Welcome to my portfolio
Simone Zhen
Data-Driven Digital Marketing & Campaign Optimization
Bridging Finance, Education & Digital Marketing
About Me
Business Analyst with 10+ years of experience across financial services, data analysis and process optimisation, supported by a Master's degree in Business Analytics from the University of Auckland.
I specialise in transforming complex data into actionable insights to support business decision-making. My experience spans customer analytics, financial performance analysis, and cross-functional process improvement, with a strong focus on delivering measurable business outcomes.
Key achievements include:
- Reduced manual reporting effort by 93% through automation of reporting and data processing workflows using Excel VBA, Power BI and structured ETL logic
- Built and maintained executive dashboards and KPI reporting frameworks to support business performance monitoring and strategic decision-making
- Improved reporting accuracy to 99.5% by reconciling and integrating complex financial and operational datasets across multiple systems
- Delivered process improvement initiatives that reduced reporting and operational turnaround times by up to 95% in regulated financial environments
- Supported 20+ business and financial analysis projects, translating complex business problems into structured analytical insights and practical recommendations
I have hands-on experience with Power BI, SQL, Python and advanced Excel, building dashboards, analysing performance trends, and supporting reporting and forecasting. My background in financial services also provides strong commercial awareness and regulatory understanding.
Currently based in Auckland, I am seeking Analyst opportunities where I can contribute to data-driven decision-making, reporting optimisation, and business performance improvement.
I bring a combination of analytical thinking, business understanding and stakeholder collaboration, and I am motivated to deliver practical, insight-driven solutions.
Feel free to connect if you're looking for someone who can turn data into meaningful business impact.
Experience
Professional Career
11/2025 - Present
Part-time
Business Intelligence Analyst / Client Success
PbearLab
Auckland, New Zealand
- •Partner with stakeholders to conduct requirement gathering, define reporting objectives, and translate business needs into dashboards, workflow improvements, automation opportunities, and actionable insights
- •Develop and maintain Power BI and Looker Studio dashboards, automated KPI reporting, and structured reporting outputs to improve visibility over commercial performance, customer behaviour, lead quality, and conversion trends
- •Perform analysis across customer journeys, lead funnels, campaign performance, and digital engagement data using GA4, CRM-connected datasets, and cross-channel reporting logic to identify optimisation opportunities
- •Support AI tool evaluation, marketing automation workflow design, and SOP development to improve efficiency, consistency, and day-to-day delivery across client projects
- •Improved reporting accuracy to 99.5% and reduced manual reporting effort by 93.8% through automation and structured reporting redesign; improved registration conversion by up to 20% through funnel analysis and landing page optimisation
Power BILooker StudioGA4SQLPythonCRM AnalyticsMarketing Automation
09/2021 - 03/2024
Full-time
Data & Research Analyst / Industry Project Coordinator
Shandong Vocational and Technical University of International Studies
Rizhao, China
- •Conducted data preparation, cleansing, transformation, and analysis across research and consulting projects using Python, SPSS, Excel, and Power BI
- •Produced analytical reports, dashboards, presentations, and structured reporting outputs to support stakeholder communication, project delivery, and decision-making
- •Worked closely with enterprise stakeholders to support requirements gathering, project coordination, reporting outputs, and structured collaboration across consulting-style initiatives
- •Delivered and supervised 50+ applied business and financial analysis projects across industry collaboration programmes; achieved 98%+ stakeholder satisfaction across partnered enterprise projects
- •Contributed analytical support to research initiatives awarded City-Level Comprehensive Research First Prize
Power BIPythonSPSSAdvanced ExcelSAP ERPCRM SystemsProject Coordination
11/2018 - 08/2021
Full-time
Financial Analyst / Relationship Manager
China Everbright Bank
Rizhao, China
