At the time of publication: Seed | Total funding raised: ~7.6mn USD
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Hiring is a high leverage activity and can make or break a young company operating in a knowledge economy. You hire someone and you are going to be stuck with the consequences for quite some time. Now imagine fast growing companies that have to hire a lot of talent every year - you end up with essentially a new company and your culture can be impacted in a big way if you are not careful.
A lot of time goes into hiring and choosing the right team mates. But the processes haven’t fundamentally changed much across the hiring journey: Search for potential candidates → Shortlist them —> Interview them → Make them an offer → Onboard. Maybe there are some productivity improvements now in the ‘search’ part of the journey and job platforms have enlarged the candidate pool to choose from. But I think there are some good startups opportunities across the hiring journey:
Filtering a high quality set of candidates with evidence rather than just credentials
Identifying candidates who are hidden gems and have a great learning slope
Accelerating on-the-job training
Getting the most out of the face time with candidates i.e. getting the most out of interviews
The Interviewing step is an interesting place to disrupt with almost no innovation in the past. That is literally the only time you get to reasonably ‘judge’ a candidate before she joins the company. It is now generally common to do three or more interviews with a candidate. And while interviewers may be good at weeding out obvious rejections, the highly intuitive nature of interviews mean there are going to be false positives and false negatives. You may have scratched your head when you think a candidate is a ‘strong hire’ and another interviewer on the panel thinks the same person is a ‘strong reject’ (both of course cannot be true at the same time.)
And there are a whole lot of biases that play on our minds because we are human:
First impression bias - judging a person based on the first piece of information or visual cue or perception we get about the person
Recency bias - judging a person based on the most recent question rather than comprehensively throughout the interview
Confirmation bias - judging a person based on what you wanted to hear rather than what maybe an interesting answer with a great thought process
Cultural and In-group bias - unwittingly favoring candidates that belong to your cultural group (however you define it)
and so on
And interviews are useful for candidates too to evaluate prospective employers. Better interviews will potentially lead to higher conversion of good quality candidates or at the least will not lead to a bad reference from them to other prospective candidates.
And as I have noted before, where there is intuition and subjectivity, there is wide variance in quality and consistency. A solution that standardizes or brings in objectivity will be incredibly useful.
Metaview is an Interview Intelligence Startup that helps companies conduct much better interviews through technology.
Metaview aims to help companies conduct high quality interviews consistently even when they are scaling rapidly. It provides actionable data and insights.
Metaview integrates with all the major application tracking systems, calendar software and video platforms. You (the interviewer) no longer have to spend time taking notes during the interview.
Metaview is like a fly on the wall during the interview (with the consent of the candidate) and transcribes the entire interview automatically and almost perfectly.
The interviewer and the team can objectively go through the transcripts and reflect on the answers to judge the candidate without relying on memory and without some of the biases.
Metaview as shown in the above picture provides actionable feedback to the interviewer with highlights on the transcript.
Metaview measures the consistency and the rigor of the interviews to provide an Interview quality score. The data is aggregated at a team level and company level and is also available at the individual interviewer level to improve.
The feedback is available to the interviewer (as a reminder) before their next interview.
Metaview helps bring much needed structure and standardization to interviews which inherently tend to lack consistency and have high variance among interviewers. (It has found that female candidates get 12% less speaking time in interviews and 23% hiring managers ask fewer than 5 questions.)
The solution will be an obvious need for startups and companies that are growing fast but yet have to maintain a high bar for talent and place a high value on culture. And in an increasingly remote-first world and the cultural challenges it brings, Metaview will become increasingly attractive to organizations.
Metaview also offers a cool feature called ‘Shadow Paths’ that helps schedule shadowing other interviewers in the organization to learn from them.
The personalized feedback suggestions are partly automated and fine tuned by in-house interview experts or ‘coaches’ and Metaview claims this is being welcomed by customers.
The startup claims on its website to have around 300 customers with some marquee names on the list.
Metaview claims that its users say the product was helpful 78% of the time which seems like a great score. It also says the transcripts are read through in 18% of interviews - which makes sense because obvious rejects are probably eliminated without looking at transcripts. But when transcripts are used, it is valuable in decision making.
The startup claims better outcomes in final stage conversion (increased by 38%) and a 28% drop in interviews per hire.
The CEO says there are two magic moments when its users appreciate the importance of the platform.
There are two consistent magic moments that our customers almost always experience. Initially, it is the first time they make use of the seamless recording and transcription to come to a different — and more informed — decision on a candidate. The shift from guess-work to evidence-based hiring is palpable at that moment.
The second magic moment is when experienced interviewers themselves — who are often also senior operators within the business — champion the quality of the insights and feedback they are receiving through the tool. They often comment on how actionable and personal the feedback is, and this delights the Talent Leadership because they know they are finally having a tangible effect on interview quality.
The pricing model is ‘per seat’. There are two tiers - one is for just transcripts and analytics and the higher tier is for additional personalized feedback.
Metaview operates in a large and growing market (knowledge economy companies) where clients have headcount as probably the single biggest expense. The remote work trend is a further tailwind for such solutions.
Solutions like Metaview will likely have low churn because it creates a new sticky habit (running more structured interviews) that users realize are how things should have been done all along! It is also great that the evidence of the solution’s usefulness is immediately evident after one or just a few interviews. And more interviews enabled via Metaview makes its product better and helps in modelling new insights for its customers.
Metaview can in the future expand its feature set and can tackle more problems in the hiring journey. It hints at that ambition in its fundraising announcement.
Weak, inconsistent, unintentional interviewing impacts all high-growth companies. It slows down hiring, leads to mis-hires, and is a major headwind for overall business velocity.
Improving interviews is just the start though.
At the moment, every collaboration, touchpoint, and decisions in the hiring, talent management, and reward process is dehydrated. The data that really matters is nowhere to be seen. Now is the time to change that.
We're building the system of intelligence for talent. Our approach is to identify the most inflectional moments in the talent lifecycle, harness the data that matters, and create delightful products that provide super-powers for the people who's most important job is to build teams.
For example, one feature can be real-time question suggestions or topic recommendations during a live interview. A more ambitious future solution can be some sort of automated ‘candidate strength scoring’ basis a candidate’s answers.
The founders Siadhal and Shariar worked in Uber and Palantir respectively and seem to have encountered these interviewing problems themselves.
I believe Metaview is a High Potential Startup: solving an important problem for a very large and growing market; great and easy to implement solution approach with low friction; almost immediate evidence of its usefulness for users and creating a new sticky way of doing things leading to low churn; potential for selling more features and solutions to its customer base; and evidence of early product market fit with its customers.