Marketing Analytics Manager

As a Marketing Analytics Manager at Strava, your primary responsibilities include leading a team of data analysts to support global marketing initiatives, driving strategic analytics projects to enhance growth efficiencies, and establishing a learning agenda for robust marketing strategies. You will also collaborate closely with product and marketing teams to design and interpret A/B tests, ensuring that data-driven decisions impact user retention and subscription growth positively.

To be a successful Marketing Analytics Manager at Strava, candidates should have over 5 years of experience in analytics or related quantitative fields, along with at least 2 years of experience leading teams. Proficiency in SQL and experience with Business Intelligence tools like Tableau are essential. Candidates should also be familiar with statistical programming languages, and possess experience in experimentation and methodologies to evaluate marketing campaign impacts.

Strava actively promotes an inclusive and supportive workplace culture for its Marketing Analytics Manager and all employees. The company embraces diversity and believes in a people-first approach. They focus on compassionate leadership, engage teammates from various backgrounds, and prioritize opportunities for professional growth through continual learning, ensuring that every team member feels valued and inspired.

Success as a Marketing Analytics Manager at Strava depends on several key skills: strong quantitative analysis capabilities, proficiency in SQL and data visualization tools like Tableau, hands-on experience with programming languages such as R or Python, and a deep understanding of experimentation methodologies. Moreover, strong leadership skills to guide and collaborate with teams are crucial for driving impactful marketing decisions.

The compensation for a Marketing Analytics Manager at Strava ranges from $230,000 to $245,000, depending on skills, experience, and location. This base salary reflects only monetary compensation and does not include potential equity or benefits. Strava emphasizes a market-based approach to compensation, ensuring competitive pay for their team members.

When answering this question, highlight specific instances where you successfully managed a team, focusing on your leadership style, how you supported their professional development, and the results achieved through collaborative initiatives. Use metrics to quantify improvements or efficiencies brought to projects.

Discuss your familiarity with various methodologies such as A/B testing, econometric methods, and time-series analysis. Share examples of how you've implemented these methods in past roles to significantly measure outcomes, and provide insight into how these analyses informed strategic decisions.

Explain your process for assessing stakeholder needs, deciding on the metrics to include, and the technologies used for implementation. Providing examples of successful tools you’ve developed or significantly contributed to can demonstrate your ability to empower teams through data.

Share a specific project detailing the challenges faced and the roles of various teams. Focus on your communication techniques, how you bridged different goals, and the measures taken to ensure everyone was aligned toward common objectives, resulting in successful project completion.

Discuss your organizational skills and how you evaluate the urgency and impact of tasks. Referring to prioritization frameworks or specific tools you use, describe how you manage your team’s workflow efficiently and ensure alignment with overarching business goals.

Outline your proficiency in programming languages like R and Python, providing examples of projects where you implemented data analysis techniques or statistical modeling. Highlight the coding practices or libraries you utilized to streamline your analysis.

Explain your methods for data validation, ensuring data integrity, and your approach to handling anomalies. Discuss any specific techniques, such as cross-referencing or employing clean-up scripts, that you’ve implemented to maintain high-quality datasets.

Share a detailed account of a specific analytical project that presented challenges and how your innovative approach led to significant outcomes. Focus on the methods used, insights derived, and the consequent business impact that made your analysis stand out.

Your response should cover your methodology for identifying and investigating discrepancies, referencing specific techniques like root cause analysis and cross-verification with different data sources. Highlight your systematic approach to resolving issues promptly.

Discuss how you transform complex data into actionable insights through effective storytelling. Mention how you use visuals, analogies, and concise messaging to engage stakeholders and ensure your analysis resonates and influences decision-making.