What does marketing data cleanup include?

Marketing data cleanup can include campaign naming cleanup, UTM standards, source-of-truth logic, metric definitions, dashboard QA, and documentation for how performance should be read.

Why does messy data hurt growth decisions?

When naming and metric logic are inconsistent, teams spend time debating the numbers instead of deciding what to test, scale, pause, or investigate.

Does marketing data cleanup help with MMM and incrementality?

Yes. Cleaner inputs make marketing mix modeling, incrementality checks, forecasting, and executive reporting more credible.

Marketing data cleanup

Make marketing reporting easier to trust.

Before a dashboard can guide spend, the inputs need to be consistent. Parthenocarpic helps teams clean campaign naming, UTMs, taxonomy, and reporting definitions so performance can be compared clearly.

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Paid media reporting Incrementality sanity checks MMM readiness

When this is useful

Marketing data cleanup makes reporting easier to compare and trust.

Parthenocarpic provides marketing data cleanup for DTC and eCommerce brands with inconsistent campaign naming, UTMs, taxonomy, source logic, metric definitions, or dashboard inputs.

This work is often the first step before paid media reporting, incrementality analysis, MMM support, or scenario planning can be useful.

What gets in the way

Messy inputs make every performance conversation harder.

Growth teams often inherit naming conventions, UTMs, ad account structures, Shopify reports, CRM exports, and agency dashboards that were never designed to work together. The result is familiar: two reports, two answers, and no clear decision.

Cleanup work is not glamorous, but it is often the difference between a dashboard people glance at and a reporting system leaders can actually use.

What we review

The reporting spine underneath better measurement.

Campaign taxonomy

Standardize naming so channel, objective, funnel stage, creative, audience, and offer are readable.

UTM and source logic

Clarify how traffic, campaigns, and channels should be tagged and grouped across systems.

Metric definitions

Align on CAC, MER, ROAS, new customer revenue, contribution, and other operating metrics.

Dashboard audit

Identify duplicate views, weak joins, unclear filters, and reporting gaps that slow decisions.

Source-of-truth map

Define which system answers which question, reducing arguments between platform and commerce data.

Insight cadence

Turn cleaned reporting into a weekly or monthly readout that explains what changed and what to do.

What you get

Cleaner data creates calmer decisions.

Less metric debate Teams spend less time reconciling reports and more time deciding what to do next.
More reliable channel reads Spend, revenue, CAC, MER, and contribution are easier to compare across campaigns and channels.
Better measurement readiness Incrementality tests, MMM support, and forecasting become more useful when the base data is cleaner.

Common questions

Questions marketing data cleanup should help answer.

What does marketing data cleanup include?: It can include campaign naming cleanup, UTM standards, source-of-truth logic, metric definitions, dashboard QA, and documentation for how performance should be read.
Why does messy data hurt growth decisions?: When naming and metric logic are inconsistent, teams spend time debating the numbers instead of deciding what to test, scale, pause, or investigate.
Does this help with MMM and incrementality?: Yes. Cleaner inputs make marketing mix modeling, incrementality checks, forecasting, and executive reporting more credible.

Start with the reporting issue that keeps coming back.

Send the report, naming problem, or metric conflict your team keeps having to explain. The first step is a practical read on what needs cleanup before the numbers can guide spend.

Request a quick measurement review