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All lessons in Digital and Web Analytics Lesson 7.4 of 32

Module 1: Introduction to Digital Analytics

  1. 1.1What is digital analytics?
  2. 1.2The four types of analytics
  3. 1.3Connecting analytics to business objectives and KPIs

Module 2: Analytics Data and Measurement Planning

  1. 2.1How to build a measurement plan
  2. 2.2Key metrics for each marketing channel
  3. 2.3Data sources, collection, and data quality
  4. 2.4Segmentation and personalization

Module 3: Web Analytics Fundamentals

  1. 3.1Users, sessions, page views, and events
  2. 3.2Traffic sources, channels, and UTM parameters
  3. 3.3Landing pages and user behavior
  4. 3.4Engagement metrics and conversion metrics
  5. 3.5Reading reports, trends, and anomalies

Module 4: Analytics Tools and Platforms

  1. 4.1Google Analytics 4 fundamentals
  2. 4.2Google Search Console performance data
  3. 4.3Adobe Analytics overview
  4. 4.4Tag management and tracking implementation
  5. 4.5Connecting tools and choosing the right stack

Module 5: Conversion Funnels and Optimization

  1. 5.1What is a conversion?
  2. 5.2Conversion funnels and drop-off points
  3. 5.3How to calculate conversion rate
  4. 5.4Prioritizing conversion improvements
  5. 5.5Introduction to A/B testing

Module 6: Digital Marketing Performance Analysis

  1. 6.1Measuring SEO and paid advertising performance
  2. 6.2Social, content, and landing page performance
  3. 6.3Comparing acquisition channels
  4. 6.4Evaluating campaigns and avoiding vanity metrics

Module 7: Reporting, Visualization, and Insights

  1. 7.1Building a marketing performance report
  2. 7.2Data visualization and dashboards
  3. 7.3Comparing periods and benchmarking
  4. 7.4Turning data into actionable recommendations

Module 8: Analytics Projects and Practical Assessment

  1. 8.1Analytics case study: Isla Coffee Co.
  2. 8.2Final assessment: digital analytics case study

Turning data into actionable recommendations

What we will go through

  • The What? So what? Now what? framework
  • What makes a recommendation strong
  • How to explain data to non-technical people

Short answer

Turn data into action with three questions. What happened, so what does it mean for the business, and now what should we do? A strong recommendation is specific, backed by data, sized by impact, and assigned to an owner with a deadline. For non-technical readers, lead with the conclusion and translate metrics into money or customers.

What? So what? Now what?

  • What: Mobile conversion rate fell from 2.1% to 1.4% this month.
  • So what: That cost about 180 orders, or roughly ₱270,000 in revenue.
  • Now what: Fix the mobile checkout bug found in session recordings this week, then A/B test a one-page checkout.

A good recommendation is specific, tied to data, sized by impact, and assigned to an owner with a deadline.

Talking to non-technical people

  • Lead with the conclusion, then show the evidence.
  • Use plain language: “visitors who left after one page” instead of jargon.
  • Translate metrics into money, customers, or time.
  • Use one clear chart per point.
  • State limitations honestly, such as tracking gaps.
  • End with a clear decision or request.

Try it: rewrite for a CEO

Quick quiz

1. Which part translates data into business impact?
2. A strong recommendation includes...

Your action

Key terms

Insight
A finding that explains why and suggests action.
What? So what? Now what?
A three-step way to turn data into action.
Owner
The person responsible for an action.
Limitation
A known gap or weakness in the data.

Questions beginners ask

How do I explain bounce rate to a CEO?

Say "visitors who left without doing anything" and show the lost sales.

Should I mention data problems?

Yes, briefly. Honesty about limits builds trust.

Related lessons

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