Customer Success Analytics Lead

At Optimizely, we\’re on a mission to help people unlock their digital potential. We do that by reinventing how marketing and product teams work to create and optimize digital experiences across all channels. With Optimizely One, our industry‐first operating system for marketers, we offer teams flexibility and choice to build their stack their way with our fully SaaS, fully decoupled, and highly composable solution.

We are proud to help more than 10,000 businesses, including H&M, PayPal, Zoom, and Toyota, enrich their customer lifetime value, increase revenue and grow their brands. Our innovation and excellence have earned us numerous recognitions as a leader by industry analysts such as Gartner, Forrester, and IDC, reinforcing our role as a trailblazer in MarTech.

At our core, we believe work is about more than just numbers — it\’s about the people. Our culture is dynamic and constantly evolving, shaped by every employee, their actions and their stories. With over 1,500 Optimizers spread across 12 global locations, our diverse team embodies the \”One Optimizely\” spirit, emphasizing collaboration and continuous improvement, while fostering a culture where every voice is heard and valued.

Join us and become part of a company that\’s empowering people to unlock their digital potential!

IntroductionOptimizely is seeking an experienced Customer Success Analytics Lead, to join our Data Services function. This role will be critical to helping optimize our decision‐making abilities across our Customer Success teams.

This position will be responsible for leading domain analysis creation, providing best in class insights and proposals for change, designing solutions and working cross‐functionally to implement process and change within the organization.

Job Responsibilities

Data‐Driven Action Requirements: Leverage customer performance data and AI to identify patterns, drivers, and actionable insights across customer health, adoption, satisfaction (e.g. NPS/CSAT), Ideal Customer Profile (ICP) and churn risk topics.

Lead GDR Modelling activities using customer segmentation, time series analysis, scenario simulation and retention curve modelling approaches.

Contribute to analysis/input required for board decks and end of quarter business reviews.

Support business modelling of the professional services organization through the transition to a subscription services model.

Provide data‐driven insights to guide customer success strategies, renewals, upsells, and proactive engagement.

Build and refine predictive and diagnostic models to support forecasting, targeting, and optimization strategies.

Partner with Sales, Marketing and Product teams to implement data‐informed actions and measure impact.

Ensure data integrity, relevance, and timeliness by collaborating with data engineering and analytics teams to define and evolve measurement frameworks and KPIs.

Storytelling Requirements: Develop clear, compelling, and audience‐appropriate narratives that explain the \”why\” behind Customer Success performance trends.

Translate technical data findings into business‐relevant insights that resonate with executive, marketing, and cross‐functional stakeholders.

Use visual storytelling techniques, including dashboards and data visualizations, to enhance understanding and engagement.

Frame insights in the context of strategic goals, highlighting opportunities, risks, and recommendations with clarity and influence.

Partner with the Chief Customer Officer and global CS and GTM leaders to ensure the Customer Success department focuses on the right metrics and improves KPIs.

Spearhead quarterly initiatives to enhance the Customer Success team\’s efficiency, scalability, and performance. This encompasses retention and product usage/adoption analysis and supporting the design of the CS Book of Business.

Act as a strategic partner to Marketing, Sales, Product, Finance and IT teams to align initiatives and streamline customer experiences.

Knowledge and Experience

5+ Years of professional experience

3+ Years of experience of Customer Success Ops at a SaaS company experience

Proficiency in SQL and at least one statistical programming language (Python, R, etc.)

Strong proficiency in SQL, Excel, and BI tools (e.g., Tableau, Power BI, Looker)

Experience with Customer Success platforms (Gainsight, HubSpot Service, Salesforce Service, etc.)

Behavioural Expectations

Strong background in bringing data to life for decision makers

Ability to work in a fast‐paced and ever‐changing environment.

Mindset of continuous improvement, self, systems, processes

Ability to identify work that supports company objectives and maintain focus on business outcomes.

Honesty when things go wrong (they will), and a plan to prevent the same thing from happening again.

Problem solving mindset (we don\’t look to blame, but to prevent future mistakes)

You create your own experiments to find solutions or answer questions, or you do research to find answers.

Transparent communication: status of work in flight, creating documentation of current state, educational learnings sharing.

Maintain intellectual and emotional curiosity.

Self‐managed work/personal life balance

Diplomacy, negotiation, and development of complex relationships

You teach yourself new skills and systems.

EducationBachelor\’s degree in a quantitative field (e.g., Mathematics, Statistics, Computer Science, Economics) or equivalent experience.

Driving Continuous Improvement

Driving for Results

Inspiring and Motivating Others

Solving Complex Problems

Our culture is the most important thing we offer. We continuously aim to provide a high‐growth space, both virtually and in person, where you can do your best work and, in the process, unlock your boundless potential. We are dedicated to providing meaningful rewards and development opportunities for our employees, recognizing performance and creating a supportive working environment.

Optimizely is committed to a diverse and inclusive workplace. Optimizely is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.

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