Jennifer Adkins Email and Phone Number

Promoted in 2024.Focusing on Microsoft Copilot metrics and A/B experimentation. Leveraging a combination of Power BI, Kusto Query Language, and Python scripts to make data about Microsoft Copilot usage accessible to decision makers.Co-Lead for Women at Microsoft Atlanta

Reporting to the Director of Marketing Sciences, responsible for presenting data science and analytics findings while serving as liaison between the Global Marketing Sciences division and Executive Leadership. Responsible for providing project-based leadership to a 5-member, globally distributed team of Senior Marketing Analysts and Data Scientists.

Reporting to the Director of Marketing Sciences within the Enterprise Marketing division, responsible for providing project-based leadership to a 5-member globally distributed team of Marketing Analysts with a focus on Revenue Modeling, Sales Lead Scoring, Customer Engagement Analysis, and Ad-Hoc Strategic Analysis for Executive Leadership. • Global Marketing Analysis – Serving as liaison between the Global Marketing Sciences Team and the Executive Leadership Team, responsible for presenting findings, predictions, and outcomes to a range of internal audiences. • Lead Scoring – As the Business Systems Analyst for the Lead Scoring Model, utilized the existing Gradient Boosting Model to identify trends and present recommendations to improve conversion rates to Senior Leadership. - As a result, the sales team achieved a 10% conversion rate on leads, up from 7% in prior years.• Client Engagement Scoring – In an effort to quantify SAS Client Engagement down to the regional, account, and individual levels, refined and expanded the legacy Client Engagement Scoring capability to include event, class, and webinar attendance, email and phone engagement, and purchase behavior of 100k accounts and over 1m contacts. - Developed, implemented, and led user training for a self-service Enterprise Customer Engagement dashboard with data refreshed daily, using the SAS Visual Analytics technology stack. • Contributed to the expansion of the Marketing Sciences team through candidate interview, assessment, and selection process, serving as a core member of the Marketing Sciences hiring team on four new analyst hires.

Reporting to the Director of Customer Insights, responsible for the SAS Global Forum Survey and the bi-annual SAS Customer Satisfaction Survey with a focus on presenting findings to leadership and developing dynamic dashboards.• Revenue Analysis – Conducted ad hoc revenue analytics projects focused on uncovering patterns and insights regarding trends in customer behavior used by executive leadership to inform strategy and resource allocation. - Led due diligence modeling for a partnership with Microsoft, verifying revenue and client growth potential. • Selected to plan, draft, execute, and present findings for the SAS Global Forum Survey focused on understanding SAS user behavior at the annual SAS Global Forum with an average of 4000 attendees from around the world. - Achieved a 15% response rate, recording responses for over 1200 SAS Global Forum attendees.• Consumer AI Sentiment – With the strategic imperative of understanding SAS Customer Sentiment regarding applied Artificial Intelligence, led a matrixed team through the process of developing, fielding, and analyzing a Qualtrics based survey of SAS’s 5000-member preferred customer panel. • Social Media Adoption and Corporate Communication – Led a matrixed team through the process of surveying 12,000 US based SAS employees with a focus on understanding how the domestic employee base uses platforms like LinkedIn, Facebook, Twitter, Instagram, and WeChat to consume corporate communications and news content.

Reporting to the Director of Market Research within the Enterprise Marketing Division, responsible for the development, analysis, and presentation of both internal and external trends, employee and client sentiment, and ad-hoc requests. • Drafted, fielded, analyzed, and presented the bi-annual SAS Customer Satisfaction Survey with 14000 survey respondents. Led the development, automation, and implementation of the SAS Customer Satisfaction Dashboard, consolidating the 20 manually populated PowerPoint presentations into a single dynamic dashboard. • To improve enterprise-wide internal communications, partnered with the SAS Internal Communications Team to draft, field, and analyze responses from 15k employees regarding their news consumption preferences. - Based on the trends of employee responses, identified a clear bifurcation of audience preferences with a high correlation to respondent age. Presented the findings to the Internal Communications team and Senior Leadership, recommending a dual track communication strategy to reach both audiences more effectively.

Within the Professional Services division, on a 12-member team, responsible for supporting a large hotel chain through the planning, design, implementation, testing, and end user training of a SAS based automated room pricing platform. • Provided project-based leadership to a team of four analysts with a focus on building accurate and scalable room pricing models based on local and regional occupancy rates, seasonality, franchise overbook preferences, available room types, and standardized pricing bands for just under 6000 Hotels & Resorts locations.AREAS OF EXPERTISE AND TECHNOLOGY PROFILEData Science Best Practices | Kusto Query Language | KQL | SQL | PowerBI Dashboards | Python | C# | Visual Studio | Scope Studio | Visio | Automated Analysis | Site-level Forecasting | Regression Models | Key Driver Analysis | SAS | Linux | Revenue Prediction | Association Measurement | Linux Scripting | Ad Hoc Analysis | Putty Shell Scripting | SAS Visual Analytics | NPS | Data Cleaning | R | Machine Learning | Database Optimization | Statistical Theory | Time Series Analysis | Experiment | Supervised Learning | Decision Maker Support | Pattern Recognition | Data Visualization | Exploratory Data Analysis | Feature Selection | Model Selection | Model Validation | Hyperparameter Tuning | Cross-Validation | Ensemble Methods | Overfitting Prevention | Regularization | Bias-Variance Tradeoff | Interpretability | Ethics | Privacy | Security | Communication | Agile Methodology | Project Management | Documentation | Collaboration | Technical Writing | Business Acumen | Critical Thinking | Problem-Solving | Creativity | Innovation | Continuous Learning

Responsible for analyzing and forecasting merchandise and fuel sales data across 1500 Kangaroo Express Locations. Assessed and reported on regional competitor pricing disparities, developed automated reports based on 6000 individual models using SAS and Linux scripts, and presented strategic insights and recommendations to leadership.AREAS OF EXPERTISE AND TECHNOLOGY PROFILEData Science Best Practices | Kusto Query Language | KQL | SQL | PowerBI Dashboards | Python | C# | Visual Studio | Scope Studio | Visio | Automated Analysis | Site-level Forecasting | Regression Models | Key Driver Analysis | SAS | Linux | Revenue Prediction | Association Measurement | Linux Scripting | Ad Hoc Analysis | Putty Shell Scripting | SAS Visual Analytics | NPS | Data Cleaning | R | Machine Learning | Database Optimization | Statistical Theory | Time Series Analysis | Experiment | Supervised Learning | Decision Maker Support | Pattern Recognition | Data Visualization | Exploratory Data Analysis | Feature Selection | Model Selection | Model Validation | Hyperparameter Tuning | Cross-Validation | Ensemble Methods | Overfitting Prevention | Regularization | Bias-Variance Tradeoff | Interpretability | Ethics | Privacy | Security | Communication | Agile Methodology | Project Management | Documentation | Collaboration | Technical Writing | Business Acumen | Critical Thinking | Problem-Solving | Creativity | Innovation | Continuous Learning