B. Key Accountabilities
Tag Management
• Own channel wise mapping and click to visit sanity
• Implement best practices in Tag Management
Adobe Analytics Implementation
• Define and own dimensions and metrics
• Define channel rules and custom implementation as per business objectives
• Own GA/AA versus DB variance in transactions and revenue
• Installing, configuring, testing, and troubleshooting Adobe software products such as Adobe Experience Manager, Adobe Analytics, Adobe Target, and Adobe Campaign.
• Providing technical guidance and best practices for Adobe software implementation and integration.
• Creating and maintaining documentation and reports on Adobe software performance, issues, and solutions.
A/B Testing & Personalization
• Drive the overall conversion optimization and landing page optimization agenda
• Co-own traffic to lead conversion with Product
Attribution and MIS Automation
• Manage the complexity of multiple truths (Adwords, DBM, Facebook, Google analytics, Internal CRM) and converge on a single truth
• Automation of critical dashboards for decision making and business insights
Channel mix modelling & Data driven attribution
• Answer the ultimate questions. How much to spend in which channel?
• Who to spanet, when to spend and which channel to use?
• How to successfully move from campaign to audience spaneting?
Other Competencies -
• Apply advanced statistical and analytical techniques, such as machine learning, predictive modeling, and optimization, to generate insights and solutions for complex business problems and opportunities
• Evaluate and validate the quality, accuracy, and relevance of the data and analytics outputs, and ensure they adhere to the standards and best
• Stay updated with the latest trends and developments in the data and analytics field, and leverage them to improve the teams capabilities and performance
• Lead and mentor a team of data analysts and engineers, and foster a culture of learning, innovation, and collaboration within the team and across the organization
Technical Competencies (Preferred domain knowledge) -
• SQL: Competency to write and execute complex queries, join multiple tables, create views and functions, and optimize the performance of your database.
• Python: Ability to use Python for data manipulation, processing, and modeling, as well as for creating web applications and APIs. You should also be proficient in using libraries such as pandas, numpy, scipy, sklearn, matplotlib, seaborn, and flask.
• R: Use R for statistical analysis, data visualization, and machine learning. You should also be familiar with popular packages such as tidyverse, ggplot2, dplyr, tidyr, caret, and shiny.
• Tableau: Ability to create interactive dashboards and reports using Tableau, as well as connect to various data sources and perform data blending and aggregation.
• Power BI: Competency to use Power BI for data visualization and business intelligence, as well as create and share reports and dashboards using Power BI Desktop and Power BI Service.
• Familiar with web services and APIs such as REST, SOAP, and OAuth.
C. Skills/Qualities Required
Strong analytical and critical thinking skills, with proficiency in data analysis tools and techniques Excellent communication skills, capable of translating complex data into clear, actionable insights for non-technical stakeholders
Detail-oriented with strong organizational skills, able to manage multiple projects and meet deadlines Keen interest in staying updated with the latest trends in data analytics and e-commerce