Ian Alexander

•Campaign Execution: Launched and managed display, online video, and Streaming TV campaigns within Amazon DSP campaigns to meet client goals. •Collaboration: Worked alongside internal media and retail teams, as well as external brand teams to align on media strategy to support growth or revenue goals from pre-launch planning stages, regular reporting and office hours call cadence, and post-campaign evaluation. •Communication: Presented programmatic topics and data to a wide-range of internal and external stakeholders in simple, effective ways.

•Program Building: Created internal documents pertaining to best practices, campaign building and execution, taxonomy, KPIs, goals, and tactics in order to grow DSP offerings. •Campaign Execution: Deployed Amazon DSP campaigns, observing and continuously optimizing campaigns to help partners achieve success. •Partner Meetings and Reporting: Conducted partner meetings to review performance metrics and align on KPIs. •Collaboration: Supported and shared knowledge with Brand Managers and Search/Social team members to aid in executing media strategy and offering knowledge of Amazon Display offerings and tactics.

•Optimization: Monitored day-to-day performance of account objectives, troubleshooting problems and implementing a/b testing in the DSP to optimize campaign performance. •Reporting: Utilized Excel, Google Data Studio, and Tableau to compile and communicate analysis of campaign performance, providing account managers and clients with an easy-to-follow summary of findings. Also built reports for internal team use documenting experimental campaigns. •Strategy: Collaborated with internal team to stay on top of industry trends and brainstorm solutions, research strategies, and identify areas of growth. •Technical Training Documentation: Responsible for creating how-to guides detailing new processes and procedures for the team, as well as technical guides on uploading different programmatic products and best practices. •Budgeting: Responsible month over month for allocating money to programmatic products for 25-30 clients totaling over $200,000, while also ensuring spend and monitoring performance towards goals specific to awareness and consideration campaigns and products. •Served ads using TradeDesk DSP and Facebook for campaigns including display banner, video, CTV, carousel, single image, catalog, and traffic. •Optimized programmatic campaigns using bid factoring, frequency, geo, and audience changes. •Built-out and optimized target demographic audiences using TradeDesk’s DMP. •Experience with Celtra and Adacado creative management platform setup and troubleshooting.

•Dashboard Creation: Collaborated with clients, analysts, and client success managers to identify relevant KPIs and create interactive dashboards tailored to serve unique business needs for a variety of industries including manufacturing, transportation logistics, marketing, health care, libraries, tourism, and more. •Data Preparation: Performed exploratory data analysis on data from a variety of sources, using Tableau and Alteryx to import, clean, join, union, blend, transform, and validate data. •Tableau Core I Concepts Training: Provided training and support on data analysis, visualization and reporting tools, tailoring trainings to meet client needs while also keeping up to date with industry trends. Concepts covered included: connecting to and preparing data, structuring data for future use, aggregation and Tableau’s order of operations, calculation types (basic, table, level of detail), actions (filtering, sorting, grouping, creating parameters and sets), common chart types (dual axis, mapping, bar, Pareto, Gantt, time series, heatmap, bullet) dashboard and story building and formatting best practices.

•Leadership: Served on the School Leadership Team, participating in tackling school problems and charting a course for school improvement. •Led a Constituency Group dedicated to helping to ease the transition process of our new staff and acquainting them with school policies and staff members. •Mentored a student teacher from Appalachian State. •Coached middle school football for 4 years, posting a 28-4 record and winning 3 conference titles, including the first undefeated season in school history. •Data Analysis and Interpretation: Interpreted data from multiple assessment sources to use effectively in planning and goal-setting purposes. Used data to inform my teaching practices, helping my students to meet expectations for student growth in year 1 and exceed expectations in every year thereafter, as measured by the SAS Institute’s EVAAS predicted student growth model. Also led professional development focused on using data to inform teaching practices. •Public Speaking: Announced football and basketball games. Also worked with graduation speakers to help edit their speeches and develop their public speaking skills. •Collaboration: Collaborated with English and History department colleagues to develop lessons, as well as coaches to develop game and practice plans. Participated in and led team building exercises. •Communication: Communicated effectively across a broad spectrum of stakeholders including students of all ages, peers, parents, and supervisors. •Planning and Organization: Prior to the school year, created comprehensive pacing guides detailing what activities, assessments and objectives would be taught in each 90-minute class period for each of the 180 days.

Graduated with Honors in History; 5-time Dean’s List selection; Chancellor’s List Selection (2012); Major GPA: 3.42

Completed coursework through the Wake Tech Business Analytics Program concerning: o Foundation in Tableau, Excel, SAS, Python, PostgreSQL o Familiar with basic syntax of SAS, Python, PostgreSQl o Data preprocessing and exploratory data analysis (Excel, SAS, PostgreSQL, Python) o Relational databases and database management (PostgreSQL, Airtable) o Graphical representation of data for visualization and reporting purposes (Tableau, Python) o Communicate data insights and identify patterns and trends in the data (Tableau) o Supervised and unsupervised machine learning and data mining methods (SAS, Python) o Classification and regression model building (SAS, Python) o Model evaluation (SAS, Python)