It’s almost the end of the year and you’re pacing yourself to meet 2023’s financial goals. You’re brainstorming, scribbling, and figuring out ways to increase revenue.
There are only 2 levers you’d need to pull right now:
We know it’s easier said than done. That’s why this blog lists a proven framework to close a higher percentage of deals on your table.
To increase your win rates, you need to figure out why it is low in the first place.
No, don’t start guessing. That’s a rookie mistake everyone makes.
Instead, take the advice of the expert in closing deals, Nate Bagley.
Nate worked as the Head of Content at Clozd helping B2B SaaS companies with innovative win-loss analysis services and technology to improve win rates, build better products, and strengthen competitive advantage.
According to Nate, to increase your win rates, you need to conduct a win-loss analysis. A win-loss analysis helps you understand why you win or lose deals.
With such vital data, you can:
In short, get the truth about why you win and lose—and then use that data to make sure you hit your target.
After every sales call, you must analyze how it went – this isn’t new information. In fact, we’ve streamlined a broken process and put a name to it, win-loss analysis. This will help you get the most out of this activity.
Every company will eventually come around and conduct a win-loss analysis. With the help of this blog, you’ll be the first in the race. Here is the difference you’ll see once you make this a part of your sales workflow:
Every company does win-loss analysis to some extent. Some have just started while others are well ahead in the game. Nate says that there is a maturity curve everyone goes through. Here is what it looks like:
Step 1: Guesswork
This is where you guess what the problem is. You want to understand the reason behind winning or losing deals and take the most obvious step: talk to your sales rep. Based on a few anecdotal stories, you make assumptions and try to change your pitch or product. A few weeks later, you see no advancement and realize your guesses were wrong.
Step 2: CRM data collection
You then take the next obvious step and look at an actual source of data: CRM. That’s better but you face yet another problem. The CRM tells you what has been happening with your deals. Sadly, it doesn’t answer the “why” element you’ve been looking for. A dropdown field in CRM is nowhere close to the insights an actual conversation will leave you with.
Step 3: Run a one-off win-loss analysis
Here, you pick a problem you want to solve. For instance, why do you keep losing to a certain competitor? You talk to 10-15 buyers who fit that profile. You get chunks of vital information you didn’t know you needed. In this step, you conduct a win-loss analysis on a project level.
Step 4: Fit win-loss analysis into your sales workflow
By this time, you know that your customers have good insights. You’ll get information on your competitors you couldn’t have found online or perceptions about your company you didn’t know existed. Now, you realize the value of this analysis.
The evolution leads you to an ongoing process where you constantly talk to your buyers (not the sales rep) and gather mind-blowing data as a part of your sales process.
Most companies are in between collecting data from their CRM (step 2) and conducting win-loss analysis on a project basis (step 3). In fact, some conduct win-loss analysis the old-school way. They look at data in the CRM and talk to their sales rep. This approach is ineffective.
Having conversations internally won’t tell you what’s on the minds of your buyers. You need to talk to the people you’re solving problems for. Talk to your buyers and understand your strengths and weaknesses from their lens.
There are six simple steps you need to follow. These are:
Narrow down one problem to solve. For instance, you not being able to sell enterprise deals. Then, look for customers who can help you find the answer to why you’re not closing big tickets.
Prepare a list of questions beforehand. This helps you stay on track and follow a single line of thought. However, keep some room for flexibility.
This might sound easy but it’s going to need some really good convincing power. Enter your buyer’s shoes: why would they agree to a 30-minute call if they didn’t have a good experience with you?
Create an outreach plan and incentivize them.
Don’t throw the ball at the first go. Build a rapport first. Put your buyer at ease and win their trust. During the interview, dig deeper whenever possible. You could ask a few open-ended questions that’ll get them talking.
A good question to ask: if the timing and pricing of our product and competitors were the exact same, who would you go ahead with?
Another tip is to repeat back to the buyer what you understood. This way, they often clarify something or go deeper into the answer.
Collect data from all the interviews and identify 3-4 themes that get repeated in most of the calls.
After conducting hundreds of win-loss analyses, Nate identified an important trend: the purchase decision is influenced by 3-4 factors, not just one.
With data, insights, and trends in hand, you need to pass the baton to people who can make a change. If you find gaps in the customer success team or incorrect direction of the product team, let them know before it’s too late.
Data that is 3-6 months old will do no good. So, be quick and efficient in reaching out to the right teams.
While it’s different for every company, customer-facing teams like sales, marketing, or revenue-generating teams should own this. A product marketer can run this on a daily basis.
Marketers, this is your opportunity to revamp yourself from a cost center to a profit center. If you’re tired of hearing the sales complaint of not generating good leads, win-loss analysis is the solution. This will open you to the factors that impact your buyer’s purchasing decision. You’ll take responsibility for facilitating sales and helping them close more deals.
Yes, it absolutely works. Nate pointed out an interesting incident where win-loss data increased profit by 30%.
While studying the data from the analysis, their client noticed a recurring theme. Their existing customers said they loved the product so much that they were willing to pay more for the same. So, the company raised its prices by 30% and was met with no objections. They were still selling the same number of deals but at a higher profit margin now.
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