Build Sales Performance Dashboards

This skill is expert in designing, building, and optimizing sales performance dashboards that drive revenue growth and team performance, covering sales metrics, KPI hierarchies, visualization, and actionable insight design. Its metric framework spans primary revenue metrics (MRR/QRR, revenue growth rate, average deal size, sales velocity), pipeline and conversion metrics (pipeline value by stage, lead-to-opportunity and opportunity-to-close conversion rates, sales cycle length, pipeline coverage typically needing 3-5x quota), and activity/performance metrics (activities per rep, quota attainment, win/loss rates by rep/product/territory/deal size, customer acquisition cost). It defines three role-specific dashboard views: an executive summary with top-line metrics and status flags, a sales manager view via SQL ranking reps by quota attainment with performance-status labels (Exceeding, On Track, At Risk, Needs Attention), and an individual rep view calculating personal quota progress, weighted pipeline, and activity counts. It covers visual design principles (a four-color status system, chart-type guidelines matching metric to visualization), predictive pipeline scoring that adjusts a stage-based base probability by deal age, engagement score, champion presence, and budget confirmation, cohort-based customer revenue analysis, and a real-time dashboard class that polls multiple API endpoints on an interval and updates widgets in place.

Use this skill when building a sales dashboard that needs to serve multiple audiences with role-appropriate detail and drive specific behaviors, not display vanity metrics. Its implementation best practices insist on starting from business outcomes (what decisions should this dashboard drive), progressive disclosure (summary first, details on demand), data quality validation and lineage tracking, self-service customization and alerting, usage monitoring, and monthly stakeholder reviews to refine metrics. It covers performance optimization for dashboards handling real query load - pre-calculated aggregates, materialized views, incremental loading, caching, and async data loading that prioritizes critical metrics first - plus mobile-first design with offline capability and push alerts. It is not meant for a static, single-audience report - the whole design is built around role-differentiated views and driving daily sales workflow behavior.

### Pipeline probability calculation
def calculate_deal_probability(deal_data):
    base_probability = deal_data['stage_probability']
    
    adjustments = {
        'deal_age': min(deal_data['days_in_stage'] / 30 * 0.1, 0.2),
        'engagement_score': deal_data['engagement_score'] / 100 * 0.15,
        'champion_identified': 0.15 if deal_data['champion'] else -0.1,
        'budget_confirmed': 0.1 if deal_data['budget_confirmed'] else -0.05
    }
    
    final_probability = base_probability + sum(adjustments.values())
    return max(0, min(1, final_probability))

Given a sales team's CRM data, the skill produces role-specific dashboard configurations (executive summary object, manager SQL query, individual rep metrics function), the deal-probability scoring function above, cohort-based customer revenue SQL, a real-time dashboard class polling revenue/pipeline/activity/performance endpoints on a refresh interval, and an async data-loading function that prioritizes critical metrics before detailed analytics.

Sales operations and RevOps teams building dashboards for executives, managers, and individual reps who need role-appropriate views and predictive pipeline insight rather than one generic report. It suits teams that want the dashboard to drive daily behavior - self-service customization, alerting, and monthly metric reviews - and that need performance-conscious data loading for dashboards under real query load.