Machine learning for digital marketing and consumer behaviour prediction
PhDs and postgraduate research
Funded PhD Project (UK and EU students only)
School of Computing
4 May 21
Candidates applying for this project may be eligible to compete for one of a small number of bursaries available; these cover tuition fees at the UK rate for three years and a stipend in line with the UKRI rate (£15,609 for 2021/22). Bursary recipients will also receive a £1,500 p.a. for project costs/consumables.
The work on this project could involve
- Machine Learning and Data Mining
- Big Data
- Consumer behaviour analysis and prediction
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 the University of Portsmouth 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. The project is expected to expand in this direction.
You 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.
You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) 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.
You should have a background in computer science, software engineering or a related subjects and good programming skills using Python or relevant languages (e.g. Java, R, C++). Experience in machine Learning, Big Data and/or data mining is welcomed. You should have a keen interest in practical problem-solving and excellent interpersonal and organisational skills.
How to apply
We’d encourage you to contact Dr Mohamed Bader-El-Den (Mohamed.Bader-El-Den@port.ac.uk) to discuss your interest before you apply, quoting the project code.
When you are ready to apply, you can use our online application form. 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. If you want to be considered for this funded PhD opportunity you must quote project code COMP5860521 when applying.