Banner

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. Offering a practical use of data science for marketing insights it aims to help you enhance customer acquisition, retention and grow your customers 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.

Apply now 

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 demonstrate how to set up an example campaign, walking you through how each stage of the decision process and emphasizing the types of data that need to be collected or used and introducing the range of tools and techniques that help analyse this data. You will gain a better understanding of the role different sources of data can play in making sense of customer engagement, including aspects such as:

  • Customer segmentation
  • Behavioural data and metrics
  • Churn prediction

 You will have access to real data about website traffic and use journeys, and will learn about how to use different sources of data and analysis methods to generate insights about visitors and their behaviour. You will learn to measure activity within a site and to track the key events that signal engagement, and how these can then be used to build customer profiles and predict the behaviour of future visitors. You will learn how to combine this data with content from social media to produce more meaningful insights.

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.

It is important to us that during the course you will not just learn how data science could help your marketing team, but you will actually learn to use data science techniques yourself. This will put you in a much stronger position to manage web and social media channels in your organisation, since you will have fundamental experience of how this environment works.

You will be asked to complete activities each week and submit three 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:

  • Understand the role data science plays in modern digital marketing
  • Understand what data needs to be collected to ensure that the digital marketing process can be optimized.
  • Understand the roles of different sources of data and how they can be, in combination, be used to produce added value
  • Use data science techniques to test performance of a campaign, and identify ways to improve it
  • Measure the success rate of a campaign

Learning outcomes by week

Week 1

  • By the end of this week, you will be ready to complete the main content of the course.

Week 2

  • Understand the potential for data science to enhance digital marketing insights and performance
  • Track activities against goals and understand the results

Week 3

  • Track website activity against specific goals
  • Evaluate the effectiveness of page content for creating results
  • Track events such as video views and downloads

Week 4

  • Describe different ways in which social network analysis can be used to gain marketing insights
  • Analyse data from Twitter to identify key members of your audience using social network analysis
  • Describe how to use Facebook tools to create a larger audience that you can advertise to using characteristics of your audience
  • Use reviews or user generated content and use a visualisation tool to create a world cloud to home in on the keywords that your customer base actually uses and use it to update some of the site keywords

Week 5

  • Explain what machine learning is and what it can be used for
  • Describe how machine learning applies to customer segmentation, churn prediction, and customer engagement

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: Plan a marketing campaign using a number of different ads, keywords and formats, and make a table of features that could be used to track performance.

Coursework 2: Produce a report based on website data (Google Ads/Analytics, Facebook Ads Insights) and discuss what you have learned from it, including user journeys, demographics, traffic sources, ‘actions’, etc.

Coursework 3: Using the supplied dataset, and one of the suggested tools, train a machine learning model to predict future customer behaviour, and write a short report on your process (detailing training set size, chosen model, etc.) and the results that you’ve gained and think about opportunities and challenges in working with Big Data.

Syllabus

Week 1 - Preliminaries

Before the main course starts, students are expected to familiarise themselves with tools such as Google Ads. Week 1 should then provide all the background on stats and marketing basics that they’ll need in the course.

Topics

  • Basic stats
  • Regression
  • A vs B testing
  • How do people interact with sites and setting goals
  • Keywords and what you expect a site to do
  • Creating site collateral
  • Googleverse
  • Basic site design - where people look, how they read, what they like and don’t like. What Google wants and how to check it via webmaster and other toolsets

Week 2 - Introduction to data science

We now move on to covering the role of digital marketing and data science, and how campaigns can be set up in order to use these techniques.

Topics

  • Welcome to the course
  • What is digital marketing? (we expect them to already know this, but this is framed from the PoV of the data)
  •  What will the course cover?
  • What is the opportunity to use data science with digital marketing?
  • Data science toolkit
  • Applications to customer segmentation, churn prediction etc.
  • How traffic gets to a site
  • How Google Adwords operates
  • Testing ads
  • How analytics works
  • Search console and how it works. How does it help you identify organic traffic?
  • Facebook - post boosting vs advertising
  • How Facebook lets you play in the big data space
  • Instagram and mobile marketing - how is it different?

Week 3 - Basic data analytics

Week 3 looks in more detail at how to understand the results of a campaign compared to your goals.

Topics

  • How analytics works
  • Setting goals
  • Measuring events and link activity
  • Reporting generally
  • Traffic and site journeys
  • Relating sources to conversions
  • Creating a dashboard
  • How does traffic integrate with other business metrics

Week 4 - Social network analytics

Week 4 now looks at how more advanced data science techniques, focusing on social network analysis, can be used to understand your audience better.

Topics

  • Facebook advertising and audience targeting
  • Graph theory for network analysis
  • Social network data analysis for communities
  • Social networking potential for viral marketing
  • SNA toolkit

Week 5 - Machine learning

Week 5 looks at how more advanced data science techniques, focusing on machine learning can be used to further leverage your campaigns and increase the returns from them.

Topics

  • Personalisation and segmentation in marketing (e.g., target story)
  • Can we predict when someone will leave? Churn prediction leading on to:
  • What is machine learning?
  • Different types of ML methods/models for marketers
  • Decision trees for customer behaviour
  • SVMs
  • Cluster analysis
  • How can ML be applied to the example site? Can we predict customer churn?

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
  • Examples of Big Data case studies in digital marketing
  • Cloud computing and distributed computing architectures such as Spark and Hadoop
  • Data analysis in the cloud
  • Distributed data analysis
  • The future of data science and digital marketing
  • Voice recognition and mobile searches
  • Context-aware pricing
  • A vision of marketing in the the machine learning age
  • Communication to both individuals and their AI gatekeepers
  • Using data to personalise offers - the effect on the 4 Ps
  • Modelling intent and training AI to respond to customer input.

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 Cebit / 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/southamptondata and follow the instructions on the website. Please use the same email address that you used when you applied to the course.