The Bowery Capital team had a conversation with Brendan Berry, a Director of Product Management at Ripple, centered around the current state of cross-border payments and what the blockchain-powered future means for international payments.
Intelligent recruiting has become a holy grail in the software world. Hiring is a pain point everyone has experienced personally. Every company in the world has to hire, so the market is enormous. And most existing solutions just don’t cut it: terrible legacy UX / UI, cumbersome workflow, and time-consuming manual processes are rampant. Moreover, we theoretically have access to a range of candidate data, existing employee data, job spec data. These all sound like perfect problems for next-gen software to tackle. Multi-tenant cloud platforms can streamline collaboration; mobile apps can make life easier for the candidate and HR professional on-the-go; applied machine-learning could pick the needle out of the haystack for your role. What scores of failed recruiting startups over the last decade should tell us, however, is that like all ubiquitous problems (email, web conferencing, food delivery, etc.), it’s a lot easier said than done. The challenge in recruiting is time. Most companies can’t afford to sift through every resume out there, knowing that 95% won’t be fits. Optimizing for quality over quantity isn’t easy either. If you’re a startup founder, it’s difficult to know what even makes a good candidate. Looking at your best employees, they excel in the same function for completely different reasons; they may even excel only in specific environments, companies stages or teams. Understanding the perfect employee for a role, again, simply takes time: time to understand your own company’s culture and how employees succeed. In our view, therefore, the real winners in this space will have to focus on three forms of time savings:
1. Screening. Get your opportunity in front of as many candidates as possible, while only letting the relevant ones through, sans manual vetting.
2. Process. Easy, “consumerized” UX that lives in your day-to-day channels (e.g. web, mobile, slack) but with a backend flexible enough to fit any organization, from a startup to a Fortune 100 enterprise.
3. Data. Leverage data to help recruiters understand what actually makes for a successful hire and apply that data back to the Screening and Process to decimate time spent and maximize hire quality.
Of the three above, Data likely holds the most promise for a breakthrough. “Smart matching” algorithms may well be the future of hiring—with an exception. Qualitative characteristics in candidates—culture fit, personality, ambition—are nearly impossible to capture and leverage in any structured way. But given the range of other potential inputs, algorithms can already be used to expedite Screening and Process. For example: automate the logistics of gathering resumes and structure that data, eliminating low-value applicants off the bat. Leverage artificial intelligence or video + prompts to conduct initial screenings. Auto-initiate work history, reference and background checks. But what’s the true potential of data and algorithms in intelligent recruiting?
Jordan Wan, the founder of CloserIQ, a career platform for tech sales professionals, finds the recruiting process too complex to be simplified to an algorithm. As more information and data about people are available to companies, the consequences of overlooking a great hire outweighs the efficiency of an automated process. Ultimately, an emotional connection is more valuable than an algorithm telling you where to work based on your skills and past experiences.
Even without algorithm-based recruiting, companies can still begin to automate the prospecting processes. According to Elise Voss, the CEO and Co-founder of UpScored, a career discovery platform, 60-70% of hires come from referrals in startups. Being entirely dependent on referrals however leads to a lack of diversity in the workplace or a mismatch of skill sets. The most intelligent recruiting method is to combine the referral process with better technology. For example: take a candidate who may not have the specific background in digital marketing but has transferable skills. When an algorithm looks at work experience, education, and skills and compares those to job eligibility and requirements, it will identify synonymous words and phrases that can translate into traits specific to digital marketing. Such processes are extremely effective in technology roles where developers can use their skills across multiple languages. Combining better technology and the referral process will optimize hiring, decrease wasted time, and improve the efficiency of intelligent recruiting.
While some may contend that marketplace models such as Hired optimize the hiring process and are examples of intelligent recruiting, they do not encapsulate all of the qualified labor force. These models disregard candidates who do not meet these thresholds, despite transferable skills and qualifications which can add more value to a company compared to the ideal candidate with relevant prior experiences. Even though these models may make the recruiting process more effective in identifying the same type of hire, they do not consider the other qualified candidates who may not have made the threshold but are just as valuable.
One of the biggest obstacles recruiting teams face that’s preventing them from automating the hiring process is mentality. Companies need to understand the problems and inefficiencies of their current models and use their capital to change that. Companies are implementing big data to optimize advertising and sales, but not hiring. While it is so much more difficult to put metrics on a person and to see performance results in the short run, talent is a company’s most important asset. Companies need to start using data to create better technology for intelligent recruiting.
As we’ve seen, there is a variety of opportunities to digitize the hiring process. Recently, there has also been an increase in recruiting platforms for specific industries. Market shifts and consolidations aside, we anticipate this trend to continue and, hopefully, companies will be able to shorten the process without compensating for the quality of new hires.
If you liked “Software Opportunities In Intelligent Recruiting” and want to read more content from the Bowery Capital Team, check out other relevant posts from the Bowery Capital Blog.
Bowery Capital and Balance explain how B2B marketplaces can think about payments.