Before You Start
You don't need a marketing background to do this work well. Read through this page before you open a single spreadsheet — it will make everything else in the playbook click faster.
These will come up constantly. Come back to this page any time you see an unfamiliar term.
Conversion — When someone takes the action you wanted: a purchase, a sign-up, a form fill. This is almost always the end goal of marketing. Everything else is in service of the conversion.
CTR (Click-Through Rate) — The percentage of people who saw something and clicked it. CTR = Clicks ÷ Impressions. A 2% CTR means 2 out of every 100 people who saw the ad clicked it.
CPC (Cost Per Click) — How much you paid on average for each click. CPC = Total Spend ÷ Clicks. Lower CPC usually means your targeting and creative are well-matched.
CPA (Cost Per Acquisition) — How much you paid to get one conversion. CPA = Total Spend ÷ Conversions. If you spent $500 and got 10 leads, your CPA is $50. Lower is better — but only if conversion quality holds up.
ROAS (Return on Ad Spend) — Revenue earned for every $1 spent on ads. A 4x ROAS means $4 came back for every $1 spent. This is the primary efficiency metric for paid advertising.
Bounce Rate — The percentage of visitors who left a page without taking any action. A high bounce rate often signals a mismatch between the ad and the landing page it sends people to.
Session Duration — How long someone spent on a page or site. Short sessions on content-heavy pages can indicate the content isn't resonating with the right audience.
Segment — A specific slice of your data: by channel, device, age group, campaign, or geography. Segmenting is how you find out whether a trend is happening everywhere or just in one place.
Benchmark — A comparison point. Last month's numbers, a previous campaign, or an industry average. Without a benchmark, a number means nothing. "CTR was 2.4%" — is that good? You need a benchmark to know.
GA4 (Google Analytics 4) — Tracks website traffic, user behavior, and conversions. This is usually your primary data source. Ask for view access early — it sometimes takes time to set up.
Meta Ads Manager — The reporting dashboard for Facebook and Instagram ads. You can pull performance data at the campaign, ad set, and individual ad level.
Google Ads — Reporting for Google search and display advertising. The Campaigns tab and Search Terms report are where most associates start their analysis.
Google Sheets — Where you'll organize and clean data before analysis. You don't need to be an expert, but be comfortable with sorting, filtering, and basic formulas (SUM, AVERAGE, division).
Looker Studio — A free Google tool for building visual dashboards. Most clients prefer a visual deliverable over a raw spreadsheet — this is usually how you'll present your final report.
The client's existing dashboards — Always ask in discovery what reports the client already uses and trusts. Start there before building anything new.
Project Setup
Name the project, define your role, and make the business question explicit before you start.
- Write the client’s main business question in one sentence.
- List the people who need to review your findings.
- Note any known constraints: access, timing, or tools.
Client Discovery
Capture what the client cares about, which reports they already trust, and what success looks like.
- What are the top business goals this project supports?
- Who is the audience for the final recommendations?
- Which metrics will the client use to judge success?
- What decisions should the analysis enable?
Analytics Brief
Build a concise brief that documents the project purpose, success criteria, and analytics scope.
- Project purpose: Why are we doing this analysis?
- Primary audience: Who will use the findings?
- Key questions: What do we need the data to answer?
- Priority metrics: Which metrics matter most?
- Deliverables: What should we hand off?
- Known risks: What data access or quality issues exist?
Use a measurement mindset. Be precise, avoid vague statements, and write recommendations the client can act on.
Checkpoint: Brief Review
Review the analytics brief with the mentor to confirm the project focus before data work begins.
- Is the business question clearly defined?
- Are the priority metrics aligned with client goals?
- Is the intended deliverable format clear?
Nick Berry is the industry mentor for this analytics track — confirm the brief with him before moving to data validation.
Data Inventory
List every source, tool, and report you can use for this analysis.
- Website analytics (GA4, Adobe, heatmaps)
- Ad reporting (Meta, Google Ads, display networks)
- CRM / email / CRM campaign data
- Owned content performance and conversion tracking
- Any existing dashboards or BI tools
- Do you have access to the source data?
- Is the data complete for the analysis window?
- Are the definitions consistent across sources?
Checkpoint: Data Readiness
Verify the data is usable and the project can proceed with confidence.
- Confirm all required data sources are accessible.
- Agree on the timeframe and key metrics.
- Document any gaps or assumptions.
Nick Berry should sign off on the data plan before analysis starts.
Metrics Plan
Define the metrics that will answer the business question and support recommendations. Don't measure everything — pick the 2–3 metrics that directly answer the business question and make those your north star.
