ServiceNow Data Analyst Case Interview — Now Platform Product & Experiment Analytics

This case simulates how a Data Analyst at ServiceNow partners with a Now Platform product team (e.g., IT Service Management or Customer Service Management) to improve customer outcomes using product telemetry and experimentation. It reflects common elements from real ServiceNow interviews: product-metrics design for B2B SaaS workflows, hands-on SQL, experiment evaluation, and an executive-style readout tailored to ServiceNow’s enterprise customers and scale. What the case covers: - Business and product context: You’ll work with anonymized Now Platform event data (e.g., incidents, requests, knowledge views, virtual agent sessions, usage telemetry) to diagnose friction and quantify impact on enterprise KPIs such as incident deflection, MTTR, SLA breach rate, backlog aging, and feature adoption. Expect discussion of how these metrics tie to customer value and renewal/expansion health in a multi-tenant, enterprise setting. - Metric design and instrumentation: Define north-star and operational metrics for a workflow (e.g., Virtual Agent deflection for ITSM). Call out leading/lagging indicators, guardrails (e.g., CSAT/NPS, SLA breaches), and how you’d implement durable, self-serve definitions with Performance Analytics/KPI scorecards and consistent event taxonomy across products. - SQL/data wrangling: Write or describe SQL to build cohorts and baselines (e.g., weekly deflection rate by customer tier/region; MTTR by priority and assignment group). Handle common realities in ServiceNow data—late-arriving events, duplicates, P1/P2 skew, tenant-level partitioning/row-level security, and join keys across product logs. - Experimentation and causal inference: Propose and evaluate an A/B test for a Virtual Agent intent/routing change (or a knowledge-recommendation model). Specify hypothesis, unit of randomization (user, session, or account), guardrails, power, clustering at enterprise level, and how you’d interpret p-values, confidence intervals, or lift. If randomization is constrained, outline quasi-experimental options (e.g., diff-in-diff, CUPED, matched cohorts) and bias checks. - Process insights and automation: Interpret a simplified process-mining artifact (Celonis-style) to pinpoint bottlenecks (e.g., reassignment loops, knowledge gaps) and recommend workflow or IntegrationHub/Flow Designer automations to reduce cycle time without harming quality or compliance. - Data storytelling and stakeholder alignment: Deliver a concise 10-minute exec readout that frames the problem, insights, and tradeoffs for product, design, and customer success leaders. Emphasize ServiceNow’s customer-outcome focus and “win-as-a-team” collaboration, including clear next steps and an experiment or rollout plan. Format & timing (typical): - 5 min: Problem brief and data schema overview - 25 min: Metric design + hands-on SQL reasoning (you may write pseudo-SQL if time is tight) - 15 min: Experiment plan or analysis of provided test results (with guardrails) - 10 min: Executive readout (recommendations, risks, and decision) - 5 min: Q&A on data quality, governance, and dashboarding in Performance Analytics Evaluation rubric (how you’re assessed): - Technical depth: Correctness and clarity of SQL/metrics; appropriate experimental design for enterprise customers; ability to reason about clustered data and guardrails. - Product sense for the Now Platform: Choosing KPIs that matter to ITSM/CSM workflows and articulating the customer/business impact of movement in those metrics. - Communication: Crisp structure, executive presence, and the ability to translate data into action for cross-functional partners. - Judgment and bias-to-action: Practical tradeoffs, risk management, and a realistic rollout/validation plan. - Craftsmanship and governance: Attention to data quality, lineage, and reliable metric definitions suitable for Performance Analytics and self-serve consumption. What to prepare: Comfort with SQL over event-style tables; product analytics for B2B SaaS; A/B testing fundamentals; dashboarding/storytelling; knowledge of ServiceNow domains (ITSM/CSM), Virtual Agent/Knowledge bases, and how process mining-style insights translate into workflow changes.