High Precision and Recall: Quickly Scout the Best Startups

Startup scouting is essential to any company’s open innovation strategy. However, with the enormous number of startups emerging globally, manual scouting is inefficient, inexhaustive, and inaccurate. This is why companies are increasingly leveraging data-driven startup scouting platforms such as the StartUs Insights Discovery Platform. Our SaaS uses Big Data and Artificial Intelligence to scout startups with high precision and recall.

In this article, you get the answers to the following questions.

• What are precision and recall and why do they matter for data-driven startup scouting?
• Why do startup scouting platforms generally have poor recall and precision?
• How does the StartUs Insights Discovery Platform scout startups with high recall and precision?

What are Precision and Recall?

Walber, CC BY-SA 4.0, via Wikimedia Commons / Modified from original

For testing the performance of any machine learning model that classifies entities, precision and recall are two important measures. Precision measures the fraction of predicted positive cases that are actually positive, whereas recall measures the fraction of actual positive cases that are correctly predicted by the model.

These two measures provide complementary information about its ability to correctly classify cases. High precision means that the model is able to make accurate predictions, while high recall means that it is able to identify all of the positive instances in a dataset. If a machine learning model is trying to detect dogs in images, a high recall means that the model is good at detecting all the images with dogs. High precision means that when the model predicts the presence of a dog in an image, it is rarely wrong.

Relevance to Data-driven Startup Scouting

Why are precision and recall relevant when your objective is to scout startups? Because data-driven startup scouting platforms use machine learning to identify if startups are relevant to your search criteria.

High precision means that nearly all of the startups scouted are relevant to what you’re looking for. High recall means that you get a large number fraction of the total relevant startups out there globally. Ideally, you scout startups in a way that delivers both high recall and high precision. This ensures that you identify all promising startups with a high degree of confidence. However, most startup scouting platforms fail to get both high precision and high recall.

Poor Recall and Precision Mean You Don’t Know All Relevant Solutions

There are several reasons why a machine learning model has poor precision and recall such as a lack of sufficient training data or model tuning. These problems impact the performance of data-driven startup scouting platforms as well.

Insufficient data.

AI models need to be trained on large and diverse datasets to be accurate. The quality of data impacts both precision and recall. If the model is not trained on a large and diverse enough dataset of startups, scaleups, and emerging companies, it may not be able to accurately identify all relevant startups, leading to poor recall. It may also suggest irrelevant startups and have poor precision.

Poor model tuning.

Model tuning is the process by which the weights assigned to each parameter are optimized to have the best performance. Data-driven startup scouting platforms with poor model tuning assign too much weight to irrelevant parameters and too little weight to parameters that matter for your search. Thereby, poor model tuning impacts both precision and recall, preventing you from identifying highly relevant startups.

Outdated data.

The recency of data is of utmost importance to any startup scouting process. Depending on where a startup scouting platform sources its data from, and how often, its data could be outdated. Consequently, you will miss out on newer startups or startups that have pivoted since the data was sourced. This does not accurately reflect the current startup landscape, leading to poor recall.

Biased data.

Any model is only as good as the data it receives. If the data used to train the model is biased, it will not accurately represent the underlying population of startups, leading to poor recall and precision. Startup scouting platforms often have a heavy bias towards particular geographies or industries which limits their recall and precision.

How to Improve Precision and Recall

There are several ways that data-driven startup scouting platforms can improve precision and recall. Some of these include:

1. Sample a larger dataset of startups and emerging companies.
2. Properly tune the model to achieve the desired performance, i.e. Which firmographic data points should weigh more than others?
3. Regularly update the data in the startup database.
4. Reduce bias in the data by sampling firmographic information from different sources.

Overall, improving precision and recall in data-driven startup scouting requires careful attention to the data and regular updates to ensure accurate predictions.

Get Industry-leading Precision and Recall with the Discovery Platform

High Recall thanks to Exhaustive Firmographic Data

The StartUs Insights Discovery Platform provides high recall for both broad and narrow searches. When the topic is broad, other platforms only deliver better-known solutions whereas the Discovery Platform delivers under-the-radar startups as well. For narrower topics, other platforms have very low recall as they generally feature data only from a few sources. The Discovery Platform, on the contrary, provides an exhaustive list of relevant startups.

How does it do that? The Discovery Platform maps the world’s information on startups, technologies, and innovations to track startup activity around the world. It delivers actionable insights by scraping over 1,8 billion innovation data points from the web, startup ecosystems, company aggregators, publications, etc. This gives you:

• Firmographic data on over 3 million startups, scaleups, and tech companies globally with data on each startup validated by an average of 53 sources.
• Insights on 23 500+ technology trends, including their development and impact over time.

This exhaustiveness of data on the startup ecosystem ensures that you do not miss a relevant solution, regardless of how niche or complex your query is.

Achieve High Precision with Best-in-Class Proprietary Technology

We also achieve high precision with our extensive experience in ranking startups and the Discovery Platform’s best-in-class proprietary technology. The precision is much higher compared to what alternatives offer as the Discovery Platforms captures even the relationship between technologies.

Some of the features of the Discovery Platform that contribute to its high precision include:

• Knowledge graph that maps the relationships between technology trends and their drivers.
• Semantic search that captures the context of your search query and provides more accurate results.
• The Similar Companies feature allows you to find more startups and scaleups similar to the ones you already know.

Not only does the Discovery Platform provide greater and more accurate results than any other startup scouting process, but it also does so within mere seconds. This allows you to focus on engaging relevant startups instead of spending time identifying them.

Validate the Discovery Platform’s Performance

It is easy to give the impression of high precision. If a startup scout finds a number of startups, they can weed out irrelevant ones later such that the remaining list has a larger proportion of relevant startups. Recall, however, isn’t easy to fake.

How can you validate the recall of the Discovery Platform (and any other startup scouting tool or your internal process)? Take a list of relevant startups from preliminary research. Compare them to the output of the Discovery Platform. If most startups from your list feature in the scout’s output, you can be certain that the search is an exhaustive one. Further, the number of additional startups is also a measure of its recall.

How to Scout Relevant Startups Exhaustively

Based on your method of choice for startup scouting, the following are the potential outcomes:

• Low recall, low precision. You get a few startups and most of them are irrelevant. This is generally the outcome of a manual scouting process.
• High recall, low precision. You get a lot of startups but a few are relevant to you. Expect this when using startup scouting platforms that only focus on amassing firmographic data.
• Low recall, high precision. You get a few, highly relevant startups. This is what most startup scouting platforms deliver when you’re looking for something specific.
• High recall, high precision. This is a powerful combination that the Discovery Platform delivers for your startup scouting process. You get an exhaustive list of highly relevant startups.

If you’re looking to scout highly relevant startups exhaustively, the Discovery Platform is the best choice for you. Here are a few ways you can use it for your startup scouting needs:

• Get more and better applicants for your startup programs. It finds the most relevant potential applicants for your startup programs or startup calls. This ensures the success of your program.
• Tackle internal bottlenecks. Your business units often run into challenges that other startups are already tackling. Working with these startups allows you to resolve these challenges and improve productivity.
• Scout potential build-buy-partner opportunities. The Discovery Platform’s search is far quicker and exhaustive, allowing you to focus on engaging startups.
• Generate leads faster. It identifies thousands of relevant startups in a few seconds. This allows you to quickly discover potential leads and grow your business.

With its high precision and recall, the Discovery Platform takes the hassle out of your startup scouting process. Using the Discovery Platform you cut costs by making your innovation discovery more efficient & exhaustive. Want to see it for yourself? Get in touch today to book a demo!