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.
- Primary conversion metric (e.g. lead volume, purchase rate)
- 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, spend, content volume.
- Outputs: leads, clicks, conversions.
- Outcomes: revenue, enrollment, pipeline growth.
Analysis & Findings
Use the metrics plan to guide your analysis and surface the strongest insights.
- 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.
- What did the data tell you that surprised you?
- What was the biggest risk in the project and how did you manage it?
- What would you do differently on the next analytics project?