Step 10: From onboarded customer to referral

Outputs doc: outputs.md, Step 10 section. Fill it in as you work through the steps below. Raw referral-ask results and advocate conversations go in captures.md. This is the last step of the spine; its output closes the loop back to the top of the funnel.

Jump to: Diagnostic · Step 10.1 · Step 10.2 · Step 10.3 · Step 10.4 · Step 10.5 · Step 10.6 · Step 10.7 · Step 10.8 · Summary · Assumption sweep

What this step is: The motion that turns happy customers into a source of new customers. It is not a campaign and it cannot be made into one. A referral is a consequence: it happens when a customer reached the promised value in Step 9, felt good getting there, and is willing to put their own reputation behind telling a peer. You do not build referrals; you do the ten steps well and then earn them. What Step 10 does build is the system around that consequence: making the referral natural, easy, and well-timed, asking the right customers at the right moment, and catching the warm leads that result. A successful customer is also the cheapest source of new revenue you have, through expansion, so this step captures that too, before turning the happiest customers into referrers. The output is a referral motion that follows from a great customer experience, plus the loop that feeds its leads back into the top of the funnel, warmer than anything lead generation can produce.

Why it comes after Step 9 and why it is the last step: Step 9 produced customers who reached value and champions who were made to look good. Step 10 is the natural overflow of that, and it cannot exist before it: anyone who treats referrals as a campaign has skipped Step 9, and is about to ask customers who are not happy to recommend a product that has not yet proven itself. The quality of a referral lead cannot be matched by any lead-generation effort, because the trust that Step 7 spends weeks building is already there, vouched for by a peer. That is why this is the last step and also the one that closes the loop: a referral lead re-enters near the top of the funnel but arrives pre-trusted, skipping most of the cold work below. It is the cheapest, highest-converting lead you will ever get, and you cannot buy it, only earn it. If referrals never come, the cause is almost never the referral mechanics; it is that Step 9 is not producing genuinely happy customers, or the value in Step 3 never really landed.

What finishing this step produces: A written referral motion in the Step 10 section of outputs.md: an honest check that Step 9 is producing advocates, a definition of who those advocates are, a map of why and when they would refer, a motion that makes referring natural and easy at the moments of highest goodwill, a fast-track path that catches referral leads warm, and the identified stall with its fix. Backed by real referral asks to real advocates, not by a referral program switched on and left to run.

Diagnostic: is Step 10 actually done?

Before building anything, answer these questions in writing. Vague answers mean the step is not finished.

1. Is Step 9 actually producing happy, activated customers who reached the promised value? This is the gate. If customers are not reaching value and feeling good, there is nothing to refer, and a referral push will only expose how few advocates you have. If you cannot point to activated, satisfied customers, stop and fix Step 9 first.

2. Do you know which customers are advocates, or are you about to ask everyone? Referrals come from the customers who reached value and would genuinely recommend you, not from your whole customer list. If you cannot tell an advocate from a merely-paying customer, you will ask the wrong people and get awkward silence or weak referrals.

3. Do you know the moments of high goodwill when a referral is natural, or do you ask at random? There are moments, usually tied to a Step 9 win, when a customer is most willing to recommend. Asking then feels natural; asking at renewal or out of the blue feels like a tax. If you cannot name the trigger moments, the timing is left to luck.

4. Have you made referring effortless and given the referrer a real reason? Customers refer to look good, to help a peer, to be the person who knew about the thing. If referring is hard work, or the only reason offered is a bribe, the best advocates will not bother. The motion has to be easy and has to make the referrer look good.

5. When a referral lead arrives, does it enter warm on a fast track, or get dumped into the cold funnel? A referral lead carries borrowed trust. If it is treated like a cold lead, you waste the trust and risk the referrer’s reputation with a clumsy experience. There should be a warm, fast path for referred leads that protects the person who sent them.

6. Are you growing the accounts you already won, not only chasing new ones? A successful customer is the cheapest source of new revenue you have, through expansion, before they ever refer anyone. If you only measure new logos and never map the path to more value, and more spend, inside existing accounts, you are leaving the easiest growth on the table.

