Data Analyst | Customer Journey & Attribution

Motorola Solutions is hiring a Data Analyst to track and analyze performance across digital customer lifecycle touchpoints and one-to-one Customer Success efforts. The role builds attribution models, connects engagement and customer-success data, optimizes campaign journeys, and delivers reliable reporting to show impact on adoption, retention, and revenue.

Responsibilities:

• Design & Custom Attribution Architecture: Build, maintain, and iterate on multi-touch attribution models to quantify the isolated and combined impact of adoption efforts, digital touchpoints and CSA human interventions and its correlation with product usage and customer retention
• Hybrid Touchpoint Tracking: Establish data pipelines connecting digital engagement metrics (email opens, in-app triggers, product usage, event engagement) with Gainsight CTA outcomes, meeting logged events, and CRM lifecycle stages
• Full-Funnel ROI & Revenue Impact: Connect early-stage adoption metrics (TTV, active usage, feature adoption) to long-term business outcomes, including net revenue retention (NRR), expansion revenue, and customer retention
• Feedback Loop Optimization: Provide CSA leadership and Campaign Strategists with data-backed recommendations to refine intervention thresholds, CTA targeting rules, and playbook messaging based on holistic performance trends
• Audience & Behavioral Segmentation: Partner with Campaign Strategy and Product Insight to identify customer segments and usage patterns, refining trigger logic for automated campaigns and automated Gainsight CTAs
• Journey Bottleneck Analysis: Analyze drop-off points and conversion velocity across integrated campaign workflows to pinpoint where digital engagement stalls and where targeted intervention creates maximum incremental lift
• Data Hygiene: Act as a flexible operator across our systems—finding, cleaning, transforming, and organizing messy data from disparate sources into reliable datasets
• Data Consistency: Partner with Product Insights and CS Ops to unify identity resolution, standardizing tags hygiene and user event logging across the post-sale customer journey
• Reporting & Dashboards: Build clear, reliable dashboards (using Tableau or Looker) that provide visual transparency into campaign performance, attribution splits, and channel efficiency
• AI Integration: Use AI tools (like Claude) to speed up writing SQL, scripting, data cleaning, and complex analysis

Requirements:

• Legal authorization to work in the U.S. indefinitely is required
• 5+ years of experience in data analytics, marketing analytics, or Revenue/CS Operations, including at least 2 years focused on attribution modeling or lifecycle analytics in SaaS
• Proven track record as a hands-on Individual Contributor (IC) with a strong Business Operations background—capable of balancing complex technical data work with practical operational problem-solving
• Must be a US Citizen
• Direct, hands-on experience building multi-touch attribution models using SQL, Python, or R to quantify isolated and combined touchpoint impact
• Advanced SQL skills; hands-on experience with Snowflake, BigQuery, or Redshift
• Proven ability to build clear, functional dashboards using tools such as Tableau, Looker, or Power BI
• Familiarity or hands-on experience with Gainsight, Salesforce, or HubSpot
• Demonstrated ability to clean, transform, and organize messy data from disparate sources into unified, reliable datasets for identity resolution and event tracking
• Ability to translate complex analytical concepts and multi-touch models into clear, actionable insights for business stakeholders (Campaign Strategy, Product Insights, and CS Ops)
• Hands-on experience using AI tools (such as Claude, ChatGPT, or Copilot) to accelerate daily analytical tasks, SQL query writing, data preparation, and scripting
• Deep experience connecting hybrid digital engagement metrics (in-app triggers, email engagement) directly with Gainsight Call-to-Action (CTA) outcomes, meeting events, and CRM lifecycle stages
• Deep familiarity with early-stage SaaS metrics (Time-to-Value, active usage, feature adoption) and their mathematical correlation to long-term financial metrics like Net Revenue Retention (NRR) and expansion revenue