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November 5, 2020 in Fintech, Healthtech

Tips for Sourcing a Diverse Accelerator, from Village Capital and IBM

Tap into a broad network, and take steps to reduce implicit bias.

Last year, the venture capital community hit a milestone when women-led companies received a record level of venture capital funding: 2.2% of all VC dollars, up slightly from the year before. That low bar counted for progress in an industry that tends to favor founders who are white, male and well-networked.

The pandemic has since wiped out that progress: women-led startups are back to receiving under two percent of VC funding. Indeed, some investors have sounded the alarm that unconscious bias can be heightened in a crisis.

Earlier this year, Village Capital and IBM teamed up to run a global accelerator for startups working on financial inclusion and healthtech solutions. The cohorts receive access to Village Capital’s investment-readiness curriculum and technical support from IBM engineers, as well as $120,000 in cloud credits to utilize toward IBM products and services including IBM Cloud Hyper Protect Services, a critical tool to help them protect sensitive customer data from hackers and data breaches.

When we launched the selection process for the accelerator, we set out to ensure that our cohort wouldn’t suffer from the same “innovation blind spots” around race or gender that too often cause investors to miss out on the best ideas. Too often, technology is built for people who look like the people building it or investing in it –  leading to all sorts of pitfalls and biases, whether it’s facial recognition that doesn’t detect gender or darker skin, or missed opportunities for companies to connect with more diverse customer bases.

We’re proud that 50% of the companies in Cohorts 2 and 3 have at least one female founder and 50% have at least one founder who is a person of color. Those statistics were the result of an intentional, four-month recruitment and selection process, during which we focused on reaching founders outside our traditional networks, and using an inclusive and equitable diligence process. This more intentionally inclusive process resulted in identifying inclusive tech like My Normative, a company founded by ‘womxn for womxn.’ While most health tracking apps compare users to a baseline profile of 18-23 year old, white males, My Normative instead integrates and contextualizes a wide range of female-specific metrics.

Here’s what we did differently – and what any decision-maker in tech: any investor, accelerator leader or corporation, can do to reduce implicit bias when they’re working with early-stage entrepreneurs.

Sourcing: tap into a broad network to unlock pipeline

Too often, decision-makers in tech complain about a lack of diverse pipeline of qualified entrepreneurs. This myth has been disproven many times. There are plenty of qualified women founders and founders of color out there: they just don’t always show up on the radar of investors or corporate accelerators because they lack access to networks (or access to a “friends and family” round). Decision-makers need to find these entrepreneurs, and go out of their way to do so.

We did this in two ways: by engaging a diverse advisory board, and by reaching out to a wide network of intermediary organizations.

(1) Advisory board. When we started the recruitment process, the first step was to build an advisory board of industry experts, startup investors, and serial entrepreneurs that reflected the composition of the cohort we were looking to recruit. This board was tasked with sharing the opportunity among their networks, encouraging companies they mentored to apply to the program, and raising awareness around the opportunity by highlighting their engagement on social media. On a volunteer basis, they also were asked to review blocks of top applicants and provide us with feedback about their fit with the IBM program.

To make sure we had broad geographic representation, we made a point to select Advisory Board mentors from across the planet. That resulted in an applicant pool that was almost 50% from outside the US, far more than we would have seen otherwise.

 (2) Intermediaries. We also actively engaged intermediary organizations: we reached out to more than 100 mission-driven accelerators, tech hubs, and NGOs that drive opportunity and investment to women-led companies and founders of color. For instance, we partnered with the Female Founders Alliance to hold webinars, fireside chats, and one-on-ones to inform their network of the opportunities to increase awareness among diverse. To reach a more geographically disperse community, we partnered with MEDICI and held a webinar highlighting what different funders around the world are doing to leverage data and provide fintech and healthtech solutions to their end customers. 

Up to this point, our efforts led to an applicant pool with founders from more than 60 countries, and more than 50% of startups founded or co-founded by a woman. The next step was selecting entrepreneurs for the cohort.

Due diligence: reduce implicit bias with a multi-step process

Startup selection is ripe for implicit bias. One study found that the average venture capital firm reviews approximately 1,200 companies in order to make 10 investments. Managing the “top of the funnel” can be exhausting, and can quickly lead to cognitive overload. Investors often rely on shortcuts: seeking out patterns to separate signals from noise.

We know that our teams are not immune to implicit bias. We did two things to mitigate that potential for bias.

(1) Standard app. We asked each of the 300+ startups that applied to use a standard application, inspired by Village Capital’s Venture Investment Level framework: categories like Program Fit, Team, Product, Market, and Scale. This process helped entrepreneurs understand exactly what we were looking for. And we used this standardized rubric for the review and diligence process to help us evaluate startups from very different geographies and backgrounds in a systematic way.

(2) Standard Process / Multiple reviewers from different backgrounds. During the first round of the scoring process, each startup was reviewed independently by three separate reviewers, to ensure that any individual person’s biases did not stand out.. For our final selection interviews,  eight interviewers across IBM and Village Capital rotated into different interviews, with two team members interviewing each company.Having multiple perspectives from different regions, teams, and backgrounds helped reduce the potential for implicit bias of one team member affecting who would be selected to participate in the program. 

To meet some of these companies, get involved, or learn more about IBM Hyper Protect Accelerator Cohorts 2 & 3, read our blog here or contact Sean Siegrist at Village Capital ( Applications for IBM’s next cohort will be launching in 2021.

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