If you answered all six clearly and in writing, Step 10 may already be done. Jump to the Step 10 section of outputs.md, fill in the fields, check the checklist, and you have completed the spine. If not, work through the steps below.

Step 10.1. Pull the inputs from Step 9 (and above)

Duration: 30 minutes

Step 10 does not start from a blank page, and it does not start from a referral-software signup. It starts from the advocates Step 9 produced and the clustered, connected segment Step 2 chose precisely because wins travel within it. The fastest route to referrals is to find the customers who already feel the value and make it easy for them to tell the peers they already talk to.

What to do:

1. Open the Step 9, Step 2, Step 3, and Step 1 sections of outputs.md and copy the following into the “Inputs from Step 9 and above” field in the Step 10 section:

   • From Step 9: the customers who reached the promised outcome and felt good, the first-value moment and when it lands, the champions who were made to look good, the signals of a genuinely happy customer, and the early detractors and why they soured. The happy customers are your advocates; the detractors are who not to ask.
   • From Step 2: the clustered and connected segment and where they gather. Step 2 chose a beachhead partly because the segment talks to each other; that connectedness is what makes referrals travel. The buying context tells you who the advocate would refer (a peer in the same role with the same problem).
   • From Step 3: the value proposition and the language buyers used. Advocates describe you to peers in their own words; the language from Step 3 and Step 4 is how they will tell the story.
   • From Step 1: the problem and the workaround. A referral usually starts with the referrer recognising the same problem in a peer; the problem is the bridge.

2. Write one sentence: “Our advocates are the customers who reached [first-value moment] and feel [the value]; they talk to [the clustered peers from Step 2] in [where they gather], and the most natural moment for them to refer is [the goodwill moment from Step 9].” It frames referral as connecting existing advocates to existing peers.

3. If Step 9 is not yet producing happy, activated customers, this step cannot run. Note it in the Step 10 scope notes in outputs.md and log it in assumptions.md (Step: 10, Status: Untested). The honest move is to go back to Step 9 (or further) rather than to launch a referral program that asks unhappy customers to lie.

Step 10.2. Check the precondition and define the advocate

Duration: 30-45 minutes

Before designing any referral motion, confirm the thing referral depends on actually exists: customers who reached value and would genuinely recommend you. This is the step that separates a referral motion from a referral campaign. A campaign asks everyone and hopes; a motion identifies real advocates and makes it easy for them. If the precondition fails, the most useful output of this step is the finding that Step 9 is not done, which is worth more than any referral mechanic built on top of unhappy customers.

What to do:

1. In the Step 10 section of outputs.md, under “Advocate definition and readiness,” fill in the table below (it is also in outputs.md). Define who counts as an advocate, using the Step 9 success signals, and gauge how many you actually have.

   Element	Definition	How you observe it
   The advocate (reached value and would recommend)
   Signals that distinguish an advocate from a paying customer
   Roughly how many advocates you have now
   Who is not an advocate (do not ask)

2. Use a leading measure of advocacy, not a guess. The willingness to recommend is measurable: ask customers how likely they are to recommend you and why, watch for unprompted praise, repeat usage, and customers who already brought others. A customer who reached the Step 9 first-value moment and speaks warmly about it unprompted is an advocate; a quiet payer is not, yet.

3. Be honest about the count. If you have only a handful of true advocates, that is the real state, and it tells you referral will be small until Step 9 produces more. A small number of genuine advocates is a better foundation than a large list of indifferent customers, but it caps the volume, and that cap is information, not a problem to spin.

4. Identify who not to ask. The detractors and the stalled customers from Step 9 are not referral sources, and asking them surfaces dissatisfaction at the worst moment. Asking only happy customers is not cherry-picking; it is the whole point. Name the exclusion so the motion does not blast the entire base.

5. The advocate count and the readiness signal are an assumption until you actually ask. Log them in assumptions.md (Step: 10, Status: Untested). Step 10.7 reveals whether the people you labelled advocates will really refer.