- •Managed end-to-end customer relationship and service delivery across lending and retail banking products, ensuring high-quality service, issue resolution, and consistent client engagement
- •Processed 150+ loan applications annually, supporting documentation preparation, workflow coordination, and approval processes in line with internal policies and compliance requirements
- •Reviewed customer documentation and financial information to support lending-related analysis, file accuracy, and structured escalation through approval workflows
- •Prepared structured reports and presentation materials to support workflow updates, service observations, and internal communication
- •Maintained 98%+ customer satisfaction while managing complaint resolution and service recovery within SLA timelines; awarded Service Excellence Recognition for consistently high-quality customer engagement
Customer Relationship ManagementLending OperationsFinancial DocumentationCompliance ReportingExcel Automation
07/2014 - 09/2018
Full-time
Operations Teller / Relationship Manager
Bank of Rizhao
Rizhao, China
- •Handled high-volume daily banking operations across account services, transactions, lending support, and settlement activities, processing 50-100 transactions per day while ensuring accuracy and service stability
- •Supported customer onboarding, account services, and lending-related processes, providing product explanations and improving service responsiveness and customer experience
- •Maintained structured records and documentation to support compliance requirements, audit readiness, and operational consistency; supported AML/CDD compliance and regulatory adherence
- •Reduced loan-related calculation processing time from 2 hours to 10 minutes by developing an automated Excel-based solution, significantly improving operational efficiency and accuracy
- •Recognised as Outstanding Employee for strong performance in customer service and operational delivery; delivered structured training to approximately 12 staff members on front-line operations
Transaction ProcessingCustomer OnboardingAML/CDD ComplianceAdvanced ExcelStaff Training
Community Impact
Online Op Shop Volunteer
SPCA New Zealand
10/2025 - Present | Auckland, New Zealand
- •Supported fulfilment of customer orders
- •Monitored transaction records to enhance process efficiency
- •Researched donated goods history and collaborated with the Manager on pricing decisions
Retail OperationsPricing StrategyInventory Management
Event Planner Assistant
Posh Styling Entertainment
02/2025 - 03/2025 | Auckland, New Zealand
- •Improved project efficiency by 20% through structured Excel and MS Project tracking
- •Coordinated communication across agencies, designers and organisers for NZ International Fashion & Culture Week
Stakeholder coordinationCross-functional CommunicationTime Management
Volunteer
Beyond International Media Group
01/2025 | Auckland, New Zealand
- •Assisted at the People's Daily Overseas Edition booth
- •Engaged with 200+ attendees and promoted interest in the publication
Public RelationsBilingual CommunicationEvent Promotion
Community Impact
Projects
UnionAID Data Automation & Donor Analysis
- Objective: Automate donor data processing workflows and analyze transaction patterns to improve reporting efficiency and inform strategic fundraising decisions
- Methods:
- ETL Automation: Developed VBA scripts to extract donation data from Xero, apply rule-based transformations, and load cleaned data into Keela-compatible templates
- RFM Segmentation: Applied RFM (Recency, Frequency, Monetary) framework to capture donor engagement and value, followed by K-Means clustering (k=4)
- Dashboard Design: Created Power BI dashboards with clear visualization for real-time KPI monitoring
- Cohort Analysis: Tracked retention trends and identified drop-off points to inform engagement strategies
- Key results:
- Reduced Processing Time: Cut data processing time by 94% (from 4 hours to 15 minutes) using custom Excel VBA solution
- Donor Segmentation: Segmented donors into 4 distinct clusters (e.g., 'Loyal Mid-Spenders', 'Inactive Low-Value') to guide campaign decisions
- Retention Insights: Conducted cohort analysis to track donor retention trends, identifying key drop-off points
- Tools and tech: Excel VBA, Python (Clustering), Power BI
Excel VBAPythonPower BIK-Means
View Details Shopping Trends Analysis & Customer Segmentation (US Fashion Retail)
- Objective: Analyze a 2,500-customer shopping dataset and create actionable customer segments to support product and marketing decisions
- Methods:
- Built Power BI dashboards to monitor purchase amount, review ratings, subscription rate, and breakdowns by age group, income group, season, category, and shipping type
