It would be so nice to be able to predict the probability of winning a bid. It would so help with resource allocation. But there are just two problems:
- None of the algorithms that make the attempt to calculate your percentage chance of winning have statistically significant data to base their calculations on. They have no basis to claim accuracy. How many leads, comparing apples to apples, have you run through your algorithm and correlated with winning? Dozens? It’s probably not enough to establish statistical significance for a single variable let alone all the factors that could impact award. You can get more data by considering more companies, but then you also decrease the likelihood of having an apples to apples comparison. Even within the same company it’s hard to compare apples to apples when the customer, offering, evaluation criteria, and other circumstances can be so different. You don’t have enough data for it to average out. Garbage in, garbage out.
- All algorithms that make the attempt to calculate a percentage are guesses piled on top of guesses. Since the chances of the customer accepting your proposal are not numerically calculable, we use proxies. Instead of quantified events we use indicators and guess at some numerical value. Some of these indicators are quite subjective. What is your level of customer intimacy? What past performance score will you get? What are your strengths and weaknesses? If you try to quantify these, you at best have guesses. But what weight will you give each of them? How much do your indicators matter when compared to each other? Which will impact the customer's decision more? Converting the indicator into a number with a guess and using a guess for the weight means multiplying your guesses as well as your margin of error. Garbage in, exponential garbage out.
Combine a statistically unreliable result with a huge margin of error and you get something not worth considering. Using guesses as input for guesses with no statistical significance can’t be made scientific. Even if you use the word “probability” and assign it a number. Does anyone ever go back and compare their predicted win probability with their win rate to see if it’s accurate?
By expressing win probability as a percentage, you may actually reduce your ability to guess your win probability accurately. It’s not just that you have a fake probability. You potentially have a misleading probability.
You really don’t need a number. Sure, it’s nice to allocate your resources by percentages. And maybe a guess is the only way to do that. But when it comes to making decisions, you don’t need win probability to be expressed as a percentage. What you need are the indicators. You need good quality indicators, so that when you guess it’s based on the best quality input possible.
What things impact your win rate the most?
Start with what you think impacts the likelihood of winning. Go ahead and guess. Guess a lot. Collect as many potential indicators as you can manage. Then track them over all of your bids. You might not have enough bids to achieve statistical significance, but using some data to test your beliefs about indicators is better than just your beliefs alone. And over time you might approach statistical significance if you control your variables. Just use the data to establish the correlations between potential indicators and winning instead of calculating a probability percentage. Knowing what actions correlate with winning is more valuable than being able to claim a percentage as your “win probability.”
Just don’t trust people who say they know what it takes to win. Rules of thumb aren’t. Your customers, your relationship with them, the nature of your offering, your ability to turn information into a winning proposal, and your circumstances, add up to a unique context. I have seen the way hundreds of companies conduct their pursuits. Most of them are guessing. Some have convinced themselves that they are experts even though they are guessing. Be data driven.
Put effort into finding indicators that are objective, so that the results aren’t as influenced by wishful thinking, misapplied incentives, and the convenience of the moment. And use a little logic. Knowing the customer for a long time has no impact on your likelihood of winning something new. But having an information advantage, calculated by the number of questions you can answer, is a potential indicator.
In fact, if you only had to choose one indicator, having an information advantage would be a great guess. But still, confirm that by correlating with your win rate. Just look out for apples, oranges, and statistical significance. And laugh at win probability numbers.
I considered the following alternatives for the title for this article:
The ugly truth about win probability that no one talks about
Lies, damn lies, and win probability
Your carefully calculated win probability is wrong
Calculating win probability is like intentionally following a mirage
Your win probability comes with certainty of being wrong
Don't count on your win probability
What are the chances that your win probability is correct?
Carl is the Founder and President of CapturePlanning.com and PropLIBRARY
Carl is an expert at winning in writing. The materials he has published have helped millions of people develop business and write better proposals. Carl is also a prolific author, frequent speaker, trainer, and consultant and can be reached at firstname.lastname@example.org. To find out more about him, you can also connect with Carl on LinkedIn.