Step 10.3. Map the expansion path (land and expand)

Duration: 45 minutes

The precondition you just confirmed, a happy customer who reached value, unlocks two growth motions, not one. Referral, which the rest of this step builds, brings new customers and closes the loop. But before a customer refers anyone, they are the cheapest source of new revenue you have: expansion, selling more to the account you already won. Net revenue retention, expansion minus churn across the existing base, is the quiet engine of durable B2B growth, because it compounds without paying acquisition cost again. This step maps that path so a successful customer’s value is captured fully, not left on the table while you chase new logos.

Expansion and referral are distinct: expansion is more revenue from the same customer, no new acquisition; referral is new customers, and it is what closes the loop. Both come from a healthy Step 9, and neither can be forced on a customer who is not actually succeeding.

What to do:

1. In the “Expansion path” table in the Step 10 section of outputs.md, map the ways a successful account grows. Pull the shape from the Step 3 packaging: more seats or usage (the value metric grows as they succeed), a tier upgrade (Good to Better to Best), a cross-sell into an adjacent need, or deeper adoption of what they already have.

   Expansion path	The signal they are ready	The value that justifies it	Who owns the conversation
   More seats / usage
   Tier upgrade (Good to Better to Best)
   Cross-sell (adjacent need)
   Deeper adoption

2. Anchor the readiness signals on the Step 9 success markers. A customer who hit the first-value moment and is using the product heavily is ready to expand; a customer who is struggling is a churn risk, not an upsell target. Expand the successful and save the struggling, and never confuse the two, because an upsell email to a struggling account accelerates the churn.

3. Tie expansion to the value metric and packaging from Step 3.7. The reason to pick a value metric that grows with the customer’s success is exactly this: expansion then happens as a natural consequence of them getting more value, not as a squeeze. If expansion feels like extraction, the value metric or the packaging is wrong, and that is a finding to feed back to Step 3, not to push through.

4. Set the measure: net revenue retention, expansion revenue minus churn and contraction across the existing base. Above one hundred percent means the base grows without a single new logo, which is the compounding engine most durable B2B businesses run on. Name what you will track, and separate it from the referral measure, which counts new customers.

5. The readiness-to-expand signals are an assumption until accounts actually expand. Log them in assumptions.md (Step: 10, Status: Untested); the same Step 9 data that flags a healthy account tests whether your expansion triggers predict who grows.

Example (continuing the example): Expansion paths for the follow-up product: more AE seats as the sales team hires (the value metric is active AEs, so revenue grows with headcount without a new sale), a tier upgrade to Better for the closes-analysis, and a future cross-sell of an onboarding module. The readiness signal: the account hit activation and just added a fourth AE. The struggling accounts flagged in Step 9 get a save motion instead, never an upsell. The NRR target is to keep existing accounts expanding faster than any churn, so growth compounds before referral adds new logos on top.

Step 10.4. Map why and when customers refer

Duration: 45 minutes

A referral happens at the intersection of a willing advocate, a real motivation, and a natural moment. You have the advocates from Step 10.2; now map the why and the when. People refer for reasons that are mostly about themselves and their peers, not about you: to look knowledgeable, to help someone they like, to be associated with something good. And they refer most readily at specific moments of high goodwill, usually right after a win. Mapping both is what lets the motion in Step 10.5 feel like a natural extension of a good experience rather than an interruption.

What to do:

1. In the Step 10 section of outputs.md, under “Why and when they refer,” fill in the two tables below (they are also in outputs.md).

   The motivations: why an advocate would actually refer, in their terms.

   Motivation	What it looks like for this segment	How the motion can support it
   Looks good / social currency (knew about the thing first)
   Helps a peer they like
   Reciprocity (you helped them, the champion was protected)
   Identity (the kind of operator who fixes this)

   The trigger moments: when goodwill peaks, tied to Step 9.

