Timeline Analysis in Q-Revenue helps you understand how deals move over time, how long they stay in each stage, and how those movements impact your revenue forecast.

Instead of only seeing where your deals are, Timeline Analysis shows you:

• When deals entered each stage

• How long they remained there

• When probabilities changed

• How forecast values evolved

• Where delays or bottlenecks occur

This allows you to improve forecasting accuracy and sales efficiency.

What Is Timeline Analysis?

Timeline Analysis compares:

Baseline Timeline
→ The expected number of cumulative days a prospect should take to move through stages (based on your Sales Template).

Actual Timeline
→ The real number of cumulative days the prospect has taken so far.

By comparing these two, Q-Revenue identifies:

• On Track prospects

• Stalled prospects

• Past Due prospects

• Timeline deviations

Understanding the Timeline Dashboard

1️⃣ Filters Section

You can refine the analysis using:

• Owner – View timeline data for specific sales reps or all owners

• Temperature – Filter by deal status (e.g., Hot)

• Sales Template – Compare based on different sales cycle templates

The dashboard shows:

Showing 239 prospects matching filters

This reflects the total prospects included in your current analysis view.

2️⃣ Status Breakdown

The dashboard automatically categorizes prospects:

i. On Track

Prospects progressing within expected baseline timeline.

ii. Stalled

Prospects not moving stages within expected time.

iii. Past Due

Prospects that have exceeded the expected cumulative days.

These metrics immediately highlight pipeline health.

Timeline Comparison Chart

The Line Chart is the core of Timeline Analysis.

It displays:

Cumulative Days (Baseline vs Actual)

• X-axis → Sales Stages (e.g., Contacted)

• Y-axis → Cumulative Days

• Two lines:

  • Baseline timeline

  • Actual timeline

Example (based on your dashboard snippet):

Stage: Contacted
Cumulative Days: 3–6 vs 127 (actual cumulative deviation example)

This shows whether deals are moving faster or slower than expected.

What “Cumulative Days” Means

Cumulative days represent:

Total days from the beginning of the sales cycle up to the current stage.

Example:

Baseline:

• Contacted: 3 days

• Qualified: 6 days

• Proposal: 12 days

If a deal is at Proposal in 20 days, it is 8 days behind baseline.

How Q-Revenue Determines Deal Status

i. On Track

Actual cumulative days ≤ Baseline cumulative days

ii. Stalled

Deal has not moved stages within expected stage duration

iii. Past Due

Actual cumulative days significantly exceed baseline expectation

How Timeline Analysis Helps Sales Managers

Managers can quickly identify:

✔ Which reps close faster
✔ Which stages cause delays
✔ Which deals are forecast risks
✔ Where pipeline bottlenecks occur
✔ Whether baseline timelines are realistic

Practical Use Cases

1️⃣ Forecast Risk Detection

If many deals are in Past Due, forecast projections may be overly optimistic.

2️⃣ Sales Performance Evaluation

Compare:

• Reps who consistently stay On Track

• Reps with high Stalled counts

3️⃣ Process Optimization

If most prospects exceed baseline at “Contacted,” you may need:

• Better follow-up tools

• Improved qualification scripts

• Automation reminders

Exporting Timeline Data

Use the Export button to:

• Download timeline analysis data

• Share with leadership

• Conduct deeper performance reviews

Use Clear Filters to reset and analyze broader pipeline trends.

Why Timeline Analysis Is Powerful

Most CRM systems show stage position.

Q-Revenue shows:

• Stage position

• Time spent

• Deviation from plan

• Risk signals

• Performance trends

It shifts forecasting from stage-based assumptions to time-based precision.

Best Practices for Using Timeline Analysis

✔ Review timeline dashboard weekly
✔ Investigate Stalled prospects immediately
✔ Adjust baseline timelines if unrealistic
✔ Combine timeline data with forecast reports
✔ Use Sales Template comparisons to optimize cycles