Repeat sales

Customers buy once and disappear.

Find a useful reason for the next purchase. Test whom to contact, when and with which relevant offer.

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 business with purchase history and a real repeat need. Establish the value already delivered before planning another contact.
Data needed
Pseudonymous customer and order IDs, purchase dates and items, net amounts, returns, costs, last contact, channel, permission to contact and unresolved complaints.
AI and people
AI proposes an explainable segment, useful next step and draft message. Staff check the need, terms, permission and contact frequency.
What the company keeps
Return journey, segmentation rules, contact scenario and observation calendar. Message opens are not sales; use a period appropriate to the repeat cycle.

Three AI scenarios. People remain accountable.

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

Next useful contact · starting point

AI prepares
Purchase + usage cycle → reason, timing and offer, with an explanation
A person checks
Checks the need and permission to contact

Personalised draft

AI prepares
Approved reason + verified facts → a message reflecting the purchase
A person checks
Checks facts, tone and frequency before sending

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

What to measure and compare

01

Share of repeat buyers

Initial-cohort customers with a repeat paid purchase in the window / all customers in that initial cohort.

02

Contribution per cohort customer

Repeat-order contribution after variable costs / initial number of customers.

03

Time to purchase

Interval from the start event; report customers without a purchase at the cut-off separately.

04

Quality and costs

Unsubscribes, complaints, returns, discounts, services, preparation and handling time.

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

Return journey, segmentation rules, contact scenario and observation calendar. Message opens are not sales; use a period appropriate to the repeat cycle.

  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 prompt02

Define a cohort and a useful reason

RAFT working template · English Bootcamp adaptationRead material

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.

AI prompt02

Prepare a useful customer contact

RAFT working template · English Bootcamp adaptationRead material

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.

AI prompt02

Check repeat purchases

RAFT working template · English Bootcamp adaptationRead material

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.

Research02

Churn risk is not responsiveness to an intervention

[R03] Ascarza, E. (2018). Retention Futility: Targeting High-Risk Customers Might Be Ineffective.Read material

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 source
Research02

Perceived progress can support returning

[R04] Nunes, J. C.; Drèze, X. (2006). The Endowed Progress Effect: How Artificial Advancement Increases Effort.Read material

Nunes 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 source
International case02

Starbucks · contextual recommendations

[C03] Microsoft / Sokolowsky, J. (06.05.2019). Starbucks turns to technology to brew up a more personal connection with its customers.Read material

Microsoft’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 source
International case02

Tesco · Clubcard Challenges

[C04] Tesco (29.04.2024). Tesco brings its AI game to new ‘Clubcard Challenges’. Eagle Eye: Tesco Clubcard Challenges.Read material

Tesco’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 source
Quote02

not all customers are created equal

Peter Fader · Wharton interview · 1 November 2022Read material

A 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 equal
Original source
Book02

Peter Fader, Bruce G. S. Hardie, Michael Ross · The Customer-Base Audit

[B03] Fader, P.; Hardie, B. G. S.; Ross, M. (2022). The Customer-Base Audit: The First Step on the Journey to Customer Centricity.Read material

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 source
Book02

Peter Fader, Sarah Toms · The Customer Centricity Playbook

[B04] Fader, P.; Toms, S. (2018). The Customer Centricity Playbook: Implement a Winning Strategy Driven by Customer Lifetime Value.Read material

Connect 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 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

3

The team is not adopting new tools

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.

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.

Tools

GRI Pulse — repeat sales and retention

AIStart360

B2B customer-base monitoring: missed purchase cycles, gaps in contact and a next action for repeat sales. A resource for growth lever 02 — retention.

Research

Generative AI at Work

Brynjolfsson, Li & Raymond / NBER

Field evidence on productivity and heterogeneous effects across worker experience.

Books

Continuous Discovery Habits

Teresa Torres

Connects desired outcomes, opportunities, solutions and assumption tests in a weekly cadence.

Books

Product-Led Growth

Wes Bush

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

Frameworks

SaaS Metrics 2.0

David Skok

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

Frameworks

The SPICED Framework

Winning by Design

A shared discovery language across Situation, Pain, Impact, Critical Event and Decision for customer-centered revenue conversations.

Frameworks

Revenue Architecture

Winning by Design

A six-model GTM operating system connecting revenue, customer lifecycle data, math, execution and growth stages.

Full library

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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.

Find a reason to return

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