Where We Are in the Course

Lesson Learning Outcomes

By the end of this lesson you should be able to:

The lesson is organised into six short sections, each ending with a quick knowledge check.

1.1 What Marketing Analytics Means

In plain terms: it is how a business turns everything it records — clicks, purchases, sign-ups, social posts — into numbers it can learn from.

Instead of guessing what works, the business measures what works and does more of it.

1.2 Why It Matters

Marketing analytics has three main purposes:

• Understand effectiveness — which campaigns, channels, and messages are actually working.
• Improve customer experience — give people more of what they want and less of what they don't.
• Maximise return on investment (ROI) — get the most value from every dollar of marketing spend.

1.3 The Four Core Functions

Marketing analytics works in four steps:

• Data Collection — gather data from websites, social media, email campaigns, ads, and offline sources.
• Data Analysis — make sense of it using statistics, machine learning, and predictive models.
• Performance Measurement — track metrics such as ROI, conversion rate, click-through rate, and engagement.
• Optimisation — use what we learn to improve targeting, messaging, and budget allocation.

The rest of Section 1 focuses on the third step — how we measure performance.

1.4 Key Metrics (1): Reach and Cost

Most marketing runs through a simple funnel: many people see an ad, some click, fewer buy.

1.5 Key Metrics (2): Conversion and Return

1.6 What Marketing Analytics Is Used For

• Ad spend tracking — compare spend and results across platforms to allocate budget better.
• Creative testing — use A/B testing to see which ad or message people respond to most.
• Budget forecasting — simulate scenarios to predict the effect on sales and profit.
• Personalised recommendations — tailor content to each person. Netflix reports that recommendations influence around 80% of what people watch.
• Dynamic pricing — adjust prices in real time based on demand and stock, as Amazon does.

Knowledge Check — Section 1

2.1 Four Types of Marketing Data

Data is grouped by who it comes from and how much we can trust it.

• Zero-party — shared on purpose by the customer (surveys, quizzes). Accurate and privacy-friendly.
• First-party — collected directly from your own channels (website, CRM). Most reliable.
• Second-party — another company's first-party data, shared or sold under agreement.
• Third-party — bought from outside sources (data brokers). Broad, but declining due to privacy concerns.

2.2 Where the Data Comes From

Marketing data is pulled from many sources, for example:

Knowledge Check — Section 2

3.1 Five Modelling Approaches

3.2 Activity 1 — Match the Model

1. Forecast which customers are likely to stop buying (churn).
2. Evaluate the long-term sales impact of TV versus digital ads.
3. Understand which ad click led to a final purchase.
4. Group users by behaviour to send personalised messages.
5. Combine online and offline results for total campaign ROI.

Discuss, then check the next slide.

3.3 Activity 1 — Answers

4.1 Three Types of Analytics

Each type answers a different question, and together they build on one another.

4.2 Trend: Personalisation and Targeting

To stand out, brands analyse data from several sources and tailor their marketing:

• Website analytics (descriptive) — tools like Google Analytics 4 show which pages and content people engage with.
• Social media engagement (descriptive) — platforms like Hootsuite and Sprout Social measure interactions.
• Purchase history (descriptive) — CRM tools like Salesforce reveal buying patterns.

Machine learning then groups customers into segments and sends each group tailored campaigns (for example through Mailchimp), which lifts conversions.

4.3 Trend: Growth of Predictive Analytics

Predictive analytics helps businesses anticipate what is coming and prepare for it:

• Data collection (descriptive) — gather demographics, transaction history, and market trends.
• Predictive tools (predictive) — platforms like Microsoft Azure and Google BigQuery build models with little coding.
• Model creation (predictive) — regression finds relationships, such as between marketing spend and customer loyalty.

Models are refined with new data over time so they stay accurate as trends change.

4.4 Trend: Advanced Attribution and AI

4.5 Metric Spotlight: Customer Lifetime Value

$$\text{CLV} = \text{Average purchase value} \times \text{Purchase frequency} \times \text{Customer lifespan}$$

4.6 Activity 2 — Case Study

1. What was the challenge the company faced?
2. How did they use analytics to solve it?
3. What were the outcomes?

Example starting point: JPMorgan Chase using contract-intelligence AI to save large amounts of manual work.