- Enriched the dataset by web-scraping US state per-capita personal income (PCPI) and merging it into the shopping dataset by state
- Standardized key numerical variables and applied PCA, then used K-means clustering (k=3) with silhouette score evaluation to validate segmentation quality
- Key results:
- Overall metrics: 2,500 customers, average purchase amount $59.67, average rating 3.75, and subscription rate 0.42
- Spending-level segmentation showed clear separation in average purchase (High $86.54 vs Medium $54.68 vs Low $29.06), supporting targeted strategies
- Delivered three data-driven customer clusters based on PCA-reduced features, with silhouette scoring used to assess cluster separation
- Tools and tech: Power BI, R (dplyr, ggplot2, rvest, httr, FactoMineR, factoextra, cluster), PCA, K-means, Silhouette Score
Power BIRK-MeansCustomer Segmentation
View Details Predicting SEO Underpricing
- Objective: Predict Seasoned Equity Offering (SEO) underpricing using market volatility indicators to inform investment strategies and pricing decisions
- Methods:
- Machine Learning (Random Forest), Feature Engineering, VIX Volatility Integration
- Collected and merged financial datasets from CRSP (stock prices and returns) and CBOE (VIX volatility index)
- Engineered features combining firm-specific characteristics with market-wide volatility measures
- Trained Random Forest classifier to predict underpricing probability based on combined feature set
- Conducted comparative analysis between models with and without VIX features
- Key results:
- Model Performance: Developed a Random Forest model achieving an AUC of 0.83 to predict Seasoned Equity Offering (SEO) underpricing
- Key Discovery: Proved that market volatility (VIX index) is a critical predictor of underpricing, significantly outperforming models that relied solely on firm-specific features
- Data Integration: Successfully merged financial datasets (CRSP and CBOE) to analyze the impact of market conditions on stock issuance
- Tools and tech: Python, Random Forest, Financial Databases (CRSP, CBOE), scikit-learn, pandas
PythonRandom ForestMachine LearningFinancial Analysis
View Details Fraud Detection Analysis for Bank A
- Objective: Develop and deploy machine learning models to detect fraudulent transactions and identify fraud patterns to inform strategic risk management decisions
- Methods:
- LightGBM, XGBoost, SMOTE (Oversampling), Stratified Sampling
- Applied advanced sampling techniques to handle class imbalance in transaction data
- Trained and compared multiple classification models (LightGBM, XGBoost, Logistic Regression) to identify the best-performing fraud detection algorithm
- Conducted temporal and value-based analysis to identify high-risk transaction patterns
- Key results:
- Top Model Accuracy: Achieved highest performance with the LightGBM model (ROC AUC 0.84, Balanced Accuracy 0.75), outperforming Logistic Regression
- Fraud Patterns: Identified that fraud peaks between 3:00-5:00 AM and in low-value transactions ($50-$150)
- Strategic Recommendations: Proposed 'Night-Time Transaction Locking' and 'Auto-Locking' features for high-risk hours to reduce fraud incidents
- Tools and tech: Python, LightGBM, XGBoost, Logistic Regression, Pandas, SMOTE
PythonLightGBMMachine LearningFraud Detection
View Details Client Activity and Churn Risk Prediction
- Objective: Predict client churn probability and analyze activity levels to develop targeted retention strategies and increase customer engagement for banking clients
- Methods:
- Activity Level Modeling: Built linear regression model (R² = 0.915) to analyze client activity levels based on savings, transactions, credit cards, checking accounts, income, and age
- Churn Prediction: Developed logistic regression model with cost-benefit analysis to predict churn probability, optimizing cutoff selection based on economic loss (FP=$20, FN=$100)
- Model Comparison: Tested 6 model variants with different variables and cutoff thresholds to minimize economic cost
- Variable Importance Analysis: Used Wald statistics and odds ratios to identify the most influential predictors of client behavior
- Key results:
- High Prediction Accuracy: Achieved 97.1% overall accuracy in churn classification with optimized cutoff at 0.1
- Activity Level Insights: Identified savings, number of transactions, and credit card usage as top three factors affecting client activity (explaining 91.5% of variance)
- Key Churn Predictors: Pinpointed age, transaction frequency, savings, investment, and credit card as most significant churn indicators
