Controlled marketing personalisation
How can you prototype automated, controllable and measurable marketing personalisation before a pilot deployment?
3500 € per session, excl. VAT
In short
Two days to design a prototype linking customer data, marketing rules and personalised message generation. You prepare the inputs, set boundaries for content, simulate triggers and test exceptions. You leave with an operational dossier and a pilot protocol, with no deployment to production during the training.
Why this training?
Multiplying campaign variants without explicit rules creates inconsistent offers, excessive contact and messages that are difficult to verify. Personalisation must start from authorised data and traceable marketing decisions. Generative AI can write the variants; it must not decide eligibility on its own or invent a promotion. Separating rules, generation and validation makes errors detectable before anything is sent. Running case study: Cime Papier, a fictitious online stationery retailer, wants to personalise its repeat-purchase emails according to the categories purchased and purchase recency. A catalogue and fictitious profiles allow recommendations, exclusions and messages to be simulated without sending anything. Transfer to the workplace: Have the prototype validated by those responsible for CRM, data and compliance, then launch a limited pilot once authorised, with a weekly review of errors and the option to revert to the standard message.
Objectives of this training
- Map the triggers, exclusions and required data
- Formalise a verifiable personalisation matrix
- Assemble a prototype for generation within defined constraints
- Test content, exceptions and the fallback mechanism
- Define a comparative pilot and its stopping conditions
Who is it for and what are the prerequisites?
Roles and tasks concerned
- CRM and marketing automation managers
- Digital marketing project managers supported by a data specialist
Prior skills
- Have practical experience of CRM campaigns and their metrics.
- Know how to structure instructions for generative AI.
- Work with tabular files and understand a conditional workflow.
What you will work on
Define the scope of the repeat-purchase journey
Objective: Choose a relevant trigger. Purchase recency, marketing pressure, exclusions and purpose.
Formalise the input requirements
Objective: Limit the data transmitted. Data minimisation, channel-specific consent, missing values and retention.
Write the marketing rules
Objective: Separate decision-making from copywriting. Deterministic eligibility, priorities, frequency and permitted recommendations.
Set boundaries for generation
Objective: Produce verifiable variants. Catalogue sources, tone, formats and a ban on invented promotions.
Assemble the prototype
Objective: Link rules and generation. Structured input, selection, copywriting, validation and output.
Set up the checks
Objective: Block non-compliant variants. Fact checks, mandatory fields and human approval.
Test the exceptions
Objective: Verify the robustness of the workflow. Incomplete profile, duplicate, unavailability and malicious source content.
Measure editorial quality
Objective: Assess relevance and fidelity. Scoring rubric, factual errors and intrusive personalisation.
Design the comparative pilot
Objective: Isolate the effect of personalisation. Control group, random allocation, clicks, conversions and unsubscribes.
Prepare the launch decision
Objective: Organise a reversible pilot. Responsibilities, minimum logging, stopping thresholds and reversion to the standard process.
What you take away
- Eligibility and personalisation matrix
- Documented prototype with versioned instructions
- Test suite and anomaly report
- Pilot plan with metrics and a fallback procedure
Method and assessment
- Short input sessions followed by immediate practice on the case study used throughout.
- Individual work followed by work in pairs, with participants reviewing each other’s outputs.
- Guided group review: what works, what needs correcting and what remains a human decision.
- Preparation: Plan to use only tools authorised by the organisation and check the available simulation options. Bring editorial guidelines and an anonymised campaign example; the practice profiles are fictitious.
Observable criteria: Each test profile receives the expected assignment according to the documented rules. ; Each recommendation points to a reference in the supplied catalogue. ; No unapproved message passes through the sending simulation. ; Missing data and errors trigger the planned fallback. ; The protocol specifies the control group, indicators, observation period and stopping conditions..
Frequently asked questions
Do you need to know how to code?
No; the workflow can be simulated, but its rules and outputs must be testable.
Can you use customer data?
The exercises use only fictitious or anonymised data in authorised tools.
Will any campaigns be sent?
No, triggers and sends remain simulated.
What is measured over two days?
The prototype’s compliance and robustness; the commercial impact is assessed in the subsequent pilot.
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