Business Operations Intern, STACK Fintech
STACK (now acquired by Californian-based Credit Sesame) was a financial technology company that provided users with a fee-free card that rewarded them on everyday spending.
Amidst COVID-19 uncertainties, I analyzed consumer shopping trends, aiding STACK with strategic insights. Collaborating with the marketing team, I enhanced promotional effectiveness, resulting in a 6% post-promotion behavior increase. I also addressed software disruptions by analyzing their impact and developing proactive user notifications. Additionally, partnering with the fraud team, I automated fraud detection, flagging 250 suspicious accounts.
Impact Analysis of COVID-19: COVID-19 brought a lot of uncertainty to many organizations, including at STACK. My curiosity into the changes of consumers’ shopping trends lead me to conduct a thorough analysis of the impact COVID had on the organization. By diving into financial and user behaviour data, I was able to identify patterns in spending habits and recommend initiatives to the executive team during a company-wide presentation.
Promotion Impact Analysis: Working closely with the marketing team, I conducted multiple tests to assess the efficacy of various promotional and reward/offer material. By creating guidelines for measurement and success, I was able to analyze a pre-post test for a frequency-based promotion, implementing recommendations that led to a 6% increase in desired post-promotion behavior.
Downtime Optimization: Due to dependence on various third-party banking platforms and APIs, occasional disruptions would occur within our software. To address this, I conducted an analysis to assess the impact and frequency of these disruptions and collaborated closely with the product team to developed banners and other tools to proactively inform users about potential delays and provide clear expectations for their experience.
Fraud Detection: Collaborating with the fraud team, I assisted in identifying fraudulent behavior, evaluating its impact, and implementing automated fraud detection using SQL-based parameters. This initiative led to the identification and flagging of approximately 250 new suspicious accounts.