- Strategic Recommendations: Proposed social media marketing for activity increase, reward mechanisms and complaint management for churn reduction
- Tools and tech: SPSS, Python, Logistic Regression, Linear Regression, Cost-Benefit Analysis
SPSSLogistic RegressionChurn PredictionPython
View Details ANZ Financial Advice Market Landscape Report
- Objective: Analyse the Australia-New Zealand financial advice and wealth planning market to identify consumer access barriers, adviser-side workflow pain points, regulatory risks and digital solution opportunities
- Key results:
- Synthesised New Zealand and Australian financial advice market data, including adviser counts, consumer advice usage, access barriers and compliance pressure
- Identified core needs around digital onboarding, adviser productivity, client segmentation, operational reporting and hybrid advice delivery
- Produced a bilingual market landscape report and visual materials for business analysis and solution consulting portfolio positioning
- Translated market findings into solution themes relevant to fintech, wealth platforms, reporting and client workflow improvement
- Tools and tech: Market research, regulatory data analysis, business analysis, report writing, data visualisation, Python
Market ResearchBusiness AnalysisData VisualisationPython
View Details Education Website SEO Project + Basketball & Maths Lead Capture Analytics
- Objective: Supported education and training providers through two parallel workstreams: improving enquiry-to-booking funnel performance and developing an education website with SEO-friendly structure
- Lead Funnel Optimisation:
- Developed sample enquiry datasets for 102 basketball leads and 48 maths assessment leads, analysing funnel performance across enquiry, booking, attendance and paid conversion stages
- Identified key drop-off points and recommended improvements including simplified enquiry forms, same-day confirmation and segmented follow-up scripts, supporting estimated uplift in booking and attendance rates
- Produced lead source and intent analysis to support campaign budget allocation and prioritised follow-up strategy
- Website Development & SEO:
- Built a website structure for an education provider, covering course information, parent-focused content, contact flow and enquiry pathways
- Applied AI-assisted SEO optimisation techniques during website setup, including keyword-informed page naming, title/meta optimisation, heading hierarchy and content structuring aligned with parent search intent
- Tools and tech: Google Forms, Excel, CSV modelling, funnel analysis, HTML, SVG, AI tools for SEO/content optimisation, reporting prototype
SEOLead GenerationFunnel AnalysisWebsite Development
View Details Sports Equipment Export Lead Generation & Clearance Landing Page
- Objective: Help a Chinese B2B sports equipment factory reduce dependency on Alibaba.com, improve enquiry quality, and position existing inventory for bulk overseas buyers through an independent landing page and lead qualification strategy
- Key results:
- Developed a market entry and B2B lead generation strategy covering market opportunity, buyer personas, channel mix, Google Ads keyword themes, negative keywords and CRM lead scoring
- Built a high-converting clearance landing page focused on bulk buyers, product showcase, factory credibility, MOQ-based pricing and structured RFQ capture
- Repositioned the offer away from retail consumers and toward distributors, schools, clubs, gyms, facility contractors and OEM buyers
- Designed a 90-day MVP plan to test website, Google Search campaigns, lead quality and cost per qualified lead before scaling budget
- Tools and tech: Market research, Google Ads strategy, SEO content planning, landing page copywriting, HTML, CRM lead scoring
Google AdsB2B Lead GenerationLanding PageSEO
View Details Forecasto - Advanced Project Management & Optimization
- Objective: Optimize the launch timeline for a new weather forecasting platform through advanced project management techniques, minimizing costs and maximizing efficiency
- Methods:
- Applied PERT (Program Evaluation and Review Technique) and 3-point estimation to calculate probabilistic timelines for each task, enabling uncertainty quantification
- Performed Critical Path Method (CPM) analysis to identify the longest sequence of activities (critical path) that dictated the minimum project duration
- Created Linear Programming models to minimize project costs under a series of time and resource constraints, using decision variables and constraints to balance cost versus time trade-offs
