Analysis of won and lost deals in a CRM: how to understand why a business gains or loses customers

Analysis of won and lost deals helps identify this difference. The team compares successful and unsuccessful sales and looks for patterns in manager performance, customer behavior, sales cycle length, lead sources, segments, and funnel stages.

One lost deal does not prove anything. But when dozens of successful deals share common characteristics while unsuccessful deals have different ones, the business gains data that can inform specific changes to the sales process.

Next, we’ll look at how analyzing won and lost deals differs from a typical analysis of lost opportunities and what data is needed to do it effectively.

Won and lost deal analysis (Win/Loss Analysis) is a systematic comparison of successful and unsuccessful sales that helps identify the factors that influence the outcome.

Traditional lost-deal analysis shows the reasons for lost sales. However, it does not always explain what the team is doing right in successful cases. For example, customers may cite a high price. But if companies with the same budget are making purchases, it is worth examining not the pricing itself, but how sales managers present the product’s value.

This gives the team a broader context rather than just the formal reason for a lost deal. To generate these insights consistently, the first step is to establish standardized rules for collecting data.

The quality of the analysis directly depends on the quality of the data in the CRM. If managers fill out deal records selectively and describe reasons for lost deals using free-form text, it will be difficult to compare deals accurately.

There is no need to collect dozens of parameters "just in case." Start with information that genuinely influences the customer’s decision and that the team is prepared to enter consistently.

Pay particular attention to standardization. Consistent loss reasons, statuses, and data-entry rules make it easier to compare deals across managers, segments, and time periods without manually cleaning the data.

Once the data is organized, you can move on to analysis. It is best to look at several dimensions at the same time to avoid mistaking a random coincidence for a recurring pattern.

Six key areas for comparison:

• By funnel stage. Look at where deals are most often lost and after which stages the percentage of successful closes increases.
• By sales cycle length. Compare the timelines of won and lost deals. If most sales are completed within 14–20 days, while deals are frequently lost after 40 days, this is a signal that warrants further investigation.
• By customer segment. Compare conversion rates by business type, company size, or other characteristics of the target audience.
• By lead source. Evaluate not the number of inquiries, but the share of won deals and revenue generated by each channel.
• By manager. Look at conversion rates together with response time, sales cycle length, and frequency of follow-up contacts.
• By actions in the sales process. Check whether demos, meetings, follow-up contacts, or personalized proposals occur more frequently in won deals.

This type of analysis reveals correlations but does not automatically prove a cause-and-effect relationship. If the first follow-up contact occurs within two days in won deals but takes five days in lost deals, this is a reason to test the hypothesis that response speed has an impact.

A pattern is useful only after it has been validated. The next step is to turn it into a specific change in how the sales team operates.

A report alone does not change anything. The practical logic looks like this: data → pattern → hypothesis → change → remeasurement.

The process should not be changed after every individual loss. A sufficient sample size and a recurring signal are needed; otherwise, the team risks reacting to random fluctuations.

That is why won and lost deal analysis should be conducted regularly — for example, monthly or quarterly. After each change, compare the new results with the previous period and check whether the metrics have actually improved.

To analyze won and lost deals, sales data needs to be stored in one place. In Uspacy, managers can see not only the final status but also the full context of customer interactions.

In the deal record, the team can track the lead source, segment, deal value, assigned manager, and other relevant parameters. Custom fields help adapt the CRM to the company’s processes, while funnel stages make it easier to see exactly where deals are most likely to stall or be lost.

For example, if a particular source generates many leads but most deals are lost, the business gets a signal to evaluate the quality of that channel. If losses are concentrated at a specific stage, it makes sense to investigate the problem there.

A CRM does not draw conclusions for the manager. It provides structured data that can be used to identify patterns and test hypotheses.