How to Analyze Sales Cycle Trends and Spot Hidden Problems in Your CRM

In today’s fast-paced business world, understanding your sales data is crucial to making informed decisions and driving growth. One trend we've been analyzing closely is how sales cycle lengths are changing. This data isn’t just numbers on a dashboard—it’s a window into the health of your sales process.

In this post, we’ll walk you through how to identify problems in your sales cycle, analyze key metrics, and uncover deeper issues lurking beneath the surface. Let’s dive in!

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Why Sales Cycle Length Matters

Your sales cycle length is a critical metric for forecasting revenue, managing resources, and optimizing your sales strategy. But here’s the thing: not all data tells the full story. Metrics like the average days to close can often be misleading due to outliers, which is why it’s essential to dig deeper.

Step 1: Start with the Basics – Average vs. Median Days to Close

When analyzing sales data, we first look at two metrics:

1. Average Days to Close: Gives a broad sense of the time it takes to close deals, but is sensitive to outliers.
2. Median Days to Close: Represents the middle value, giving a clearer picture of what’s typical for most deals.

For one of our clients, we noticed that the average days to close had doubled over a few quarters, jumping from 51 to 102 days. That’s a huge red flag! However, when we examined the median days to close, the increase was much smaller—from 10 to 20 days.

This discrepancy highlighted the importance of looking beyond the average. The outliers were skewing the numbers, but there was still a trend worth investigating.

Step 2: Break Down the Data by Deal Size

Once we noticed the longer sales cycles, we dug into the deal sizes. Here’s what we found:

• The majority of closed deals were in the smallest bucket (annual contract value of $0–29K).
• These smaller deals accounted for over 85% of all closed deals in recent quarters.

This was unexpected. The client had aimed to target larger deal sizes, but the data told a different story. Smaller deals were closing more frequently, and the larger deals that should have driven growth were missing.

Step 3: Investigate What’s Driving the Trends

If smaller deals dominate your sales pipeline, your sales cycle should theoretically be shorter—they’re simpler and quicker to close. So why was the average time to close increasing?

We identified a few potential reasons:

1. Pipeline Targeting: Were we attracting the right leads? The influx of smaller deals suggested that lead targeting needed adjustment.
2. Outliers in the Data: A few high-value deals took exceptionally long to close, skewing the averages.
3. Sales Process Efficiency: Was the sales team equipped to handle larger, more complex deals?

Step 4: Addressing the Root Cause

By analyzing the data further, we uncovered key insights:

• Smaller Deals: While they increased revenue in the short term, they weren’t aligned with the client’s long-term goals.
• Larger Deals: These were missing from the pipeline entirely, signaling a need to refine targeting and improve enterprise sales strategies.

To address this, we recommended:

1. Refocusing Lead Generation: Adjust targeting criteria to prioritize larger prospects.
2. Optimizing the Sales Process: Equip the team with tools and training to handle complex, enterprise-level deals.
3. Cleaning the Data: Outliers skew results, so regular data hygiene is essential for accurate reporting.

Why Digging Deeper Matters

Analyzing sales data is like peeling back layers of an onion. You start with one issue—say, increasing sales cycle lengths—and uncover deeper, systemic problems, like misaligned targeting or an inefficient sales process.

To illustrate, think of spotting mold in a wall. At first, it seems like a small fix, but once you open it up, you might discover a much larger issue. Similarly, your CRM might show a seemingly minor metric like longer sales cycles, but digging in reveals bigger challenges that need addressing.

How to Apply This Analysis to Your Business

Ready to dig into your own sales data? Here’s how:

1. Check Your Data Quality: Ensure your CRM (HubSpot, Salesforce, etc.) has clean, reliable data.
2. Compare Metrics: Look at both the average and median days to close to avoid being misled by outliers.
3. Segment by Deal Size: Identify trends based on annual contract value or other relevant criteria.
4. Investigate Outliers: Dig into deals that took abnormally long to close. Were they worth the effort?
5. Adjust Your Strategy: Use these insights to refine your targeting, sales processes, and forecasting.

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At the surface level, longer sales cycles might seem like a minor issue, but when you dig deeper, you may uncover misaligned strategies or inefficiencies. Taking the time to analyze your sales data thoroughly can save you from going down the wrong path—and help you identify opportunities for growth.

If this process feels overwhelming, or if your team struggles with data quality or analysis, we’re here to help. This is what we do for our clients every day: uncover hidden insights, refine sales strategies, and drive meaningful results.

Let’s take your sales data to the next level.

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