sent per user | Average number of invites each referrer sends. User referrals are a brand-building channel as much as an acquisition one: most people have a following and share one-to-many across WhatsApp, Slack and LinkedIn, so reach is part of the return. |
| Visit to signup, signup to activation | Conversion at each step after the invite lands. Referred users often need more activation energy and know less about the product than users from other channels, so activation is the step that usually leaks. |
Two numbers a month in, one number out. Nothing is submitted anywhere and nothing is saved: it recomputes as you type.
Two numbers per month: active users, and new users arriving through direct traffic plus brand search. Where those come from is in section four. Everything recomputes as you type.
| Month | Active users | New WoM users | Coefficient | Remove |
|---|---|---|---|---|
| 0.042 | ||||
| 0.044 | ||||
| 0.041 | ||||
| 0.043 | ||||
| 0.042 | ||||
| 0.042 |
Volume-weighted: total word-of-mouth users (1,377) divided by total active users (32,500) across all 6 valid months, not the average of the per-month ratios. Bigger months count for more.
An R² of 0.99 means active users explain most of the month-to-month movement in your word-of-mouth intake, so the coefficient is stable enough to plan against. Annualized, word of mouth is worth 50.8% of your active base per year, which is at or above the Atlas's best-in-class AUCC benchmark of 49%. Treat that as a direction, not a like-for-like score: AUCC counts customers, and this counts users.
Each dot is one month. If active users really drive your word-of-mouth intake, the dots sit close to the line.
Reforge's method, run light: Google Analytics and nothing else. It assumes active users predict new word-of-mouth users. Start here, and only tighten the definitions if the relationship does not hold.
| Step | What to do |
|---|---|
| 1. Define the denominator | Segment returning users in Google Analytics. That is your active-user count for the month. |
| 2. Define the numerator | Identify new users arriving through direct traffic and brand search. That is your word-of-mouth intake. |
| 3. Run the analysis | Export both series by month and put them in the table above. The coefficient is the division. |
| 4. Validate it | Check R² on the plot. At or above 0.7 the relationship holds and you can forecast against it. |
If the light version is not predictive, tighten the definition of an active user around the actions that plausibly generate a recommendation, and run it again. Only mature businesses with genuinely complex marketing mixes need the heavy, multivariate version. Start simple and scale the method as the number starts mattering to your planning.
The canonical home for the affiliate budget lookup tables. Chapter 08 keeps the method and the two inputs that set the order of magnitude; the full set lives here.
| Form | How it pays |
|---|---|
| Pay-per-click | The affiliate gets a reward for every successful unique click on a referral link. |
| Pay-per-lead | The affiliates get a reward for an inbound lead or a specific event. |
| Pay-per-sale | The affiliates get a percentage of the revenue generated by new paying customers. |
| Typical range | SaaS pays the highest commission fees of any category. The rate Cello sees in practice is 15% to 30%. The higher your CLV, the higher the absolute payout can be and the lower the percentage needs to be. |
| Input | What to work out |
|---|---|
| Average Customer Lifetime Value (CLV) | Understand how much revenue the average customer brings in over their lifetime. This is the ceiling every other number sits under. |
| Profit margins | Consider your profit margins on the products or services being promoted. You'll want to ensure that you're still maintaining a healthy profit after paying out commissions. |
| Market competition | Research what your competitors offer in their affiliate programs. To attract top affiliates, your offer needs to be competitive. |
| Program management costs | Account for the cost of running the program: software, personnel, and marketing materials. |
| Estimated acquisition through affiliates | Estimate how many new customers you expect to acquire through the affiliate program. |
| Trial and error | Start with a budget you're comfortable with and adjust as you go. Set up two campaigns and A/B test them to understand what actually incentivizes your user base. |
| Variable | Why it changes the payout |
|---|---|
| Two-sided rewards | Incentivizing the referee with a discount as well as the affiliate makes it more attractive for both parties: the affiliate participates, and the new user has a reason to sign up. |
| Retention-based pay-per-sale | Offer revenue shares that extend over time, with an initial and possibly reduced ongoing payment, for example 20% of monthly generated revenue. Recurring fits the subscription model and doubles as a fraud deterrent, since a fraudulent deal that churns after a month stops paying. |
| Payout thresholds | Establish a minimum earning for withdrawal to motivate continuous content creation, like Fiverr's $100 threshold, which is a consumer-marketplace bounty rather than a SaaS commission benchmark. |
| Gamification and tiering | Introduce reward levels for hitting milestones, and add leaderboards and performance boosters to sustain engagement. |
The canonical home for the affiliate type taxonomy. Chapter 08 keeps the six types and what each one is; every attribute lives here.
| Attribute | Newsletter publisher | Blogger collaboration | Podcast hosts | Social media creators |
|---|---|---|---|---|
| What | Placement of an affiliate link in a newsletter (e.g. newsletter sponsorship) | Placement of an affiliate link in a blog post (e.g. Top 10 AI tools) | Short ad placements plus an affiliate link in the podcast bio | Link placements in posts (LinkedIn, X, YouTube) |
| Pro | Targeted reach plus high conversion rates | High trust and authority within blogger networks | Brand awareness and high trust in the host's community | Broad audience and potential reach |
| Con | Short time of visibility plus costs not linked to channel performance | Time-consuming to create content and to be mentioned | Slow-burn approach plus costs not linked to channel performance | Less control of content plus costs not linked to channel performance |
| Reach | Medium | Low to medium | Low to medium | High |
| Costs | Niche/vertical products = more targeted affiliates (higher cost). Broad/horizontal products = less targeted affiliates (lower cost). | Same as newsletter publisher | Same as newsletter publisher | Same as newsletter publisher |
| Effort (own content creation plus affiliate maintenance) | Medium | High | Medium | Low |
| Example | Lenny's Newsletter | Neil Patel | SaaStr podcast | Marina Mogilko |
| Attribute | Coupon and deal websites | Affiliate marketplaces |
|---|---|---|
| What | Place affiliate links on coupon websites | Access a wide range of affiliates |
| Pro | Attracting price-sensitive customers looking for a deal, plus high conversion rates | Broad access to various affiliates plus acquisition cost control |
| Con | May devalue the product's perceived value over time | Network fees can reduce overall margin |
| Suited for | Early-stage SaaS with a horizontal targeting trying to acquire their first customers | Post-PMF SaaS looking for a diverse range of affiliate types, those new to affiliates |
| Reach | Medium | Medium |
| Costs | Low | Low |
| Effort (own content creation plus affiliate maintenance) | Low | Medium |
| Example | AppSumo | impact.com |