Value moments
- AI prepares
- Permitted feedback → possible invitation moments with a quote or source ID
- A person checks
- Confirms the actual benefit and that no problem remains unresolved
AIStart360 / growth lever / 05
Satisfied customers exist. Referrals happen by chance.
Make a good experience easy to recommend. Test the path from a relevant invitation to a new customer’s first purchase.
These are test directions, not growth promises. Agree one primary metric, costs and decision criteria for the chosen challenge.
AI must not invent prices or terms, send important messages without agreed oversight, change agreements or execute payments.
Confirmed first purchases by new customers with a permitted referral source; also report per member of the initial referrer cohort.
Invitation → voluntary referral → new enquiry → first payment.
New-purchase contribution minus rewards, handling, services and additional costs.
Complaints, duplicates, self-referrals, cancellations, returns and unsupported rewards.
GRI is an author-developed analytical framework, not independent certification, an investment rating or a growth guarantee.
Find an observable loss and test possible explanations against the facts.
Define the segment, value, offer, channel, funnel step, economics and accountability.
Specify the inputs, expected output, limits and employee’s role.
Agree a metric and comparison method, check quality, include costs and decide the next step.
The shared Bootcamp structure. Data, actions and the metric are tailored to the chosen lever.
Discuss the challenge, available examples, team involvement and scope. Agree the work before the programme begins.
Days 1–701
Review the workflow and data, choose a constraint and one lever, define the GTM action and experiment rules.
Session: map the customer journey and locate the loss
Process map and challenge statementCollect examples; check IDs and dates
Data dictionary and missing-data listReview relevant GRI areas and test two or three explanations
Evidence-backed constraint mapSession: choose one lever, segment and GTM action
GTM on one pageDescribe the usual process, a non-AI improvement and an AI variant
Technology role and comparison variantsDefine cohort, metric, observation window, costs and stopping rules
Experiment brief v1Checkpoint: agree the solution scope
Build / revise / prepare dataDays 8–1402
Prepare context, build the AI workflow, test typical cases and exceptions, and define human handover rules.
Session: create a context base and first prompt
AI scenario v0.1Run typical cases and verify source facts
Error and correction logTest missing data, conflicts, duplicates and refusal
Exception set and human handover rulesSession: independent quality review on held-out examples
Readiness assessment and critical defectsRun in shadow mode without contacting customers
Time measurement and staff usefulness reviewPrepare the log, group assignment and instructions
Launch pack with prompt versionCheckpoint and owner decision
Limited-test approval or correction planDays 15–2103
Check execution, compare available outcomes, account for costs and limitations, and prepare team handover.
Session: check the first live events and stopping procedure
Working limited testReview deviations and protocol adherence
Event and error logReconcile customers, orders, payments, costs and time
Observed-results tableReview group comparability and alternative explanations
Limitations of the conclusionSession: calculate measures and economics
Draft report and decisionPrepare instructions, materials and continuation plan
Handover packReview: continue, change, stop or extend
Approved report and 30/60/90 planA long sales or repeat-purchase cycle may require more than 21 days to assess a monetary effect. In that case, the result is tested execution and a plan for continued observation.
Proposed format: six 90-minute working sessions and three 30-minute checkpoints. Indicative time for the experiment owner: 7–9 hours per week plus 2–3 hours of preparation. This is not an approved package; scope and involvement are agreed separately.
Trigger map, messages, new-customer journey and attribution rules. Define reward conditions and duplicate protection in advance.
A 90-day plan is a document, not 90 days of included implementation and support. Production integration, data migration, licences and ongoing support require separate agreement.
05 / Customer referrals
Research and cases inform hypotheses; they do not guarantee the same result in your company. External companies are not Bootcamp clients or partners. Not every case concerns generative AI.
Materials: 10 / 10
Using [confirmed value events], [feedback] and [permission], identify an appropriate moment to invite a referral.
R — You are a careful business workflow analyst for this specific task. A — Using [confirmed value events], [feedback] and [permission], identify an appropriate moment to invite a referral. Do not turn neutral feedback into praise or request other people’s contacts without consent. F — Evidence and source IDs; observations; missing facts; draft action; human review checklist; metric, costs and limits. T — Clear, specific and without growth promises. Before starting, ask the clarifying questions needed to understand the context, objective and limits. Do not proceed until the inputs and rules are clear.
Limits of the findingUse permitted or anonymised data. A person verifies facts and approves consequential actions.
Bootcamp practiceReplace bracketed inputs with approved context. Test typical cases, exceptions and stopping rules before operational use.
Using [approved offer] and [referral rules], draft an invitation and an optional short forwarding message.
R — You are a careful business workflow analyst for this specific task. A — Using [approved offer] and [referral rules], draft an invitation and an optional short forwarding message. Do not impersonate a customer or invent a review. The new customer must choose to make contact. F — Evidence and source IDs; observations; missing facts; draft action; human review checklist; metric, costs and limits. T — Clear, specific and without growth promises. Before starting, ask the clarifying questions needed to understand the context, objective and limits. Do not proceed until the inputs and rules are clear.
Limits of the findingUse permitted or anonymised data. A person verifies facts and approves consequential actions.
Bootcamp practiceReplace bracketed inputs with approved context. Test typical cases, exceptions and stopping rules before operational use.
Using [referral IDs], [new customers], [payments], [returns], [rewards] and [costs], reconcile first purchases.
