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Data Science for Digital Marketing - an online course

About this course

This course is a practical introduction to how you can use data and data science techniques to improve marketing insights and manage customer interaction in web-based environments. Its content is aimed at people charged with delivering real engagement with their organisation’s clients in a digital age.It aims to help you understand the potential of data science and machine learning to enhance customer acquisition, retention and to grow your customer's lifetime value.

Taking place over six weeks, each week will contain a mix of taught material, self-study, activities and practical exercises all carried out online.

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Course overview

The amount of data produced by customers engaging with digital marketing channels provides new opportunities for marketers to leverage more effective, targeted and personalised campaigns, track customer journeys, improve customer segmentation and predict their behaviour. This course will teach you the fundamental concepts and tools in data science that equip marketers to make use of these new opportunities effectively.

The course will begin with the current state of the art in web analytics, including the types of data that can be collected and the range of tools and techniques that can be used to analyse that data, including social media campaigns and the use of micro-segmentation. These are powerful tools to understand customer journeys, and you will learn about how to use different sources of data and analysis methods to generate insights about visitors and their behaviour.

But data science has the potential to go beyond this, and in the second part of the course you will learn how social media analytics can give you new insights into customer behaviour, and how content analysis methods can help to reveal customer reactions and sentiment. You will also learn about machine learning techniques, and how they can be applied to problems such as churn predication. As well as their strengths and potential weaknesses, their place in the business pipeline, and potential applications in attention, persuasion and retention. Finally, you will learn about big data and explore its potential for new applications.

Throughout the course you will also be asked to consider how you would use the techniques in your own campaigns and reflect on the use of advanced machine learning and big data technologies to reduce costs and improve outcomes.

You will be asked to complete activities each week and submit four pieces of coursework. While you will complete all of them individually, you will also work in small groups with the dedicated supervision of a course tutor. The course tutors will be available to provide in-depth assistance should you have any problems.

Aims and learning outcomes

After completing this course, you will be able to:

• Describe the current ecology of tools for web analytics

• Understand how customer journeys and sources can be tracked using those tools

• Describe different ways in which social network analysis can be used to gain marketing insights

• Apply content analysis method to gain insights into your customer base

• Explain what machine learning is and what it can be used for

• Describe the potential for machine learning for addressing the challenges of attention, persuasion and retention

• Understand the possible limitations of machine learning

• Understand what Big Data is about and how it applies to your business problems

Learning outcomes by week

Week 1

Describe the current ecology of tools for web analytics
Track activities against goals and understand the results

Week 2

Understand how journeys and sources can be tracked
Create effective dashboards and reporting

Week 3

Describe different ways in which social network analysis can be used to gain marketing insights
Describe how to use Facebook tools to target specific micro-segments of your potential audience.
Understand the fundamentals of graph theory
Apply content analysis method to gain insights into your customer base

Week 4

Explain what machine learning is and what it can be used for
Describe how machine learning fits into the business pipeline
Choose appropriate training data and evaluation techniques

Week 5

Describe the potential for machine learning for addressing the challenges of attention, persuasion and retention
Understand the possible limitations of machine learning
Match machine learning techniques with particular goals and data sets

Week 6

Understand what Big Data is about and how it applies to your business problems
Have an overview of technologies and tools that help solve Big Data challenges

Assessment

Coursework 1: Analyse and contrast two existing websites in terms of their site goals, and their fit to google search. Plan a traffic analysis and explain the potential of micro-segmentation for understanding their audience.

Coursework 2: A short online test on the principles on graph theory and its use in social media analysis

Coursework 3: A short online test on the principles on machine learning and its potential role within marketing

Coursework 4: A report based around a mini case study of a sample dataset, where you will select a machine learning algorithm, and discuss how it could be applied to gain useful insights, and what potential limitations there might be with the results. 

Syllabus

Week 1: introduces common web analytic tools, and looks at how we can use them to track activities around specific goals.

Topics

Googleverse

How do people interact with sites and setting goals

Basic site design – what Google wants

Google Analytics and search console

Facebook Advertising

Week 2: Analytics for Digital Marketing

We now move on to how these tools can give insights for digital marketing, look at reporting, and how campaigns can be set up as part of a broader strategy.

Topics

Google Analytics 

A vs. B testing

Reporting

Goals

Traffic and site journeys

Sources and conversations

Dashboards

Integrating traffic with other business metrics

Week 3: Social Network Analysis

Week 3 looks in more depth at how social media can be used for marketing, and in particular how analysing social networks as a graph, and analysing the content posted within those networks, can be used to better understand your audience.

Topics

The role of social networks

Facebook advertising campaigns

Micro-segmentation

Graph Theory

Tools for Social Media Analytics

Content analysis methods

Week 4: Machine Learning

Week 4 moves from more traditional analytic approaches to machine learning, and explores a number of key machine learning techniques, and how they fit with different types of data. 

Topics

Definitions of data science

What is machine learning?

Different types of ML methods/models for marketers

Decision trees

SVMs

Cluster analysis

How can ML be applied to website data? Can we predict customer churn?

How do you choose training data, and evaluate the effectiveness of machine learning?

Week 5: Application of Machine Learning in Marketing

Week 5 looks in more depth at the potential of machine learning in digital marketing. We also look at the limitations of machine learning, and your role in deploying it. 

Topics

Using machine learning for attention

Using machine learning for persuasion

Using machine learning for retention

Your strategic role

Limitations of AI and ethical considerations

Mini case studies showing examples of how it might be applied

Week 6: Big data and future applications

Week 6 discusses future possibilities for digital marketing and data science, covering the topic of big data and how this creates the potential for many advanced applications in this space.

Topics

What is Big Data?

Volume, variety, velocity, and veracity dimensions and how they apply to marketing data sources

Cloud computing and distributed computing architectures

Data analysis in the cloud

Novel visualisations and applications

Future technological directions

Fees

The Data Science for Digital Marketing course is £1500 per person, inclusive of VAT.

For corporate packages, please see here.

How to Pay

You can pay by phone, email or Flywire, using the application form here.

Paying Online

  • To make a payment using this method, fill out the application form and select "Pay by Credit / Debit Card".
  • A 2% fee is charged for payment by credit card and we do not accept American Express. 
  • Fees paid by this method will be charged in British pounds sterling.

Paying by Phone

  • To make a payment using this method, fill out the application form and select "Pay by Phone". 
  • A 2% fee is charged for payment by credit card and we do not accept American Express. 
  • Fees paid by this method will be charged in British pounds sterling.

Pay by Email

  • To arrange a payment using this method, please contact us once you have received confirmation of your place. Contact your course advisor, agent, or email us on payments@southamptondata.science 

Flywire

  • To make a payment using this method, fill out the application form and select "Pay by Flywire".
  • Best for international participants: accepting over 70 currencies via credit card, debit card, or bank transfer.

  • Simply visit www.flywire.com/pay/southamptondata and follow the instructions on the website. Please use the same email address that you used when you applied to the course.