Explainable segmentation
- AI prepares
- Order history → groups based on observable rules and a list of exceptions
- A person checks
- Checks the meaning of the groups; calculations use formulas
AIStart360 / growth lever / 02
Customers buy once and disappear.
Find a useful reason for the next purchase. Test whom to contact, when and with which relevant offer.
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.
Initial-cohort customers with a repeat paid purchase in the window / all customers in that initial cohort.
Repeat-order contribution after variable costs / initial number of customers.
Interval from the start event; report customers without a purchase at the cut-off separately.
Unsubscribes, complaints, returns, discounts, services, preparation and handling time.
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.
Return journey, segmentation rules, contact scenario and observation calendar. Message opens are not sales; use a period appropriate to the repeat cycle.
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.
02 / Repeat sales
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 [purchase history], [usage cycles] and [contact rules], propose transparent segments and one repeat-purchase cohort.
R — You are a careful business workflow analyst for this specific task. A — Using [purchase history], [usage cycles] and [contact rules], propose transparent segments and one repeat-purchase cohort. Separate facts, assumptions and missing data. Apply [exclusions] and retain the initial cohort for measurement. 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 [confirmed purchase], [customer need] and [approved offer], propose a relevant contact time and message.
R — You are a careful business workflow analyst for this specific task. A — Using [confirmed purchase], [customer need] and [approved offer], propose a relevant contact time and message. Respect permission, recent contacts and unresolved complaints. Do not invent a need or use a discount as a substitute for value. 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 [initial cohort], [orders], [payments], [returns] and [costs], calculate repeat buyers within [window].
R — You are a careful business workflow analyst for this specific task. A — Using [initial cohort], [orders], [payments], [returns] and [costs], calculate repeat buyers within [window]. Include zero-purchase customers and late outcomes. Keep opens and clicks separate from purchases. 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.
Ascarza studies why the highest-risk customers are not necessarily the best targets for a retention offer. Responsiveness to intervention matters.
Limits of the findingA risk prediction does not show that a message or discount will change behaviour. Context and offer matter.
Bootcamp practiceDefine an initial customer cohort and test a useful contact against an appropriate comparison.
Original sourceNunes and Drèze study endowed progress: perceived advancement can affect effort and completion.
Limits of the findingThis is not proof that every loyalty programme is profitable. Rewards have a cost and the mechanism must not mislead customers.
Bootcamp practiceTest a transparent progress mechanism and include reward costs in contribution calculations.
Original sourceMicrosoft’s May 2019 account describes reinforcement learning using purchase history, availability, time and weather.
Limits of the findingMachine-learning personalisation, not a generative chatbot or a controlled repeat-purchase effect.
Bootcamp practiceTest one relevant recommendation and an appropriate customer observation window.
Original sourceTesco’s April 2024 announcement and supplier material describe personalised challenges with time-limited rewards.
Limits of the findingLaunch and mechanism evidence; the supplier has a commercial interest. This is not an independent retention or profit estimate.
Bootcamp practiceAgree task relevance, reward cost and actual repeat-purchase measures.
Original sourceOriginal sourceA discussion excerpt for the repeat purchase 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.
not all customers are created equalOriginal source
Audit observed customer behaviour and cohort economics before proposing a repeat-purchase action.
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 practiceAudit observed customer behaviour and cohort economics before proposing a repeat-purchase action.
Original sourceConnect customer lifetime-value thinking to an explicit, testable customer strategy.
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 practiceConnect customer lifetime-value thinking to an explicit, testable customer strategy.
Original sourceSelected from AIStart360 Growth Labs, edition 2.0, 26 October 2026. Each item retains its provenance, limitations and links to original publications.
Team
Examine a real employee task: where the tool helps, where it adds work and how mistakes can be corrected. Agree on expectations and responsibility. Measure successful task completion, not only logins.
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.
Repeat sales · AI
Compare customer cohorts, reasons to return and time between orders. AI can surface patterns, but a person decides whether contact is appropriate.
External research and cases inform hypotheses; they do not promise the same outcome. Check current conditions and relevance to the specific challenge.
B2B customer-base monitoring: missed purchase cycles, gaps in contact and a next action for repeat sales. A resource for growth lever 02 — retention.
Field evidence on productivity and heterogeneous effects across worker experience.
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.
Patterns for deciding when an agent is appropriate, designing orchestration and applying layered guardrails.
Empirical patterns of how AI supports tasks, occupations and human–AI division of work.
A systems view of growth across market, product, channel and model fit—useful for testing whether a GTM engine can compound.
A detailed operating guide to CAC, LTV, churn, payback and the metrics that explain recurring-revenue growth.
Twelve metrics for diagnosing the health, efficiency and scalability of a SaaS growth engine.
A shared discovery language across Situation, Pain, Impact, Critical Event and Decision for customer-centered revenue conversations.
A six-model GTM operating system connecting revenue, customer lifecycle data, math, execution and growth stages.
What changes first: strategy, or the culture that must execute it? Microsoft's renewal suggests that the question is often a false choice. Cloud investment, subscription relationships, developer tools and cross-functional learning developed together. Revenue growth cannot be attributed solely to one leader, one cultural practice or a slogan.
Read the caseKaspi.kz illustrates connected value between consumers and merchants. The lesson for a smaller business is not to build a super app. It is to identify adjacent tasks that become easier when information and service continuity are connected. Distinguish the company's reported scale from our interpretation of the mechanisms behind it.
Read the caseDigital retail transformation need not mean abandoning physical stores. Decathlon's model connects product design, distribution, digital services and the product lifecycle. The practical question is how a customer continues a task between channels, rather than whether a sale is credited to the website or the store.
Read the caseThe useful question is not whether AI can replace people, but which requests it can resolve well at a lower complete cost while preserving trust and access to human help. Klarna's public figures must be read as company statements with defined periods and channels, rather than a universal staffing formula.
Read the caseTerms 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.