   Trigger moment (from Step 9 or the relationship)	Why goodwill is high here	The natural ask at this moment

2. Lead the trigger moments with the Step 9 first-value moment and other wins. The best time to ask is just after a customer feels a real result: the first saved deal, a milestone, a visible success they are pleased about. Goodwill is highest there, and the ask connects naturally to the thing they just experienced. Asking at renewal, by contrast, collides with a money decision and feels like a toll.

3. Make the referral something that makes the advocate look good, not just something that helps you. The strongest referrals are the ones where recommending you raises the referrer’s standing: they introduced their peer to the fix, they were ahead of the curve. Design the ask so saying yes is a way for the advocate to be the helpful, in-the-know one. People share what makes them look good and what is genuinely useful to the person they share it with.

4. Map who the advocate refers to, using the clustered Step 2 segment. Advocates refer peers with the same problem, usually in the same role and the same circles Step 2 identified. The referral travels along the connections that made the beachhead a good choice in the first place. This is also why the referred lead is so well-qualified: it comes pre-filtered to the segment.

5. The motivations and trigger moments are assumptions until advocates confirm them. Log them in assumptions.md (Step: 10, Status: Untested), especially the trigger moment you plan to build the ask around.

Step 10.5. Design the referral motion, not a campaign

Duration: 60-90 minutes

Now build the motion: the specific, low-friction way you turn a willing advocate at a goodwill moment into an actual referral. The discipline that separates a motion from a campaign is that every element follows from a real advocate and a real moment, and that referring is made genuinely effortless. The advocate’s goodwill is finite; spend none of it on friction. This is also where you decide what to ask for, because “refer us” is vague, and the easier and more specific the ask, the more likely the yes.

What to do:

1. In the Step 10 section of outputs.md, under “Referral asks,” map the asks in the table below (it is also in outputs.md). Each row is one type of ask, tied to a trigger moment and made easy. Different advocates and moments suit different asks; you do not need all of them.

   Ask	Best advocate and moment	How you make it effortless	What the referrer gets (how it makes them look good)
   Warm introduction to a named peer
   Public testimonial or review
   Case study or story
   Bring-a-peer / direct referral

2. Make every ask as effortless as possible. The advocate should not have to write the email, find the link, or think about how to phrase it. Hand them a forwardable one-pager, a pre-drafted introduction they can edit, a direct link, a two-click path. Every unit of effort you remove raises the rate. The most common reason a willing advocate does not refer is that it was mildly inconvenient at the moment they were willing.

3. Arm the advocate to refer, the way Step 8 armed the champion to sell internally. Give them the words and the proof to make the case to their peer: a short version of the deal-decay story, the result they personally got, the one-line description of the problem the peer also has. An advocate who has to invent the pitch refers less and refers worse than one you have equipped.

4. Be careful with incentives. A reward can help, but the wrong incentive cheapens a genuine recommendation, attracts referrals motivated by the bribe rather than fit, and can even insult an advocate whose motivation was to help a peer. If you use an incentive, prefer one that fits the relationship (a benefit to both the referrer and the referred, or a donation, or simply recognition) over a transactional bounty, and never let it replace the real motivations from Step 10.4. The best referrals usually need no bribe; they need to be easy and well-timed.

5. Build the anti-referral list. Some referral tactics are tempting and quietly corrosive. Capture them in the table below (also in outputs.md).

   Tempting tactic	Why it looks effective	Why it hurts

   Common entries: the mass “refer a friend for a discount” blast to the whole base, the ask before the customer has reached value, the cash bounty that attracts junk referrals, the program that makes referring more work than it is worth, and asking at renewal. Naming them keeps the motion from sliding back into a campaign.

6. The motion’s design is a set of bets until advocates run through it. Log the asks and the effort-removal choices in assumptions.md (Step: 10, Status: Untested).

Example (continuing the Step 1 to Step 9 example):

The advocates are VPs of Sales who saw their reps adopt the cadence and watched the first warm deals get saved, and whose founders noticed. The trigger moment: just after the first-value dashboard, or after a quarter of recovered deals they can quantify. The asks: a warm intro to a peer VP in the same boat (made effortless with a pre-drafted, editable intro and a forwardable one-pager), and a short LinkedIn post or testimonial titled something like how their team stopped losing deals to silence, which makes the VP look like a sharp operator to their network. No cash bounty; the motivation is looking good and helping a peer. Anti-referral: a base-wide “refer a friend, get a month free” email that would pull junk and cheapen the VP’s recommendation.

