A PWIN calculation should include a combination of qualitative intuition, data analysis, and continuous monitoring.
PWIN is one of the most important numbers your company can track for each opportunity.
Hundreds of thousands of dollars may be dedicated to creating a bid for a government contract, and this money is often allocated based on the PWIN for each opportunity in your pipeline.
Yet there are so many times when PWIN is just estimated by gut feel, and bid and proposal dollars are allocated willy nilly with little justification.
Do you expect your company to compete with four other competitors? Oh, that means you have a 20% PWIN, right?
Have you executed a similar project in the past for another agency? That definitely means you have at least a 75% chance of winning, right?
Did you go to high school with the contracting officer? Booyah! 90% PWIN, right? Right?
I kid. A little.
You may be calculating PWIN all wrong. Or maybe you’re doing it sorta right. But I’m pretty sure you can track it more effectively and calculate it more accurately.
(Wrong) Ways to Calculate PWIN
There are a number of ways that you can calculate PWIN. Most of them are wrong. Or at least incomplete.
PWIN Based on Gut Feel
The first and simplest way to calculate PWIN is by pure intuition and gut feel.
A business development rep might have a good gauge of the probability of winning the bid, use their gut to take an educated guess, and enter the PWIN into CRM software or a spreadsheet.
I’m not saying that this PWIN estimate is more or less accurate than other approximations. BD reps certainly know their craft, and their input is absolutely essential to the PWIN calculation.
The problem with this method is that there is very little justification of why the PWIN is what it is, and what can be done to increase it.
While it’s an educated guess, it’s still a guess. And personally, I wouldn’t allocate hundreds of thousands of bid and proposal dollars on a guess. Would you?
PWIN Approximation Based on Sales Stage
Many private sector companies use sales stages to approximate their PWIN for their deals, and almost all CRMs accommodate this method of PWIN calculation.
And this could not be more wrong for government contractors.
A company might set up seven (7) stages of the BD process and assign a PWIN for each stage. These stages might look something like this:
- Opportunity identified (15%)
- Opportunity qualified (25%)
- Capture plan created (45%)
- Customer requirements gathered (65%)
- RFP analyzed (75%)
- Proposal created (85%)
- Closed Won (100%)
As the opportunity progresses, it is moved along into subsequent deal stages. The assumption is that the further along the opportunity is, the higher the PWIN.
This is just as arbitrary as gut feel and is probably more inaccurate.
Using this method, you can skip stages and still increase your PWIN, which makes no sense.
For example, if you analyzed the RFP and wrote a proposal without truly learning about your customer’s requirements, your PWIN should be MUCH lower than 85%.
What happens if a super strong competitor rears its ugly head in a later stage? That will certainly be detrimental to your PWIN, but isn’t reflected in this model.
What if the contracting officer you’ve built a relationship with suddenly moves to another job while your deal is in stage 5? Yikes, your PWIN will drop like a rock.
The core assumption – that the further along the sales process you are, the higher your PWIN is – just doesn’t work.
PWIN via Competitive Analysis
Many government contractors calculate PWIN purely in the context of how they stack up against the possible competition.
The most simplistic version of this, which I call the “Split the Difference” method, is not much of an analysis at all, but is surprisingly used very often.
Contractors will guess that there will be five firms, including themselves, bidding on this contract. Thus, they have a 20% chance of winning the bid.
If you use this method, come on! You’re better than that!
A more involved method of competitive analysis might look like this:
- Identify potential competitors
- Break down the criteria that may be used to analyze bids
- Score yourself and your competition on each of these criteria
- Calculate each competitor’s PWIN based on these scores
On the surface, this makes a lot of sense. After all, the buying agency will compare you to competing companies, so wouldn’t this be the best way to calculate PWIN?
This is a more analytical method of calculating PWIN, and it’s a step in the right direction. But while competition is certainly one of the most important factors in calculating PWIN, it can’t be the only factor that’s considered.
So this method is more incomplete than wrong. But that still makes it kind of wrong.
The Q&A PWIN Analysis
One of the more detailed approaches is the Q&A PWIN analysis.
In this method, capture teams come up with a bunch of questions that assess their chances of winning, assign weights to the importance of these questions, and rate them on a scale to determine the PWIN score.
The questions may include:
- How close are we to the customer?
- How much can we influence the bid?
- How strong is our team?
- How strong is the competition?
And so on and so forth.
While this is pretty good, there are still some issues with this approach.
Some of the factors, such as how close you are to the customer and how much you can influence the bid, are extremely subjective.
The answers to these questions are typically generated through gut feel.
And some teams go through the Q&A exercise only once and don’t revisit the PWIN calculation again.
Things certainly change; there can be additional competitors who emerge, team members can leave, or client contacts can change jobs. So these questions should be asked frequently and regularly to ensure your PWIN is up to date.
What’s the Right Way to Calculate PWIN?
The methods of calculating PWIN mentioned above aren’t really wrong, they’re just incomplete.
