Maturity · Use of data and AI for marketing purposes
Your marketing data and AI usage, placed on a maturity scale and translated into a roadmap.
10 themes, a 5-level scale. And the action that moves each level to the next.
The framework’s 10 themes, already written from L1 to L5. One company, one business unit, or 300 at once.
Maturity · Use of data and AI for marketing purposes
10 themes, 5-level scale.
Nordhavn Industries
53 / 100
They measure their maturity with Datamensio
An example
This could be your situation.
Take one company as an example: three sites, three spreadsheets, no shared answer.
Nobody can consolidate.
Nordhavn Industries, 2,400 people in Hamburg, Lyon and Porto. A client asks where the group stands. Each site answers in its own spreadsheet, with its own scales.
Three weeks, a single base.
One Datamensio maturity framework, aligned with CMMI levels assessment launched across all three sites at once, from the managers’ interview notes. The framework was already written, its 10 themes and levels L1 to L5 too.
Two costs avoided before being committed.
A score of 53 out of 100, with the gap concentrated on three themes. The AI companion spotted that two actions duplicated those of another audit. The committee report took one sentence to request.
What it saved them
- 3sites measured on the same base, instead of three questionnaires to reconcile
- 2duplicate actions caught before the spend
- 1committee report, with no manual rework
These figures are an example. They could be yours.
The standard imposes processes. Datamensio says where you stand.
01
The framework is already written
Themes, questions and levels L1 to L5, all written. You do not start from an empty spreadsheet.
02
The score lands the same day
Online, by self-assessment link or in interview. Theme by theme, comparable over time.
03
The gap becomes a costed plan
Every step up carries its action. The AI prioritises on expected effect, not on the order of the standard.
04
Progress can be demonstrated
Campaign after campaign, against your target and against your own past. That is what your board asks for.
The maturity scale
One level, the next, and the action that links the two.
This mechanism (a level, the level above, and the action linking the two) is what turns a finding into a trajectory.
Are the audiences and segments used in campaigns built on a common foundation and reusable?
- N1
No common foundation. Each campaign starts from one-off extractions requested from different contacts.
- N2
Reference segments exist for some teams, but their definitions diverge and they are rebuilt from campaign to campaign.
- N3
Segments are defined in a shared repository, named and documented, and reused by most campaign teams.
- N4
Audiences are automatically refreshed from the customer foundation, their use is tracked and their performance compared across campaigns.
- N5
Definitions are reviewed periodically based on measured results, obsolete segments are retired and revisions documented.
Action to move from L2 to L3
Publish a catalogue of reference segments with, for each one, its definition, owner and refresh rule, then make its use mandatory when validating the campaign brief at the monthly marketing committee.
« With Datamensio, we meet our objectives far more efficiently. The ERDF inspection services and our supervising ministry particularly appreciated an approach that gives them reliable data. »
Director, CCI 94CCI Île-de-France
« We believe this is the most suitable solution to scale our transformation project and measure impact according to our needs. »
Maja SucekChief Operating Officer, Interreg Danube
Rarely on its own
Frameworks combine. Put several together to cover your business, or have the AI write yours.
Take your first measurement
What this framework covers
This framework assesses how a marketing department uses its data and AI models. It covers specific objects: the customer data foundation and its level of reconciliation, consent databases, segmentation and scoring, activation to channels, personalisation of content and journeys, attribution measurement, use of generative AI for creative production, predictive models for propensity and churn. It is not a standard but a progression framework: each practice is placed on five maturity levels, from isolated action to industrialised, measured capability.
In practice, these usages are hard to steer because they are spread across several teams and tools. Are your audiences built once and reused, or rebuilt for each campaign by each team? Do you know what share of your content is produced with generative AI, by whom, and under what review rules? Are your scoring models tracked over time, or delivered once and never re-evaluated? And when a channel reports a performance gain, is the measurement comparable with that of other channels?
Two recent shifts have moved the subject forward. The gradual phase-out of third-party identifiers has made the value of the proprietary data foundation much more visible, and the arrival of generative AI has brought marketing teams into AI-assisted content production, without usage rules always keeping pace. One common confusion is worth clearing up: having a customer data platform says nothing about maturity level. The tool is a means, maturity is read in practices, roles and measurement.
The maturity assessment does not aim to judge good or bad. It places. For each theme, it indicates the level reached, describes the observable practice that characterises the next level, and names the action that closes the gap. The gap between the observed score and the target generates the action plan, which AI groups into a prioritised roadmap. Benchmarking across business units and comparison with your own past assessments turn the finding into a transformation programme trajectory.
The framework is ready to use and adapts to your organisation. AI adjusts the themes, rephrases the questions and refines the levels according to the CMMI method, or builds a variant from your own documents: AI usage charter, marketing plan, tools mapping. The models used can be selected, including from European solutions.
Reference standard: Datamensio maturity framework, aligned with CMMI levels
The themes assessed
Strategy and use cases
Existence of a marketing data and AI trajectory, prioritisation of use cases, link with commercial objectives, balance between experimentation and industrialisation.
Customer data foundation
Sources collected, identifier reconciliation, quality and freshness, single customer view, coverage of offline channels.
Consent and usage governance
Consent capture and propagation, minimisation, retention periods, usage rules for training and targeting data, traceability.
Segmentation, scoring and predictive models
Building and reusing audiences, propensity, churn and customer value models, documentation, performance tracking over time.
Personalisation and journeys
Level of personalisation by channel, journey orchestration, priority rules between outreach, controlled commercial pressure.
Generative AI and content production
Authorised uses, shared prompts and templates, human review, rights and brand management, measurement of impact on production timelines.
Activation and tool ecosystem
Connecting the foundation to activation channels, tool consistency, dependency on ad platforms, flow management and error recovery.
Performance measurement
Attribution model, test protocols, comparability across channels, indicators shared with sales and finance leadership.
Skills and organisation
Data and AI roles within marketing teams, upskilling, coordination with IT and agencies, practice sharing across business units.
Steering and continuous improvement
Periodic reviews of usage, lessons learned, updating of rules, comparison of successive assessments and entities.
A short version of the framework is available for the online self-assessment.
Frequently asked questions
Does this assessment lead to a certification?
No, and that is not its purpose. There is no body that certifies the use of data and AI in marketing. The framework measures a maturity level and produces a progression trajectory.
How does this differ from an audit?
An audit checks for the presence of requirements and concludes with a gap. The maturity assessment places each practice on a progressive scale and names the action that moves it to the next level. The outcome is a costed action plan, not a list of findings.
How long does the assessment take?
The self-assessment can be completed in a single working session. In collaborative mode, with CRM, content, media and data managers, allow one to two weeks, most of the time being spent gathering input from teams and agencies.
Can the framework be adapted to our organisation?
Yes. You can change the questions, levels and themes, or start from your own internal documents to build a variant suited to your sector. AI handles drafting and refining the levels, you keep the decision.
How can several brands or countries be compared?
The same framework is rolled out to each business unit, then scores are compared by theme. A cross-entity roadmap consolidates assessments and groups common actions rather than duplicating them entity by entity.
Does the framework cover the legal governance of usage?
It assesses practices: consent, minimisation, traceability of AI usage, review rules for generated content. For the requirements of the European AI regulation as such, the dedicated framework is more suitable and combines with this one.
Do respondents need a technical background?
No. The questions concern management and organisational practices, not configurations. Some require input from a data engineer or tools manager: collaborative mode allows these questions to be assigned directly to them.
Where is the data hosted?
In France, with OVH, backed up at Scaleway. No transfer outside the European Union. The AI models used can be selected, including from European solutions.