Step 10.6. Build the referral path and the loop-back

Duration: 45 minutes

A referral motion is not finished when the advocate says yes; it is finished when the referred lead has been received well and the advocate’s trust has been protected. A referral lead carries borrowed credibility, which is both its power and its fragility: handle it well and it converts far better than any cold lead; handle it badly and you damage the relationship with the advocate who vouched for you. This step designs what happens when the referral arrives, and how that lead re-enters the funnel, closing the loop the whole model has been building toward.

What to do:

1. In the Step 10 section of outputs.md, under “Referral path and loop-back,” map what happens to a referred lead from arrival to first conversation. Define a fast track that reflects the trust they arrive with.

   Stage	What happens for a referred lead	How it differs from a cold lead (the fast track)
   Arrival (the intro or sign-up)
   First contact
   Qualification
   Into the funnel

2. Treat the referred lead as warm, because it is. The referrer already did the Step 7 work of building trust; the lead arrives believing a peer they respect. Do not restart the cold sequence. Skip the parts of Step 5, Step 6, and Step 7 that exist to manufacture the trust the referral already supplied, and move quickly to a real conversation. A referral that gets a generic cold welcome wastes its single greatest advantage.

3. Protect the referrer. The advocate put their reputation on the line; a slow, sloppy, or pushy experience for the person they referred reflects on them. Respond fast, handle the referred lead with extra care, and close the loop back to the referrer (thank them, tell them how it went). Protecting the referrer is what makes them refer again; a referral motion that burns referrers gets one round and stops.

4. Define the loop-back explicitly. A referred lead re-enters the funnel near the top, but at Step 2 it is already in the segment (a peer of an advocate), at Step 5 it arrived through the best channel there is (a trusted human), and at Step 6 and Step 7 it needs far less convincing. Note where referred leads enter and what they skip, so the rest of the system treats them differently from cold leads. This is the loop the model points to: the output of the last step becomes the highest-quality input to the first ones.

5. Set the measure. The honest measures of Step 10 are the share of advocates who actually refer, the number and conversion rate of referral leads, and how that conversion compares to cold leads (it should be much higher). A vanity referral-program signup count is not the measure; referred customers won is. Name the measure you will watch.

6. The fast track and the loop-back are assumptions until referral leads run through them. Log them in assumptions.md (Step: 10, Status: Untested), especially the claim that referred leads convert better, which Step 10.7 tests.

Step 10.7. Ask real advocates and find the stall

Duration: 2-6 weeks calendar time | Target: enough real referral asks to see who refers and how the leads convert

A referral motion is a hypothesis until you actually ask real advocates and watch what happens. Step 10.7 makes real asks at real goodwill moments, measures who refers and the quality of the leads they send, and asks advocates why they did or did not refer. As in every step, when referrals do not come you have to judge whether the fault is Step 10 (asked the wrong people, at the wrong moment, made it too hard) or upstream: Step 9 customers who are not actually happy, or a value in Step 3 that never truly landed. This judgement matters more here than anywhere, because referral is the step most often blamed for a problem that lives in Step 9.

There are two halves to Step 10 validation: the referral data (who referred, how many leads, how they converted) and conversations with advocates about why they did or did not refer. The data shows the result; the conversations explain it, and they are the clearest mirror of whether the whole machine above actually produced happy customers.

7a. Make real asks and measure referrals

Duration: the bulk of the time

Make genuine referral asks to the advocates from Step 10.2, at the trigger moments from Step 10.4, using the motion from Step 10.5. Keep it small and personal at first; you are testing whether advocates refer and whether the leads are good, not running a program.

