Marketing Analytics Roadmap: What Tools to Learn and Where to Specialize (Part 2 of 3)

If you missed Part 1, read it here: Feeling Lost in Marketing Analytics? Start Here

So you’ve got the foundations:

• You understand the marketing funnel

• You’re familiar with key metrics

• You’ve started learning how marketing decisions get made

Now the next questions hit hard:

Which tools should I actually learn?
What if I don’t want to be a generalist forever? Should I specialize early?
How do I not drown in tech tutorials and still build a real career?

Let’s get into it.

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Step 1: Build a Minimum Viable Analytics Stack

You don’t need to master 15 tools. You need just enough to:

• Pull data

• Analyze it

• Share insights clearly

Here’s the minimum viable stack to get hired and be dangerous:

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🔧 Must-Know Tools

• SQL
  → Why: Almost every marketing or product dataset lives in a database. SQL gets you access.
  → What to learn: SELECT, WHERE, JOIN, GROUP BY
  → Free resource: SQLBolt

• Excel or Google Sheets
  → Why: Still used daily in 90% of roles. Great for ad hoc analysis, modeling, and stakeholder handoffs.
  → What to learn: Pivot tables, VLOOKUP/XLOOKUP, IF formulas, conditional formatting
  → Free resource: Excel Exposure or YouTube

• Google Analytics 4 (GA4)
  → Why: Web analytics is everywhere. GA4 is the new standard.
  → What to learn: User vs. session, events, conversion setup, UTM tracking
  → Free resource: Google Analytics Academy

• Looker Studio or Power BI
  → Why: You’ll need to turn raw numbers into dashboards for decision-makers.
  → What to learn: Filtering, blending data sources, building visuals that tell a story
  → Free resource: Looker Studio Help

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🧪 Optional but Powerful

• Mixpanel or Amplitude
  → For product analytics and user flows

• Google Tag Manager
  → For event tracking and learning how data actually gets collected

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Step 2: Learn Through Projects (Not Just Courses)

Tutorials are fine, but real skill comes from solving problems.

Example project idea:

• Pull data from GA4 (Google demo store)

• Analyze bounce rate vs. conversion rate by source/medium

• Visualize the trend in Looker Studio

• Write a 3-sentence summary with one business recommendation

This gives you:

• SQL or GA practice

• Funnel thinking

• Communication practice

• A portfolio piece

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Step 3: Choose One Domain to Go Deep In

Marketing analytics is wide. You don’t need to do it all.

Pick one area to go deep in, and let that shape your projects, learning, and job search.

Here are four high-leverage domains:

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🔍 1. Acquisition Analytics

• Focus: Paid ads, SEO, channel performance

• You’ll work on: ROAS, CAC, attribution, channel mix

• Good for: People who love traffic data, ads, and campaign testing

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🔁 2. Retention & Lifecycle Analytics

• Focus: Churn, engagement, repeat users

• You’ll work on: Cohorts, product usage patterns, email/CRM analytics

• Good for: People who love product and user behavior

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🧪 3. Conversion Optimization & Experimentation (CRO)

• Focus: A/B testing, landing page optimization, user flows

• You’ll work on: Test design, statistical significance, lift calculations

• Good for: People who love psychology, copy, and testing

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🎯 4. Attribution Modeling & Budget Allocation

• Focus: Multi-touch attribution, media mix modeling

• You’ll work on: Credit assignment, data blending, predictive modeling

• Good for: People who enjoy math, modeling, and big-picture strategy

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Can’t decide yet? That’s okay. Sample each through small projects.
But when you're job hunting, signal one clearly to stand out.

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Coming Up in Part 3:

• How to communicate insights that get you hired

• How to build a portfolio even without job experience

• How to grow faster through feedback, community, and learning in public

👉 If you’re not subscribed yet, do it here.

🔁 And if you missed Part 1, go back and start from the top.