Cadient Talent
Augmented Decision-Making for Hourly-Based Hiring

According to the latest labor department data, more than 78 million Americans—nearly 59 percent of the U.S. workforce—is now paid by the hour. However, when compared to the salaried jobs field, most talent acquisition companies overlook the 'work-by-the-hour' jobs. Consequently, because work-by-the-hour job candidates often resign unexpectedly, it makes recruiters' jobs more difficult. Enter Cadient Talent, a company that leverages its AI and machine learning-based software solution to help recruiters with augmented decision-making capabilities. For companies employing candidates on an hourly basis, Cadient Talent's solution helps hire reliable and long-term candidates. In an interview with CIOApplications, Jim Buchanan, CEO, and Stuart Nisbet, Chief Data Scientist shares their insights on how Cadient Talent helps recruiters and hiring managers to acquire the best suitable candidate for a specific position in a company.

Could you give us a background of Cadient Talent?

Jim: We are a Software as a Service (SaaS) company that is focused solely on talent acquisition. A unique aspect of Cadient Talent is that despite being less than a year old as a company, our solution has been leveraged by several companies for more than 15 years. We had acquired a talent acquisition business unit from a larger company, whose strategic focus was on workforce management. Their investment in talent acquisition curtailed over time, and so in a transaction with them, we carved out the business unit and established it as a separate entity. Today, we serve more than 125 clients.

What kind of upcoming trends do you expect to have an impact on the HR Tech Solution Space? How is Cadient Talent planning to leverage these trends?

Jim: One of the most significant trends that we see in HR Tech--one that everybody is trying to comprehend is how to use AI and machine learning (ML) in talent acquisition. Such advanced technologies can prove to be incredibly powerful in the talent acquisition process. We firmly believe that there are some unique aspects of our business where these technologies can be harnessed, and we can drive incredible value for our clients by using them.

Stuart: Applications of AI and ML in HR Tech space have garnered a lot of hype devoid of any robust application in the real world. In many cases, it is due to the lack of data collected over time, as it is a crucial element to build an algorithm, whether you are using ML or deep learning or AI. As explained earlier, though we are a relatively new company, we have over 15 years of accumulated data. We utilize ML to sift through the massive amount of historical data to help clients make better hiring decisions and allow them to predict which candidates would be the most effective in the long run.

Can you outline a few challenges or pain points that your clients face?

Jim: I think one of the biggest challenges that CIOs face today is choosing a talent management suite or a best-of-breed solution. For the past few years, Human Capital Management (HCM) players have invested a lot of money to promote and advertise their HCM suite solution.

It is really attractive for CIOs as it combines all of their human capital management technologies, including employee engagement, training, and career development, performance management, and people analytics in one platform. However, such diverse functionalities can sometimes be confusing and difficult to use for people.

For example, a local store manager may need to use only hiring and onboarding functions. So, it can be challenging to navigate through a comprehensive suite for just one or two services. On the other hand, there are best-of-breed solutions like Cadient that are robust and easier to use. They can be easily integrated into talent management suites, and that way, an organization can get the best of both worlds.

Stuart: Another major challenge for CIOs is not knowing what to do with the large volume of data on applicants returned by the talent management system. A lot of people ignore it and pass on the information to the archives, which is never used. Many organizations, like us, keep that data in a Data Lake—a repository where we store data to be used for the future. Thus, at Cadient Talent, our goal is to use that wealth of data to make the next hiring decisions informed using the decisions that were made in the past.

Could you walk us through the methodology, features, and benefits of your software solution?

Jim: Our solution is designed to accommodate both hourly hiring and requisition-based/salaried hiring in one platform. Typically, most of the systems are designed for salaried recruitment and are to be used by full-time recruiters. However, hourly hiring–or, as we like to call it, distributed hourly hiring—is an entirely different process. For example, a store manager is always hiring as their employee termination notice periods can really be short. So even though they are responsible for hiring, they don't really consider themselves as recruiters and thus aren't adequately trained to do it. Our talent acquisition system is designed in such a way that a store manager can see the available candidates and efficiently conduct screening and reviewing process once they onboard a person.

Stuart: For any job post, there can either be a feast or famine of applicants. Sometimes, a recruiter can see a candidate's application and realize that although he is not eligible for the specified job profile, he is perfectly fit for another opening in the same company. Here, the candidate pooling and machine learning capabilities of our software can augment a hiring manager's portfolio of decision-making choice.

The ML algorithms of our software look across not just positions that candidates have applied for but also the open positions in the company that matches their skill-set. This is unique, and it differentiates us from the rest of our peers.

What do you think are the key differentiating factors that distinguish Cadient Talent from its competitors in HR Tech Market Space?

Jim: We can do better than anybody else when it comes to providing an effective solution for distributed hourly hiring needs. So, even though there are more jobs on the hourly side, most recruiting systems don't cater to that. Employers with a high concentration of hourly workers continuously struggle to fill open positions in the economy. So, our focus is to do everything we can to hire people who will stay on the job longer and be more productive employees

Stuart: Another thing that sets us apart from our competitors is the way we utilize data analytics to make hiring decisions. We use three levels of analytics for talent acquisition. First is descriptive analytics that helps us look at the data we have today and the data we had yesterday to comprehend the current state of affairs like how many openings we have and how many employees we currently pose. Then there is predictive analytics. Based on where we are today and what we expect in the next quarter, we predict where we are going. It is a critical step for business planning as it helps us understand the seasonality of things, challenges based on the region where a company is hiring, etc. Finally, there is the cornerstone of our vision—prescriptive analytics. Prescriptive analytics allows hiring mangers to prescribe the type of candidates that they want, and the system uses analytics that helps them make the hiring decisions.

The final thing that differentiates Cadient Talent from the rest is that we believe analytics—be it ML algorithms or deep learning—are just the engine. But the fuel of the engine is data. It doesn't matter how strong the analytics is; if you don't have the data to feed the engine, it is worthless. We have 15 years of data with over a billion applications. Thus, our possession of potent analytics, along with a wealth of data, is what I believe makes us very unique in the industry.