Key Responsibilities and Required Skills for Insights Manager

🎯 Role Definition

We are hiring an Insights Manager to lead the generation of actionable customer and market insights that drive strategic decisions across product, marketing, and commercial teams. The Insights Manager combines rigorous quantitative analysis, qualitative research, and persuasive storytelling to translate complex data into clear recommendations. This role reports to Head of Insights/Analytics and partners closely with Product, Marketing, Sales, Finance, and Data Engineering to influence go-to-market strategy, product roadmaps, and performance optimization.

Key SEO / LLM keywords: Insights Manager, customer insights, market research, analytics manager, data-driven decisions, data storytelling, product analytics, marketing analytics, business intelligence.

📈 Career Progression

Typical Career Path

Entry Point From:

Senior Analyst — Customer Insights, Product Analytics, or Market Research
Analytics Manager or BI Manager with cross-functional experience
Market Research Lead or Consumer Insights Lead

Advancement To:

Head of Insights / Head of Analytics
Director of Product Analytics or Director of Consumer Insights
VP of Data & Insights / Chief Insights Officer

Lateral Moves:

Product Manager (analytics-focused)
Growth Manager / Growth Analytics Lead

Core Responsibilities

Primary Functions

Lead the end-to-end insights lifecycle: design the research question, define KPIs, build analysis plans, execute quantitative and qualitative analysis, synthesize findings, and present clear strategic recommendations to senior stakeholders.
Own and manage the insights roadmap, prioritizing research and analysis projects that align with company OKRs, revenue goals, and product priorities while balancing quick wins with long-term strategic studies.
Design, implement, and interpret A/B tests and experimentation frameworks across product and marketing to measure causal impact and recommend optimization strategies based on statistically valid results.
Build and maintain dashboards and self-serve reporting in BI tools (Tableau, Power BI, Looker) that provide executives and business partners with timely, actionable metrics and diagnostic analytics.
Perform advanced cohort analysis, customer segmentation, churn modeling, and lifetime value (LTV) analysis to inform acquisition, retention, and monetization strategies.
Partner with Product and UX research to run mixed-method studies (surveys, usability testing, interviews) and integrate qualitative insights with quantitative datasets to build a complete customer understanding.
Translate complex analytics into concise, persuasive insight decks and narratives tailored to executive, product, marketing, and commercial audiences with clear business recommendations and next steps.
Establish and enforce analysis standards and best practices for the insights function, including statistical rigor, reproducibility, documentation, and version control.
Mentor and manage junior analysts, providing coaching on analysis techniques, visualization, stakeholder engagement, and career development; hire and scale the insights team as needed.
Collaborate with Data Engineering to define data requirements, improve data quality, and prioritize instrumentation changes to ensure accurate and reliable metrics across analytics platforms.
Conduct competitive and market analysis, synthesizing external market trends, TAM/SAM/SOM, pricing intelligence, and competitor product comparisons to inform positioning and roadmap decisions.
Drive customer feedback loops with Sales and Support to quantify qualitative themes and prioritize product fixes and feature requests based on impact and effort.
Create and maintain a central insights repository (research library, playbooks, personas) that democratizes knowledge and reduces duplicated analysis across the organization.
Develop predictive models (propensity to churn, upsell likelihood, conversion probability) and present model outputs and assumptions to stakeholders with recommended business actions.
Lead cross-functional working groups for high-impact initiatives (e.g., major launches, pricing experiments, new product lines) to ensure insights guide decision-making and measurement plans.
Ensure privacy, compliance, and ethical use of customer data in all insight activities by partnering with Legal and Privacy teams for research protocols and data access.
Conduct ad hoc deep-dive analyses to troubleshoot sudden KPI shifts or to investigate emerging trends, delivering rapid, evidence-based diagnoses and corrective recommendations.
Quantify the ROI and impact of marketing, product, and growth initiatives by defining attribution approaches and creating standardized measurement frameworks for campaign and feature performance.
Represent the insights function in executive forums, board presentations, and cross-functional leadership meetings, articulating the insights narrative and influencing strategic decisions.
Standardize and automate repetitive reporting tasks using SQL, Python/R scripts, and BI tool features to free analyst time for higher-value strategic work.
Evaluate and recommend analytics and research tools (survey platforms, behavioral analytics, experimentation platforms) to expand capability and reduce time-to-insight.
Partner with Finance and Commercial Ops to build forecasting inputs informed by leading indicators uncovered in customer behavior and market research.

