Google Analytics 4 RFP Template

Before You Issue a Google Analytics 4 RFP

GA4 engagements are measurement-strategy projects that happen to end in tags. The deliverable that matters is a measurement plan your organization agrees with: which events, which conversions, which audiences, and how they map to the decisions each team makes. RFPs that lead with tag counts get implementations that are technically fine and analytically useless.

Two technical positions belong in every GA4 RFP today. First, consent: how Consent Mode is implemented against your privacy posture and what behavioral modeling means for your reported numbers. Second, the BigQuery export — whether to enable it from day one (almost always yes) and who will own the modeling of raw events into something analysts can query. Server-side tagging deserves a stated position too, with its costs and benefits quantified rather than assumed.

What the Google Analytics 4 Sections Cover

• Event and parameter schema documented before tagging
• Custom dimension and event limits you budget against
• Consent Mode behaviour and modelled data expectations
• Server-side tagging: in scope, or explicitly not
• BigQuery export as the reporting source of record
• Cross-domain, subdomain and internal traffic rules

Writing the Google Analytics 4 Scope of Work

Write the scope around a measurement plan and the property architecture that serves it. State how many properties and data streams are involved, whether a single property should cover multiple sites and apps or whether regional or brand separation is required, and what the consequences are for cross-property reporting. Then scope the event taxonomy: the event and parameter naming convention, which events are marked as key events, and how custom dimensions and metrics are registered. Registration limits are real, so the plan should say which parameters earn a registered dimension and which stay in the raw export only.

Be explicit about who owns the container and the deployment path. Say whether your tag manager is client-side, server-side or both, who holds publish rights, what the approval process is for a container change, and whether the vendor works in your container or hands over a specification. If server-side tagging is in scope, it brings infrastructure with it: a hosting decision, a cost that scales with traffic, a monitoring requirement and a first-party domain configuration. Treat it as an infrastructure project inside the analytics project rather than a setting.

Scope consent handling as a compliance-adjacent workstream with named participants. State which consent platform you use, who defines the categories, and how consent state is communicated to tags in each market. Say explicitly what the expected effect on reported volumes will be and who briefs the stakeholders about it, because numbers changing without explanation is how analytics implementations lose credibility. If behavioral modeling is expected to fill gaps, say so and record that modeled figures are not the same as observed ones for the audiences who will read the reports.

Put the raw export and its consumers in scope. Enabling the BigQuery export is trivial; deciding the dataset location, the retention, who models the raw events into usable tables and who pays for the queries is the actual work, and it should have an owner. Then define done as reconciliation and usability rather than tag coverage: a named set of figures agrees with the order or CRM system within a stated tolerance, the debug environment shows clean events across the user journeys you care about, and a named group of internal users can build a report answering a real question without asking the vendor.

Requirements That Actually Separate Google Analytics 4 Proposals

• Registration budget discipline — require a plan for which parameters become registered custom dimensions given the platform limits, and what the fallback is for parameters that are only ever needed in the raw export.
• Cardinality and sampling awareness — ask how the proposed design avoids high-cardinality dimensions that push reports into aggregated rows, since this is a design decision that quietly ruins reporting later.
• Consent state verification — require a method for proving, per market, what is and is not collected under each consent combination, tested on the live site rather than asserted from the configuration.
• Cross-domain and identity configuration — ask how sessions are preserved across your domains and subdomains, how user identifiers are set where you have logged-in users, and what the reporting identity setting will be and why.
• Ecommerce or lead-event accuracy — require validation of transaction and lead events against the source system, including handling of refunds, cancellations, test transactions and duplicates on page refresh.
• Export modeling ownership — where the raw export matters, require the vendor to state which curated tables they will build, with what tests and freshness expectations, or to name what they are leaving for someone else.
• Migration of existing configuration — if a property already exists, ask what they will keep, what they will rename and how historical comparability is preserved or explicitly abandoned.

Common Mistakes in Google Analytics 4 RFPs

• ✕ Scoping by number of tags instead of by measurement plan and the decisions it serves.
• ✕ Consent Mode bolted on late, changing reported numbers after stakeholders have baselined them.
• ✕ BigQuery export left off, discarding the raw event history you cannot recover later.
• ✕ Ecommerce parameters implemented loosely, so revenue never reconciles with the order system.
• ✕ No enablement, leaving a powerful property that only the implementation vendor can query.

• ✕ Scoping the web property while the mobile applications send events designed by a different team under a different naming convention, making the combined property incoherent from the first day.
• ✕ Enabling the raw export without agreeing who pays for storage and queries, so the data accumulates until a finance review results in it being switched off.
• ✕ Assuming the existing tag manager container is a workable foundation when it holds years of undocumented tags, and pricing no time to audit what is firing and why.
• ✕ Defining key events to match what is easy to measure rather than what the business optimizes toward, so bidding and reporting are anchored to actions that do not correlate with revenue.

Questions Worth Asking Google Analytics 4 Vendors

1. Show a redacted measurement plan from a delivered GA4 project — events, conversions and their owners.
2. How do you implement Consent Mode for a privacy posture like ours, and what happens to the numbers?
3. Who models the BigQuery export into analyst-ready tables, and show an example schema.
4. What is your position on server-side tagging for our profile, with costs quantified?
5. How do you validate ecommerce data against the order system before calling the implementation done?

How to Weight the Google Analytics 4 Evaluation

Weight the measurement plan and the taxonomy above implementation speed. The platform imposes limits on registered dimensions and punishes inconsistent naming, so decisions made in the first fortnight determine what can be reported for years. A proposal with a serious design phase and a defined naming standard is buying you the thing that actually constrains the outcome; a proposal that starts tagging in week one is deferring the constraint.

Score raw-data capability more heavily than dashboard craft. The reporting interface is standard across every vendor and its limits are quickly reached by anyone asking interesting questions, which means the durable value sits in the exported event data and what is built on it. Weight evidence of modeling that data into analyst-ready tables above evidence of building attractive reports in the interface.

Give privacy engineering capability real weight rather than treating it as a legal box. Consent configuration determines both your compliance posture and your reported numbers, and the difference between a vendor who implements it deliberately across markets and one who applies a default template shows up as either defensible measurement or an uncomfortable conversation later. Score demonstrated multi-market consent work accordingly.