Machine Learning Comes to GovWin IQ: Introducing Smart Fit Score
In the ever-changing field of technology it can be difficult to stay up to date on all the latest innovations. Artificial intelligence (AI) and machine learning (ML) are two up-and-coming fields that often seem to be used interchangeably, so that begs the question: What exactly is the difference between the two? And why are the lines so blurred between them?
Part of the reasoning these are so easily confused is because machine learning is actually a subset of artificial intelligence with the primary difference really coming down to a series of more nuanced differences.
Artificial Intelligence vs Machine Learning
AI is most easily defined as a machine simulating human behavior. The goal of this technology is to create a human-like system to solve complex problems. With such a broad scope, this technology works towards completing various, complex tasks. Popular examples include Siri, Amazon Alexa, or customer service chat bots.
In contrast, ML allows machines to learn from past data without programming explicitly. The goal for this technology is to allow the machine to learn from data so it can give an accurate output. This means it needs a more limited scope that works towards performing the specific tasks it is trained for. Popular examples include Google search algorithms, Facebook auto friend tagging or Netflix suggestions.
Introducing Smart Fit Score
GovWin IQ’s latest beta product release, Smart Fit Score, leverages machine learning to “score” federal contracting opportunities based on historical contract award data. Each score is measured against five influential scoring criteria: NAICS codes, GovWin IQ Smart Tags, opportunity description/keywords, place of performance, and past history with an agency with scores ranging from 40-100. The criteria chosen are weighted differently to help gauge realistic fit for an opportunity.
Outside of scoring best fit opportunities easily and efficiently, Smart Fit Scores can be used for a myriad of other use cases as well. With the ability to view other companies’ scores, you can quickly identify who you might be going up against in the competitive landscape with the ability to view their score breakdown for a side-by-side comparison. Or if your company is in the market for a teaming partner, quickly assess potential leads by identifying top-scoring organizations to make strategic relationships early on in the procurement process.
How It Works: Smart Fit Score & Machine Learning
Criteria-specific scoring is performed by machine learning models trained to extract data from historical contracts won by a company. The score is then consolidated from criteria corresponding to an opportunity with each criteria scored independently and summed up to arrive at the overall Smart Fit Score.
This model aims to reflect the similarity of an opportunity to contracts won by a company, and also captures how well the company is positioned to win the opportunity with respect to the competition. This is key to scoring success because a similarity score with past contracts will not alone suffice. Most active companies in the government contracting space are into multiple industries/domains. Their relative competitiveness in each of these industries is quite variable and scores should reflect that.
After training, these models are able to predict scores on similar features found in different types of opportunities such as SAM.gov opportunities and analyst-tracked opportunities with more to come in the future.
If you’re ready to gain a better understanding of how AI and machine learning are being applied within the GovWin IQ platform – and how you can leverage this technology to source the best opportunities for your business – click the link below to register for our free webinar.
Use Machine Learning to Find Best-Fit Government Contracts
- Federal Agencies
- Technology Areas
- GovWin Recon
- State, Local and Education