Sramana Mitra: What is the customer acquisition strategy?
Josh Millet: When we were bootstrapping, we built our business on the back of Google. A lot of customers came in through the web. We did spend quite a bit of money on Google with paid ads. Over time, we built a great content strategy.
What started as paid search has now turned into an organic search. We started telling our story on the web with a blog, which was a hit. It has been web-driven and search-oriented. We get a lot of small businesses because they saw us on the web. We have a free trial of our services. They get to try it out and look at all these different assessments.
As we move up to more enterprises, it’s more difficult as we do a lot of outbound prospecting. In an enterprise, we have different ways of reaching customers as well. It’s still, to a large extent, web-driven.
Sramana Mitra: How do you analyze the competitive landscape today?
Josh Millet: The field of pre-employment assessments is not a new category. It’s been around for 50 years. When we first came to the market, we were competing with a lot of companies that were good at publishing assessments and validating them, but they weren’t great at customer acquisition on the web or even technology in general. They were more like content publishers.
That was the one area that we had an advantage in. All of the three founders of my company had a tech background. Initially, we were competing against these older companies that were new to the web. In the last three or four years as we have moved to enterprise, we still compete with great assessment businesses.
Now, we also compete with AI-driven companies that use some little assessment science along with general machine learning and AI-driven approaches to create selection algorithms with other inputs. When we are in a bake-off with other companies for big enterprise accounts, it’s going to be us, one other assessment company, and AI-driven companies.
The AI companies are more focused on finding correlations wherever they exist. We have a definite perspective on that. We do some machine learning ourselves and we have early growth. Part of our mission and what we believe is that assessments help you reduce the level of unconscious bias in a company’s hiring process.
Some of the high-profile AI attempts in the hiring space have had the opposite effect. You might have heard the story about Amazon abandoning algorithms that were supposed to promote gender equity. It had the opposite effect.
There are some problems with those approaches and, certainly, some of those are interesting. We focus on using selection algorithms where the applicants are very much aware of how they are being measured. We get nervous anytime there’s AI that is black box and opaque to the user.
Our approach is to use assessment science and combine social science with cutting-edge data science. This a good approach to take rather than just leaning on data science alone. It’s interesting because the set of competitors over the years has dramatically changed. Now, we are competing with a lot more AI companies.