Time to Close¶

Definition¶

Time to Close measures the average amount of time it takes for a deal, ticket, or request to move from initiation to resolution or closure. This metric is crucial for tracking sales efficiency, customer support performance, or operational workflows.

Description¶

Time to Close is a key indicator of sales velocity, process efficiency, and revenue forecasting accuracy, reflecting how quickly leads convert to closed deals or resolved cases.

The relevance and interpretation of this metric shift depending on the model or product:

In B2B SaaS, it highlights sales team efficiency and pipeline momentum
In Support/Operations, it reflects ticket resolution time and workflow readiness
In PLG or hybrid models, it surfaces how fast product-qualified leads move to expansion

A shorter Time to Close shows strong lead fit, clear value delivery, and effective follow-through, while a longer one often signals friction in the sales cycle or resource bottlenecks. By segmenting by rep, region, or deal size, you uncover winning playbooks and bottlenecks in the path to revenue.

Time to Close informs:

Strategic decisions, like sales resourcing, forecasting, and pipeline pacing
Tactical actions, such as deal prioritization or offer design
Operational improvements, including CRM hygiene, content readiness, and handoff optimization
Cross-functional alignment, by connecting signals across sales, CS, ops, and finance, helping teams drive faster, healthier revenue outcomes

Key Drivers¶

These are the main factors that directly impact the metric. Understanding these lets you know what levers you can pull to improve the outcome

Stakeholder Alignment: More stakeholders = longer timelines. Missing decision-makers cause stalls.
Sales Process and Deal Coaching: Deals close faster when reps control the motion and manage next steps.
Objection Handling and Competitive Positioning: Faster resolution = faster wins.

Improvement Tactics & Quick Wins¶

Actionable ideas to optimize this KPI, from fast, low-effort wins to strategic initiatives that drive measurable impact.

If close time is increasing, implement mutual action plans with buyer milestones and due dates.
Add sales stage exit criteria to reduce “stuck” pipeline.
Run internal deal reviews focused on aging deals and decision path clarity.
Refine your qualification — only stage real buyers with budget, authority, and urgency.
Partner with sales enablement to equip reps with fast objection-handling content.

Required Datapoints to calculate the metric
- Total Time to Close: The sum of all closing times for deals or tickets in a given period.
- Total Number of Cases: The count of cases resolved or deals closed during the same period.
Example to show how the metric is derived

A SaaS company tracks Time to Close for sales deals in Q1:
- Total Time Spent Closing Deals: 900 days
- Total Deals Closed: 300
- Average Time to Close = 900 / 300 = 3 days per deal

Formula¶

Formula

\[ \mathrm{Average\ Time\ to\ Close} = \frac{\mathrm{Total\ Time\ to\ Close\ for\ All\ Cases}}{\mathrm{Total\ Number\ of\ Cases}} \]

Data Model Definition¶

How this KPI is structured in Cube.js, including its key measures, dimensions, and calculation logic for consistent reporting.

DealsTickets

cube(`Deals`, {
  sql: `SELECT * FROM deals`,
  measures: {
    totalTimeToClose: {
      sql: `total_time_to_close`,
      type: `sum`,
      title: `Total Time to Close`,
      description: `The sum of all closing times for deals in a given period.`
    },
    totalNumberOfDeals: {
      sql: `id`,
      type: `count`,
      title: `Total Number of Deals`,
      description: `The count of deals closed during the same period.`
    },
    averageTimeToClose: {
      sql: `${totalTimeToClose} / NULLIF(${totalNumberOfDeals}, 0)`,
      type: `number`,
      title: `Average Time to Close`,
      description: `The average amount of time it takes for a deal to move from initiation to closure.`
    }
  },
  dimensions: {
    id: {
      sql: `id`,
      type: `number`,
      primaryKey: true
    },
    createdAt: {
      sql: `created_at`,
      type: `time`,
      title: `Created At`,
      description: `The time when the deal was created.`
    }
  }
})

cube(`Tickets`, {
  sql: `SELECT * FROM tickets`,
  measures: {
    totalTimeToClose: {
      sql: `total_time_to_close`,
      type: `sum`,
      title: `Total Time to Close`,
      description: `The sum of all closing times for tickets in a given period.`
    },
    totalNumberOfTickets: {
      sql: `id`,
      type: `count`,
      title: `Total Number of Tickets`,
      description: `The count of tickets resolved during the same period.`
    },
    averageTimeToClose: {
      sql: `${totalTimeToClose} / NULLIF(${totalNumberOfTickets}, 0)`,
      type: `number`,
      title: `Average Time to Close`,
      description: `The average amount of time it takes for a ticket to move from initiation to closure.`
    }
  },
  dimensions: {
    id: {
      sql: `id`,
      type: `number`,
      primaryKey: true
    },
    createdAt: {
      sql: `created_at`,
      type: `time`,
      title: `Created At`,
      description: `The time when the ticket was created.`
    }
  }
})

