AP is a leader in Commercial Analytics and Commercial Mix Modeling. We have consistently earned Forrester Wave's top prize for Marketing Measurement and Optimization. Our project process is to collect client marketing and economic data with which we leverage our proprietary technology, GPS-E, to conduct thorough analysis and provide optimization solutions for our clients, especially as they look to enhance the efficacy of their marketing dollars. My primary role at AP has been to support a number of our large, DTC retail clients (+$2B in Rev/yr) throughout their monthly and quarterly analysis programs. As a Consultant, I am a project manager, setting timelines and expectations with the client as well as our internal team. I lead my project's Analysts throughout modeling efforts and analysis, helping to synthesize key insights from modeled results for our clients such as optimization opportunities within their budget and business strategy. I utilize our proprietary tools to forecast future revenue and determine budget sufficiency, ultimately noting gaps in client strategy. I lead readouts and presentations for client's senior leadership, executives and media agencies to not only facilitate understanding of modeled results, but also identify $MM of opportunity for future Fiscal periods.

Transitioned roles internally to the Strategic Insights team within Growth Marketing's Actionable Insights and Analytics organization - my main role is to support the Global Commercial Marketing and Sales Segment teams with data requests and requirements, but also to help guide strategy for targeting and success for these particular sized companies.

Transitioned roles internally to Cisco’s Advanced Analytics and Insights team where I:Manage the State Machines program: a machine learning model developed by the Advanced Analytics team that is fed by both internal and external data sources to identify moments that matter in customer buying journeys. From these moments that matter, we plot customers on a purchase journey, visualize their dynamic behavior, and analyze how they purchase Cisco products to optimize our engagement at all stages of the buying process. As the manager I spend time educating stakeholders on the functionality of the tool, the intricacies of the model, as well as intaking requests for specific use cases. As the analyst, I perform analysis on account buying journeys for specific technology types or audiencesManage the Category 3 Lead Scoring and Cleansing relationship with Global regions as part of the Virtual Demand Center (VDC). The VDC is a new platform designed to send relevant leads to Cisco's call centers around the globe. Category 3 leads are identified by utilizing ML modeling techniques to score and send relevant contact information to respective call centers. Our team designed the methodology and I oversee regional relationships with stakeholders, as well as edit and maintain capacity requests in real timeManage our Engagement Points methodology, a model developed to attribute quantitative metrics to customers visiting Cisco's content. We have utilized Engagement Points as a KPI for recent global marketing campaignsManage the on-boarding of models and 3rd party vendor dataServe as the business owner of Adobe AnalyticsPrepare and present monthly demos for leadership regarding recent implementations, innovation, and updates to major team projectsAnalyze and implement the output from social listening platforms in order to map social content to account journeys, subsequently expanding our understanding around why and how accounts behave when they are likely to purchase

Hired out of undergrad as a member of Cisco's Early in Career Network, I worked on the Digital Marketing .com team. As a project specialist, I: - Organized and managed an initiative to identify and remove more than 4M pieces of old and poorly performing digital content as a part of cisco.com's larger digital transformation, including the creation of an automated portal that notifies stakeholders of said content and removes it on a regular cadence - Performed qualitative research via customer and partner interviews to determine the efficacy of Cisco’s collateral documentation- Worked with partners such as Oracle, Dun and Bradstreet, and 6Sense to build out and utilize predictive analytics in an effort to create successful competitive marketing campaigns- Worked with sales to implement predictive models that recommend additional products for customers to purchase, the best time to contact customers, and the likelihood for a customer to unsubscribe from our messages- Worked with global teams to bring predictive insights to regions outside of the US, such as Singapore, Australia, Mexico, and more- performed A/B tests on numerous cisco.com pages to determine the most effective and optimized content Cisco could provide

My primary focus for my Neuroscience Independent Study, I worked on a project titled “Exploratory Behavior in Reward Seeking,” which was comprised of multiple studies focused on understanding how the probability of reward affects human motivation to perform a specific task. Some studies were lab specific computer programs designed in MATLAB to study human motivation and motor control. Other studies were conducted within fMRI scanners equipped with ocular tracking devices provided by Duke University’s medical center. During the project, I conducted in-office studies and collected official data. The deeper data analysis was carried out under supervision of a graduate student, all in an effort to advance our understanding of reward motivation and how/if that manifests physically.

The Jones lab, prior to its departure from Duke University, created a collaborative study between Exercise Physiology and Oncology to determine the efficacy and mechanisms of exercise training on cardiovascular toxicity as well as tumor initiation and progression. The study was focused on understanding whether exercise served as an effective treatment for cancer. My role in the project included collection of blood work of patients participating in the study; helping facilitate exercise tests such as cycling intervals, treadmill intervals, and stretching with active participants; recording statistics, such as heart rate and blood pressure during exercise protocols in the research study.