R — You are a careful business workflow analyst for this specific task. A — Using [referral IDs], [new customers], [payments], [returns], [rewards] and [costs], reconcile first purchases. Identify duplicates, self-referrals and unconfirmed sources. Keep forwarded links separate from paid outcomes. F — Evidence and source IDs; observations; missing facts; draft action; human review checklist; metric, costs and limits. T — Clear, specific and without growth promises. Before starting, ask the clarifying questions needed to understand the context, objective and limits. Do not proceed until the inputs and rules are clear.
Limits of the findingUse permitted or anonymised data. A person verifies facts and approves consequential actions.
Bootcamp practiceReplace bracketed inputs with approved context. Test typical cases, exceptions and stopping rules before operational use.
A study followed roughly 10,000 German bank customers for almost three years; referred customers had at least 16% higher average value than matched customers, with segment differences.
Limits of the findingThis is a context-specific comparison, not random channel assignment or a universal 16% improvement. Referrals are not necessarily free.
Bootcamp practiceTrack confirmed first purchases, contribution, rewards and handling costs.
Original sourceBerger and Milkman study New York Times content and experiments, linking sharing to activation and other content features.
Limits of the findingSharing does not establish payment, trust or high-quality demand. Emotional pressure is not proposed as a practice.
Bootcamp practiceGive customers useful, truthful explanations and measure purchases separately from views and forwards.
Original sourceAirbnb Engineering’s March 2014 account describes redesigning referrals for web and mobile, with rewards after a referred guest’s first trip.
Limits of the findingHistorical company self-report about that programme, not all Airbnb bookings or generative AI.
Bootcamp practiceLink rewards to a confirmed event. A sent link is not a completed referral purchase.
Original sourceDropbox Help describes storage rewards for referrals, with plan-specific limits and qualifying actions.
Limits of the findingThe help page establishes mechanics, not a causal contribution to business growth.
Bootcamp practiceChoose a useful reward, cost it and check new-customer validity, duplicates and abuse.
Original sourceA discussion excerpt for the referral route. Use the source’s own context, not as a promise of programme results.
Limits of the findingAn expert quote is not causal evidence or a universal business rule.
Bootcamp practiceUse the question to sharpen your own hypothesis, metric and decision criteria.
We all want to be liked.Original source
Understand why people share information. Prepare useful messages without fabricating experience or emotion.
Limits of the findingThe AI exercise is an original Bootcamp adaptation, not content attributed to the book. The programme uses publisher descriptions and open excerpts, not a claimed analysis of every full text.
Bootcamp practiceUnderstand why people share information. Prepare useful messages without fabricating experience or emotion.
Original sourceUse customer feedback to improve service. Willingness to recommend and NPS are not actual referral purchases.
Limits of the findingThe AI exercise is an original Bootcamp adaptation, not content attributed to the book. The programme uses publisher descriptions and open excerpts, not a claimed analysis of every full text.
Bootcamp practiceUse customer feedback to improve service. Willingness to recommend and NPS are not actual referral purchases.
Original sourceSelected from AIStart360 Growth Labs, edition 2.0, 26 October 2026. Each item retains its provenance, limitations and links to original publications.
Customers
Start with recent customer situations, existing alternatives and the cost of doing nothing. Then test a concrete offer through an application, pilot or another meaningful commitment. Positive opinions about an idea are not demand.
Customer journey
Follow one customer mission from first contact to fulfilment and return. Map customer actions to staff and system work. Test a channel transition: choices, agreements and status should carry over without creating duplicates.
Referrals · AI
AI can review de-identified feedback to find repeated reasons to recommend. Do not generate fake reviews or request someone else’s contact details without consent.
External research and cases inform hypotheses; they do not promise the same outcome. Check current conditions and relevance to the specific challenge.
Frames demand around the progress customers hire a solution to make.
Connects desired outcomes, opportunities, solutions and assumption tests in a weekly cadence.
Uses customer purpose as the endpoint that organizes journeys and measurement.
A playbook for self-serve value, time-to-value and product-led acquisition.
18 interaction guidelines covering expectations, explanations, correction, feedback and control.
A field guide for finding, prioritizing and expanding AI use cases from tasks into end-to-end workflows.
A step-by-step method for positioning around competitive alternatives, differentiated value and the customers who care most.
A concise positioning workflow that starts with what customers would do if your solution did not exist.
A systems view of growth across market, product, channel and model fit—useful for testing whether a GTM engine can compound.
Explains why market-product fit precedes scalable channel and monetization choices.
Separates growth loops from one-off tactics and balances acquisition, retention and monetization around a durable engine.
Terms and participation
Check whether the challenge fits, and discuss examples and team involvement. Then agree location, language, format, scope, fees and participation terms.
A manual workflow usually needs a process owner and permitted data. Integration with operational systems requires technical participation and a separately agreed scope.
A company works on one lever per cycle. The others inform the next step; they are not six parallel projects.
Establish minimum record-keeping and test on permitted examples first. Never replace missing facts with invented data.
Document quality, costs and limitations. A justified stop helps avoid expanding an uneconomic solution.
Licences, integration development and ongoing support are included only when explicitly agreed in the scope.
It depends on the workflow and customer cycle. The 21 days cover a limited scenario; long-term effects are assessed separately.
Agree permitted materials, anonymisation, access and external services before starting. The first-contact form does not need customer databases or documents.