How to Build Custom Pipeline Analytics and Sales Reports

How to Build Custom Pipeline Analytics and Sales Reports

The Reporting Gap

Your VP of Sales asks: "What's our average deal size for Professional Services deals closed by the enterprise team in Q3?"

You open HubSpot's reporting dashboard. You can see total pipeline. You can see deals by owner. But slicing revenue by deal type AND rep AND time period? That requires exporting to Excel, hours of pivot tables, and by the time you have the answer, the question has changed.

Meanwhile, your CFO wants to know services revenue vs. product revenue. Your RevOps team needs to forecast by deal category. Your sales managers want to compare rep performance within specific segments. HubSpot's default reports can't answer these questions without manual data manipulation.

The result? Your leadership team makes decisions based on incomplete data, or your ops team spends 10+ hours per week creating custom reports manually.

The Solution: A Custom Pipeline Analytics Dashboard

Build a flexible analytics dashboard that lets you slice your pipeline data any way your business needs: by deal type, product line, sales rep, team, region, time period, or any custom property you track. With Replit Agent and HubSpot Connector, you can create sophisticated analytics views without SQL, BI tools, or data warehouse infrastructure.

This isn't another canned report. This is custom analysis built exactly for your business model, with the ability to drill down, filter, and compare across any dimension that matters to you.

What You'll Build

• Revenue breakdowns by deal type, product, or custom categories
• Rep performance comparisons showing individual and team metrics
• Time-based analysis with trends, seasonal patterns, and growth rates
• Interactive filters to slice data by any property in your CRM
• Visual dashboards with charts, graphs, and summary cards
• Export capabilities for sharing with stakeholders who need spreadsheets

Why This Matters for Revenue Operations

Answer questions instantly: No more "let me pull that data and get back to you." Leadership questions get answered in real-time.

Better forecasting: When you can see revenue by category and trend over time, forecasts become data-driven instead of guesswork.

Fair compensation: Transparent rep comparisons ensure fair quota setting and commission calculations. Everyone can see how performance is measured.

Resource allocation: Identify which product lines or deal types need more support. Invest in what's working, fix what's not.

Strategic planning: Historical analysis by segment reveals where your business is actually growing, not where you think it's growing.

Watch the Full Tutorial

See this analytics dashboard built live in our webinar, where we create custom views that slice HubSpot deal data across multiple dimensions.

Watch the webinar here →

Key timestamps:

• 50:00–55:03: Services Analytics tab walkthrough and design
• 1:05:03–1:06:05: Rep comparison views and filtering

How It Works: Step-by-Step

Step 1: Connect to HubSpot

Use the HubSpot Connector to access your complete deal dataset. This includes:

• All standard deal properties (amount, stage, close date, owner)
• Your custom properties (deal type, product line, region, customer segment)
• Deal history and stage changes
• Associated contacts and companies
• Deal source and attribution data

The connector handles authentication and provides clean, structured data ready for analysis.

Step 2: Define Your Segments

Tell Agent how your business categorizes deals. Example prompt:

"Our deals are categorized by type: Platform Licenses, Professional Services, Managed Services, and Training. Each deal has a primary product: Core Platform, Enterprise Add-ons, API Access, or Custom Development. We also track deal size: Small (<$25K), Medium ($25K-$100K), Large ($100K-$500K), Enterprise ($500K+)."

Agent understands your taxonomy and builds analytics that respect these categories.

Step 3: Design Your Views

Specify what you want to analyze. Example:

"Create a Services Analytics tab showing total revenue for each deal type with cards displaying the amount and percentage of total. Show a time-series chart of revenue by type over the last 12 months. Include a table of reps with their revenue by deal type, sortable by any column. Add filters for date range, team, and minimum deal size."

Agent builds the UI, queries, calculations, and filtering logic based on your description.

Step 4: Add Comparisons

Enable meaningful comparisons:

"Create a rep comparison view showing each seller with their total revenue, deal count, average deal size, and win rate. Include small photos or avatars. Add a sparkline showing their monthly trend. Allow filtering by team and date range. Highlight top performers in green."

Now you can see at a glance who's crushing it and who needs coaching.

Step 5: Deploy and Share

Your analytics dashboard is live at a shareable URL. Give access to:

• Sales leadership for strategic decisions
• RevOps for planning and forecasting
• Finance for revenue recognition and budgeting
• Individual reps so they can see their own performance

Everyone looks at the same data, updated in real-time from HubSpot.

