阿里巴巴LAZADA-Performance Marketing海外广告优化师-北京2

1.参与用户增长的渠道运营，对主流流量渠道有一定的了解，负责投放、优化的关键环节，譬如首页、广告内容、feeds等； 2.分析渠道绩效表现，根据不同的目标用户来调整渠道策略，通过A/B test确定渠道优化方案，提升整体流量ROI； 3.推动渠道系统优化和整合，与产品技术团队一起改进系统，提升渠道效率； 4.把与周边团队紧密配合，尤其是内容、品牌、商务团队，培养整体营销观，为平台带来更大价值。

【职位名称】：Lazada平台广告投放专家 【职位描述】： 1、负责Lazada电商平台的海外广告投放策略制定与执行，确保广告效果和ROI。 2、具备深厚的数据分析能力，能从海量数据中提炼关键信息，优化广告投放效果。 3、拥有TikTok/Meta/Google等社交媒体广告投放经验，熟悉其广告系统和用户行为模式。 4、对广告素材有敏锐的洞察力，能创作和迭代爆款广告素材，提升用户点击和转化。 5、与跨部门团队紧密合作，协调资源，确保广告活动的顺利进行和目标达成。 1、Responsible for formulating and executing overseas advertising strategies on the Lazada e-commerce platform, ensuring ad effectiveness and ROI. 2、Strong data analytical skills to extract key insights from large datasets, optimizing ad performance. 3、Hands-on experience with TikTok/Meta/Google and other social media advertising, familiar with their ad systems and user behavior patterns. 4、High sensitivity to advertising materials, capable of creating and iterating on viral ad content to increase user engagement and conversions. 5、Collaborate closely with cross-functional teams, coordinating resources to ensure smooth ad campaigns and target achievement.

职位详情 1. 负责滴滴海外国际化广告增长的效果评估及用户行为趋势，通过深度数据洞察，挖掘关键驱动因素，推动业务增长； 2. 设计并验证流量归因模型 3. 设计并实施预算分配和竞价策略的优化模型，提升投放ROI; 4. 将业务逻辑转化为明确的数据需求，校验数据集准确性，优化数据架构； 5. 参与制定数据质量标准，执行质量检查，保障分析与建模数据可靠性。 6. 搭建数据看板，实现实时数据展示与策略决策支持; 7. 与数据科学家紧密合作，定义假设，推动业务问题转化为分析模型； 8. 与数据工程师紧密配合，确保数据稳定、可扩展，满足分析需求。 9. 主导端到端数据分析项目，从需求梳理到成果交付，联动产品、工程及营销团队。 Experienced in data analytics, prefer in growth and advertising, with a strong focus on A/B testing, predictive modeling, ETL and dashboard development. Skilled in identifying actionable insights to optimize budget allocation and campaign performance. Key areas: Statistical analysis to evaluate marketing effectiveness and user behavior trends. Design and implementation of optimization models for budget distribution and campaign bidding strategies. Dashboard creation for automated performance monitoring and strategic decision-making. Close collaboration with Data Scientists to define hypotheses and translate business questions into analytical models. 🔗 Collaboration with Data Engineers Partner with data engineers to ensure data pipelines are robust, scalable, and aligned with analytical needs. Translate business logic into clear data requirements, validate output datasets, and contribute to improving data architecture. Help define data quality standards and perform QA checks to guarantee reliable inputs for analytics and modeling. 🧩 Project Management & Stakeholder Coordination Act as a bridge between technical and non-technical teams, managing timelines, deliverables, and communication for cross-functional projects. Lead end-to-end analytics initiatives from scoping to delivery, aligning with product, engineering, and marketing teams. Define priorities, set ETAs, and ensure timely execution, often using agile tools (e.g., JIRA, Asana, Notion) to track progress.

数据收集、回传及处理 1. 搭建完善的广告投放数据监测体系，实时收集、整理与分析广告投放数据，包括曝光量、点击量、转化率、成本等关键指标，深入洞察广告投放效果及用户行为路径； 2. 负责接收、回传、整合来自各大广告平台、公司内部业务系统等多渠道的数据，确保数据的完整性与准确性； 3. 对原始数据进行清洗，识别并处理数据中的缺失值、重复值和异常值，为后续分析提供高质量的数据基础； 4. 协助产研部门建立和维护数据仓库或数据库，优化数据存储结构，提高数据查询和调用的效率。​ 数据分析与挖掘​ 1. 对广告投放数据进行深度分析，挖掘数据中的潜在规律和趋势，为广告投放策略的优化提供数据支持；​ 2. 评估不同广告创意、投放策略的效果差异，为决策提供科学依据； 3. 及时发现投放中的低效，以及舞弊的流量，为公司降低损失。 4. 使用数据可视化工具将复杂的数据转化为直观易懂的图表，清晰展示广告投放效果及关键指标的变化趋势。定期撰写详细的数据报告，对广告投放效果进行全面解读，包括各项指标的完成情况、问题分析以及改进建议。 5. 积极协助产研部门优化投放工具和数据基建，提高效果广告的投放效率。 Data Collection, Transmission, and Processing 1. Establish and improve the ad performance monitoring system to collect, organize, and analyze advertising data in real time, including key metrics such as impressions, clicks, conversion rates, and costs, to gain in-depth insights into campaign effectiveness and user behavior paths. 2. Responsible for receiving, transmitting, and integrating data from multiple sources, including major ad platforms and internal business systems, ensuring data integrity and accuracy. 3. Clean raw data by identifying and handling missing values, duplicates, and anomalies to provide a high-quality data foundation for further analysis. 4. Assist product and engineering teams in building and maintaining data warehouses or databases, optimizing data storage structures to improve query and retrieval efficiency. Data Analysis and Mining 1. Conduct in-depth analysis of advertising data to uncover underlying patterns and trends, providing strong data support for optimizing advertising strategies. 2. Evaluate the performance of different ad creatives and delivery strategies to support data-driven decision-making. 3. Identify inefficiencies and fraudulent traffic in ad campaigns in a timely manner to help reduce company losses. 4. Use data visualization tools to transform complex data into clear and intuitive charts, effectively presenting trends and changes in key ad metrics. Regularly compile detailed data reports to comprehensively interpret advertising performance, including metric achievements, issue analysis, and improvement suggestions. 5. Actively support the product and engineering teams in optimizing ad delivery tools and data infrastructure to enhance the efficiency of performance advertising.