2017 Masters Research Dissertation Programme Winners Announced
The winners of the CDRC’s 2017 Masters Research Dissertation Programme were recently announced at the Data Analysts User Group Conference.
The CDRC led programme provides the opportunity for students to work directly with an industrial partner and links students’ research to important retail and ‘open data’ sources. Once again the standard of the projects was extremely high this year, with students working with a range of partners, including Sainsbury’s, Shop Direct, Local Data Company and E.ON.
Prize winner: Yifan Cao and Sainsbury’s
Estimate impact of slot availability on customer demand by using choice-based demand models
The judges liked the way that Yifan Cao spanned the scale from the strategic impact down to the detailed model selection. This project provided a very solid analysis with clear objectives analysis, and results. The output has clear implications for the data provider with the potential to introduce new efficiencies into their service delivery. Further, it was encouraging to see the use of open-source reproducible code and a focus on validation.
Runner up: Christian Tonge and Movement Strategies
An exploration of mobile data: towards proximity based passenger sensing on public transport
The judges felt that the two runner projects were great examples of dissertation programme from a commercial perspective. Both Christian Tonge and Jon Haycox explored new techniques and data sources that will have future consequences.
Christian Tonge’s project which examined the opportunities presented by New Forms of Data for understanding human mobility patterns. Such analysis is increasingly important as we seek to exploit the potential of New Forms of Data.
Runner up: Jon Haycox and E.ON
Electric vehicle charge point placement optimisation
The judges said that Jon Haycox’s project was forward-thinking, on a topic which is going to increasingly interest academics and practitioners alike. The thesis was well constructed and incorporated a broad range of techniques and data. Again, great to see use of open-source tools project where charging points represent a significant potential change in behaviour, so it requires imagination to be able to use existing data constructively.
Other projects completed this year included:
- An exploratory analysis of the temporal fluctuations in footfall around Hammersmith town centre in relation to Business Improvement District events
- Customer pathway and in-store activity – Sainsbury’s
- Customer value modelling – how sensitive is the model to change? – Shop Direct
- How competitive is propensity score matching using ‘address embeddings’ with supervised classification for record linkage tasks? – LDC
- An evaluation of the relationships between construction developments and retail vacancy – LDI and Barbour ABI
- Pricing optimisation – a key tool in successful retailing – Shop Direct
- Exploring submarket trends in London: a constrained spatial interaction modelling analysis – Cluttons
- Where next for Parsons Bakery? A preliminary quantitative location assessment exploring the link between demographics, competition and other variables. – Parsons Bakery
- Propensity modelling – moving beyond standard regression models & explorations into online engagement – Shop Direct
- Identifying Socio-Spatial Inequalities in Student Housing in London – Knight Frank
- The impact of the night tube on Westminster’s Night Economy – City of Westminster
- Can harnessing the demographic characteristics of store catchments improve the planning of promotions and pricing strategies? – Marks & Spencer
- An application of statistical and spatial analysis techniques to engineer callout data from a UK telecommunications company
2018 Retail Masters Dissertation Programme
We are now seeking proposals from businesses for new projects due to commence next spring (2018). Further Information
We hope to advertise the 2018 opportunities towards the end of the year.
Should you have any queries relating to the programme, please contact Guy Lansley.Back to Archive