Thoughts from our CEO

The Full Stack Business Model for Talent?

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I am very excited about the current popularity of the term “full stack,” the zeitgeist of which was captured by John Herrman’s recent New York Times’s article on “the stack” as the flavor-of-the-moment tech metaphor for understanding everything. The author noted that the term refers to a set of software that works together to accomplish something, but has expanded to comfortably roll off the tongues of a diverse range of non-techies in such contexts as diet (a “supplement stack”), leadership (a “talent stack”), and public policy (the “India stack” — though I suppose that’s still kind of techie). I will admit, I’m a gleeful offender, though I mostly still use the term in boring, as-intended phrases like “full stack engineer” and in sentences like, “Gosh, we’re having a hard time finding awesome full stack engineers, do you know any in Hyderabad?” (Seriously; do you know any?)

All this full stack talk has made me think about the full stack business model— distinct from a full stack tech solution or full stack platform— which happens to be what we’re building at Shortlist. In this context, full stack connotes something akin to “vertically integrated,” owning the full (in this case) recruiting value chain from job description to source to shortlist. But it also means that we act as a company’s outsourced full tech stack, bringing the “best of” a robust talent tech stack to companies so that they don’t have to go figure it all out on their own.

How does this concept play out at Shortlist?

We work with companies to deeply understand the role (and which competencies will drive success), build a candidate pool through a diverse range of channels, and most importantly, narrow down large candidate pools from many to few using a mix of sophisticated software and thoughtful human touch.

This is a bit unusual for a tech company, and earns us the occasional accusation of being a “service company,” an insult in VC/tech parlance on par with egg-throwing in a French election (Marine Le Pen, you deserved it!). It’s true that most companies in the talent tech world are obsessed with being a purely tech solution that picks one thing (video interviews, gamified assessments, social media search) and slots neatly into a company’s HR tech stack, playing nicely with the jumble of HRIS, CRM, ATS, social search, and other stuff revving the engine of sophisticated Fortune 500 HR teams around the world. If you’re a startup selling to a big company with a sophisticated talent value chain, there’s a darn good case to be made to specialize and focus, optimize the heck out of your corner of heaven, integrate promiscuously, and wait for someone to buy you.

Unfortunately, I think this is a challenging strategy for a company building a tech solution for talent issues in emerging markets, because it’s not what the market wants or is ready for.

Across industries, I’ve seen many companies fail trying to tackle just one part of a value chain, and I’ve seen how the success stories realized they needed to figure out the entire thing to succeed. We saw this in microfinance, as the success stories in India of the early/mid 2000s grew by owning the entire product and distribution value chain for financial services. We saw this in household solar products, as the success stories in East Africa of the late 2000s/early 2010s grew by owning the complete manufacturing to packaging to sales/distribution value chain, many times extending into after-sales service as well. And we’re seeing it even today as e-commerce businesses figure out new ways of solving last mile delivery and payments in order to grow their businesses.

We think it’s a similar case when it comes to talent and recruiting, albeit with less distribution and more of a tech dimension. SMEs with 1,000 or fewer employees will rarely have “talent tech stacks,” and frankly will rarely have any HR-focused software capable of integrating with cutting edge tech tools.

And when it comes time for SMEs to hire and grow, they don’t want to go out hunting for software, doing demos, comparing features and prices, negotiating contracts, waiting out tech integrations, training employees on how to use it, weathering complaints about how “the old way was better,” and praying the new tech actually works and generates ROI to write home about. No way!

When SMEs need to hire and grow, they want people, not software: they want to talk to people who understand their needs and they want to be given candidates who are great, ready to be interviewed, and ready to get to work. It’s not that these companies are opposed to software and tech — not at all! — they’d just rather someone else figure that out, so they can get back to their core business.

At Shortlist, we figure that out. We build technology that makes the human touch more efficient and effective, while not expecting it to replace humans altogether (yet). And we build for the full value chain, knowing most SMEs in particular want partners to solve their whole problem, not just part of it.

At least that’s what we always wished existed as we’ve built companies as founders, managers and investors in India and Kenya over the years.

So here’s to the full stack business model! (And here’s to full stack engineers, too, who should come talk to us if they’re in Hyderabad and ready to build something awesome.)


The Limits of Machine Learning in Recruiting: Garbage In, Garbage Out?

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Hardly a day goes by without someone asking: “So Shortlist is basically using artificial intelligence to screen CVs faster?” My response: “Well, not really…”

There’s no doubt that AI, neural nets, natural language processing, and machine learning are having a moment. They’re the shiny new toys in Silicon Valley right now, and with good reason — the capabilities are powerful, and if the vision is realized, the world will change, big-time. And no doubt there are a number of companies looking at ways to apply AI, machine learning and related concepts to recruiting.

It’s true that we incorporate sophisticated data analysis into our screening. We collect hundreds of data points from each candidate, and we’re starting to engineer even more features or variables that can predict who gets hired, who performs well, and who sticks around. These data feedback loops can get even more powerful as we do more and more hiring with a single employer, learning what they like and who does well at their company.

That said, we see glaring limitations to applying machine learning and AI to screen candidates based on their CVs and social media profiles alone — summed up in the old computer science adage of “garbage in garbage out.” GIGO is the concept that any output can only be as good as the inputs, and ultimately, any predictive algorithm is only as good as the data going in and the outcomes you’re predicting. In this case, most fancy new machine learning-based solutions in the recruiting space are using CV data as the primary input, and “similarity to other candidates/employees” or to a job description as the outcome to be predicted.

But this is myopic: CVs are shown to have only modest predictive relevance to performance in a job when considered on their own. Of course prior experience can matter, but CVs alone fail to capture individual performance and contribution, raw talent, actual competence, motivation, and a host of other factors that are important in assessing quality.