A thorough PWIN analysis will include some form of each of the above methods, as well as external data analysis and internal insight.
Each opportunity might vary, especially if you’re selling products versus services or commodities versus novelties. But for most opportunities, the key factors that contribute to PWIN typically include:
- Strength of relationship with the client
- Past performance
- Team’s qualifications and ability to execute the project
- Size of opportunity
- Timeline for proposal
Building a holistic, data-driven model that incorporates each of these factors will provide the most accurate calculation of PWIN and help you make the best bid / no-bid decisions possible.
Some of the inputs for these factors might come from internal approximations, prior internal data, or external data analysis. The important thing is that the inputs are detailed, data-driven, and continuously monitored.
Let’s break each of these factors down and see how they should be used to more accurately calculate PWIN.
Factor 1 – Strength of relationship with the client
If your potential client has no idea who you are, good luck winning that contract.
And in the situations where you think you might have a “very strong” relationship with the client, how will you prove this to your boss?
Here’s how – show the relationship-building tasks that have been completed and link them to the calculation of PWIN.
Did you attend an industry day? That certainly impacts PWIN.
Did you have a face-to-face meeting with the client to discuss the potential scope of the project? Bump that PWIN up a little.
Did your marketing team create a white paper to display your knowledge of the subject matter to the client? Nice work, crank up that PWIN some more!
Many BD reps may track their activity in a CRM, which is a great way to stay organized. But what’s missing in this traditional CRM process is the creation of a detailed, task-based client interaction plan and linking the execution of these activities to PWIN.
The more relationship-building tasks that are completed, the higher the PWIN.
That’s how you prove to your boss that your relationship with the client is “very strong.”
Factor 2 – Competition
The next important factor is competition.
The method of competitive analysis that we mentioned in the prior section is pretty solid.
But the problems with that method are:
- The criteria scores are essentially made up from gut feel
- This analysis is many times the sole factor in calculating PWIN
- Many companies will do this competitive analysis once and never again for the opportunity
Intuition and gut feel is important, but there is so much data on the competition that it should definitely be incorporated into your rating.
There are ways to analyze publicly available contract data and identify potential competitors by the size of past contracts won and related NAICS codes, for example.
And data science can be leveraged to assess competitors’ past performance in order to make inferences on their strengths and weaknesses in bidding for specific types of contracts.
With machine learning and artificial intelligence, these analyses can be continuously executed to more accurately predict which competing contractors might bid and assess how strong those competitors really are.
Factor 3 – Past Performance
Your past can help predict your future. But how much of an impact can past projects have on your opportunity?
To find out, you can dig up information on your past performance, see how closely it matches your current opportunity, and estimate how well you performed on those projects. Then you have to document these projects and model how much they may impact your PWIN.
These past project inputs will allow you to gauge how well you might perform on the opportunity in front of you and give you an idea of how they will impact the opportunity’s PWIN.
Factor 4 – Team’s qualifications and ability to execute the project
The ability of your team to execute the potential project is another important factor in whether you’ll win the contract.
The important inputs that will have a huge impact on your ability to successfully deliver what you promise include:
- The strength of your project management methodology
- Your technical approach
- The skills of key personnel
You should consider the key staff who might be placed on the project and thoroughly assess their skills.
If your current staff doesn’t have the skill set to get the job done, this factor will be detrimental to your PWIN, and you might have to consider teaming with another company to acquire these skills.
Factor 5 – Size of opportunity
Size of opportunity is another factor that may be important in determining whether you’ll have to team with another contracting firm.
Data analysis can help here as well.
Let’s say the average value of the professional services contracts your company has won is $2 million. You see an opportunity that is right in your wheelhouse but its potential value is $9 million, which is definitely out of your league.
This might be a sign that you’ll need to partner with another firm to go after this contract.
But a deeper analysis of the publicly-available contract win data might uncover other companies who have successfully won a much larger contract than their average value, and how likely this is to happen.
So you may have a shot after all.
The size of the opportunity, compared to past contract sizes, can be a big factor in your PWIN and can inform you of what you may need to do to win it.
Factor 6 – Timeline Until Proposal Due Date
The final PWIN factor that we’ll include is the time until proposal – how much time you have left until the proposal is expected to be due.
If an RFP just got released and the proposal is due in two weeks, that buyer pretty much already knows who is going to win that bid.
On the other hand, if the proposal is due in two months and you’ve built a strong relationship with the client, have a good grasp on the competition, your past performance is solid, and you have a great team waiting to be deployed, you’re in good shape.
The Timeline Until Proposal Due Date factor gauges the progress you’ve made on the opportunity as it relates to the time remaining until the proposal is due.
While there’s no standard way to calculate PWIN, there certainly are wrong and incomplete ways.
When calculating PWIN, it’s important to track all of your inputs, analyze data as much as possible, and update information as the BD process advances.
Calculating an accurate PWIN is imperative and can help you make the best decisions on where to allocate your resources.