Managing marketing performance
How can you establish reliable marketing measurement to make decisions between campaigns without confusing attribution with causality?
3500 € per session, excl. VAT
In short
Two days, comprising 14 hours excluding breaks, to design a marketing measurement prototype linking objectives, data and decisions. Participants define their indicators, build a verifiable dashboard and prepare a pilot test. Generative AI supports documentation and analysis without replacing checks on calculations.
Why this training?
An increase in clicks is not enough to justify a budget: conversion windows, duplicates and discrepancies between prospects and sales can reverse the interpretation of results. Generative AI helps formalise indicators and scrutinise anomalies. Its use requires traceable sources, independent calculations and an explicit distinction between observation and interpretation. Running case study: Orbeva Mobilier, a fictitious organisation selling professional furniture, collects quotation requests through advertising, content and emails. Its fictitious exports contain duplicates and missing source information. The team must compare channels through to the sale without counting the same conversions twice. Transfer to the workplace: Adapt the prototype to an authorised channel, have the definitions validated by the marketing and sales teams, then run the pilot before extending the measurement system.
Objectives of this training
- Link marketing objectives to priority decisions and indicators
- Specify sources, formulae and collection rules
- Build a dashboard prototype
- Verify calculations and document attribution limitations
- Define a pilot with responsibilities and acceptance criteria
Who is it for and what are the prerequisites?
Roles and tasks concerned
- Marketing and acquisition managers responsible for budget allocation decisions
- Marketing analysts and CRM managers involved in commercial reporting
Prior skills
- Understand conversion journeys and the main marketing indicators.
- Use aggregations and formulae in a spreadsheet.
- Be able to query generative AI and verify its responses.
What you will work on
Frame the trade-offs
Objective: Define the scope of the decisions to be informed. Commercial objectives, marketing funnel, outcome and diagnostic indicators.
Define the indicators
Objective: Make measurements reproducible. Cost per qualified prospect, quotation-to-sale conversion, denominators and time windows.
Map data collection
Objective: Link events and sources. Campaign tagging, identifiers, consent, data minimisation and refresh frequency.
Check the exports
Objective: Assess data reliability. Duplicates, missing values, reconciliations and exclusion rules.
Set boundaries for attribution
Objective: Avoid unjustified causal conclusions. First and last touch, delayed conversions, correlation and incrementality.
Assemble the prototype
Objective: Present useful measurements. Views by channel, period, qualification and source freshness.
Perform acceptance testing on calculations
Objective: Verify the displayed results. Reference totals, divisions by zero and time offsets.
Interpret without overpromising
Objective: Formulate evidence-based recommendations. Facts, hypotheses, limitations and further investigations.
Prepare the pilot
Objective: Organise a limited experiment. Scope, responsibilities, cadence, thresholds and stopping conditions.
Make the case for the measurement system
Objective: Determine its maturity. Business acceptance, technical reservations and prioritisation of corrections.
What you take away
- Indicator dictionary and decision–measurement matrix
- Data collection plan and checks register
- Documented dashboard prototype
- Pilot protocol and acceptance testing dossier
Method and assessment
- Brief instructional input followed by immediate practice on the case study used throughout.
- Individual work followed by work in pairs, with peer review of the outputs.
- Facilitated group discussion: what works, what needs correcting and what remains a human decision.
- Preparation: List the budget decisions and indicator definitions currently used. Prepare authorised anonymised exports; if these are unavailable, use only the fictitious files provided.
Observable criteria: Each indicator includes a formula, source, granularity, period and owner. ; Deduplicated conversions reconcile with the commercial source, or discrepancies are explained. ; Priority indicators are recalculated independently of AI. ; The prototype flags missing data, data freshness and attribution limitations. ; The pilot specifies scope, acceptance thresholds, responsibilities and the decision on whether to proceed..
Frequently asked questions
Does AI calculate the indicators?
It can assist with their formulation; the results are checked in the spreadsheet.
Can we use our own data?
Only if anonymised, with authorisation and within tools approved by the organisation.
Will the dashboard be made production-ready?
No, the two days produce a prototype and a pilot protocol.
Does the training demonstrate the causal effect of campaigns?
No; it distinguishes descriptive attribution from causality, then specifies the additional tests required.
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