There’s also the pesky reality that they’re often embellished and written to be picked up by keyword-driven screening engines, which distorts analysis and results. Think back to your own hiring experiences; how many times has that candidate who looked so great on paper disappointed in reality? And have you ever had great colleagues who didn’t go to great schools or work at fancy corporates but who have shined in the real world? So don’t ignore a CV, but don’t rely on it exclusively!

And there are limits on the availability of “outcome data” — i.e., how do people actually perform once on the job? Ultimately, you need good “training data” and time to build a good algorithm, which means you need a data set with outcomes you care about — i.e., what was the person’s productivity on the job, did they stick around, were they a great culture fit? Unfortunately, that data is rarely available when training an algorithm in recruiting contexts.

Unless you’re running predictions about actual performance, actual retention, and actual outcomes that matter — then you run a great risk that you’re just pattern matching the status quo, which may entrench the same hiring mistakes, the same biases, the same lack of diversity that we already see.

But if CV data is all we’ve got, what’s a recruiter to do? Well, we can start to generate new data and new signals. Our technology automates the collection of dozens of new, user-generated data points — raw data about experience and salary expectation, performance data drawn from cognitive and competency tests, and the meta-data about how a candidate goes through the process which can be mined for motivation, speed, curiosity. As we use this treasure trove of new data to supplement traditional CV data, we become even more excited about the promise of machine learning approaches to make sense of it, yielding better matches for companies and candidates alike.

So, it’s an exciting time for AI (particularly as my brilliant brother joins one the coolest AI companies out there — congrats Tom!), but I don’t think it will be the standalone silver bullet in recruiting for some time to come. Humans are just too darn complicated.

Can we shift the recruiting paradigm from pedigree to potential?

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All around the world, companies big and small are facing a similar problem: Hiring is so much harder than it should be.

While India adds a million people to its job market every month and Africa is set to add more people to its workforce by 2020 than the rest of the world combined, over half of emerging market companies still can’t fill the roles they have open. Startups consistently rank talent acquisition as a top barrier to growth. What gives?

I saw this dilemma firsthand while investing in financial technology startups around the world for the last five years, as the founder of seed venture fund Accion Venture Lab. Once an investment was closed and cash was in the bank, the company’s problem shifted from not having financial capital to not having the human capital they needed to be successful.

The picture is even bleaker on the jobseeker’s side. Even skilled professionals often can’t get hired because they didn’t go to the “right” school, didn’t work at the “right” company, don’t know the “right” people, or fall victim to unfair biases during the application process. They are left lobbing their CV into job board black holes, never able to show potential employers what they can do.

This needs to change. We believe that talent is equally distributed, but opportunity is not. What often appears to be a lack of talent supply in markets is more often a failure of not knowing where to look or what to look for. At Shortlist, we want to level the job search playing field, shifting the recruiting paradigm from one based on pedigree and prejudice to a new version grounded in competency and potential.

How are we doing it?

1. Bringing intelligence to technology

Technology has burst on the scene to flatten access to job opportunities and broaden candidate pools (thank you LinkedIn and Monster). But without intelligent intermediation, more tech creates more noise, more decision fatigue, more work, and more despair for companies and jobseekers — not better outcomes. Just ask any of our employers who have received 2,000+ applications to a single job posting.

We combine a chatbot questionnaire with online assessments and phone screens to help us decide who is most likely to be great in a job. This filtering layer combines technology, data, and a human touch to ensure that talented candidates don’t slip through the cracks, particularly those who risk being overlooked based on CV alone.

2. Creating signals beyond the CV

Most companies have been hiring the same way for centuries (seriously): source and skim a lot of CVs, speak with some of the candidates, then make a decision — and regret those decisions more often than they would like. Not only is it hard to discern genuine ability and fit through a CV and unstructured interview alone, but this mode of decision-making is also often riddled with bias and prejudice.

Companies often do this not because they think it’s best, but because, frankly, there’s nothing else to go on. It’s like the joke about the economist looking for his keys under a streetlamp, not because that’s where he lost his keys, but because that’s where the light is better. At Shortlist, we engage candidates digitally to user-generate more accurate signals. We screen not only for basic experience fit but layer on additional data points for cognitive ability, competencies, and motivation. To be Shortlisted for a job, it’s more important to show us what you can do, not just tell us what you’ve done.

3. Refocusing on what matters

Let’s be clear: many people who went to great schools and worked at impressive companies are great and impressive. But for the vast majority of job-seekers, particularly in emerging markets like India and Kenya (where we work), prior experience paints an incomplete and often misleading picture of a candidate’s capabilities.

Schooling and subsequent corporate experience is — in all countries — more often determined by “birth lottery” than by merit. And we all hold biases, positive or negative, about certain schools or corporate brands. Looking past pedigree and refocusing on potential is the first step towards a world where everyone gets a shot at fulfilling professional experiences. Further, reconceiving the nature and focus of talent screening matters not only for hiring fairness, but also for hiring effectiveness. Building a team based on merit and performance instead of connections and pedigree is not only the right thing to do — it’s good for the bottom line.

The Shortlist mission

At Shortlist, we are on a mission to unlock professional potential and help great companies succeed in building great teams. We’re starting with a new way to match talent with opportunity, but we’re just getting started.

We want to level the talent playing field, but we can’t do it alone! We want to learn from each of you about what you think works to find and understand great talent, and what makes a great team. Visit our website, email us, or tweet at us — we’d love to talk with you about how we can help you hire. We’ll be using this blog as one of the ways we share the ideas behind what we do and how we do it, so stay tuned…