Marketing Research and Analytics
As a marketing research enthusiast with real-world experience gathering, cleaning, mining and modeling qualitative and quantitative data, I present thoughtful visualizations and stories for key performance indicators and metrics, that, in turn, inform actionable business recommendations across various industries.
Qualitative Brand Positioning Statement Research
Pharmaceutical Case Study: Positioning a New Hemophilia Treatment in the Market
This end-to-end qualitative market research and brand strategy project allowed me to summarize how two positioning statements for a breakthrough hemophilia drug produced by a Fortune 500 biotechnology client on perform across key metrics and how hemophilia patients interpret the emotional/functional benefits of each statement through in-depth interviews. Then, I recommended a positioning statement (original copy) for Product J that not only resonates with and motivates patients to learn more about the product, but also differentiates and positions it for growth in the current and anticipated competitive treatment landscape.
Principal Component Analysis
Brand Positioning of Popular Consumer Electronics Brands
In this project, I use R to analyze survey data that position popular consumer electronics brands, such as Sony, Apple and Bose, on various key perceptual attributes using Principal Component Analysis. I provide heatmaps, correlation plots, scree plots, correspondence maps and insights for targeted marketing strategies.
Conjoint Analysis
Impossible Foods: introducing a new vegan pork product
In this project, I use conjoint analysis in R to help Impossible Foods distinguish its brand identity from competitors like Beyond Meat and create the most desirable combination of cut, flavor, packaging and nutrition to produce a new vegan bacon product aimed at the flexitarian market. Then, we conducted market share analysis to test the product's estimated contribution and market segmentation analysis to determine the appropriate target markets based on our survey results.
Survey Design and Statistical Analysis
Airbnb Case Study: Improving Customer Satisfaction
This survey project allowed me to practice effective questionnaire
development comprehensively and familiarized me with different sampling techniques. With this knowledge, I gathered, in a team-oriented environment, primary data in a survey, and produced a conference quality report examining relationships between various factors that affect Airbnb customer satisfaction through statistical tests and analysis.
Search Engine Optimization (SEO) Audit
Client: Peninsula Humane Society & SPCA, Burlingame, CA
In this client-based project, I serve as a digital marketing consultant for a non-profit organization. I use the SaaS tool SemRush to evaluate its digital marketing impact, including online buyer behavior, website design, SEO and search engine marketing (SEM), media analytics, social media marketing, content creation, email marketing and online branding. I also run an A/B test for two visuals over a week-long Facebook ad campaign and generate key findings.
Time-series and causal analysis
Forecasting Nike's sales revenue and identifying its causal factors
In this project, I use hands-on spreadsheet models and metrics to forecast Nike's sales revenue for the year 2023 and identify correlations between potential internal and external causal factors, such as advertising and promotion costs, inventory, inflation rates and sales revenue.
Statistical data mining
San Francisco Museum of Modern Art Case Study: Revolutionizing Visitor Experience
In this project, I use the statistical programming language R to analyze survey data collected from 600 museum visitors at the San Francisco Museum of Modern Art (SFMOMA). Through statistical testing methods, I answer 7 research questions to explore relationships between satisfaction levels among visitors and other factors. These are then used to generate valuable consumer insights and inform actionable business decisions in brief executive summaries.
Statistical data mining
Improving customer satisfaction with prepared meal deliveries
In this project, I use the statistical programming language R to analyze data collected by a local company that prepares meals and ships them to customers' homes. Through statistical testing methods, including ANOVA, correlations, t-tests, and chi-square tests, I answer 2 research questions regarding satisfaction levels among customers and their relationships with other factors, which are then used to generate valuable consumer insights and inform actionable business decisions for the meal delivery company.
