Bundle adviser · starting point
- AI prepares
- Customer task + verified catalogue → a suitable bundle with an explanation for each item
- A person checks
- Checks compatibility, availability and customer benefit
AIStart360 / growth lever / 03
A purchase solves only part of the customer’s problem.
Offer more useful value, not unnecessary extras. Test a bundle, service tier or a justified reason to buy more often.
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.
Sales contribution after variable costs / all included customers, not just buyers.
Revenue after the agreed treatment of discounts and returns / orders counted under the same rule.
Purchases during the period / customers in a cohort fixed in advance, including zero purchases.
Conversion, returns, complaints, compatibility failures, service time and costs.
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.
Offer matrix, recommendation rules, economics worksheet and staff instructions. Include a no-extra option: the current solution may be sufficient.
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.
03 / Order value and frequency
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 [customer task], [catalogue], [compatibility rules] and [costs], propose useful complements.
R — You are a careful business workflow analyst for this specific task. A — Using [customer task], [catalogue], [compatibility rules] and [costs], propose useful complements. Include a no-extra option and identify gaps rather than inventing compatibility or demand. 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 options], [stock] and [terms], draft a recommendation explaining how it solves the task.
R — You are a careful business workflow analyst for this specific task. A — Using [approved options], [stock] and [terms], draft a recommendation explaining how it solves the task. Do not default to the highest price, hide charges or invent special outcomes. Staff approve before use. 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 [fixed cohort], [orders], [returns] and [variable costs], calculate contribution per eligible customer.
R — You are a careful business workflow analyst for this specific task. A — Using [fixed cohort], [orders], [returns] and [variable costs], calculate contribution per eligible customer. Show order value separately; for frequency use the fixed cohort and period, including zero 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.
Iyengar and Lepper compare limited and extensive choice sets in three studies. Smaller sets sometimes supported purchase or task completion.
Limits of the findingThe findings do not establish a universal number of products or price tiers. Context and the choice task matter.
Bootcamp practiceTest an understandable, compatible set of options for one customer task.
Original sourceA meta-analysis of 50 experiments, 63 conditions and 5,036 participants found a mean effect near zero with substantial variation.
Limits of the findingOne striking study cannot show that reducing options always helps; a near-zero mean does not rule out a local effect.
Bootcamp practiceCompare recommendation quality, customer action and economics in your own workflow.
Original sourceAmazon Science’s 2019 retrospective describes item-to-item collaborative filtering for a large catalogue.
Limits of the findingA technical history, not evidence for a fixed percentage of revenue attributable to recommendations.
Bootcamp practiceStart with an approved compatibility and bundle matrix; never invent co-purchase statistics.
Original sourceA 2015 developer paper describes algorithms, offline evaluation and A/B tests aimed at retention and engagement.
Limits of the findingA subscription content service, not a shop-cart experiment or proof of higher order value.
Bootcamp practiceTransfer the testing discipline rather than another company’s monetary metric.
Original sourceA discussion excerpt for the choice 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.
be choosy about choosingOriginal source
Examine how choice is structured. Test clarity rather than assuming fewer options always sell better.
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 practiceExamine how choice is structured. Test clarity rather than assuming fewer options always sell better.
Original sourceLink offer design to willingness to pay and contribution. Do not confuse a more expensive package with more useful value.
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 practiceLink offer design to willingness to pay and contribution. Do not confuse a more expensive package with more useful value.
Original sourceSelected from AIStart360 Growth Labs, edition 2.0, 26 October 2026. Each item retains its provenance, limitations and links to original publications.
Economics
Separate revenue, variable costs and fixed costs. Compare acquisition cost with customer contribution over a consistent period. Do not treat projected LTV as fact: show scenarios and unknown inputs.
Order value · AI
AI can surface product combinations and use situations. Offer only what fits the customer need; measure margin and declines alongside revenue.
External research and cases inform hypotheses; they do not promise the same outcome. Check current conditions and relevance to the specific challenge.
A playbook for self-serve value, time-to-value and product-led acquisition.
A practical view of freemium as a deliberate acquisition and monetization system.
How to make disciplined experimentation a repeatable organizational capability.
A commerce case spanning conversational discovery, recommendations and product knowledge quality.
A field guide for finding, prioritizing and expanding AI use cases from tasks into end-to-end workflows.
Forty-four discovery and validation experiments compared by cost, setup time, duration and evidence strength.
A decision-first lens that separates objectives, actions, uncertainty and evidence before choosing models.
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.
A warning against false precision in early-stage unit economics and a guide to the data maturity needed for credible ratios.
Twelve metrics for diagnosing the health, efficiency and scalability of a SaaS growth engine.
Why did an early digital-camera prototype not secure a profitable digital future? Kodak's case concerns the replacement of a business model, not a company that never noticed digital technology. It invested in multiple forms of renewal. The commercial question was how to replace the economics of film and printing with a durable new source of customer value and profit.
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 caseBooking.com's lesson is not a target number of experiments. It is a management system that makes assumptions testable and permits evidence to challenge seniority. Hypotheses, measurement and accountable teams matter more than the prestige of an A/B-testing tool.
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.