CB Insights vs Dealroom: Two intelligence providers benchmarked by Osterus
Previously we’ve focused on the venture capital and recruitment industries, but today we’re taking a look at the technology realm by comparing two companies that focus on utilizing data to connect investors with viable companies and help businesses make more informed decisions.
Dealroom and CB Insights are two software-based companies with ties to the investing and business industries. Both of these companies are relatively new with CB Insights being founded in 2009 and Dealroom in 2013. Unlike some of the other companies we’ve analyzed, they don’t have billions in market cap. Instead, CB Insights reports around $63 million while Dealroom shows a latest funding round of $7 million in 2021.
As a result, both of these companies retain a lower number of employees, which is between 51 and 250. This is a stark difference from some of the other companies with an employee count around 10,000. Does company size impact the employment specifics found in these companies? We would assume that because of the similarities in company size and market cap that the employment standards found would be similar; however, this might not be the case.
Let’s first start by looking at the experience each job title has. The category with the highest experience is software development with an average of 6 ½ years for Dealroom and nearly 6 years for CB Insights. The next highest category is C suite with both Dealroom and CB Insights reporting nearly 5 ½ years of experience.
The experience in these categories makes sense considering that both software development and C suite job duties require extensive knowledge and experience to make the most informed decisions. Next, we see design and board members carrying high experience. It’s interesting to see that no employees at either company have over 7 years of experience. You would assume that each company needs a few personnel that has extensive experience to effectively lead others.
Moving on to the work experience distribution, we see some contrasting results from the average work experience by job title. The highest category for both companies is the 5–10 year category with 33.7% and 39.6% for Dealroom and CB Insights, respectively. This does support the information we previously uncovered in the software development and C suite experience by job titles.
In the 10–15 and 15+ year categories there are 23.8% and 22.7% and 14.2% and 11.9% for CB Insights and Dealroom, respectively. This directly contrasts the average job experience by title information, indicating that the companies may be moving employees between positions where they are less experienced.
There are minimal employees in the 0–1, 1–3, and 3–5 year categories which tells us that the company is not looking to bring on inexperienced talent, such as a recent graduate. To have the most successful chances of receiving a job at one of these companies, you should have around 5 years of experience under your belt.
Looking at the current employment duration distribution is also very telling of company operations surrounding employment. Most employees have been with these companies less than a year with CB Insights reporting 46.2% and Dealroom showing 53.5%. Both of these companies are facing a large learning curve with these new employees while they are training and learning company policies. As a result, productivity and profitability may be lower.
The 1–3 category is the next highest, showing 31.7% and 31.1% for Dealroom and CB Insights, respectively. The 3–5 and 5–10 year categories show minimal current employees while the 10–15 and 15+ year categories are nonexistent. Since these companies are relatively new, we wouldn’t expect to see a high level of employees in the 10–15 and 15+ categories; however, we would want to see a few more employees in the 3–5 and 5–10 categories, indicating that there are employees growing with the company since inception.
The previous employment retention distribution further supports that there are minimal employees sticking around from the start. Most employees work at these companies for 1–3 years, indicating a high turnover. CB Insights shows 56% while Dealroom reports 57.9% of employees fall in the 1–3 year category. This is more than half, highlighting that both companies need to revisit their policies to keep qualified talent in the company.
The next highest category is the 0–1 category with 25.3% and 18.1% of employees found there for Dealroom and CB Insights, respectively. The high amounts of employees in these categories are alarming. What is causing these employees to jump ship so soon? Poor work environment? Low pay? There are very few employees past the 10-year mark, indicating internal problems.
The gender distribution could be playing a role in the high turnover each company is generating. Most positions in both companies are male dominant with the software development category highlighting 81.82% males to 9.09% females for CB Insights and 75.86% males to 20.69% females for Dealroom. This means for every 8 males, there are only 2 females.
The data science and sales categories further show the male dominance in these companies. The data science and analysis category for CB Insights reports 76.47% males to 11.76% females while Dealroom shows 55% males to 45% females. The sales category is even worse with 80% males to 20% females in CB Insights and 70.45% males to 29.55% females in Dealroom.
Clearly, both companies have areas of improvement as women are only dominant in the intern position at CB Insights and the customer service position in Dealroom. Despite the male dominance, the females actually have more experience compared to males. This means that the men hired on often don’t need as much experience, highlighting potential employment discriminatory practices.
The information utilized to derive these employment statistics was from Osterus, a software program that analyzes hundreds of data points to create useful statistics regarding employment. Not only does this information help us understand what is truly going on behind closed doors for employees, but we can also use these statistics to determine if these are companies we would want to work at.
This is just the start of what software-based data programs, like Osterus, can provide us. In no time, we will be able to take this information a step further and decide what quality of life and living costs would be associated with these companies.