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Smart Digital Marketing and Consumer Behaviour Prediction using Machine Learning and AI


   School of Computing


About the Project

Applications are invited for a self-funded, 3-year full-time or 6-year part time PhD project.

The PhD will be based in the School of Computing and will be supervised by Dr Mohamed Bader-El-Den, Dr Alaa Mohaseb and David Henderson (CTO Fresh Relevance).

The work on this project could involve:

  • Machine Learning and Data Mining
  • Big Data 
  • Consumer behaviour analysis and prediction

Project description

The market share of online/e-commerce sales has been rapidly increasing during the last three decades. In 2018, for the first time, the amount of total online sales has exceeded the in-store sales in the USA. Moreover, Google and Facebook generated 116.3 and 55.8 billion US dollars respectively in 2018 form online advertising only. Unlike in-store sales, digital marketing and online sales generate big and valuable data about products, consumers' engagement and behaviour which was not available for business before (e.g. where customers are coming from? what devices they are using? what items do they buy? or view and for how long? how shoppers respond to digital marketing ads and emails? and much more). Moreover, social media has changed how companies advertise their products, engage with potential consumers and understand market segments’ needs. There is a huge demand for new computational intelligence and Machine Learning (ML) methods to improve and optimize digital marketing and online business operations. 

This project is based on an existing collaboration/projects with Fresh Relevance (FR), a leading digital marketing company. The collaboration between UoP and FR has resulted in a few novel ML methods for consumer behaviour prediction e.g. Price Affinity Predictor, an ML model developed to predict the price level that is likely appeal to each new website visitor, this early prediction allows the automatic customisation of the landing pages to show the consumers the most relevant products based on the predicted price level. [here]. The project is expected to expand on this direction. The PhD student will have the opportunity to define his project in one or more of the following topics based on the supervisor team direction. 

  • ML for digital strategy optimization and automatic discovery.
  • Recommender Systems and content-based Recommender systems
  • Computational intelligence for automatic consumer segmentation and clustering.
  • Neural Networks and Machine learning for consumer behaviour prediction (classification).
  • Purchase prediction. 

The successful candidate will be co-supervised by Dr Mohamed Bader-El-Den, the director of the Data Science and Analytics subject group at School of Computing and Fresh Relevance.

General admissions criteria

You'll need a good first degree from an internationally recognised university or a Master’s degree in an appropriate subject. In exceptional cases, we may consider equivalent professional experience and/or qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.

Specific candidate requirements

  • Programming skills using Python or relevant languages (e.g. Java, R, C++) are essential [essential].
  • Background in computer science, software engineering or a related subject [essential].
  • Machine Learning, Big Data and/or Data Mining experiences [desired].
  • A keen interest in practical problem-solving
  • Excellent interpersonal and organisational skills

How to Apply

We encourage you to contact Dr Mohamed Bader-El-Den () to discuss your interest before you apply, quoting the project code below.

When you are ready to apply, please follow the 'Apply now' link on the Computing PhD subject area page and select the link for the relevant intake. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our ‘How to Apply’ page offers further guidance on the PhD application process. 

When applying please quote project code:COMP5881023


Funding Notes

Self-funded PhD students only.
PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK students only).

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