- Conducted sensitivity analysis to evaluate the impact of activity duration changes on the entire project schedule
- Key results:
- Reduced overall project duration from an initial estimate of 40 weeks to 33 weeks by identifying opportunities for task parallelization and shortening critical activities
- Achieved an optimal cost-time point of $495,386 and 33 weeks, representing a 25% budget savings over the less-optimized scenarios
- Detected that crashing "Backend Development" (the longest critical path activity) by 10 weeks offered the highest cost-effectiveness, enabling prioritized resource allocation
- Provided stakeholders with a range of scenarios (including crash and normal durations), allowing informed decision-making based on risk appetite and budget flexibility
- Tools and tech: Microsoft Project, Linear Programming (LP), PERT, CPM, Gantt Charts, Sensitivity Analysis
Microsoft ProjectPERTLinear Programming
View Details Predicting House Prices
- Objective: Build and evaluate machine learning models to predict house prices based on property characteristics, improving understanding of key price drivers and model performance in a real-world style dataset
- Methods:
- Collected and prepared structured housing data (e.g., size, rooms, age, location) from the provided course dataset
- Performed data cleaning and preprocessing, including handling missing values, encoding categorical variables, and feature scaling/engineering
- Conducted exploratory data analysis to identify important features and relationships between property attributes and sale prices
- Trained and compared regression models (e.g., linear regression and tree-based methods) to predict house prices
- Evaluated model performance using error metrics such as RMSE/MAE and refined features and parameters to improve accuracy
- Key results:
- Identified key property attributes that have strong impact on house prices and quantified their effects through model coefficients/feature importance
- Achieved a predictive model with reasonable error metrics on validation/test data, demonstrating the ability to forecast sale prices from tabular property data
- Tools and tech: Excel, Python (pandas, scikit-learn, visualization libraries) / R (for data analysis and modelling), Jupyter Notebook, basic SQL for data handling
ExcelPythonMachine LearningRegressionscikit-learn
View Details Impact of Population Dynamics on Housing Prices in NZ
- Objective: Analyze how population trends (growth, age structure, migration) affect housing price dynamics, including demand-supply interactions, affordability, and policy implications in New Zealand
- Methods:
- Collect demographic and housing market data from national statistics, census, and reputable sources
- Apply econometric models to estimate price elasticities, demand shifts, and supply constraints
- Develop scenario analyses to simulate future housing prices under different population trajectories
- Create dashboards (Power BI/Excel) to visualize key indicators and projections
- Key results:
- Quantified relationships between population growth and housing price movements under multiple scenarios
- Provided policy and investment implications to improve housing affordability and market stability
- Tools and tech: Excel, Power BI, R/Python (for data analysis), SQL (data extraction), econometrics packages
Power BIExcelPythonR
View Details Adventure Works Product Sales Performance Analysis
- Objective: Design and implement a comprehensive data warehouse solution to analyze product sales performance across multiple dimensions including channels, promotions, regions, and time periods
- Methods:
- Star Schema Design, ETL Process, OLAP Cube Creation
- Designed Star Schema with 4 dimension tables (DimChannel, DimPromotion, DimGeography, DimDate) and 1 fact table (FactSales)
- Implemented advanced SSIS transformations including Lookup, Derived Columns, and Conditional Split to handle data quality issues
- Built SSAS cubes with calculated measures (Revenue, Profit) and advanced features (perspectives, translations, aggregations)
- Key results:
- Successfully processed 169,312 rows of sales data with zero errors through ETL pipeline
- Enabled multi-dimensional analysis across 4 sales channels, 16 promotion types, 110 regions, and 1,461 dates
- Created 9 perspectives and 5 translations for global stakeholder access
- Delivered actionable insights on product profitability and channel performance through SSAS cubes
- Tools and tech: SQL Server, SSIS, SSAS, Star Schema, MDX
SQL ServerSSISSSASData Warehouse
