Marketing Testing & Optimization Analyst at null,Hyderabad (Job in Hyderabad Secunderabad)
(Not Shown) (Please mention IndiaDynamics.com when contacting)
Job Description:Responsibilities: Review test hypotheses, help develop comprehensive test plans and success metrics, performing quality assurance on test cells, and calculating the final test results and deep dive analysis of the test results and craft Test Summaries using both behavioral and voice of the customer analytics to provide actionable insights for key business stakeholders. Use experimental design to optimize website and marketing activities as well as new in-product initiatives. Utilize best-in-class analytics tools, including the Adobe Marketing Cloud (eg. Target, Analytics, Ad Hoc Analysis etc.) to analyze test results and provide interpretation, guidance and recommendations to aid marketing decision making Partner with Marketing to identify key visitor segments, draft user stories for the ideal customer experience for each segment Collaborate with other team members to synthesize learnings from other analyses/sources to present holistic analysis and recommendations to stakeholders. Ensure solutions are scalable, repeatable, effective, and meet the expectations of stakeholders. Skills/ Qualifications: Bachelors degree required along with practical business experience in analyzing data. MBA or Masters in a quantitative field like Economics, Statistics, Engineering, or IT a plus 3-6 years of experience in analytics and/or marketing analytics. Experience in B2C and eCommerce is preferred. Expert in A/B and Multivariate testing, design of experiments, the mathematics of statistical hypothesis testing coupled with the ability to draw meaningful business insights across multiple tests. Good understanding of Microsoft Excel, RDBMS, Visualization and experience with Big Data tools like Hadoop and Hive. Experience with web analytics tools such as Adobe Analytics and Discover (strongly preferred), Google Analytics, or CoreMetrics Knowledge of test design, predictive modeling, and combining disparate data sources is a plus.