Order value and frequency

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.

Challenge and test scope

These are test directions, not growth promises. Agree one primary metric, costs and decision criteria for the chosen challenge.

Who it is for
For a partially solved customer task, unclear packages or a useful missing complement. Choose either purchase composition or frequency for the test.
Data needed
Customer and order IDs, dates, items, quantities, net price, returns, product costs and variable expenses. Supply the approved catalogue, stock, compatibility rules and permitted bundles.
AI and people
AI suggests a compatible option and explains its relevance. A consultant verifies specifications, stock and terms. Compare conversion, contribution, costs and returns.
What the company keeps
Offer matrix, recommendation rules, economics worksheet and staff instructions. Include a no-extra option: the current solution may be sufficient.

Three AI scenarios. People remain accountable.

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

Compare service levels

AI prepares
Approved packages → a clear comparison relevant to the customer’s task
A person checks
Checks the scope of promises and terms

Replenishment reminder

AI prepares
Confirmed usage cycle → an appropriate time for the next purchase
A person checks
Checks the actual need and permission to contact

AI must not invent prices or terms, send important messages without agreed oversight, change agreements or execute payments.

What to measure and compare

01

Contribution per eligible customer

Sales contribution after variable costs / all included customers, not just buyers.

02

Average order value

Revenue after the agreed treatment of discounts and returns / orders counted under the same rule.

03

Frequency: an alternative sub-route

Purchases during the period / customers in a cohort fixed in advance, including zero purchases.

04

Guardrail measures

Conversion, returns, complaints, compatibility failures, service time and costs.

From GRI findings to a test

GRI is an author-developed analytical framework, not independent certification, an investment rating or a growth guarantee.

  1. GRI

    Understand the constraint

    Find an observable loss and test possible explanations against the facts.

  2. GTM

    Connect the change to the customer

    Define the segment, value, offer, channel, funnel step, economics and accountability.

  3. AI

    Build an assistant for the task

    Specify the inputs, expected output, limits and employee’s role.

  4. TEST

    Build a basis for a decision

    Agree a metric and comparison method, check quality, include costs and decide the next step.

Explore the methodology

The first 21-day cycle

The shared Bootcamp structure. Data, actions and the metric are tailored to the chosen lever.

00

Before the start

Discuss the challenge, available examples, team involvement and scope. Agree the work before the programme begins.

  1. Days 1–701

    Choose and design

    Review the workflow and data, choose a constraint and one lever, define the GTM action and experiment rules.

    Constraint map · GTM on one page · experiment brief
    Day-by-day calendar
    1. Day 1

      Session: map the customer journey and locate the loss

      Process map and challenge statement
    2. Day 2

      Collect examples; check IDs and dates

      Data dictionary and missing-data list
    3. Day 3

      Review relevant GRI areas and test two or three explanations

      Evidence-backed constraint map
    4. Day 4

      Session: choose one lever, segment and GTM action

      GTM on one page
    5. Day 5

      Describe the usual process, a non-AI improvement and an AI variant

      Technology role and comparison variants
    6. Day 6

      Define cohort, metric, observation window, costs and stopping rules

      Experiment brief v1
    7. Day 7

      Checkpoint: agree the solution scope

      Build / revise / prepare data
  2. Days 8–1402

    Build and test

    Prepare context, build the AI workflow, test typical cases and exceptions, and define human handover rules.

    Tested workflow version · error log · limited-test approval
    Day-by-day calendar
    1. Day 8

      Session: create a context base and first prompt

      AI scenario v0.1
    2. Day 9

      Run typical cases and verify source facts

      Error and correction log
    3. Day 10

      Test missing data, conflicts, duplicates and refusal

      Exception set and human handover rules
    4. Day 11

      Session: independent quality review on held-out examples

      Readiness assessment and critical defects
    5. Day 12

      Run in shadow mode without contacting customers

      Time measurement and staff usefulness review
    6. Day 13

      Prepare the log, group assignment and instructions

      Launch pack with prompt version
    7. Day 14

      Checkpoint and owner decision

      Limited-test approval or correction plan
  3. Days 15–2103

    Observe and decide

    Check execution, compare available outcomes, account for costs and limitations, and prepare team handover.

    Report · instructions · next-step decision
    Day-by-day calendar
    1. Day 15

      Session: check the first live events and stopping procedure

      Working limited test
    2. Day 16

      Review deviations and protocol adherence

      Event and error log
    3. Day 17

      Reconcile customers, orders, payments, costs and time

      Observed-results table
    4. Day 18

      Review group comparability and alternative explanations

      Limitations of the conclusion
    5. Day 19

      Session: calculate measures and economics

      Draft report and decision
    6. Day 20

      Prepare instructions, materials and continuation plan

      Handover pack
    7. Day 21

      Review: continue, change, stop or extend

      Approved report and 30/60/90 plan

A 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.