Analytics Views to Build

Revenue Analysis:

• Total closed-won revenue by time period
• Revenue by product line or deal type
• Average deal size trends over time
• Revenue by customer segment (new business vs. expansion vs. renewal)
• Year-over-year growth by category
• Quarterly revenue comparisons

Pipeline Health:

• Pipeline value by stage
• Stage velocity (average time in each stage)
• Conversion rates between stages
• Deals at risk (stalled in stage too long)
• Pipeline coverage ratio by rep
• Weighted pipeline forecast

Rep Performance:

• Quota attainment by rep and team
• Activity metrics (calls, meetings, emails)
• Win rate by rep
• Average deal size by rep
• Deal cycle time by rep
• Pipeline created by rep

Deal Source Analysis:

• Revenue by lead source
• Conversion rates by source
• ROI by marketing channel
• Sales cycle length by source
• Deal size by source

Product Analysis:

• Revenue by product or SKU
• Product attach rates (what sells together)
• Upsell and cross-sell analysis
• Product mix trends over time
• Pricing analysis by segment

Geographic Analysis:

• Revenue by region or territory
• Win rates by geography
• Market penetration by area
• International vs. domestic performance

Advanced Analytics Capabilities

Cohort Analysis: Track deal cohorts by close month. See how long different cohorts took to close, their average size, and their win rates. Identify seasonal patterns.

Funnel Analysis: Visualize conversion at each pipeline stage. Where do most deals drop out? Which stages have the longest duration? Optimize your process based on data.

Forecasting Models: Use historical data to project future revenue. Apply seasonal adjustments, growth rates, and probability weighting to predict quarterly outcomes.

Anomaly Detection: Automatically flag unusual patterns. Alert when a rep's activity drops 30%, when conversion rates suddenly change, or when deal velocity slows significantly.

Goal Tracking: Set targets for revenue, deal count, or average deal size. Track progress in real-time with visual indicators showing on-track, at-risk, or exceeded.

Benchmarking: Compare individual rep performance against team averages, top performers, or historical baselines. Identify coaching opportunities.

Real-World Use Cases

Quarterly Business Reviews

Your CEO wants to understand Q3 performance. Instead of spending three days in Excel, you open your analytics dashboard:

• Total revenue: $4.2M (18% above plan)
• By deal type: Platform $2.8M (67%), Services $1.0M (24%), Training $400K (9%)
• Top performers: Sarah ($850K), Mike ($720K), Jennifer ($690K)
• Deal size trend: Up 12% vs. Q2, driven by larger enterprise deals
• Win rate: 28% overall, 42% for enterprise segment

You export these views to PDF and the QBR presentation is done in 15 minutes.

Commission Calculation

Finance needs to calculate rep commissions based on closed revenue by product line, with different rates for new business vs. renewals. Your dashboard shows:

• Each rep's revenue split by product and deal type
• Accurate attribution for team deals
• Override tracking for special pricing
• Month-by-month breakdown for quarterly accelerators

Finance downloads the data and commission calculations are complete in an hour instead of a week of back-and-forth.

Resource Planning

Your services team is overwhelmed. Are you selling too much Professional Services relative to your delivery capacity? Your analytics show:

• Services bookings trending up 35% quarter-over-quarter
• 67% of services deals include implementation (up from 45% last year)
• Average services engagement size grew from $35K to $52K
• Current backlog: 8 weeks for new projects

You share this data with the COO. The decision to hire two more services consultants is made immediately with clear justification.

Sales Coaching

You notice a rep's average deal size is $18K while team average is $32K. Drill into their analytics:

• They're closing 15% more deals than average (volume is good)
• But 80% are "Quick Start" packages instead of "Enterprise"
• Their demo-to-close rate is strong at 31%
• They're not losing deals—they're just selling to smaller segments

Insight: This rep is great at closing but needs coaching on qualifying up-market. You adjust their territory to focus on SMB and their performance improves immediately.

Dashboard Design Best Practices

Start with Summary Cards: Lead with the numbers that matter most. Total revenue, deal count, win rate, average deal size. Users should see the headline metrics immediately.

Use Visual Hierarchy: Most important metrics should be largest and highest on the page. Supporting details below. Don't make users hunt for key numbers.

Color Code Meaningfully: Green for exceeding goals, yellow for on-track, red for below expectations. Use color to communicate status at a glance.

