5 Metrics to Track Deal Aging in Sales Pipelines
5 Metrics to Track Deal Aging in Sales Pipelines
Stalled deals hurt your sales pipeline and revenue forecasts. Tracking deal aging metrics helps you spot bottlenecks, clear out stagnant opportunities, and focus on deals that are most likely to close. Here are five key metrics every sales team should monitor to improve pipeline health and win rates:
Average Age by Stage: Reveals how long deals stay in each phase, highlighting where they tend to stall.
Percentage of Deals Over Age Thresholds: Flags stagnant deals that exceed expected durations, signaling potential issues.
Pipeline Composition by Age: Shows the age distribution of deals, helping distinguish between active and dormant opportunities.
Win Rate by Age Cohort: Tracks how win rates change as deals age, offering insights into when deals are most likely to close - or fail.
Time to Disposition for Aged Deals: Measures how quickly aged deals are resolved, ensuring outdated opportunities don’t clutter your pipeline.
These metrics, combined with AI tools, such as AI lead scoring platforms, can boost forecast accuracy and streamline your sales process. For example, companies using AI-driven insights have seen a 25% improvement in forecast accuracy and a 76% increase in revenue. By monitoring deal aging, you’ll identify bottlenecks, act on at-risk opportunities, and improve overall sales performance.
::: @figure [Image: 5 Key Metrics to Track Deal Aging in Sales Pipelines]{5 Key Metrics to Track Deal Aging in Sales Pipelines} :::
1. Average Age by Stage
Average Age by Stage tracks how long deals stay in each phase of your sales pipeline. It’s a key metric that reveals where processes are smooth and where deals tend to stall.
Relevance to Identifying Deal Bottlenecks
By monitoring "Days in Stage", you can pinpoint exactly where deals are slowing down. For instance, if a significant number of opportunities get stuck in the demo or proposal phase, it might indicate deeper issues, such as the need for better scripts, improved pricing strategies, or more effective follow-ups.
"If a ton of opportunities are getting stalled between demo and proposal, it could be a sign your team needs to sharpen its pitch or signal a problem with pricing." – Close [7]
Stalled deals are no small matter - they close 31% slower [10]. Leveraging historical data can help you set up alerts when deals linger too long, giving your team a chance to step in before opportunities fade.
Impact on Pipeline Health and Win Rates
When deals sit in a stage longer than expected, they’re more likely to end up as "Closed Lost." This not only hurts win rates but also skews your forecasts [12][13]. A case in point: Frontify used Gong's Revenue AI Platform to track these metrics in real time, leading to a 20% improvement in forecast accuracy and a 30% jump in conversion rates [10].
Actionable Insights Provided
Start by calculating the historical average time for each stage in your pipeline. For example, in B2B sales, it typically takes about 84 days to move from Lead to Opportunity, and 18 days to go from Opportunity to Close [11]. These benchmarks can help you set thresholds to flag deals that exceed the expected timeframe.
You can also break down aging data by factors like deal size, industry, or sales rep performance. For instance, if one rep’s deals consistently stall in negotiations while another’s move quickly, it’s a great opportunity for targeted coaching. Tools like Salesforce, HubSpot, and Close make it easy to track these metrics in real time [3].
"An AI-backed analysis may show that dozens of deals get stuck or lost during the demo stage of your sales process. Coaching about how to run demos and close interested prospects may help unclog your pipeline." – Gong [10]
2. Percentage of Deals Over Age Thresholds
This metric measures how many deals linger beyond their expected stage durations, highlighting potential bottlenecks in your sales pipeline. While average age metrics provide an overview, this focuses on outliers - those stagnant deals that could be clogging the flow.
Relevance to Identifying Deal Bottlenecks
Age thresholds serve as early warning signs for stalled opportunities. For example, if a deal spends more than 10 days in Discovery or 15 days in Proposal, it may indicate a delay. A high percentage of deals exceeding these benchmarks suggests systemic issues that need immediate attention. Addressing these bottlenecks could involve targeted sales strategies or clearing out dormant deals [14].
"A stagnant opportunity 20 days past the median close time for that segment isn't just 'taking longer', it's a red flag." – Drivetrain [14]
Impact on Pipeline Health and Win Rates
When too many deals surpass their age thresholds, the pipeline can become bloated with stalled opportunities. These deals not only consume resources but can also distort your pipeline's value, leading to unreliable forecasts. Research shows that such deals often take much longer to close [10]. Many of these "zombie" opportunities arise when factors like budget shifts or the departure of an internal champion derail progress [14].