What to measure:

• The share of asked advocates who actually refer. A low rate among genuine advocates points at the motion (the ask, the moment, the effort) or at the advocates not being as happy as you thought.
• The number and quality of referral leads: are they in the segment, do they have the problem, are they warm? Referral leads should be the best-fit leads you get.
• The conversion of referral leads compared to cold leads, through the fast track from Step 10.6. The gap is the proof of the step’s value.
• Whether referrers refer again, and whether the experience protected them. A one-and-done referrer signals the loop-back is leaking trust.

Find the stall and name it specifically: not “referrals are low” but “advocates say yes to a warm intro but never get around to sending it,” which points at an effort problem in the motion, or “advocates decline because they are not actually confident in the results,” which points upstream at Step 9.

7b. Ask advocates why they did or did not refer

Duration: light, a handful of short conversations

The referral data shows the result; advocates explain it. Talk to a few who referred and, more valuably, a few who you expected would and did not. Keep it warm and genuinely curious.

What to ask those who referred:

• What made you comfortable recommending us? Who did you think of, and why them?
• Was it easy to do? Where did you hesitate?

What to ask those who did not:

• You are happy with the product but have not referred anyone. What is in the way?
• Is it that it has not come up, that it was awkward, or that you are not quite confident enough to put your name to it? (the last answer points upstream)
• What would make it easy and natural?

You are listening for whether the gap is a fixable Step 10 problem (never asked, asked at the wrong time, too much effort, no natural moment) or an upstream signal (the customer is not actually confident in the value, which means Step 9 or Step 3 is the real issue). That judgement decides whether you fix the motion or go back a step.

7c. Capture immediately after the asks and conversations

Do this while the detail is fresh. Add entries to the Step 10 section of captures.md using this structure:

Referral asks [number] | Advocate segment + trigger moment:
Asks made:

Referrals received (count, and the ask type):

Referral lead quality (segment fit, warmth):

Referral lead conversion vs cold (if known):

---

Advocate conversation [number] | Referred / did-not-refer:
Advocate (role, segment fit):
Why they did or did not refer (verbatim):
What would make it easy and natural (verbatim):
Confidence in the value (does it point to Step 9/Step 3?):
Is the gap a Step 10 fix or an upstream signal?:

Surprises (anything you did not expect):
Any Step 10 assumptions this confirmed or challenged:

The referral-lead quality and conversion, and the verbatim reasons for not referring, are the most valuable output. The first proves the step’s payoff; the second tells you whether to fix the motion or go back upstream. Capture both exactly.

7d. Synthesise and fix the stall

Do this once enough asks have been made. Go through your captures in captures.md and update the Referral synthesis fields in the Step 10 section of outputs.md, covering:

Referral rate and lead quality. What share of advocates referred, and were the leads as well-fit and warm as expected? Compare referral-lead conversion to cold. The gap is the step’s value, and the size of it tells you whether to invest more here.

The stall and its likely cause. Name where the motion stalls (the ask, the moment, the effort, the follow-through) and what the conversations say is behind it. Be specific about Step 10 versus upstream: a hard or mistimed ask is a Step 10 fix; advocates who are not confident in the value are not. This judgement is the most important output of the step.

The honesty check on Step 9. Did the advocates turn out to be as happy as you assumed? If many “advocates” declined because they were not confident in the results, that is Step 9 (or Step 3) telling you the value is not landing as well as you thought. This is the most valuable thing referral can reveal, and the reason it cannot be faked with a campaign.

The loop-back. Did referral leads convert better on the fast track, and were referrers protected and willing to refer again? If referred leads were treated cold or referrers were burned, fix the path before scaling.

Is the constraint upstream. If advocates will not refer and the reason is confidence rather than mechanics, the constraint is above Step 10. Say so and go back to Step 9 or Step 3. A referral motion cannot manufacture advocacy that the experience did not earn.

The fix and the re-test. State the one change you are making (to the ask, the timing, the effort, or upstream) and what you expect it to do, then ask again. One change at a time.

After synthesis, mark the relevant rows in assumptions.md as Validated or Invalidated, and note what the evidence showed. If the constraint is upstream, fix it there. Referrals are a consequence; you cannot fix a consequence by adjusting the consequence.

