VIVAHR is a finalist in the 2017 Arizona Innovation Challenge
VIVAHR, a ground-breaking applicant tracking software that focuses on culture marketing, becomes a finalist for the 2017 Arizona Innovation Challenge.
Phoenix, AZ- It was announced Wednesday that VIVAHR is a finalist in the 2017 Arizona Innovation Challenge. The Innovation Challenge is powered by the Arizona Commerce Authority and is a program in which local businesses compete to win a $250,000 prize. Every six months, the ACA awards six companies with their highest honor for small businesses, the Innovation Challenge prize. The award is meant to boost startups and small companies to the next level of business growth.
VIVAHR set out in 2017 to disrupt the way companies hire. Increasingly, millennials are looking for company culture as they seek either their first jobs or the next big opportunity. Companies who know this and are willing to adapt to this new way of attracting candidates are calling for tools to help them communicate company culture better. With culture marketing, hiring managers report conversion rates that are five times higher than before. VIVAHR helps recruiters and hiring managers keep track of candidates while also giving them ways to express and promote their company culture online.
Ryan Naylor, Founder and CEO of VIVAHR, expressed his gratitude for being considered as a finalist. “Arizona is on the upswing when it comes to innovation and business creation. I am proud that VIVAHR can be a part of the dialogue and can be recognized as a great new business in our state. VIVAHR is committed to growing and hiring in Arizona as we continue this grow this recruitment marketing software. ”
About VIVAHR (https://vivahr.com)
VIVAHR is a proven software for HR teams to attract and convert the best people to their organization for their hiring needs. Although loaded with many features, VIVAHR focuses on two main challenges to the recruiting process. 1)Drives job posting awareness through job board partners and syndication, 2) Offers AI and machine learning to create predictable scoring and automations.
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