CDP MVP Scoping: How to Define Scope, Tech Stack, and Requirements for a Customer Data Platform

The Scenario

A French data-marketing services company — operating platforms that help advertisers, agencies, and publishers run targeted and personalized campaigns — set out to build an advanced customer data platform (CDP). The organization had a clear business ambition: improve conversions across the customer buying cycle by unifying and activating customer data at scale.

Rather than jumping straight into development, the company first commissioned a structured MVP Scoping phase. The goal was to define the project's scope with enough precision to minimize execution risk, confirm the technical and business requirements, select an appropriate tech stack, and produce a credible roadmap before a single line of production code was written.

The Main Challenges

1. Defining scope across an uneven brief

The incoming brief was large but uneven — some areas were heavily specified while others contained only a handful of requirements. The scoping team needed to produce time and cost estimates, an initial product roadmap, and a list of project milestones from this material without losing fidelity to the client's intent.

2. Keeping complexity under control

A marketing platform of this breadth inherently carries the risk of scope creep. One of the core objectives of the scoping phase was to draw explicit boundaries: what would be in the MVP, what would follow in later phases, and where the project risked becoming too large to execute reliably.

3. Resolving technical and business unknowns

Even a detailed brief leaves gaps. In this case, significant questions remained — particularly around how the various platform components would transfer data between each other, and what the precise behaviour of key features should be. These unknowns needed to be surfaced, documented, and resolved before development could begin.

4. Selecting the right tech stack

The tech stack selection had to account for several non-trivial requirements simultaneously: data portability (moving data cleanly between components), user profile deduplication, and real-time reporting. Each of these demands has meaningful implications for the choice of programming languages, frameworks, infrastructure, and databases.

The Approach

Breaking the scope down into user stories

The first move was analytical: the full project scope was decomposed into smaller, more manageable sections using user stories as the unit of work. This process produced 12 distinct components and over 500 user stories in total — a granularity that made estimation and prioritization tractable.

Modularizing into independent workstreams

Beyond decomposition, the project was reorganized into separate, independent modules — including a marketing automation platform, a tag manager, and an audience manager. For each module, priorities were analyzed, a product roadmap was created, and a story map was produced. Treating these as independent workstreams allowed teams to reason about each module's dependencies and delivery sequence without the full system needing to be coherent at every moment.

Questionnaires, mockups, screen flows, and proof-of-concept work

To close the gaps between the written brief and the client's actual intent, the scoping team developed structured questionnaires for the client to work through. The responses fed into a decision log — a living document that captured each unclear requirement, the resolution reached, and the reasoning behind it.

Alongside the decision log, the team produced technical mockups, screen flows, and proof-of-concept (POC) work. The POCs were particularly important for validating assumptions about data flow between components — the kind of question that can derail a project if left unresolved until implementation.

Tech stack selection based on POC findings

With the requirements clarified and the POCs complete, a tech stack was selected that satisfied the core constraints: data portability across components, deduplication of user profiles, and real-time reporting capability. "Happy paths" between components were also mapped to confirm that the selected stack could support the intended data flows end to end.

Implementation Considerations

A scoping phase of this nature succeeds or fails based on a few structural choices:

• Domain expertise matters early. Technical challenges like data portability and user profile deduplication are well-understood problems in MarTech, but they're not obvious to teams approaching the space for the first time. Having practitioners who have navigated these problems before compresses the learning curve significantly.
• The decision log is a first-class deliverable. Documenting not just what was decided but why — and what the alternatives were — creates a shared reference that prevents revisiting settled questions during development.
• Modular architecture reduces risk. Breaking a large CDP into independent modules means each can be scoped, estimated, and delivered on its own terms. It also means failures or scope changes in one module don't cascade unpredictably into others.
• POCs before stack selection, not after. Running proofs of concept specifically around the unknowns (rather than the comfortable, already-understood parts) ensures that the tech stack choice is grounded in evidence rather than preference.

Outcomes and Tradeoffs

The MVP Scoping phase produced:

• A breakdown of 12 components and 500+ user stories
• A product roadmap and story map for each module
• A decision log resolving the key technical and business unknowns
• Technical mockups, screen flows, and POC results
• A selected tech stack validated against the core requirements
• A clear foundation for the subsequent MVP development phase

The primary value of this kind of scoping investment is risk reduction. Organizations that skip it tend to discover their unknowns mid-development, when they are expensive to resolve. The tradeoff is time and cost upfront — but for a platform of this complexity, that investment typically pays back in reduced rework and clearer team alignment.

The approach is particularly well-suited to CDP builds because CDPs tend to span multiple technical domains (data collection, identity resolution, activation, reporting) that each carry their own architectural decisions. A scoping phase that treats these as separate, linked modules — rather than one monolithic system — reflects how the work will actually unfold in practice.