Win/Loss Analysis Data: CRM Reason Codes, Interviews, or Surveys?
This guide compares four sources of data often used for Win/Loss Analysis:
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- CRM reason codes
- Sales team surveys
- Buyer surveys
- Buyer interviews
The tradeoffs of in-house vs outsourced data collection and analysis are also detailed.
Align Your Win/Loss Data Source With Your Use Case
The best data source for your Win/Loss Analysis will depend on your use case.
For example, to calculate competitive win rates you’ll need data on which vendors were downselected in each deal, and whether the outcome was a win or a loss. Good data sources for this use case are: CRM reason codes and notes, a sales survey, or a buyer survey.
Alternatively, qualitative data is best when you want to learn why you’re losing deals and how to win more often. Buyer interviews reveal the issues affecting their decision making, and how prevalent they are. Win/loss buyer interviews also allow you to learn why something’s important to buyers, how your performance on it compares to other vendors’, and why. This is essential data when your goal is to improve or fix the issues you identify.
Pros And Cons Of Four Win/Loss Data Sources
| Win/Loss Data Source | Quick Take | Accuracy | Efficiency | Pros | Cons |
|---|---|---|---|---|---|
| 1. CRM Reason Codes | The most common source of data for basic Win/Loss Analysis, but living up to its potential requires significant commitment from sales leadership |  |  | • Most CRM implementations already collect Closed/Lost reason codes • Large sample size, potentially 100% of closed opps • Closed reason notes can be mined for more detail | • Secondary data based on AE’s interpretation • Data hygiene requires substantial effort from sales leadership |
| 2. Sales Team Survey | A quick way to generate baseline win/loss data when it’s not already available in your CRM | | | • Quickly generate baseline win/loss data • Large sample size, potentially 100% of closed opps • Ongoing data collection can be automated by triggering a survey when an opportunity is closed | • Secondary data based on AE’s interpretation and memory • Data hygiene requires substantial effort from sales leadership |
| 3.1 Buyer Survey — In-house | Scalable, continuous tracking of buyer satisfaction on key metrics and essential win/loss data after time consuming initial setup |  |  | • Primary data direct from the buyer • Large sample size • Scales efficiently | • Requires significant time and experience up-front • Low response rate from buyers in Closed/Lost opportunities |
| 3.2 Buyer Survey — Outsourced | Resources permitting, surveys run by specialists can produce higher accuracy data from buyers on key metrics and gather essential win/loss data | | | • Primary data direct from the buyer • Higher response rate from buyers in Closed/Lost deals increases sample size and accuracy • Better questionnaire design increases completion rate and accuracy • Scales efficiently | • Significant up-front effort to select vendor and set up • Requires ongoing funding |
| 4.1 Buyer Interviews — In-house | These deep dives into the story behind buying teams’ decision making often stall because of the substantial effort that’s required from an in-house resource | | | • Supports broad, high stakes change when analyzed in a cluster of at least 20 interviews • Can yield the most complete and candid data including “how” and “why” decisions were made | • Abundant time and skill required to interview and analyze large data set • Buyers in lost deals are reluctant to talk openly with company employees • High variable effort per interview |
| 4.2 Buyer Interviews — Subscription | Quarterly installments of 5-10 interviews support continuous tracking of buyers and competitors, but more efficient buyer surveys and CRM reason codes are available | | | • Can yield the most complete and candid data about each quarter’s critical deals • Continuous source of up-to-date buyer and competitor data • Supports closed loop tracking of improvements | • Pattern analysis requires waiting 3-4 quarters to collect win/loss data from sufficient deals • Limited data from recent quarters reduces accuracy • High variable cost per interview |
| 4.3 Buyer Interviews — Clustered | Cluster of 20-30 deep dive buyer interviews conducted by a win/loss analysis service produce the highest accuracy insight in one quarter or less | | | • Supports high stakes change with insight into the “why” and “how” on 20-30 recent deals • Findings available in one quarter or less • Can yield the most complete and candid data | • Closed loop tracking of improvements requires pairing with buyer surveys or other ongoing data source • High variable cost per interview |
You Can Increase Your Win/Loss Program’s Efficiency By Using More Than One Data Source
Choosing a data source for Win/Loss Analysis isn’t a one and done decision.
Most B2B software vendors already analyze their wins and losses in some fashion. When they decide to invest in improved Win/Loss Analysis it’s usually because there’s a high profile need to improve GTM outcomes. Broad change requires insights that are incontestable because they’re based on highly accurate data. Buyer interviews are the most common data source in this scenario.
After improvements have been made, the use case shifts to closing the loop with new buyers. Do they perceive the changes as improvements? Or do we need to iterate? During this period of benchmarking a lower fidelity data source like buyer surveys can be used to bring down the program’s overall cost.
Read our post about the Win/Loss Lifecycle for more on the cyclical nature of win/loss data collection.
Related Resources
Sample Size For Win/Loss Analysis
How to decide on the best sample size for your Win/Loss Analysis using statistics and other factors.
How To Conduct A Win/Loss Analysis
This is the real guide to conducting a Win/Loss Analysis based on independent buyer interviews. The seven steps described here have been proven through years of in-the-trenches experience.