Sales Operations Analyst

Jobgether

United States5d ago

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Key details

Location
United States
Work style
Remote
Level
Mid level
Job type
Full-time
Function
Sales & Solutions
Focus area
other
Posted
5d ago

Skills

business intelligencecrmcustomer servicedata extractiondata qualitypower bipredictive modelingtableau

About the role

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Sales Operations Analyst based in the United States.

The Sales Operations Analyst will play a key role in optimizing sales performance, strengthening customer relationships, and driving sustainable revenue growth through data-driven insights. This position offers the opportunity to transform complex operational and customer data into actionable intelligence that supports sales strategy, account management, and customer success. Working closely with Customer Operations, Account Management, Customer Success, and IT teams, you will develop dashboards, automate reporting, and identify opportunities to improve retention and expand customer value. You will also contribute to the adoption of AI-powered analytics and predictive modeling to enhance business decision-making. In a fast-evolving, technology-enabled services environment, your work will directly influence operational efficiency and commercial outcomes. This role is ideal for an analytical, customer-focused professional who combines technical expertise with strong business acumen.

Accountabilities

    • Sales Performance and Customer Analytics: Develop and maintain analytical frameworks that help customer-facing teams understand account performance, identify growth opportunities, and make informed commercial decisions. Analyze order frequency, dispute rates, response times, service levels, and other key indicators to assess customer health and business performance.

    • Customer Retention and Renewal Management: Build customer health scoring models and generate weekly reports highlighting at-risk accounts, including the primary drivers of score changes. Develop predictive churn models capable of identifying potential customer losses 30–60 days in advance and automate renewal dashboards incorporating win probability estimates.

    • Revenue Growth and Expansion Analysis: Identify opportunities to increase revenue from existing customers through monthly analytics, market share assessments, and wallet share tracking. Monitor customer service behavior and account activity to identify potential service-level agreement (SLA) upgrades and other expansion opportunities.

    • Business Reporting and Performance Reviews: Produce quarterly customer value reports and prepare executive-level presentations for monthly and quarterly business reviews. Deliver reliable, actionable insights to support strategic planning, customer discussions, and executive and board-level decision-making.

    • Reporting Infrastructure and Automation: Maintain integrations between internal databases, customer relationship management (CRM) platforms, and business intelligence tools such as Power BI and Tableau. Build and maintain automated dashboards, establish alerts when customer health scores cross predefined thresholds, and standardize reporting processes and data definitions.

    • Data Quality and Cross-Functional Collaboration: Work closely with Operations and IT to identify and resolve data quality issues at their source. Translate business questions into clear analytical requirements and ensure that customer-facing teams have access to accurate, consistent, and timely information.

    • AI and Advanced Analytics: Lead the adoption of AI-powered analytics and collaborate with IT to operationalize analytical models. Develop AI prompts and self-service query interfaces that enable Account Managers and Customer Success Managers to access insights independently, while validating AI-generated outputs for accuracy and reliability.

    • Continuous Improvement and Business Impact: Measure the effectiveness of analytics initiatives and continuously improve reporting efficiency, customer retention insights, and commercial decision support. Work toward reducing accounts with critical health scores by 30% week over week, cutting manual data extraction time for Customer Success Managers by 50%, and achieving 100%

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