Dennis Pong

• Developed and refined self-service reporting in Power BI for various marketing functions, including Field and Partner Marketing, Branding, Events, and Sales Funnel and Pipeline Reporting.
• Conducted predictive modeling analysis with DataRobot notebooks to assist with marketing field event planning, campaign optimization, and revenue incrementality analysis.
• Collaborated cross-functionally with IT to build accurate data sources and pipelines, powering marketing reporting, and developing intermediate dashboards for data quality monitoring.
• Evaluated and integrated vendors such as Dataiku and CaliberMind to enhance the internal marketing ecosystem.
• Established daily guidelines to address roadblocks in data and metrics availability, accuracy, and usability.
• Developed and implemented strategies to increase customer satisfaction and loyalty.
• Acted as a 'player-coach' to enhance the impact of the Analytics team, ensuring the right work was done at the right level of depth, and within deadlines.
• Partnered with internal stakeholders to ensure efficient project execution.
• Collaborated with Data Engineering teams to define internal data projects, improving self-service capabilities, and analyst efficiency.
• Reinforced a strong team culture in a remote-first work environment.
• Supported the career development of individual contributors and team leads.

Key Achievements:

• Successfully led the design and implementation of predictive models for customer segmentation and CLV analysis.
• Executed deployment of advanced algorithms to enhance business growth.
• Provided strategic insights on conversion rates, and recommended strategies to enhance customer experience and increase conversions.
• Developed and refined multi-touch attribution models to understand the impact of different marketing channels on customer conversions.

• Analyzed data from various sources to identify trends and patterns.
• Developed complex financial models for forecasting and budgeting purposes.
• Prepared presentations on results of analysis for management team.
• Conducted research to compare competitive products in the marketplace.
• Created detailed reports that documented findings and presented recommendations to senior leadership.
• Coordinated with other departments to ensure accuracy of data collection and analysis.
• Provided technical support to junior analysts regarding data analysis techniques and methods.
• Assisted with design of experiments utilizing advanced statistical methods such as ANOVA or regression analysis.
• Leveraged machine learning algorithms such as decision trees or random forests for predictive analytics projects.
• Created and designed business intelligence databases, spreadsheets or outputs.
• Analyzed competitive market strategies through related product, market or share trends.
• Performed ad-hoc analyses using SAS and PROC SQL scripts to build scorecard and reporting for weekly business reviews.

• Served as the leading expert in everything experimentation, from instrumentation to Google Tag Manager, to interpretation of A/B testing results for product analytics, growth analytics, and marketing analytics.
• Defined the next level of OKRs that are aligned with the expanding needs for changes to the websites, with ownership of funnel analytics and website optimizations.
• Provided support for Media Mix Modeling optimizations with advanced scaling techniques in Python vectorizations and leverage of concepts in circulant matrices.
• Built a predicted LTV (pLTV) in connection with FRM segmentation techniques for guiding product roadmap and marketing campaign segmentation needs.
• Achieved the business goals of providing churn modeling support with an intermediation strategy, which resulted in a 4% retention improvement.
• Helped guide experiments by constantly providing consultation services on choices of primary metrics, secondary metrics, and guardrail metrics.
• Created Experimentation Base and Post-Analysis templates to allow scale-up of experiments and monitoring of multiple experiments.
• A/A tests to make sure of the readiness from an instrumentation perspective as we moved to GasV3 and away from Segment. We also discovered issues with tracking and data quality.
• Crafted document for Causal Inference (or quasi-) experimentation setup work – when to use, what methods to choose.
• Provided thought leadership on experimental designs, from full-factorial designs to fractional factorial designs, for multivariate testing
• When to use block designs, when to use sequential testing, when to use instrumental variables, when to use difference-in-difference along with synthetic controls.

• Analyzed campaign results from slack-eligible traits and gave strategic recommendations on next steps to improve conversions
• Created global and personalized real time reports system for executive stakeholders and processes in Tableau, Redash (Redshift) and Databricks (Spark, Python and R)
• Created ETL scripts to create session-based aggregated tables to target customer journeys based on contact type and purchasing behavior based on product bundling and had a gain of efficiency of 15%

• Introduced new testing strategies to the shift of customer purchasing behaviors due to pre-pandemic gathering restrictions.
• Performed statistical modeling to financial planning exercises to hedge risks of improving/declining department of sales.
• Built the capability to source data from the Adobe API and built an innovative machine learning model for personalization initiatives to drive margin, AOV, and conversion.

• Spearheaded the effort in revamping the Referral Program
• Brought in an additional 1.2 K Subs / quarter
• Built an end-to-end API solution in retrieving and approving friend conversions in order to sieve out fraudulent activities
• Analyzed original segmentation program for retention and came up with new churn prediction scoring to help reduce churn rates
• Leveraged clustering approach to match different treatments to customers with varying level of churn risks
• Assessed Cohorted Retention Rates associated with incremental attributions associated with price increase email notifications
• Multi-company discounts expiring attrition risk evaluation and reporting
• Evaluated incremental cancels for price increase notification and billing phases
• Assisted Group Marketing leaders in evaluating the potential risks from various credit card cleanups efforts
• Evaluated incremental saves using Observe point eVars tags and click stream data
• Trial Abandoner Segmentation design using various methods like looking at the frequencies of 28 key product usage metrics or a more scientific method called Principal Component Analysis (PCAs)
• Developed dashboard for marketing partner to come up with a segment for a new marketing initiative called Delayed Launchers

• Boosted the SEM tactics performances by launching Big Ticket Item product categorizations by an estimated sales of $3.75 Million
• Promotion Optimizations spurred sales of about 110% MoM starting in Nov 2017
• Identified gaps in existing affiliate campaigns – Increased ROIs YoY by 25.4%
• Identified gaps in promotional campaigns – steady increase in monthly sales in FY 2016 of about 6.2 - 12.3%
• Performed Free-shipping threshold testing and experimental design
• Performed promotion optimizations for Google’s Product Listing Ads (PLAs)
• Performed Big Ticket product type optimizations
• Performed Time Series Analysis on current SEM Sales Data via Channel Advisor (CA) and Affiliate Sales Data
• Created tableau charts examining the distribution of certain key segmentation variables like gross margins

• Presented AEP weekly results to the management team, as well as senior-level audiences.
• Provided programming support to KPIs used in marketing.
• Produced a comparison of results based on pre-fixed and post-fixed marketing response data to evaluate the impact of the fix.
• Delivered statistical test results to Sr. Management and other stakeholders within the Medicare Direct Marketing team.
• Leveraged SAS data steps to create datasets ready for some SAS statistical procedures, like PROC LIFETEST.
• Helped the company save $975K by successfully implementing new householding logic for AEP Direct Mail Campaigns.
• Identified Latino segments and helped evaluate the results of the pilot programs.