Team involvement · proposed format

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.

What the team keeps

Offer matrix, recommendation rules, economics worksheet and staff instructions. Include a no-extra option: the current solution may be sufficient.

  1. 01Constraint map
  2. 02GTM plan
  3. 03Experiment brief
  4. 04AI workflow or prototype within the agreed scope
  5. 05Log and calculations
  6. 06Staff instructions
  7. 07Final decision and 30/60/90 plan

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.

Prompts, research, cases and books

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

AI prompt03

Find a useful complement

RAFT working template · English Bootcamp adaptationRead material

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.

AI prompt03

Build a recommendation

RAFT working template · English Bootcamp adaptationRead material

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.

AI prompt03

Evaluate offer economics

RAFT working template · English Bootcamp adaptationRead material

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.

Research03

More choice can sometimes hinder decisions

[R05] Iyengar, S. S.; Lepper, M. R. (2000). When Choice Is Demotivating: Can One Desire Too Much of a Good Thing?Read material

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 source
Research03

Choice overload is not a universal effect

[R06] Scheibehenne, B.; Greifeneder, R.; Todd, P. M. (2010). Can There Ever Be Too Many Options? A Meta-Analytic Review of Choice Overload.Read material

A 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 source
International case03

Amazon · item relationships

[C05] Hardesty, L. / Amazon Science (22.11.2019). The history of Amazon’s recommendation algorithm.Read material

Amazon 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 source
International case03

Netflix · recommendations tested through behaviour

[C06] Gomez-Uribe, C. A.; Hunt, N. (2015). The Netflix Recommender System: Algorithms, Business Value, and Innovation.Read material

A 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 source
Quote03

be choosy about choosing

Sheena Iyengar · interview, episode 862 · 4 May 2023Read material

A 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 choosing
Original source
Book03

Sheena Iyengar · The Art of Choosing

[B05] Iyengar, S. (2010). The Art of Choosing. Twelve / Hachette.Read material

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 source
Book03

Madhavan Ramanujam, Georg Tacke · Monetizing Innovation

[B06] Ramanujam, M.; Tacke, G. (2016). Monetizing Innovation: How Smart Companies Design the Product Around the Price.Read material

Link 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 source

Working templates

Selected from AIStart360 Growth Labs, edition 2.0, 26 October 2026. Each item retains its provenance, limitations and links to original publications.

Articles and practical guides

2

More from the library

External research and cases inform hypotheses; they do not promise the same outcome. Check current conditions and relevance to the specific challenge.

Books

Product-Led Growth

Wes Bush

A playbook for self-serve value, time-to-value and product-led acquisition.

Articles

What the Freemium

Elena Verna

A practical view of freemium as a deliberate acquisition and monetization system.

Books

Experimentation Works

Stefan Thomke

How to make disciplined experimentation a repeatable organizational capability.

Articles

The Technology Behind Rufus

Amazon Science

A commerce case spanning conversational discovery, recommendations and product knowledge quality.

Tools

The Experiment Library

Strategyzer

Forty-four discovery and validation experiments compared by cost, setup time, duration and evidence strength.

Frameworks

SaaS Metrics 2.0

David Skok

A detailed operating guide to CAC, LTV, churn, payback and the metrics that explain recurring-revenue growth.

Full library

Related leadership decisions

Kodak

Kodak: Why Inventing Digital Photography Was Not Enough

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 case
Kaspi.kz

Kaspi.kz: Building an Ecosystem Around Everyday Customer Needs

Kaspi.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 case
Decathlon

Decathlon: Connecting Stores, Digital Services and the Product Lifecycle

Digital 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 case
Booking.com

Booking.com: Building a Management System Around Experiments

Booking.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 case

The challenge first. Then the terms.

Check whether the challenge fits, and discuss examples and team involvement. Then agree location, language, format, scope, fees and participation terms.

Test a complementary offer

Before we speak

Do we need a technical specialist?

A manual workflow usually needs a process owner and permitted data. Integration with operational systems requires technical participation and a separately agreed scope.

Can we cover all six levers?

A company works on one lever per cycle. The others inform the next step; they are not six parallel projects.

What if we have little data?

Establish minimum record-keeping and test on permitted examples first. Never replace missing facts with invented data.

What if the hypothesis is not supported?

Document quality, costs and limitations. A justified stop helps avoid expanding an uneconomic solution.

Are software and implementation included?

Licences, integration development and ongoing support are included only when explicitly agreed in the scope.

When should we expect sales?

It depends on the workflow and customer cycle. The 21 days cover a limited scenario; long-term effects are assessed separately.

How is data used?

Agree permitted materials, anonymisation, access and external services before starting. The first-contact form does not need customer databases or documents.

Discuss your challenge