Conversion Rate — Percentage of visitors who took the desired action. If 1,000 people visited a page and 40 filled out a form, conversion rate is 4%. This is often the most important metric to improve.
CPC (Cost Per Click) — What the client paid, on average, for each ad click. Useful for comparing efficiency across channels or campaigns. Lower CPC isn't always better — cheap clicks from the wrong audience waste budget.
CPA (Cost Per Acquisition) — What the client paid to get one conversion. This is the efficiency metric that matters most to most clients. Ask them what a conversion is worth — that tells you whether the CPA is acceptable.
ROAS (Return on Ad Spend) — Revenue generated per dollar spent on ads. A 3x ROAS means $3 back for every $1 spent. Most businesses need at least 2–3x to be profitable after costs.
CTR (Click-Through Rate) — Percentage of people who saw an ad or link and clicked it. High CTR on a low-conversion page means the ad is working but the landing page isn't. Low CTR usually means the ad creative or targeting needs work.
Bounce Rate — Percentage of sessions where the user left without any further interaction. A bounce rate above 70% on a key landing page is usually a red flag worth investigating.
ROAS vs. ROI — ROAS only measures ad spend. ROI accounts for all costs (creative, labor, tools). For APEX projects, ROAS is usually the right metric — you likely won't have full cost data.
- Primary conversion metric (e.g. lead volume, purchase rate, enrollment)
- Engagement metrics tied to business outcomes
- Efficiency metrics (CPC, CPA, ROAS, cost per lead)
- Quality metrics (bounce rate, session duration, CTR)
Use this framework to keep analysis focused: Inputs → Outputs → Outcomes.
- Inputs: traffic, ad spend, content volume — what the client put in.
- Outputs: leads, clicks, conversions — what happened as a result.
- Outcomes: revenue, enrollment, pipeline growth — the business impact.
Most analytics reports stop at Outputs. The strongest analyses connect Outputs to Outcomes — that's where real business value is.
Analysis & Findings
Use the metrics plan to guide your analysis and surface the strongest insights. This is the hardest step — take your time here.
Reporting says: "Our CTR was 2.4%."
Analysis says: "Our CTR of 2.4% was 40% above industry average, but it was driven entirely by the retargeting campaign — cold traffic CTR was actually below average, which suggests our top-of-funnel creative needs work."
Your job is analysis, not reporting. Every number needs context, comparison, and a "so what."
Follow these steps in order. Don't skip to conclusions before you've looked at the data.
- Get everything in one place. Export the relevant data from each platform (GA4, Meta, Google Ads, etc.) into a single Google Sheet. One tab per source is a clean way to start.
- Build a comparison table. Create a simple table: this period vs. the previous period (or this campaign vs. another). Use the same metrics across all rows. This alone will surface most of the interesting patterns.
- Sort by your primary metric. What's highest? Lowest? Most changed? You're looking for outliers — the things that are unusually good or unusually bad are where the story usually lives.
- Segment before you conclude. If you see a trend, ask: does this hold across all channels, or is one channel driving the whole result? Same for device type, geography, and audience. A top-line number can hide very different realities underneath.
- Write one sentence per finding. Before moving on from any pattern, write one sentence that explains what it means for the client — not just what the number is. This forces you to actually interpret the data instead of just restating it.
- Note data quality issues. If a metric looks wrong, flag it. Missing tracking, inconsistent definitions across platforms, or gaps in data all need to be documented — they affect how confident you can be in your conclusions.
- Baseline current performance and identify trends.
- Segment by audience, channel, and campaign.
- Test assumptions against actual data.
- Document any data quality issues and how you addressed them.
Checkpoint: Insight Review
Share your early findings with the mentor or client and make sure the recommendations are on target.
- Are the insights aligned with the brief and business goals?
- Do the recommendations flow from the data?
- Have you identified the highest-impact next steps?
Nick Berry should review this checkpoint to confirm the strategy before reporting.
Insights & Strategy
Turn your analysis into clear recommendations and a practical plan for the client.
- What should the client stop, start, and continue?
- Which channel or campaign actions matter most?
- What should be measured next to validate progress?
- Link each recommendation to the data source that supports it.
- Include expected impact, confidence level, and timing.
- Flag any follow-up analysis needed after implementation.
Reporting & Handoff
Create the final client deliverable and capture the story behind the numbers.
- Start with the most important conclusion.
- Use visuals to support each recommendation.
- Document next steps and any data limitations.
Project Reflection
Capture what worked, what you learned, and what you would improve next time.