Marketing Analytics Hackathon Challenge 2026

Hackathon details

Goal of the challenge

The goal of this challenge is to identify the key factors that influence lender decision-making in prosocial crowdfunding, as reflected in funding speed.

Using loan-level data from 2016 to 2025, participants are invited to investigate what drives some loans to attract funding more quickly than others in subsistence marketplaces. Teams are also encouraged to examine how persuasive patterns differ across meaningful segments and how they evolve over time. Strong submissions will not only identify influential drivers, but also demonstrate a clear and compelling understanding of how persuasion operates in a dynamic environment.

The ultimate objective is to generate rigorous, data-driven insights that can inform more effective strategies for borrowers and crowdfunding platforms operating in resource-constrained environments.

What is the annual Marketing Analytics Hackathon?

The Marketing Analytics Hackathon is an annual competition organized by the School of Marketing at UNSW. It attracts top-tier students with strong marketing and analytical skills to participate. Participants form teams to compete in solving real-world problems, using their marketing knowledge and analytical skills within a limited time period (e.g., one week).

What to expect in 2026?

• A global competition – collaborate and compete with talented participants from around the world.
• A fresh, real-world challenge – tackle an intriguing and meaningful problem using real data.
• Recognition and opportunities – see the prize section below.

Who can participate?

Students from both Australia and international universities, who are interested in solving real-world marketing problems using analytical tools and possess necessary marketing knowledge and analytical skills are welcome to compete.

• Both undergraduate and postgraduate (i.e., master) students are welcome.
• Form a team of 2-5 students, and register your team here
• Download the sample dataset after your registration.

What is the prize?

Up to eight teams will advance to the final competition. The winning teams will be announced at the final presentation day.

• 1^st place winner: $1,000 e-gift card per team, two tutor positions at School of Marketing *.
• 2^nd place winner: $600 e-gift card per team.
• 3^rd place winner: $300 e-gift card per team.
• Certificates for all eight finalists.

_{* Tutor positions are subject to a separate selection process and eligibility requirements, e.g., visa conditions.}

Partners and Sponsors

Partners

Sponsor

Business School SDG Committee

(To find out more about becoming a sponsor, please contact MA.Hackathon@unsw.edu.au)

Data

The dataset used in this challenge is sourced from Kiva, a global nonprofit crowdfunding platform that connects individual lenders with borrowers in underserved communities around the world. Kiva is widely described as the world’s first online lending platform, and it enables people to lend small amounts (often as little as $25) to support small businesses, household needs, agriculture, education, and other livelihood-related activities. Since its founding in 2005, Kiva has helped over 5 million people across diverse regions and sectors.

For this challenge, we focus on loans from countries with purchasing power parity (PPP) GDP per capita below US$4,500, a threshold commonly associated with low- and lower-middle-income economies under the World Bank classification. The analysis therefore centres on subsistence marketplaces.

Participants will work with a loan-level dataset containing 1,453,846 loans issued between January 1, 2016 and December 31, 2025. Each record represents a single loan and includes loan features along with key dates. In particular, the difference between “fundraising date” (when the loan was posted) and “raised date” (when the loan was fully funded) provides a measure of funding speed, which serves as the primary lender decision outcome in this challenge.

Teams are encouraged to formulate their own research questions rather than answer a predefined analytical task.

A data dictionary can be found using this link.

After the team registration, you will find a link to download a sample dataset.

• Helps you understand the dataset and its structure
• Allows you to test your code and analytical approaches
• Assists you in developing your research question and proposal

Key dates

• Registration: Now until 16 Aug 2026
• 17 Aug 2026: Proposal submission opens
• 24 Aug 2026 (5pm): Proposal submission deadline
• 27 Aug 2026 (9am): Finalist teams announced
• 4 Sep 2026: Final presentations and award ceremony

Final presentations

The final presentation is the culmination of the Marketing Analytics Hackathon 2026. Up to eight finalist teams will present their projects and compete for the championship. Audience members are welcome to attend, network with participants and guests, and vote for the Audience Choice component of the competition.

During the event, we will also host an expert panel discussion on "Marketing Analytics, AI, and Social Impact", bringing together experts from industry and academia to discuss how analytics and AI can be used to address real-world social-impact challenges.

The final presentation will be a hybrid event.

• In person: UNSW Business School Building, Level 6, Business Lounge
• Online:  Details to be announced

Tentative schedule (subject to change):

• 09:30 - 09:45: Opening Introduction
• 09:45 - 11:10: Presentations
• 11:10 - 11:30: Coffee Break
• 11:30 - 12:55: Presentations
• 12:55 - 13:55: Lunch and Networking
• 13:55 - 14:45: Expert Panel Discussion
• 14:45 - 15:00: Award Ceremony and Closing Remarks

Interested in attending? Register as a guest here

We will provide event updates and participation details via your registered email.

Judging criteria

Proposal judging criteria

• Insightfulness and originality (30%): Does the proposal offer a thoughtful and creative approach to identifying the key drivers of lender decision-making? Does it move beyond surface-level analysis to uncover meaningful patterns?
• Analytical rigor and relevance (30%): Will the proposed analysis generate robust, data-driven insights into what influences funding decisions? Are the methods appropriate, well-justified, and aligned with the research objective?
• Strategic depth and evolutionary perspective (20%): Does the proposal consider how persuasive drivers may differ across segments or evolve over time? Does it demonstrate an understanding of the dynamic nature of lender behaviour?
• Project feasibility within one week (10%): Can the proposed project be realistically completed within the available timeframe and using the provided dataset?
• Clarity, structure, and communication quality (10%): Is the proposal clearly written and logically organized, and effective in presenting objectives, methods, and expected outcomes?

Final presentation judging criteria

Judging panel (80% in total)

• Originality of research question (20%): Does the team pose a novel, interesting, and impactful question that advances understanding of prosocial crowdfunding and funding dynamics in subsistence marketplaces?
• Analytical approach and execution (20%): Are the chosen methods appropriate for the research question, and are they applied rigorously and effectively?
• Insights and communication (20%): Does the presentation offer meaningful, data-driven insights? Are the findings clearly interpreted and communicated in a compelling and accessible manner?
• Practical implications (20%): Are the insights translated into actionable implications for borrowers, crowdfunding platforms, or related stakeholders?

Audience choice (20%)

• Based on live audience voting. The top-voted team will receive full 20% marks, with other teams receiving proportionally scaled scores.
• Audience score = 20% × (Team votes ÷ Highest team votes)

Submit your proposal

Registration 

• Register via this webform
•  After registering, you will receive a link to the sample dataset.

Requirements

Specify the aim of your project, outline the methodologies, the data items you plan to use, the expected outcomes from the analyses, and the expected managerial implications. (You are not expected to complete your full analysis at the proposal stage.)

• Recommended Proposal Structure:

- Title, names, and affiliations

- Project aim and research questions

- Proposed analytical approaches

- Data items to be used

- Expected outcomes and managerial relevance

• Maximum word count: 1,500 words (excluding references).

Submission

• Submission window: 17 Aug (9am) ~ 24 Aug (5pm) 2026
• Email your proposal to: MA.Hackathon@unsw.edu.au

What happens after your submission?

• Proposal evaluation
  Your proposal will be evaluated by a judging panel. Those who advance to the final presentation will be notified via the registered email.
• Finalist teams work on the project
  Up to eight teams will advance to the final competition. The finalist teams will receive the full dataset, and have one week to work on the project and prepare for the final presentation.
• Final presentation
  The finalist teams will present their work on the final presentation day. Winners will be announced on the day.