Note: This is a reference implementation and should be used as a starting point. You’ll need to adapt it to match your own data model and schema

Positive & Negative Influences¶

Negative influences

Factors that drive the metric in an undesirable direction, often signaling risk or decline.
- Number of Stakeholders: An increase in the number of stakeholders involved in a deal typically leads to longer Time to Close due to the complexity of aligning multiple decision-makers.
- Missing Decision-Makers: The absence of key decision-makers can cause significant delays in the Time to Close as approvals and decisions are stalled.
- Complexity of Sales Process: A more complex sales process can extend the Time to Close as it requires more steps and approvals.
- Ineffective Deal Coaching: Lack of effective deal coaching can result in longer Time to Close as sales reps may struggle to manage the sales motion and next steps efficiently.
- Poor Objection Handling: Inadequate handling of objections can prolong the Time to Close as it may take longer to address customer concerns and move the deal forward.
Positive influences

Factors that push the metric in a favorable direction, supporting growth or improvement.
- Effective Stakeholder Alignment: Ensuring all stakeholders are aligned and involved early can reduce the Time to Close by streamlining decision-making.
- Proactive Sales Process Management: Sales reps who proactively manage the sales process and control the motion can significantly decrease the Time to Close.
- Efficient Objection Handling: Quick and effective resolution of objections can lead to a faster Time to Close by addressing customer concerns promptly.
- Strong Competitive Positioning: A strong competitive positioning can shorten the Time to Close by making the value proposition clear and compelling to the customer.
- Deal Coaching and Training: Providing sales reps with robust deal coaching and training can enhance their ability to close deals faster, thus reducing the Time to Close.

Involved Roles & Activities¶

Involved Roles

These roles are typically responsible for implementing or monitoring this KPI:

Product Marketing (PMM)
Revenue Operations
Sales Manager
Activities

Common initiatives or actions associated with this KPI:

Sales Enablement
Pipeline Management
Proposal Acceleration
Objection Handling

Funnel Stage & Type¶

AAARRR Funnel Stage

This KPI is associated with the following stages in the AAARRR (Pirate Metrics) funnel:

Acquisition
Type

This KPI is classified as a Lagging Indicator. It reflects the results of past actions or behaviors and is used to validate performance or assess the impact of previous strategies.

Supporting Leading & Lagging Metrics¶

Leading

These leading indicators influence this KPI and act as early signals that forecast future changes in this KPI.
- Deal Velocity: Deal Velocity directly impacts Time to Close by measuring how quickly deals move through the pipeline. Improved deal velocity signals a shorter Time to Close, making it a strong predictor and operational lever for efficiency.
- Lead Response Time: Lead Response Time measures how swiftly teams engage with leads after initial contact. Faster response times typically result in reduced Time to Close by maintaining momentum and preventing delays early in the funnel.
- Ticket Volume: Ticket Volume indicates the workload on support or sales teams. High ticket volume can lengthen Time to Close due to resource constraints, while lower volumes often correlate with faster resolution or deal closing.
- First Contact Resolution: First Contact Resolution reflects the rate at which issues or inquiries are resolved in the initial interaction. High FCR rates often signal operational excellence and directly forecast shorter Time to Close across tickets and cases.
- Onboarding Completion Rate: Onboarding Completion Rate tracks how effectively new users or customers complete setup processes. High completion rates are predictive of reduced Time to Close, especially in sales or service onboarding scenarios.
Lagging

These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.
- Average Resolution Time: Average Resolution Time provides a retrospective measure of how long it takes to resolve cases or tickets, offering feedback to recalibrate Time to Close targets and highlight process bottlenecks.
- Sales Cycle Length: Sales Cycle Length is a lagging indicator that quantifies the end-to-end duration of sales processes. Analyzing this metric helps refine the predictive accuracy of Time to Close and guides improvement strategies.
- Activation Conversion Rate: Activation Conversion Rate measures how efficiently users progress from onboarding to activation. High conversion suggests that underlying factors driving Time to Close are being addressed, informing adjustments to leading indicators.
- Close Rate: Close Rate tracks the percentage of deals successfully closed. A high close rate may validate the effectiveness of Time to Close reduction efforts, while lower rates could suggest issues in the closing stage requiring recalibration of upstream metrics.
- Drop-Off Rate During Onboarding: Drop-Off Rate During Onboarding identifies friction points that extend Time to Close for new customers. Insights from this metric help optimize onboarding flows, influencing leading indicators for future cohorts.