In 2025, Verse.ai used Gong's platform to tackle aging deals earlier in the process. By tracking deals that exceeded their thresholds, they boosted forecast accuracy by 25% and increased total revenue by 76% [10].
Ease of Integration with Sales Tools
Modern CRMs, including Salesforce, HubSpot, Clari, and Gong, simplify tracking this metric. These tools automatically calculate stage durations using timestamps and deal fields, sending real-time alerts when thresholds are breached [6].
Actionable Insights Provided
Start by setting benchmarks for each stage based on historical performance - such as 1–2 months for SMB SaaS deals, 3–4 months for mid-market deals, and 6–9 months for enterprise deals [14]. Use thresholds set at roughly 1.5 times these averages to trigger alerts. Then, segment the data by deal size, industry, or sales rep to identify patterns. Regularly review aging reports in team meetings to focus on high-value, at-risk opportunities.
3. Pipeline Composition by Age
This metric examines how deals are distributed across various age brackets and stages within your pipeline. It helps differentiate between new opportunities and those that have been sitting for a while.
Relevance to Identifying Deal Bottlenecks
Pipeline composition highlights where deals tend to stall. For instance, if a large number of deals have been stuck in the proposal stage for over 30 days, you've pinpointed a bottleneck [13]. This breakdown also helps clarify the difference between having a "full" pipeline and a "healthy" one. While a pipeline might look promising in terms of total value, if it’s filled with aged deals, the chances of conversion are slim [12]. Comparing how long deals stay in each stage across teams can reveal inefficiencies. For example, one region may have a 15% lower conversion rate because their reps take longer to follow up [13].
"A full pipeline might feel promising, but it can hide bottlenecks or a collection of deals that were never going to close in the first place." – CaptivateIQ [12]
This analysis lays the groundwork for assessing the overall health of your pipeline.
Impact on Pipeline Health and Win Rates
When a pipeline is dominated by aged deals, it can distort revenue forecasts. Older deals inflate the perceived value of the pipeline but often come with a lower likelihood of closing [2][10]. Deals that show signs of aging, such as being stuck in a stage too long, take 31% longer to close on average [10].
A real-world example: Frontify leveraged Gong's Revenue AI in 2025 to boost forecast accuracy by 20% and improve conversion rates by 30% [10].
Ease of Integration with Sales Tools
Modern CRMs make it easy to track metrics like "Days in Stage" and "Total Age" using timestamps. Many systems even allow conditional formatting to flag deals that have lingered for over 30 days [1][15]. For smaller teams, a simple spreadsheet formula like =TODAY() - [Created Date] can do the trick [15].
Actionable Insights Provided
Once you’ve set up accurate tracking, the next step is to analyze the age distribution of your pipeline. Use charts to visualize where the bulk of your deals fall. If you notice a heavy concentration of older deals, it’s a sign your pipeline might be drying up [8]. To address this, establish age thresholds for each stage, set up automated alerts, and regularly review "closed-lost" deals to uncover common failure points and refine your qualification process with intent-driven outbound platforms [12].
4. Win Rate by Age Cohort
Taking a closer look at win rates by age cohort can provide valuable insights into how deals progress through your pipeline. This approach groups deals based on when they first entered the pipeline and tracks the percentage that successfully close as they age. Instead of viewing all deals as a single entity, cohort analysis allows you to identify patterns over time - much like monitoring graduation rates in education.
Relevance to Identifying Deal Bottlenecks
Cohort analysis is a powerful tool for pinpointing where deals tend to stall or fall apart. For instance, you might discover a strong win rate early in the process, followed by a sharp decline after a specific timeframe. As David Sacks, Co-Founder of Craft Ventures, explains:
"This cohorted view often reveals unexpected trends. For example, you might learn that many opportunities are lost in the 2nd month, but opportunities that are open past then are won at a very high rate." [4]
In most cases, the longer a deal lingers in the pipeline, the less likely it is to close. Breaking down win rates by segments - such as SMB, mid-market, or enterprise - can further sharpen your ability to forecast and identify bottlenecks [4].
Impact on Pipeline Health and Win Rates
Cohort analysis can significantly improve both revenue growth and forecast precision. Research shows that stagnant deals take 31% longer to close on average [10], while win rates increase by 50% when sales reps follow AI-recommended actions [10]. Additionally, companies that fine-tune their sales forecasting processes are 10% more likely to achieve year-over-year revenue growth [5].