View Details Retirement Planning Decision Analytics
- Objective: Analyze retirement savings strategy for a 30-year-old professional to determine if current investments will meet long-term retirement goals and provide data-driven recommendations
- Methods:
- Built Excel simulation models using normal distribution to predict salary growth (3.5% annual average with 0.7% standard deviation)
- Analyzed investment allocation across three funds (Conservative 40%, Balanced 30%, Growth 30%) with varying volatility and return rates
- Conducted scenario analysis to test different savings rates (2.5% to 6%) and contribution rates (3% to 6%) to identify optimal strategies
- Calculated projected retirement expenses based on NZ average life expectancy (83 years) and annual expenditure ($30,700)
- Key results:
- Identified Savings Gap: Current plan shows $125,600 shortfall between retirement savings ($427,000) and required funds ($552,600)
- Recommended Strategy 1: Increase individual savings rate from 2.5% to 6% to close the retirement gap while maintaining current contribution levels
- Recommended Strategy 2: Increase individual contribution from 3% to 6% (with employer matching) to strengthen retirement fund growth
- Validated both strategies through 50+ simulation runs, confirming they eliminate the retirement shortfall and maintain quality of life post-retirement
- Tools and tech: Microsoft Excel, Monte Carlo Simulation, Normal Distribution Modeling, Financial Planning Analysis
ExcelFinancial ModelingSimulationDecision Analytics
View Details Skills & Highlights
Technical Skills
ExcelPythonSQLPower BIGoogle AnalyticsSalesforce Marketing CloudCustomer SegmentationCampaign AnalyticsCRM SystemsData Visualization
Languages
English - ProfessionalMandarin - Native
Certifications
| Year | Certification Name |
|---|---|
| 2025 | Google Ads Search & Display Certification |
| 2025 | Google Analytics Certification |
| 2025 | Salesforce Certified Agentforce Specialist |
| 2025 | Microsoft Certified: Power BI Data Analyst Associate |
| 2022 | Higher Education Teacher Qualification |
| 2021 | Securities Investment Fund Practitioner |
| 2020 | Certified International Psychological Consultant |
| 2015 | Banking & Insurance Practitioner Certificates |
| 2015 | AFP (Associate Financial Planner) |
| 2013 | Accounting Practitioner Certificate |
Honors & Awards
| Year | Achievement |
|---|---|
| 2024 | A+ in Excel Course (University of Auckland) |
| 2023 | Third Prize: BRICS Vocational Skills Competition (Fintech Category) – Instructor |
| 2023 | Third Prize: Provincial "Smart Finance" Skills Competition – Instructor |
| 2022 | First Prize: City-Level Comprehensive Research Competition |
| 2022 | First Prize: Teacher's Skills Competition (Comprehensive Finance) |
| 2017 | Advanced Worker Award (Bank of Rizhao) |
Education
Master of Business Analytics
University of Auckland | Auckland, NZ
Apr 2024 - Jun 2025
A comprehensive curriculum bridging technical data skills with business strategy. Core focus areas include Predictive Business Analytics and Decision Analytics for data-driven problem solving, alongside practical applications in FinTech, Project Management, and Consultancy Practice. Proficient in using industry-standard tools for Data Visualisation and interpreting complex datasets to drive strategic business decisions.
Bachelor of Finance & Cultural Industry Management (Double Degree)
The Shandong University of Finance and Economics | Jinan, China
Sep 2010 - Jun 2014
Major in Finance
Core curriculum focused on quantitative economics and banking operations. Key coursework included Political Economy, Western Economics, Monetary Banking, Public Finance, Accounting, and Corporate Finance. Developed strong analytical skills through Advanced Statistics, Probability Theory, and Econometrics. Gained practical knowledge in Commercial Bank Management, International Finance, Securities Investment, and Financial Markets, supported by a solid foundation in Economic Law.
Major in Cultural Industry Management
Specialized management training for the creative and media sectors. Core studies covered Media Communication, Social Media Marketing, and Cultural Management. Gained strategic insight into the Creative Economy, Cultural Policy & Regulations, and International Cultural Trade. Broadened perspective through courses in Art Theory, Global Cultural Industries, and Cultural Consumer Behavior, bridging the gap between artistic content and market strategy.
Get in Touch
Let's connect and explore opportunities together
© 2025 Simone Zhen. Crafted with precision for data-driven excellence.