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Key Responsibilities and Required Skills for Customer Insights Analyst

💰 $60,000 - $95,000 (USD, typical)

AnalyticsMarketingCustomer ExperienceData Science

🎯 Role Definition

The Customer Insights Analyst synthesizes quantitative and qualitative customer data to generate hypotheses, design and run analytics and experiments, build predictive models, and present clear, prioritized recommendations to business leaders. This role owns customer segmentation, lifetime value (CLV) modeling, churn analysis, funnel and cohort analysis, and translates complex findings into clear actions for marketing, product, sales and customer success teams. The analyst partner with data engineering and BI teams to ensure data quality and scalable reporting while evangelizing a customer-centric, test-and-learn approach across the organization.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Junior Data Analyst or Business Analyst with a focus on customer metrics
  • Marketing Analyst or CRM Specialist transitioning into analytics
  • Market Research Associate or Consumer Insights Coordinator

Advancement To:

  • Senior Customer Insights Analyst
  • Customer Insights Manager / Analytics Manager (CX)
  • Head of Customer Analytics or Director of Customer Insights
  • Chief Data Officer or VP, Customer Experience (with broader leadership path)

Lateral Moves:

  • Product Analyst / Product Insights
  • Marketing Analytics Manager / Lifecycle Marketing
  • Revenue Operations / Growth Analytics

Core Responsibilities

Primary Functions

  • Design, implement, and maintain end-to-end customer segmentation frameworks that combine behavioral, transactional, demographic, and channel data to identify high-value cohorts and personalize marketing and product strategies.
  • Conduct lifecycle and funnel analyses (acquisition → activation → retention → monetization) to quantify drop-off points, diagnose root causes, and recommend prioritized interventions that improve conversion and retention metrics.
  • Build and maintain robust SQL-based reporting and dashboards (Tableau, Power BI, Looker) that track key customer KPIs (CAC, LTV/CLV, churn, NPS, repeat purchase rate) and provide weekly/monthly insights to stakeholders.
  • Develop predictive models for churn, propensity to buy, next-best-offer, and CLV using statistical approaches and machine learning (logistic regression, random forest, gradient boosting), then operationalize those models with engineering teams.
  • Lead A/B tests and multivariate experiments for pricing, onboarding flows, email campaigns, and product features, ensuring proper randomization, sample size calculations, instrumentation, and rigorous statistical interpretation of results.
  • Translate raw behavioral and product telemetry data into business-ready metrics and KPIs, defining event taxonomies and ensuring consistent definitions across analytics and business reporting.
  • Collaborate with marketing and CRM to design and measure lifecycle campaigns, using cohort-level measurement and uplift analysis to quantify incremental impact and ROI.
  • Perform ad-hoc deep-dive analyses to answer business-critical questions (e.g., impact of pricing changes, channel mix optimization, feature adoption barriers) and deliver clear, evidence-based recommendations.
  • Use SQL, Python or R to clean, transform, and analyze large customer datasets from multiple sources (CRM, CDP, web/mobile analytics, transactional systems) and document data lineage for reproducibility.
  • Partner with data engineering to define ETL requirements, improve data reliability, and build scalable pipelines for customer-focused datasets and model scoring.
  • Conduct voice-of-customer analysis by synthesizing qualitative sources (surveys, interviews, customer support tickets) with quantitative behavior to surface drivers of satisfaction and dissatisfaction.
  • Establish and maintain customer health scoring and risk frameworks that identify at-risk customers and prioritize retention actions by revenue exposure and renewal likelihood.
  • Quantify customer acquisition economics by channel and campaign, conducting unit economics analyses (CAC payback, cohort ROAS) to optimize marketing spend allocation.
  • Regularly present findings and roadmaps to senior stakeholders and cross-functional teams, creating executive summaries and compelling visualizations to drive alignment and decision making.
  • Create and maintain a centralized insights repository and playbooks (e.g., segmentation, onboarding optimization, churn interventions) so teams can quickly access repeatable analyses and recommended tactics.
  • Drive continuous improvement in analytics practices by implementing automations, anomaly detection, and monitoring to quickly surface shifts in customer behavior or data quality issues.
  • Evaluate and recommend analytics tools, CDPs, and experimentation platforms to support personalization, orchestration, and measurement at scale.
  • Partner with product managers to define measurable success metrics for new features, instrument tracking requirements, and run post-launch analyses to inform roadmaps.
  • Perform pricing sensitivity and elasticity studies, including basket analysis and willingness-to-pay segmentation, to support revenue optimization initiatives.
  • Mentor junior analysts and act as a subject-matter expert on customer behavior, analytics methodologies, and measurement best practices across the organization.

