PRIVATE RETAIL COMPANY (APR-2241)

Retail Trade Predictive Model

Engineering, IT, Mathematics and Statistics

ABOUT THE INDUSTRY PARTNER

• This internship is only available to students from The University of Melbourne.
• A small but growing retail group with some experience in using data to optimise decision making.

WHAT’S IN IT FOR YOU?

• Opportunity to work directly with the owner/CEO
• Work with large data sets in a cross-disciplinary collaboration
• Collaboratively steer the direction of data analysis to uncover new insights of strategic value

RESEARCH TO BE CONDUCTED

Build a probability model to give a confidence interval of predicted retail trade based on location (GPS coordinates).

Source industry data necessary to test statistical models
Build variables to be tested such as but not limited to;

• Proximity to competition
• Proximity to major supermarkets
• Proximity to local population, possibly age adjusted
• Franchise branding
• Others (students may choose to identify and test other variables like local traffic data from Google, opening hours etc)

SKILLS WISH LIST

If you’re a postgraduate research student and meet some or all the below we want to hear from you. We strongly encourage women, indigenous and disadvantaged candidates to apply:

Essential:

• Excellent statistical analysis skills
• High level ability to build predictive models
• Programming skills required to handle large data sets efficiently

Desirable:

• Proficiency with data sources such as Google APIs and ABS

RESEARCH OUTCOMES

Deliver a model with high statistical power to inform;

• business acquisition (acquire currently underperforming businesses)
• new business start-up locations

Other insights to inform optimal decision making, such as;

• choice of optimal franchise for a given location
• relocation of existing businesses to improve performance enough to justify costs

ADDITIONAL DETAILS

The intern will receive $3,000 per month of the internship, usually in the form of scholarship payments.

It is expected that the intern will primarily undertake this research project during regular business hours and maintain contact with their academic mentor throughout the internship either through face-to-face or phone meetings as appropriate.

The intern and their academic mentor will have the opportunity to negotiate the project’s scope, milestones and timeline during the project planning stage.

Please note, applications are reviewed regularly and this internship may be filled prior to the advertised closing date if a suitable applicant is identified. Early submissions are encouraged.

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