High employee turnover is often a hint that a company’s human resources strategy isn’t working, but it’s also difficult for managers to identify who is thinking of leaving. HR Signal wants to make retention easier by using algorithms to predict which employees are most likely to seek another job. The worker analysis startup, which has benchmarked 50,000 job positions, announced today it has raised $1.6 million in pre-seed funding from Gammite Ventures and Aaron Grossman.
One of HR Signal’s algorithms, called the Employee Retention Engine, crunches billions of data points from public sources, market data and peer career path data, including a million anonymized resumes. The startup uses very little internal data from its clients in order to preserve employee privacy and make onboarding new clients faster, the founders say.
Co-founder Sagy Cohen came up with the idea for HR Signal while working as a senior data scientist for a venture-backed computer vision startup. Data scientists and computer vision AI specialists are in short supply and Cohen was constantly approached by recruiters, even though he was happy at his job. This prompted him to build an algorithm to help recruiters contact only people who are at a point in their careers where they are likely to leave their current positions.
Cohen and co-founders Andrew Spott, Daniel Gilon and Aaron Goodman began working on the algorithm in early 2020. At first, their goal was to direct recruiters toward people who are under-promoted or under-recognized. Then at the end of the year they had an internal case study predicting an abnormally high number of people were ready to move on from their jobs, and realized there was going to be large “turnover event” because many people felt stagnated as the result of the pandemic.
After that, HR Signal’s team decided to focus on boosting employee retention instead of recruitment. “There’s this narrative that people are less loyal to their jobs and are constantly quitting, but when we analyze the data in a large enough data set, the trend really is potentially different,” said Spott. “Employers seek leadership positions externally and are not promoting enough from within.”
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To help clients retain employees, HR Signal’s algorithms consolidate data from external and public data sources. In addition to the million anonymized resumes, other examples of data include earnings reports, mandatory filings and IRS reports. To get more information about different job positions, HR Signal uses salary information and market data, like how many job ads are in each region for each type of position, how long have they been posted, how long they took to fill and whether that number is growing or falling. All data is GDPR and CCPA compliant.
“If we’re looking the resume of a person and comparing them to their peers in that position, with the same tenure and salary grade, we can create, even anonymously, a profile of a person and look at their career path and next position or salary trends,” said Spott. This helps companies figure out who in their workforce is most likely to want another job or get poached by a competitor.
An example of the insights produced by HR Signals include internal mobility, or the percentage of people who change positions, usually through a promotion, in a workforce. “Internal mobility is the thing that really explains business continuity and preservation of talent and tacit knowledge,” said Spott.
He added that if there are 10 companies of a similar size in the same industry, “we could just look at internal mobility and know which one’s going to have a better culture and more likely to have a better financial performance.”
Many companies already have “stay interviews” with employees to discuss what they like and don’t like about their jobs. What HR Signal can do is make the process more efficient by providing a suggested workflow for an employee’s stay interview. For example, it can help employers create a path towards promotion, including what projects or outside credentials someone should complete before moving on to a higher position.
HR Signal’s founders say thats algorithms can be used to help diversity, equity and inclusion initiatives. “Our customer agreement is very specific that this information is to be used for good,” said Spott. “We do not want to use it to exercise biases or inequity. In fact, all our information or analysis of career paths, is entirely devoid of any sort of demographics. It’s not based on trend reports and historical analysis, not based on gender, not based on age.”
The startup’s clients include HR consulting firm ERC, which is used by 1,500 companies that want to improve their workplace culture and promote from within. So far, HR Signals has been in prolonged soft beta mode to refine its algorithm’s output and it has worked with employers ranging from 30 people up to 3,600. The founders say their ideal client has least 100 clients because that gives HR Signal more data to use.
“We are tasked with being in the middle of the employee-employer mix, but we are really trying to be beneficial for both sides,” said Cohen. “And we think that this is a great role to have because there’s a lot of tension, but also a lot of good things that can come out of the length of a relationship and retention.”
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HR Signal helps companies figure out who is most likely to quit and why by Catherine Shu originally published on TechCrunch