Secondary Functions

Support ad-hoc data requests and exploratory data analysis.
Contribute to the organization's data strategy and roadmap.
Collaborate with business units to translate data needs into engineering requirements.
Participate in sprint planning and agile ceremonies within the data engineering team.
Provide training and enablement sessions to business partners on how to interpret insights and use dashboards effectively.
Help build personas and customer journey maps that are refreshed with primary and secondary research findings.

Required Skills & Competencies

Hard Skills (Technical)

Expert SQL skills for complex cohorting, window functions, joins, and performance-optimized queries against large datasets.
Proficiency with at least one analytic programming language (Python or R) for statistical modeling, data cleaning, and automation.
Hands-on experience building dashboards and operational reports in Tableau, Power BI, or Looker with an emphasis on usability and maintainability.
Practical knowledge of experimentation platforms and A/B testing methodology (e.g., Optimizely, Google Optimize, VWO) including test design, power calculations, and result interpretation.
Strong understanding of web and product analytics tools (GA4, Mixpanel, Amplitude) and event instrumentation best practices.
Experience with statistical methods (regression, hypothesis testing, clustering, time-series analysis) and data science concepts (classification, survival analysis).
Familiarity with SQL-based analytics warehouses and cloud platforms (BigQuery, Redshift, Snowflake) and basic understanding of ETL/data pipelines.
Ability to build and validate predictive models (churn, LTV, propensity) and translate model outputs into business actions.
Advanced Excel skills for ad hoc financial modeling and scenario analysis, including pivot tables and complex formulas.
Experience with survey design, conjoint analysis, and qualitative research synthesis tools (Dovetail, UserTesting, Qualtrics).
Knowledge of data governance, privacy considerations (GDPR, CCPA), and secure data access patterns.

Soft Skills

Exceptional data storytelling skills: convert complex analyses into concise, actionable recommendations for executives and frontline teams.
Strong stakeholder management: influence cross-functional partners, negotiate priorities, and align multiple teams around measurement plans.
Strategic thinking with business acumen: tie insights directly to revenue, retention, acquisition, and product KPIs.
Leadership and people management: coach, mentor, and scale a high-performing insights team.
Project management and prioritization: manage multiple concurrent analyses with clear scope, timelines, and deliverables.
Intellectual curiosity and critical thinking: challenge assumptions, validate sources, and ask the right questions.
Excellent written and verbal communication, capable of building executive-level presentations and facilitating workshops.
Adaptability and problem-solving: work effectively in ambiguous, fast-moving environments.
Collaborative mindset: build trust across Product, Marketing, Sales, and Engineering to turn insights into action.
Attention to detail: ensure analytical rigor, reproducibility, and accuracy in all deliverables.

Education & Experience

Educational Background

Minimum Education:

Bachelor’s degree in Data Science, Statistics, Economics, Business, Marketing, Psychology, or a related quantitative/social science discipline.

Preferred Education:

Master’s degree (MS, MSc, MBE) in Analytics, Data Science, Statistics, Business Analytics, Economics, or MBA with strong analytics coursework.
Certifications in analytics/experimentation (e.g., Google Analytics, Optimizely, Tableau Desktop Specialist) are a plus.

Relevant Fields of Study:

Data Science / Analytics
Statistics / Applied Mathematics
Economics
Business Administration / Marketing
Psychology / Behavioral Science

Experience Requirements

Typical Experience Range:

5–10+ years of relevant experience in analytics, market research, or consumer insights roles; 2+ years of people management preferred.

Preferred:

7+ years with a track record of leading insights programs that influenced product or marketing strategy.
Demonstrated experience in both quantitative and qualitative methods, experimentation, dashboarding, and stakeholder influence.
Experience working in SaaS, e-commerce, consumer tech, or fast-scaling B2B/B2C companies and familiarity with subscription or multi-product business models.