Independent measurement for DTC & ecommerce

Marketing analytics consultant

DTC measurement without another opaque annual contract.

Assess the decision and the data first. Then build the right evidence system: marketing mix modeling, an incrementality test, or both.

Talk with Eric
Client-owned outputsDecision-first scopeUncertainty disclosed

The problem

Every platform can claim the sale. The business still has to allocate the next dollar.

Platform reports conflict. Last-click reporting rewards demand capture. Upper-funnel value is hard to see. And before choosing a methodology, a team needs to know whether MMM is appropriate at all.

Brinker Analytics helps leaders turn that ambiguity into a bounded measurement decision—not another dashboard full of competing answers.

A strong fit

Enough signal to make a decision.

  • Roughly $3–5M+ annual sales and $1M+ annual media spend
  • Three to five meaningful marketing line items, with real allocation tension
  • 12+ months of sales, revenue, and media history
  • A decision owner ready to act and learn from a test

GA4 history is preferred; a GA4-to-BigQuery setup is especially strong, but it is not a universal prerequisite.

Better to wait

When the question cannot yet be answered usefully.

  • A very small brand or too little history and variation
  • One paid channel dominates, with no meaningful organic baseline or tactical variation
  • No accountable owner to act on the recommendation

The right next step may be cleaner data, a narrower question, audience diagnostics, or waiting for a credible test opportunity.

The readiness diagnostic

Choose the method after reviewing the decision, data, and feasible learning path.

This paid diagnostic produces a feasibility finding and data-gap register. It recommends the smallest useful next step.

  1. 01

    MMM package

    For portfolio allocation questions with sufficient reconciled outcome, media, and business-context history.

  2. 02

    Experiment package

    For a bounded intervention where outcome and traffic can be observed by geography or another credible treatment design.

  3. 03

    Both, in sequence

    When a test can anchor a consequential causal question while MMM supports the broader allocation system.

The approach

Triangulation is a model-risk review, not three independent votes.

The work uses client-owned data and multiple viable model specifications to identify findings that persist, surface conclusions that rely on weak signal or strong assumptions, and make uncertainty visible. Reasonable response and carryover assumptions are tested—not treated as fact.

A relevant incrementality test anchors the causal claim for the intervention and conditions it tested. When a recommendation is unstable, the result is an explanation and next-test plan, not false precision for every channel.

ActEvidence supports a bounded change.
WatchDirection is plausible; use a guardrail.
TestDisagreement matters and can be resolved.
Do not score yetThe data cannot support a useful read.

Engagement paths

A package follows the question—not a fixed software plan.

01

MMM package

Client-owned modeled tables and outputs, model-health documentation, data lineage and reconciliation, and a decision-ready allocation view.

02

Experiment package

A test-design brief, feasibility or power assessment, measurement plan, execution guardrails, and a clear decision rule.

03

Combined path

A deliberate sequence that uses experimentation to calibrate a high-stakes claim and MMM to guide the broader media portfolio.

Scope follows the readiness finding and the decision at stake. Consultations are used to determine whether the diagnostic makes sense; no fixed prices are published.

What you receive

Decision assets your team can keep using.

Fixed decision report

Findings, uncertainty, recommended actions and guardrails—built for a decision, not an open-ended custom dashboard project.

Channel classification & roadmap

Act, Watch, Test, or Do not score yet, plus a 30/60/90-day action and learning roadmap.

Live working session

A direct readout with the decision owner(s), focused on what changes now and what needs validation next.

Open-issues register

Unresolved questions, unscored channels, assumptions to revisit, and the reason each one remains open.

Client-owned handoff

The report is fixed. The learning system is yours.

Core outputs can live in the client’s BigQuery environment. The standard handoff includes suggested prompts for exploring the client-owned data lake with the team’s preferred LLM between formal readouts.

An optional AI context layer and refresh runbook can document definitions, table locations, and responsible reuse. AI can reduce some compute and repetitive implementation cost; expert judgment, validation, and adoption remain the work.

Read why disagreement can be useful

A better decision shift

Reward incremental demand, not just the click closest to the sale.

A lower-funnel channel that looks exceptional in last-click reporting may be demand capture with limited marginal scale. The operating response could be a stricter last-click ROAS or CPA guardrail—not aggressive scale.

An upper-funnel channel with supported incremental effect may deserve a controlled increase, scenario plan, and validation test even when click credit is weak.

Questions to ask before committing

Why not just buy software?

Software can be a good choice. This work is for teams that need an independent measurement system shaped around their own data and decision—not a standard workflow that may skip an important edge case.

Why use more than one model?

Multiple specifications are a model-risk review, not a vote. The goal is to see which findings survive reasonable assumptions, understand material differences, and test the questions that matter.

What if the models disagree?

That can be useful evidence. A relevant, well-designed incrementality test anchors the causal claim for the conditions tested. Elsewhere, consequential disagreement becomes an explained limitation or a prioritized test plan—not a manufactured score.

What if we are not ready?

The readiness diagnostic can recommend data work, a narrower question, an experiment package, or waiting for more variation. A full model build should not begin when it cannot support a useful decision.

A practical first conversation

Talk with Eric about the measurement problem in front of you.

Determine whether a readiness diagnostic makes sense—and whether MMM, an experiment, or both is the useful next move.

Talk with Eric about your measurement problem