7e. If you genuinely cannot make real asks yet

If you have too few advocates to ask in the time available, that scarcity is itself the finding, and it usually points back to Step 9. Still, reduce the unknowns and mark the gap.

• Ask the one or two real advocates you do have, by hand. A single warm intro from a genuine advocate teaches more than a program built for advocates you do not have.
• Look for referrals that already happened unprompted. Did any customer already bring someone, mention you publicly, or vouch for you without being asked? Unprompted referrals are the purest signal that the experience earns advocacy.
• Pressure-test the precondition. If you cannot find advocates to ask, treat that as evidence about Step 9, not as a reason to build referral mechanics anyway.

Document each in the Step 10 section of captures.md, noting it is secondary. Log the absence of real asks as an assumption in assumptions.md. A referral motion never tested with real advocates is a guess, and if the reason you cannot test it is that you have no advocates, the work is in Step 9.

Step 10.8. Write the final referral motion

Duration: 30-45 minutes

You now have a confirmed precondition, an advocate definition, a why-and-when map, a motion, a loop-back, and a diagnosed stall. Lock the motion.

A complete Step 10 output has seven parts. Write a line or two for each:

1. The precondition and the advocate: the honest state of whether Step 9 produces advocates, who they are, and roughly how many.
2. The expansion path: how successful accounts grow (seats, tier, cross-sell, adoption), the readiness signals, and the net-revenue-retention measure, kept separate from the save motion for struggling accounts.
3. Why and when they refer: the motivations and the goodwill moments, tied to Step 9 wins.
4. The motion: the asks, each tied to a moment and made effortless, with the advocate armed to refer and incentives handled carefully.
5. The referral path and loop-back: the fast track that catches referral leads warm, protects the referrer, and re-enters them near the top of the funnel.
6. The stall and the fix: where the motion stalls, its likely cause (Step 10 or upstream), and the change you made.
7. The upstream finding, if any: whether the asks revealed that Step 9 or Step 3 is not producing genuine advocacy, so it is visible.

Keep it as structured fields. Format does not matter. Earned advocacy and an effortless ask do: this is the step where “we have a referral program” is not good enough.

What to do:

1. Write the final motion directly into the matching fields in the Step 10 section of outputs.md.
2. Read it once as an advocate who is genuinely happy but busy. Is the ask easy enough, well-timed enough, and flattering enough that you would actually do it this week?
3. Run the diagnostic from the top of this page one more time. If all six questions now have clear written answers, Step 10 is done, and so is the spine.

What you’ve built

After completing the steps above, the Step 10 section of outputs.md should contain:

This is the last output on the spine. It does not constrain a next step; it feeds back into the first ones.

Assumption sweep

Before moving on, scan the Step 10 section of outputs.md for any field you filled in from reasoning rather than evidence. Common ones at Step 10:

• That your advocates are genuinely happy (did they refer when asked, or did you assume the satisfaction?)
• The expansion-readiness signals (did healthy accounts actually expand, or did you assume the upsell path, or worse, aim it at a struggling account?)
• The trigger moment (did asking then actually produce referrals, or is it your best guess at when goodwill peaks?)
• The motivations (did advocates refer for the reasons you mapped, or for others?)
• That referral leads convert better (did the data show it, or are you assuming the trust transfers?)
• That referring is easy enough (did willing advocates follow through, or did effort stop them?)

Each unconfirmed field is an assumption. Log it in assumptions.md now if you have not already. Whether your advocates are genuinely happy is the highest-impact assumption in the step, and the one that most often reveals an upstream problem; if untested, mark it leap-of-faith and make real asks before trusting the motion.

What this step hands back: closing the loop

This is the last step of the spine, so it does not hand off to a tenth phase. It closes the loop. A referral lead is the output of Step 10 and the highest-quality input to the steps at the top:

• It enters at Step 2 already inside the segment, because advocates refer peers with the same problem in the same circles the beachhead was chosen for.
• It arrives through Step 5’s best possible channel, a trusted human, which no paid channel can match.
• It needs far less of Step 6 and Step 7, because the trust those steps work to build was already supplied by the referrer. The cold-start work is largely done.

