30-60-90 day plan for marketing analysts

A 30-60-90 day plan for a new marketing analyst should spend the first 30 days listing every data source, writing a metric dictionary and reproducing the main dashboard's numbers; days 31-60 fixing the tracking gaps that matter, owning the recurring reports and agreeing how attribution is read; and days 61-90 running one incrementality test and delivering an analysis that changes a budget decision. As of November 2026, two settings change most marketing numbers: Google Analytics 4 offers three attribution models (data-driven, paid and organic last click, and Google paid channels last click), and California's CCPA regulations require businesses that sell or share personal information, including for cross-context behavioral advertising, to honor opt-out preference signals such as Global Privacy Control.

This plan is for the marketing manager, marketing director or analytics lead hiring a marketing analyst. Campaign execution and UTM rules on each send are in the 30-60-90 day plan for marketing coordinators; budget and channel ownership in the 30-60-90 day plan for marketing managers; and company-wide metrics and SQL review in the 30-60-90 day plan for data analysts. Nothing here is legal advice.

What does a marketing analyst own that a marketing manager or data analyst does not?

What does the 30-60-90 day plan look like?

30-60-90 day plan — [Name], Marketing Analyst, [company]
Reports to: [marketing manager / analytics lead]    Start date: [date]
Channels: [paid search, paid social, email, SEO, events]
Systems: [analytics property, ad platforms, CRM, warehouse, BI tool]
Budget in scope: [$ per quarter]    Planning cycle: [month / quarter]

DAYS 1-30 — Sources, definitions and settings
Goals:
- List every source feeding marketing reports, with owner and refresh
- Write the metric dictionary: name, definition, formula, source,
  filters, known gaps; get it signed off by [marketing manager]
- Reproduce the main dashboard's last full month from the sources;
  explain every difference larger than [5] percent
- Audit analytics settings: key events, attribution model,
  conversion windows, data retention, consent mode, filters
- Meet the marketing manager, channel owners, RevOps, the web
  team and the privacy or legal contact
Deliverables by day 30:
- Signed metric dictionary; tracking gap list with owners

DAYS 31-60 — Fix, own and agree
Goals:
- Fix the [3] tracking gaps that affect budget decisions most
- Own the weekly and monthly reports; add notes on what changed
- Agree how attribution is read in reviews and where it stops
- Design one incrementality test (holdout or geo split) with the
  channel owner: hypothesis, metric, size, duration, stop rule
Deliverables by day 60:
- Reports delivered on schedule with commentary
- Test design approved; test running

DAYS 61-90 — Test, recommend, plan
Goals:
- Read the test result against its stop rule
- Recommend one budget change, with the evidence and its limits
- Draft the next two quarters' measurement plan: tests, data
  needed for a marketing mix model, reporting changes
Deliverables by day 90:
- Budget recommendation reviewed by [marketing manager]
- Measurement plan agreed

Check-ins: weekly with [manager]; reviews at day 30, 60 and 90.

Numbers in brackets are examples. Size them to the budget, the channels and the data the company really has.

Which platform settings and privacy rules change what a marketing analyst can measure?

Each item below is stated as of November 2026 from the platform's or regulator's own page. Platform features change often, so re-check them at each quarterly review; this is not legal advice, and legal should confirm what applies outside California.

The analyst does not decide these legal questions, but should know their effect on the data: opted-out users drop out of audiences and some reports, and consent choices change what the analytics property records. The 30-60-90 day plan for privacy officers covers the full CCPA calendar.

How should a marketing analyst write the metric dictionary?

Most arguments in marketing reviews are about definitions, not performance. One row per metric ends them:

Take spend from the ledger rather than from ad platform dashboards when finance is in the room; platform spend and invoiced spend often differ by fees, credits and currency.

Attribution, experiments or a marketing mix model: which answers which question?

Google's Meridian is one open-source marketing mix model, which Google says can take experiment results as priors. The analyst's day-90 plan should say which questions each method will answer next, not pick one method for everything.

Which numbers should a new marketing analyst baseline?

What does "on track" look like at day 30, 60 and 90?

A filled example

How should the hiring manager use this plan in the final interview?

Give every finalist the same brief: a disguised month of channel data from two systems that do not agree, and the question "where should the next $50,000 go?" Score the 30-60-90 plan with the rubric in how to evaluate a 30-60-90 day plan in an interview. Strong candidates reconcile before they recommend and say what the data cannot show. Questions that test it:

• "The ad platform and the analytics property disagree by 40 percent. Walk me through finding out why."
• "A channel looks great in attribution reports. How would you test whether it causes sales?"
• "Legal says we must honor opt-out signals. What changes in your reports?"
• "What would you put in a metric dictionary first, and who signs it off?"

The interview guide for marketing roles covers the full loop, including a campaign brief exercise that pairs well with the data brief above.

What should the marketing manager ask at the check-ins?

• Day 30: Which numbers in our dashboard do you not trust yet, and why? Which settings surprised you?
• Day 60: Which gap, once fixed, changed a number we act on? What is the test, and what result would change our plan?
• Day 90: What should we stop funding, with what evidence? What do we need to collect now for a mix model next year?

What the marketing manager owes a new marketing analyst

• Admin or analyst access to the analytics property, ad platforms, CRM reports and the warehouse in week one.
• Authority to define metrics, with a named approver, so definitions do not change by meeting.
• Web team time to fix tags, and a privacy or legal contact for consent questions.
• Willingness to hold spend back for a test, and to act on a result that is unwelcome.

Common mistakes

• Building dashboards before definitions. A new dashboard on old disagreements adds a fourth version of the truth.
• Treating attribution as cause. Credit shows paths; experiments show effects.
• Ignoring settings. Windows, models, retention and consent change numbers more than campaigns do in a month.
• Forcing systems to match. Document why they differ instead.
• Reports without commentary. Numbers with no "what changed and why" do not get used.

Adapting the plan

• B2B with long sales cycles: measure to qualified pipeline and revenue in the CRM, and agree stage definitions with RevOps before reading any channel.
• Ecommerce: add the order system, returns and margin, so channel reads use contribution, not revenue.
• Organic search heavy: add search console data and the AI Assistant channel to the dictionary, and agree with the SEO owner which pages and queries the reports track.
• Agency analysts: run the dictionary and the settings audit per client, and agree the reporting tolerance in each statement of work.