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.

Required Skills & Competencies

Hard Skills (Technical)

  • SQL: advanced querying, window functions, performance tuning, and working with large production datasets.
  • Statistical analysis: hypothesis testing, confidence intervals, p-values, power analysis, A/B test design and interpretation.
  • Scripting & modeling: proficiency in Python (pandas, scikit-learn) or R for data cleaning, modeling, and automation.
  • Data visualization: expert-level experience with Tableau, Power BI, Looker, or equivalent to build dashboards and executive reports.
  • Experimentation platforms: experience designing and analyzing experiments with platforms such as Optimizely, Google Optimize, or in-house frameworks.
  • Customer data platforms and CRMs: familiarity with CDPs (e.g., Segment, Tealium) and CRM systems (Salesforce) and integrating these data sources for analytics.
  • Big data & cloud SQL engines: working knowledge of Redshift, BigQuery, Snowflake, or similar cloud data warehouses.
  • Machine learning & predictive modeling: experience building churn, CLV, propensity models, and operationalizing model outputs.
  • Data engineering awareness: ability to define ETL requirements, understand data pipelines, instrumentation, and data governance considerations.
  • Analytics instrumentation & web/mobile analytics: familiarity with Google Analytics, Mixpanel, Amplitude, or equivalent event-level analytics tools.
  • Excel: advanced skills for ad-hoc modeling, pivot tables, and financial-style analyses.
  • SQL-based reporting automation and scheduling (dbt is a plus).

Soft Skills

  • Business acumen: ability to translate analytics into strategic, revenue-driving recommendations that align to business goals.
  • Storytelling & data visualization: craft compelling narratives and slide decks that move stakeholders to action.
  • Stakeholder management: influence cross-functional teams (marketing, product, sales, CS) and senior leaders with clear, consultative engagement.
  • Problem solving: structured, hypothesis-driven thinking and the ability to decompose complex business questions.
  • Communication: clear written and verbal communication tailored for both technical and non-technical audiences.
  • Time and project management: prioritize analyses, manage timelines, and deliver high-quality insights under tight deadlines.
  • Curiosity and experimentation mindset: eagerness to test, iterate, and learn from both successes and failures.
  • Collaboration: work effectively in cross-functional squads and mentor junior team members.
  • Attention to detail: rigorous approach to data quality, documentation, and reproducibility.
  • Adaptability: comfortable navigating ambiguity and shifting priorities in fast-moving environments.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in a quantitative or business-related field such as Statistics, Economics, Mathematics, Computer Science, Marketing, or Business Analytics.

Preferred Education:

  • Master's degree in Analytics, Data Science, Business Analytics, Statistics, or an MBA with strong analytical coursework is preferred for senior roles.

Relevant Fields of Study:

  • Statistics / Applied Mathematics
  • Data Science / Computer Science
  • Economics
  • Marketing / Consumer Behavior
  • Business Analytics / Operations Research

Experience Requirements

Typical Experience Range:

  • 2–5 years of hands-on analytics or insights experience with a focus on customer behavior, segmentation, or lifecycle analytics.

Preferred:

  • 3–7+ years of progressive experience analyzing customer data in e-commerce, SaaS, retail, or subscription businesses, with demonstrable impact on retention, revenue, or product metrics. Prior experience owning end-to-end experiments, predictive models, and stakeholder-facing initiatives is highly valued.
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