Ease of Integration with Sales Tools
Modern CRMs simplify the process of tracking cohorted win rates by automatically maintaining a detailed history of each opportunity. These systems create snapshots of deals at various stages, helping you understand how they evolve over time [4]. Setting age thresholds within your CRM can also trigger alerts when deals surpass your typical sales cycle, making it easier to identify and address stalled opportunities.
Actionable Insights Provided
Direct your team’s efforts toward the most promising cohorts - those with the highest win rates - rather than wasting time on deals that have been stuck too long. Regularly auditing your pipeline to remove stagnant deals and focusing on high-performing cohorts can lead to more efficient resource allocation. When paired with metrics like average deal age and age thresholds, cohort win rate analysis helps refine closing probabilities. These insights can also guide more accurate forecasting by factoring in both the deal’s stage and its age. Leveraging this data, along with top AI SDRs for data synchronization, can streamline pipeline management and improve overall sales performance.
5. Time to Disposition for Aged Deals
Time to disposition measures how quickly aged deals are marked as either Won or Lost, shedding light on stalled opportunities. This metric complements aging measurements by ensuring that outdated deals are addressed without delay.
Relevance to Identifying Deal Bottlenecks
Tracking how long it takes to resolve deals can reveal both internal and external obstacles. For example, internal delays like slow legal approvals or external factors like a prospect’s indecision can cause deals to stall. If you notice a pattern of deals getting stuck between the demo and proposal stages, it might indicate issues with your team's pitch or how pricing concerns are being handled.
Impact on Pipeline Health and Win Rates
Older deals tend to have lower chances of closing successfully [2]. Leaving these aged deals unresolved clutters your pipeline, making revenue forecasts less reliable. Additionally, deals that linger beyond their expected timelines drain resources that could be better spent on more promising opportunities.
Ease of Integration with Sales Tools
Modern CRM systems simplify tracking by automatically monitoring metrics like "Days in Stage" and "Total Age" for every deal. You can establish age thresholds for each stage of your pipeline, triggering alerts when deals surpass those limits. AI-driven tools like Gong provide real-time insights into pipeline health, helping you identify and address at-risk deals before it’s too late.
In 2025, Verse.ai adopted pipeline analytics to track aged deals and improve visibility into their sales data. Under Julie Smutko, Director of Demand Generation, this initiative shortened sales cycles, improved forecast accuracy by 25%, and boosted total revenue by 76% [10].
Actionable Insights Provided
Incorporate time-to-disposition data into your pipeline management process for a more streamlined and predictable sales funnel. Set specific age limits for each stage of your pipeline and use automated alerts to flag deals that exceed them. Regularly review your pipeline and close stagnant deals to focus on high-value opportunities.
Dig into the reasons behind lost aged deals to identify recurring issues, whether it's pricing, competition, or poor initial qualification. Reps who prioritize timely follow-ups and complete outstanding tasks can cut their average deal duration by 11% [10]. Use this data to pinpoint areas where your team could benefit from coaching, such as handling objections, improving pitches, or refining qualification strategies.
How AI SDR Tools Help Manage Deal Aging
AI SDR tools take aging metrics to the next level by offering proactive management and simplifying follow-up processes. These tools automatically track aging deals by monitoring interactions across various channels, ensuring your pipeline data stays up-to-date and accurate in real time. This eliminates the need for manual updates and ensures that aging metrics reflect the current status of deals rather than outdated information [10].
One standout feature of modern AI SDR tools is their ability to generate predictive "Deal Scores." These scores are calculated using data like stage duration, periods of inactivity, and shifts in close dates. Compared to traditional CRM-based forecasts, these AI-driven scores predict win likelihood with 20% greater accuracy [10]. This allows sales teams to focus on opportunities that show strong engagement signals rather than just how long a deal has been sitting in the pipeline [16]. These predictive insights are seamlessly integrated into automated systems, prompting timely actions.
Automated alerts are another game-changer. These alerts notify reps when deals exceed specific age thresholds or show warning signs, such as repeated close-date changes. For example, in April 2025, Frontify adopted Gong's Revenue AI Platform to centralize customer interaction data. Under the leadership of Naya Tsoukala, Head of Revenue Operations, the company used automated alerts to flag at-risk deals, resulting in a 20% improvement in forecast accuracy and a 30% boost in conversion rates [10].
