--- name: sales-analytics description: Analyze sales performance data to drive insights on pipeline health, rep performance, forecast accuracy, conversion rates, and revenue trends. Use when building sales dashboards, analyzing win/loss patterns, measuring sales efficiency, tracking KPIs, evaluating quota attainment, or creating executive sales reports. Triggers on phrases like "sales report", "pipeline analysis", "win rate analysis", "rep performance", "sales forecast accuracy", "conversion funnel", "revenue attribution", "sales KPI", "deal analytics". --- # Sales Analytics & Performance Intelligence Transform sales data into actionable insights for performance improvement and strategic decision-making. ## Workflow ### 1. Pipeline Analysis 1. **Pipeline health assessment**: - Calculate pipeline coverage ratio (total pipeline / remaining quota) by rep, team, territory - Analyze pipeline aging: % of deals >30, 60, 90 days old per stage - Stage distribution: ensure healthy funnel shape (no bottlenecks at any stage) - Identify "zombie deals": opportunities with no activity in 30+ days 2. **Conversion rate analysis**: - Calculate conversion rates between each pipeline stage - Benchmark conversion rates against historical averages and industry standards - Identify stages with abnormal drop-off (flag deviations >10pp from average) - Segment conversion rates by: rep, region, product line, lead source, deal size 3. **Pipeline velocity calculation**: - Formula: (Number of Deals × Average Deal Size × Win Rate) / Sales Cycle Length - Track velocity trends monthly and quarterly - Identify factors accelerating or decelerating velocity - Segment velocity by product, persona, and source ### 2. Revenue Forecasting Analysis 1. **Forecast accuracy tracking**: - Compare forecasted vs actual closed revenue by period (weekly, monthly, quarterly) - Calculate forecast accuracy: 1 - (|Forecast - Actual| / Actual) - Track forecast bias (consistent over/under-forecasting by rep or team) - Set accuracy targets: >85% monthly, >90% quarterly 2. **Forecast methodology optimization**: - Compare multiple forecasting methods: pipeline-based, historical-based, rep-commit, AI-driven - Weight forecast inputs based on historical accuracy of each method - Implement deal scoring models using historical close probability data - Calibrate deal stage probabilities using actual close rates by stage 3. **Risk & opportunity identification**: - Flag at-risk deals: stalled progression, negative sentiment signals, competitor presence - Identify upside opportunities: accelerated timelines, expansion potential - Calculate best case, committed, and pipeline scenarios with confidence intervals - Weekly forecast variance analysis with root cause for deviations >10% ### 3. Rep Performance Analytics 1. **Individual performance scorecards**: - Revenue attainment: actual vs quota (monthly, quarterly, YTD) - Activity metrics: calls, emails, meetings, demos, proposals - Quality metrics: win rate, average deal size, sales cycle length - Pipeline metrics: pipeline generated, pipeline conversion, coverage ratio - Consistency metrics: month-over-month variance in performance 2. **Cohort analysis**: - Compare performance by hire vintage (new hire ramp, tenured rep) - Track 90-day, 6-month, 12-month ramp curves - Identify high-potential reps: consistent over-performers with strong activity - Flag at-risk reps: declining performance trends, activity drops, low engagement 3. **Performance benchmarking**: - Rank reps within team on composite score (revenue + activity + quality) - Identify top-quartile behaviors correlated with high performance - Compare performance across territories controlling for market factors - Generate performance distribution curves; flag outliers (top/bottom 10%) ### 4. Win/Loss Analysis 1. **Deal outcome tracking**: - Categorize every lost deal: competitor win, budget/timing, product fit, relationship, pricing - Capture win factors: differentiated value, relationship strength, timing, competitive advantage - Track win/loss by: competitor, product line, sales cycle length, deal size, lead source 2. **Competitive win/loss matrix**: - Build head-to-head win rate against each competitor - Identify winning/losing conditions by competitor (price wars, feature gaps, relationship plays) - Update competitive intelligence with deal-level insights - Share competitive wins/losses in weekly sales meetings 3. **Root cause analysis**: - Run regression analysis on deal outcomes to identify key predictors - Segment analysis: do certain personas, industries, or deal sizes have lower win rates? - Identify process gaps correlated with losses (e.g., deals without executive sponsor lose 40% more) - Translate findings into actionable sales process improvements ### 5. Sales Efficiency Metrics 1. **Cost and productivity analysis**: - Calculate CAC by: lead source, channel, campaign, product line - Track CAC payback period trend (target: <18 months) - Revenue per rep: monthly, quarterly, annual averages - Sales headcount efficiency: revenue per FTE, quota coverage percentage 2. **Sales cycle