Knowledge Check — Section 4

5.1 The 3 Cs, with Data

Last week introduced the 3 Cs framework. Analytics gives each of them an evidence base:

• Customer — people constantly share data (loyalty cards, apps, social media) that reveals their needs, wants, and behaviours.
• Company — the business collects this data and uses it to build better products and choose the right channels.
• Competitor — the same techniques help a business see where it stands against rivals.

Sections 5.2 and 5.3 look at the Customer and the Company in more detail.

5.2 Understanding the Customer

• Analytics can help keep customers using your product instead of switching to a competitor.
• It can also encourage customers to buy more often and spend more each time.

These goals are reached by understanding two ideas we study later in the course:

• Customer churn — the rate at which customers stop buying.
• Customer lifetime value (CLV) — how much a customer is worth over time.

5.3 Understanding the Company

Companies need to know which channels their customers prefer, then invest in those channels to keep existing customers and attract new ones. Techniques include:

• Conjoint analysis — design products and services that match what customers want.
• SEO and A/B testing — find the messages and channels that engage people most.
• Conversion funnel analysis — retain current customers and acquire new ones.

6.1 Choosing a Platform

The best choice depends on business needs, scale, and what it must connect to. Each platform is strong in one area:

• Google Analytics — best for web analytics.
• Hootsuite and Sprout Social — best for social media management.
• HubSpot — best all-in-one marketing platform.
• Tableau — best for data visualisation.

The next slides look at each in turn, then compare them side by side.

6.2 Google Analytics — Web Analytics

Website performance and marketing insights.

• Built-in automation that surfaces useful insights.
• Machine-learning models for analysing customer behaviour.
• Funnel exploration to visualise the user journey.
• Data collection and management through an API.
• Segment overlap to identify new customers.
• Advertising workspace to assess spend across channels.

6.3 Hootsuite — Social Media Management

Managing social content and engagement in one place.

• Social analytics — performance and follower insights.
• Social publishing — create and schedule content.
• Campaign optimisation — spot trends to improve results.
• Social engagement — manage conversations in one view.
• Reports and dashboards — customisable and exportable.
• Content and collaboration — shared calendar, library, and inbox.

6.4 Sprout Social — Social Media Management

Focused on monitoring, engagement, measurement, and growth.

• Monitoring — a Smart Inbox that consolidates messages.
• Engagement — insights on interactions and trends.
• Measurement — analytics at profile, group, and roll-up levels.
• Growth — publishing tools and a team content calendar.

6.5 HubSpot — All-in-One Platform

A comprehensive CRM platform with several modules ("hubs").

• Sales Hub — email, automation, and pipeline management.
• Marketing Hub — multi-channel marketing, content, and analytics.
• Customer Service Hub — messaging and a knowledge base.
• CMS Hub — content distribution using CRM data.
• Operations Hub — data pooling and process automation.

6.6 Tableau — Data Visualisation

An advanced tool for turning data into clear visuals.

• Intuitive drag-and-drop interface.
• A wide range of chart types.
• Real-time, interactive dashboards.
• Connects to many data sources at once.
• Advanced calculations and custom scripting.
• Geo-spatial analysis for mapping data.

6.7 Comparing the Platforms

6.8 Activity 5 — Reading a Dashboard

1. Which information matters most to you as an analyst?
2. How would you use it to make a business decision?
3. What is missing that you would also want to see?

Knowledge Check — Section 6

Reading the Story: Bigger Budget, Better Results?

These are the same six campaigns from your notebook. Put spend and revenue side by side and a pattern appears.

Reading the Story: Are All Customers Worth the Same?

The same four customer segments from your notebook, compared by lifetime value.

Summary — What to Take Away

• Marketing analytics collects, analyses, and acts on data to measure performance and improve ROI.
• Data ranges from zero-party (given by the customer) to third-party (bought externally).
• Key metrics — CTR, CPC, conversion rate, ROAS, ROI, and CLV — are mostly simple divisions and multiplications.
• Descriptive, predictive, and prescriptive analytics answer "what happened", "what will happen", and "what should we do".
• Software — Google Analytics, Hootsuite, Sprout Social, HubSpot, and Tableau — each has its own strength.