How to do win-loss analysis with a small team
Sprout Social has more than 700 employees. We have more than 350 people on our Sales and Customer Success teams alone. We have dozens of competitors in nearly every region of the world. We have thousands of trials every single month. And we have only two people doing competitive win-loss analysis.
That’s just the nature of a competitive intelligence (CI) team in SaaS. It will always feel like there are more competitors to know, trends to monitor, opportunities to exploit and research to read than you could ever have time for. Small CI teams have limited resources, so they must ruthlessly prioritize projects to maximize their impact on the business.
The most important initiative a CI team, especially a small one, can do is win-loss analysis. It shows you where you’re weak today. It reveals your biggest competitive advantages for future deals. It gives you product feedback on what tech works best and what falls short. It tells you if your pricing strategy is working or not. There are more insights to pull from win-loss analysis than anywhere else for a CI team.
This data should ground nearly every decision you make. More importantly, it empowers you to influence several departments across the organization. When we break down our win-loss performance at Sprout, we use the insights to influence future work on:
- Sales content (Sales & Success)
- Trainings (Sales & Success)
- Messaging and positioning (Marketing & Product Marketing)
- Pricing and packaging (Sales & Finance)
- Product direction (Product & Engineering)
- Corporate development (Executive Team)
However, when you have a small team, it makes it incredibly difficult to do the deep qualitative analysis that most win-loss articles recommend as the cornerstone of this work. With a small team, I recommend you prioritize quantitative analysis to ensure you have confidence in the applicability of your findings across your customer base.
At the end of every quarter, we review every single deal in Salesforce that was lost to a competitor. We scour the notes that the SDR, AE and SE wrote. We track the size of the deal and the products involved. We look at customer segment and region of the world. We listen to Gong clips for additional details. And we pull all of this info into an Excel doc where we dive into the analysis.
We’ve been able to build out a process that allows us to analyze over 500 competitive deals every quarter at a minimum. This is a fairly representative set across the SMB, Mid-Market, Agency and Enterprise segments. Because we have such large sets of data, I feel confident making recommendations based on the sample size.
Now compare that to what we could do if we were trying to do qualitative interviews with customers after they made their decision. To do this well, it takes weeks and months of effort to collect enough interviews (most customers say no or do not respond) to get a decent sample size. Then there is the question of how much the customer will offer up. I’ve done interviews where the responses the customer offered were borderline useless. It can feel like pulling teeth to get anything beyond surface-level answers. This is true for both wins and losses. Finally, especially in the case of losses, getting customers to tell the honest truth can be incredibly difficult.
For a company like Sprout or any company with a high volume of deals, the question then becomes, “do we trust what this small number of customers said is reflective of all our customers?” Candidly, I do not, and the risk of being wrong could have too large of implications to risk it.
Now, I know this isn’t an either/or proposition. The absolute best win-loss analysis initiatives pair both quantitative and qualitative together. Many larger organizations can afford to outsource the qualitative portion to third-party vendors. This can improve both the validity and reliability of the data.
But most small teams don’t have the budget to do this. And most don’t have the time to invest in ongoing qualitative interviews without sacrificing in other areas. That’s why they should prioritize losing some depth for a broader data set that is more representative. The insights might not be quite as rich, but the confidence in your finds should be higher. Numbers don’t lie, but customers might.
If you have a small team that is responsible for win-loss analysis, I recommend you focus on the numbers to start. Here are the basic steps:
- Build a process to review deals on a regular cadence, likely monthly or quarterly
- Understand what insights individual departments would benefit from most
- Create a framework to evaluate every deal the same way to ensure consistency
- Build your Salesforce reports for both wins and losses
- Learn to live in Excel and love pivot tables
- Develop a repeatable format for how these insights are distributed
It probably took me about a year to get it just right, so don’t be discouraged if your first few efforts are rocky. The insights you can pull from a quantitative approach like this can transform your organization, speed up your career development and get you into bigger, more strategic conversations, so the work is worth it.
If you’re looking for help to get started, please reach out. I’d love to help empower the next generation of CI specialists to build powerful win-loss programs.