analysis**: - Median and average sales cycle by: stage, product, deal size, rep, region - Identify cycle lengthening trends (early warning of market friction) - Break down cycle time by stage to find bottlenecks - Correlate cycle length with deal size and complexity 3. **Revenue quality assessment**: - New logo vs expansion revenue mix - Gross margin by product line and deal - Discount analysis: average discount rate, discount impact on margin - Revenue retention: NRR, GRR, expansion rate ## Templates & Frameworks ### Sales Analytics Dashboard Structure ``` EXECUTIVE SALES DASHBOARD ========================= Section 1: Revenue Overview □ Bookings vs Plan (MTD, QTD, YTD) □ ARR/MRR Trend □ Revenue by Product Line □ New Logo vs Expansion Split Section 2: Pipeline Health □ Total Pipeline & Coverage Ratio □ Pipeline by Stage (funnel visualization) □ Pipeline Aging Report □ Pipeline Velocity Trend Section 3: Forecast Accuracy □ Forecast vs Actual (last 6 months) □ Accuracy Trend Line □ Rep-Level Forecast Bias Heatmap Section 4: Performance Metrics □ Win Rate by Segment □ Average Deal Size Trend □ Sales Cycle Length Trend □ Conversion Rate by Stage Section 5: Efficiency Metrics □ CAC & Payback Period □ Revenue per Rep □ Quota Attainment Distribution □ Activity-to-Revenue Correlation ``` ### Win/Loss Analysis Report Template ``` WIN/LOSS ANALYSIS — Q2 2025 ============================ OVERALL METRICS: Total Deals Closed: 156 Win Rate: 24% Lost Deals: 119 Average Won Deal Size: $145,000 Average Lost Deal Size: $112,000 LOSS REASONS (Top 5): 1. Competitor — Acme Corp: 34 deals (29%) 2. Budget/Timing: 28 deals (24%) 3. Product Fit/Feature Gap: 22 deals (18%) 4. Incumbent Stickiness: 18 deals (15%) 5. Pricing Objection: 12 deals (10%) WIN FACTORS (Top 5): 1. Superior Integration Capabilities: 28% of wins 2. Executive Champion Development: 22% of wins 3. Competitive Differentiation on Security: 19% of wins 4. Faster Implementation Timeline: 15% of wins 5. Total Cost of Ownership Advantage: 12% of wins COMPETITIVE WIN RATE: vs Acme Corp: 38% (improving +5pp QoQ) vs Beta Inc: 52% (strong position ✓) vs Gamma LLC: 41% (stable) KEY INSIGHTS: □ Deals without executive sponsor lose 40% more often □ Average deal size increasing by 15% QoQ — premium positioning working □ Acme Corp competitive play needs refresh — update battlecards □ Budget-related losses concentrated in Q1 — consider financing options ``` ## Integration Points - CRM (Salesforce, HubSpot): Primary data source for pipeline, deal, and activity data - BI/Analytics platforms (Tableau, Looker, Power BI, Sigma): Dashboard and report building - Revenue intelligence (Gong, Chorus): Call metrics, sentiment analysis, engagement data - FP&A systems: Budget comparison, revenue recognition data - Marketing automation: Lead source attribution, campaign-to-deal mapping - Finance systems (NetSuite, QuickBooks): Actual revenue, margin, and billing data - Compensation platforms: Quota attainment, earnings data - Data warehouse (Snowflake, BigQuery, Redshift): Centralized analytics data layer ## Edge Cases - **New sales org (no historical data)**: Use industry benchmarks for first 6 months; establish internal baselines by Month 3; calibrate forecasts monthly - **Data quality issues**: Implement "trust score" for analytics; flag metrics derived from incomplete data; run data quality remediation in parallel - **Sales org restructuring mid-quarter**: Prorate quotas; adjust historical comparisons; document all changes for audit trail - **M&A impact on analytics**: Separate legacy vs combined metrics for 12 months post-close; identify data mapping issues early - **Seasonal fluctuations**: Adjust expectations and benchmarks for known seasonal patterns (e.g., Q4 close surge, Q1 slowdown) ## Output ### Weekly Analytics Summary ``` SALES ANALYTICS — Week 12, Q2 2025 =================================== KEY METRICS vs TARGET: Pipeline Coverage: 4.6x (target: 3-4x) ✓ Win Rate: 24% (target: 20%) ✓ (+4pp) Avg Deal Size: $145K (target: $130K) ✓ Sales Cycle: 52 days (target: <60) ✓ Forecast Accuracy: 91% (target: >85%) ✓ TREND ALERTS: ✓ Pipeline velocity up 12% vs last week ⚠ Conversion rate at "Proposal" stage down 3pp — review proposal templates ✓ Win rate against Beta Inc improved to 58% (was 52%) 🔴 Southwest territory coverage at 2.4x — below minimum 3x threshold ACTIONABLE INSIGHTS: 1. Top 3 performing reps averaging 28% win rate vs 24% org average — schedule peer coaching 2. Deals with 3+ meetings have 3x higher win rate — reinforce meeting cadence standards 3. Average discount rate increased to 18% (from 15%) — review pricing guidelines 4. Acme Corp competitive losses up 8% — urgent battlecard update needed ``` ## Trigger Phrases "sales analytics", "pipeline analysis", "win rate", "win/loss analysis", "rep performance", "sales report", "dashboard", "forecast accuracy", "conversion funnel", "revenue attribution", "sales KPI", "deal analytics", "quota attainment", "sales efficiency", "CAC analysis", "sales cycle metrics", "competitive win rate", "pipeline velocity", "revenue trends", "sales productivity"