Sales teams don't care about website metrics. They care about pipeline: leads in-progress, deal velocity, win rates, forecast. Analytics teams don't care about individual deals. They care about aggregate trends and channel performance. But these teams need a shared view so sales knows which leads came from which channels and marketing knows whether channels are driving qualified leads. This chapter covers building dashboards that serve both teams.
What Sales Dashboards Show
A sales dashboard displays: pipeline by stage (leads, opportunities, proposals, won/lost), deal velocity (how many deals move through each stage per week), win rates by stage, forecast (predicted revenue by month based on pipeline), and channel/campaign source of leads.
This is different from marketing analytics: marketing cares about lead volume and CAC. Sales cares about deal velocity and win probability. Both are important. Dashboards need to show both perspectives.
Building the Dashboard
Data sources
Pull data from: CRM (pipeline, deals, win rates), analytics (lead source, campaign), email (engagement), accounting (closed revenue). All data flows to a business intelligence tool that creates the dashboard.
Key metrics
For sales: pipeline by stage, deals in each stage, days in each stage, win rate by stage, forecast.
For marketing: leads by source, cost per lead by source, quality by source (% that become opportunities, win rate), revenue by source.
Shared: lead quality assessment, campaign performance, channel effectiveness.
Sales-Specific Considerations
Timeliness: Sales needs data fresh (daily or real-time). A lead that arrived yesterday is old news if you don't know about it immediately.
Ownership: Each deal is owned by a rep. Dashboards should segment by rep, territory, and product line so reps see their own deals first.
Accuracy: Salespeople are skeptical of data if it doesn't match their CRM. Ensure dashboard totals match CRM exactly. If they diverge, find and fix the discrepancy.
Actionability: Dashboards should drive actions: "This lead source has 40% win rate; prioritize these leads." Don't show metrics without implications.
Common Dashboard Failures
Too complex: Dashboard shows 50 metrics. Sales team ignores it because it's overwhelming. Solution: start with 5-7 core metrics. Add others only if asked.
Not trustworthy: Totals don't match CRM. Sales team doesn't trust dashboard, doesn't use it. Solution: reconcile daily. If totals diverge, fix before publishing.
Slow to update: Dashboard updates once per week. Deals are stale by then. Solution: automate updates to happen nightly at minimum.
No insights: Dashboard shows data but no interpretation. Sales team doesn't know what to do with it. Solution: include commentary: "Pipeline is up 20% vs. last month. At current velocity, we'll exceed forecast."
Dashboard Evolution
Phase 1 (month 1): Basic dashboard showing pipeline, revenue forecast, new leads. Focus on accuracy and timeliness.
Phase 2 (month 3): Add lead source, campaign performance, channel quality. Enable filtering by rep, territory, product.
Phase 3 (month 6): Add predictive metrics: deal probability, predicted win rates, predicted revenue. Identify at-risk deals.
Should sales team use a sales dashboard or rely on CRM?
Both. CRM is the source of truth and task management. Dashboard provides bird's-eye view of pipeline and performance. Sales reps use CRM daily for deal management; they check dashboard weekly for performance review and coaching. They're complementary, not competing.
What's the best cadence for dashboard updates?
Nightly minimum for sales dashboards (data updated each morning). Real-time is nice-to-have if you have the technical infrastructure. Avoid weekly updates; deals change too fast. Automate updates so they're not manual.
How do I calculate deal probability and pipeline forecast?
Simple: forecast = sum of deal amounts × probability by stage. Example: 10 deals in proposal stage, historical 30% win rate = 10 × avg amount × 30%. Sophisticated: use historical data by rep or product to refine probabilities. Most CRMs have built-in forecasting; use it as a starting point.
How do I present channel/campaign quality to leadership if volume is high but quality is low?
Show both metrics: organic drives 100 leads/month but 5% win rate. Paid ads drives 30 leads/month but 20% win rate. Cost per closed deal = lead cost / win rate. This shows that paid ads, despite lower volume, is more efficient. Use this to justify budget reallocation.
How do I handle deals that span multiple quarters or fiscal years in forecasting?
Use expected close date for forecast, not creation date. Example: deal created in Q3 but expected to close in Q1 next year goes into Q1 forecast. This is more accurate than using creation date. CRM systems support this; configure your forecast by expected close date, not created date.
Should I show individual rep data on dashboards if sales team is competitive?
Yes, but carefully. Public competition can be motivating or demoralizing. Show rep data on their own dashboards (private), and aggregate data on team dashboards (public). Use data for coaching, not punishment. The goal is mutual improvement, not shame-based competition.