"Gong's AI has changed our approach to sales engagement. We now have actionable insights that we can leverage. We don't need to add CRM notes - you can just ask AI to give you a summary."
– Naya Tsoukala, Head of Revenue Operations, Frontify [10]
AI tools also handle repetitive tasks like summarizing calls, syncing CRM data, and sending follow-up reminders, ensuring that data stays current. Sales reps who act on AI-recommended tasks have seen their win rates increase by 50% [9][10]. Platforms like AI SDR Shop (https://aisdr.shop) make it easy to explore and compare over 80 AI-powered SDR tools, helping you find the ideal solution for managing your pipeline effectively.
Conclusion
Keeping an eye on deal aging metrics is like having an early warning system for your revenue forecast. It helps you focus on deals that have a real shot at closing. By tracking data like the average age by stage, win rate by age group, and time to disposition, you can step in before opportunities slip through the cracks and keep your pipeline healthy [2][6].
Accurate revenue forecasts don’t happen by chance - they require consistent pipeline cleanups and a willingness to face hard truths about which deals are solid and which are just fluff. As Gert-Jan Lagas, AVP in Revenue at Mural, wisely pointed out:
"We stopped treating every deal like it was real just because it was in Salesforce. Scrubbing the data is sometimes uncomfortable. But the truth works for us, instead of against us" [5].
On top of these metrics, AI-powered tools are changing the game for pipeline management. These platforms can automatically flag deals that have gone stale, predict win likelihood with up to 95% accuracy [3], and keep your CRM data fresh without requiring constant manual updates. Sales teams that follow AI-recommended actions for follow-ups are seeing better win rates [10], proving that combining smart metrics with AI-driven insights makes a real difference to your revenue.
Predictive analytics takes things a step further by shifting your focus from analyzing past performance to spotting future opportunities. It helps you identify bottlenecks before they slow you down and directs resources to deals with the most momentum. Tools like AI SDR Shop even let you compare over 80 AI-powered SDR solutions for free, so you can find the best fit for your pipeline. When you combine these insights with AI automation, you move from simply reacting to problems to actively driving results and boosting revenue.
FAQs
How can tracking deal aging metrics improve your sales pipeline?
Tracking deal aging metrics - such as days in stage, total deal age, and average sales cycle length - gives you a clear picture of how opportunities are moving through your sales pipeline. These metrics are essential for spotting bottlenecks, identifying deals that may be at risk, and making smarter decisions about where to focus your team's time and energy. Take this as an example: deals that sit idle for 30 days or more beyond the usual sales cycle tend to have much lower chances of closing. By keeping an eye on these patterns, sales managers can step in early to re-evaluate opportunities, refine strategies, or take other actions to keep things moving. On top of that, knowing your average sales cycle length helps you set realistic goals, benchmark team performance, and improve the accuracy of your sales forecasts. The real advantage comes from acting on these insights. By doing so, you can shift focus to high-value opportunities, weed out deals that have gone stale, and keep your pipeline healthy and balanced. Tools like AI SDR Shop can make this process even more efficient. With AI-powered sales development representatives handling tasks like outreach and follow-ups, you’ll be able to act faster on aging trends and keep deals progressing smoothly.
How can AI help track and manage aging deals in a sales pipeline?
AI is a game-changer when it comes to tracking deal aging. By analyzing CRM activities - like stage changes, email interactions, and meeting notes - it calculates how long deals linger at each stage. If a deal starts to drag beyond the usual timeframe, AI can flag it, giving sales teams an early warning. Predictive models also come into play, identifying potential risks and sending alerts for aging deals. This helps teams act quickly to keep prospects moving forward instead of stalling out. For companies leveraging AI-powered Sales Development Representatives (SDRs), tools with built-in deal-aging tracking and automated follow-ups make the process even smoother. Platforms like AI SDR Shop integrate seamlessly with CRMs, ensuring no deal slips through the cracks. This allows sales teams to concentrate on what they do best - closing deals efficiently.
Why is it important to monitor the average deal age at each stage of the sales pipeline?
Tracking the average deal age at each stage of the sales process helps pinpoint where deals are getting stuck. This insight allows sales teams to identify bottlenecks and take action to keep the pipeline flowing. By tackling delays head-on, managers can provide targeted coaching, direct resources to the right areas, and improve forecast accuracy by addressing outdated or stalled opportunities. This metric is essential for keeping the sales pipeline on track, ensuring deals move forward efficiently and sales objectives stay within reach.