Make It Interactive: Clicking a product line should filter the entire dashboard to show only those deals. Selecting a date range should update all charts simultaneously.

Show Trends: Static numbers don't tell the story. Add sparklines, arrows (↑↓), or mini-charts showing "up 15% from last month."

Include Context: Don't just show "$2.3M in pipeline." Show "$2.3M in pipeline (3.2x quota coverage, +18% vs. last quarter)."

Enable Drill-Down: Users should be able to click summary numbers to see the underlying deals. Transparency builds trust in the data.

Data Refresh and Performance

Real-Time vs. Cached: Balance freshness with performance. Deal data can refresh every 5 minutes. Historical trends can cache for an hour.

Incremental Updates: Don't reload all deals every time. Fetch only changed records since last refresh for faster updates.

Aggregation Strategy: Pre-calculate expensive aggregations (like revenue by rep by product by month) and store results. Refresh calculations hourly.

Query Optimization: Filter data at the source. Don't pull 10,000 deals if you're only analyzing last quarter's 500.

User Expectations: A dashboard that loads in 2 seconds beats one that loads in 10 seconds with 5% more features. Speed matters.

Metrics Glossary

Pipeline Coverage: Total pipeline value ÷ quota remaining. Healthy = 3x or higher.

Win Rate: Closed-won deals ÷ (closed-won + closed-lost). Percentage of opportunities that convert.

Average Deal Size: Total revenue ÷ number of deals. Tracks whether you're moving upmarket or downmarket.

Sales Cycle Length: Average days from create date to close date. Shorter is generally better, but varies by segment.

Stage Conversion Rate: Percentage of deals that advance from one stage to the next. Identifies bottlenecks.

Deal Velocity: (Number of deals × average deal value × win rate) ÷ sales cycle length. Measures how fast revenue is generated.

Quota Attainment: Actual revenue ÷ quota. Above 100% = exceeding plan.

Pipeline Created: Value of new opportunities created in a time period. Leading indicator of future revenue.

Integration with Other Systems

Data Warehouse: Export your analytics to Snowflake, BigQuery, or Redshift for long-term historical analysis and ML models.

Slack: Send daily summaries of key metrics to sales channels. Alert on significant changes (pipeline dropped 20%, win rate jumped).

Google Sheets: Enable one-click export for users who want to do their own analysis or create presentations.

BI Tools: If you already use Tableau or Looker, your Replit analytics can feed those platforms while providing faster, simpler views for day-to-day use.

Financial Systems: Push closed revenue data to NetSuite, QuickBooks, or your ERP for revenue recognition workflows.

Get Started with Pipeline Analytics

Stop spending hours in Excel. Build analytics dashboards that answer your hardest questions instantly.

Watch the full webinar →

See how to build multi-dimensional analytics from HubSpot deal data, with real revenue breakdowns and rep comparisons.

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Why Build with Replit Agent + Connectors?

No SQL required: Describe your analytics needs in plain English. Agent writes the queries.

Pre-built HubSpot connection: Access all deal properties immediately. No data pipeline infrastructure needed.

Flexible views: Change what you're analyzing anytime. New product line? Just tell Agent to add it to your dashboard.

Iterate fast: Test different visualizations and metrics. See changes immediately.

Enterprise-ready: SOC 2 compliance, role-based access control, audit logging.

Common Questions

Can I analyze data from before I started using Replit?
Yes. The HubSpot Connector pulls your complete deal history. Analyze deals from any time period.

What if I track custom properties HubSpot doesn't have by default?
Agent works with all your custom properties. If you track it in HubSpot, you can analyze it.

Can I compare current quarter to same quarter last year?
Yes. Build year-over-year comparisons, quarter-over-quarter trends, or any time period analysis.

How do I handle deals with multiple products?
Define how you want to count them: by primary product, by highest value product, or split revenue proportionally across all products.

Can non-technical users create new views?
Yes. Users can describe new analyses to Agent, which generates the code. No SQL knowledge needed.

Does this work with Salesforce?
Salesforce connector support is coming. The same analytics concepts apply to any CRM.

Can I schedule reports to be emailed?
Yes. Set up automated reports that email PDF snapshots daily, weekly, or monthly to stakeholders.

Start Building Today

Your pipeline data tells a story. Build the analytics dashboards that reveal it.

Watch the complete tutorial →

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