So the model is a loop, not a line: do the ten steps well, earn referrals, and those referrals re-enter near the top warmer than anything you could generate, which is why referral leads convert better than any other source and why this step cannot be bought, only earned.

On the Brand result, the thread. Brand is not an eleventh stop on the spine and has no phase page. It is the result of doing these ten steps well, and referral is its most visible sign: when customers vouch for you unprompted to their peers, that reputation is the brand, accumulating across every step rather than built in one. You do not build a brand. You do ten other things well, and then you get one. If you have worked the spine from Step 1 to here with evidence at each step, the brand is the compounding consequence, and the referral loop is where it shows.

Common failure modes

Referral is run as a campaign. A referral program is switched on, a “refer a friend” email goes to the whole base, and it produces little because most customers are not advocates and the ask is generic. Referral is a consequence of Step 9, not a campaign; identify real advocates and make it easy for them.

You asked before Step 9 produced advocates. The push went out before customers reached value, so you asked unhappy or indifferent customers to recommend a product that had not proven itself. Confirm the precondition first; if there are no advocates, the work is in Step 9.

You asked everyone instead of the advocates. Blasting the whole base surfaces dissatisfaction and produces weak or no referrals. Ask only the customers who reached value and would genuinely recommend you.

The timing ignored goodwill. The ask came at renewal or out of the blue, colliding with a money decision or feeling random, instead of riding the wave of a Step 9 win. Tie the ask to the moment of highest goodwill.

Referring was too much work. A willing advocate was asked to write the email, find the link, and phrase the pitch, and did not bother. Make referring effortless: a forwardable asset, a pre-drafted intro, a two-click path.

The incentive cheapened it. A cash bounty attracted junk referrals motivated by the bribe and insulted advocates who wanted to help a peer. Lean on the real motivations, looking good and helping a peer, and use incentives carefully if at all.

The referral lead was treated cold. A warm, vouched-for lead got dumped into the cold sequence, wasting its trust and risking the referrer’s reputation. Build a fast track that reflects the trust the lead arrives with, and protect the referrer.

You tried to fix referral by adjusting referral. Referrals were low, so you tweaked the program endlessly while the real cause was customers who never reached value. A consequence cannot be fixed by adjusting the consequence; when advocacy is missing, the cause is upstream in Step 9 or Step 3.

“We have a referral program.” Said with a signup count and a bounty, and no idea how many genuine advocates exist or how referral leads convert. If you cannot point to real advocates, an effortless and well-timed ask, and referral leads that convert better than cold, it is a campaign, not a motion.

Sources

• The Ultimate Question 2.0 by Fred Reichheld. On advocacy as the engine of growth, the measurable willingness to recommend, and the distinction between promoters and the merely satisfied. Behind the advocate definition and readiness measure in Step 10.2.
• Customer Success by Nick Mehta, Dan Steinman, and Lincoln Murphy. On net revenue retention as the compounding engine of B2B growth, expanding successful accounts (seats, tiers, cross-sell) while saving the at-risk ones, and why expansion is cheaper than acquisition. Behind the land-and-expand step in Step 10.3.
• Contagious by Jonah Berger. On why people share and talk: social currency, triggers, emotion, public visibility, practical value, and stories. The foundation for the motivations and trigger moments in Step 10.4.
• The Referral Engine by John Jantsch. On building referrals as a system that follows from a genuinely good customer experience rather than a bolted-on program, and on making referral a natural part of the relationship. Behind the motion design in Step 10.5.
• Talk Triggers by Jay Baer and Daniel Lemin. On the deliberate, operational differentiator that earns word of mouth, and why word of mouth is a designed consequence of doing something worth talking about. Background for Steps 4 and 5.
• Word of Mouth Marketing by Andy Sernovitz. On the practical mechanics of word of mouth: the talkers, the topic, the tools that make sharing easy, and tracking the result. Behind the effort-removal and the asks in Step 10.5 and the measure in Step 10.6.