In May 2022, Ben Eubanks, Chief Research Officer for Lighthouse Research & Advisory, shared in a blog post, “According to some sources, the number of HR technology firms is more than 4,000 providers. We estimate that number to be 5,000+ due to the simple velocity of new companies entering the space in recent years.” No wonder it’s so noisy out there. And the noisiness becomes downright frenetic when you consider how many players are involved. You have upwards of 5000 sales reps vying for prospects, hoping to seal the deal and make their numbers. Not to mention the PR and marketing folks in the background working to build product awareness and generate new leads. Pure chaos. 

So, to help make sense of the market, today we will take a journalistic approach (5 W’s and an H) and focus on a single solution: Cadient Decision Point®. 


First things first, who is Cadient? Cadient is a talent acquisition solutions company with software built to meet the needs of high-volume, hourly hiring industries like retail, restaurants, hospitality and healthcare. Next up, what is Cadient Decision Point? This particular solution would best be described as a recommendation engine (sometimes referred to as a recommender system).

In the software world, recommendation engines work by analyzing available data and making suggestions. It gets much more complicated than this, with different approaches and ways of filtering, but for the sake of clarification, most people encounter recommendation engines when they shop online. Sites like Amazon and others will deduce that people who bought product X will also like product Y and use this insight for promotional purposes. 

In the case of Decision Point, the recommendation engine uses machine learning and augmented intelligence to analyze candidate data and make suggestions, which brings us to the when, where and why almost simultaneously. Decision Point sits at the application stage of the recruiting funnel and works alongside the applicant tracking system.

As candidates apply, Decision Point sorts through their information in seconds and identifies the candidates it sees as good hires (more on the how later). As for why the Decision Point approach saves hiring teams the time it takes to sort and review applications and offers data-driven recommendations that can decrease turnover and predict future hiring needs. 


Having covered the who, what, when, where and why, it’s time to tackle the how – and the “how” is what differentiates Decision Point in this noisy, chaotic space. 

Through machine learning and augmented intelligence, Decision Point develops algorithms specific to the business in question. That typically means compiling data based on years of historical applicant records, hired status and employee tenure. By examining this dataset, Decision Point starts to learn which attributes led to good hires, leveraging length of tenure as a key determiner.

From business to business and industry to industry, the attributes that result in good hires vary and can even vary within a company depending on geography or economic climate. Before the analysis is complete, Decision Point takes the process one step further and removes attributes associated with bias, such as name, age, gender and race. 

Here, Decision Point is ready to go, able to sift and sort through stacks of applications in seconds, a task apt to take hours when done manually. 

Use Cases & Benefits

Ultimately, Decision Point makes hourly hiring easier by identifying quality candidates quickly, eliminating guesswork and mitigating bias. These top-line benefits mirror much of what companies want to achieve through recruiting optimization: faster time to fill, better quality of hire and the potential for increased diversity.

There are also secondary benefits that factor into what Decision Point offers: reduced turnover, decreased operating costs and increased revenue. Sourcing candidates more likely to stay corresponds with new hires who are more likely to stay. Those hires invested in the company become more likely to produce. Over time, longer tenure and higher productivity can support a drop in recruiting spend and a bump in profits. 

Analysis of Cadient clients validates this thinking, looking at how many actual hires Decision Point would recommend versus how many were hired that Decision Point would not recommend. To determine this, the Cadient team estimated the impact of hiring fewer hires who lasted longer than repeatedly hiring ill-suited employees and multiplied the saved hires by $2,500 – the average cost per hire.

For a large national business services firm, this resulted in $28M in savings for the business (not to mention thousands of potential hires saved). Cadient Decision Point produced similar results for a national retail chain, national convenience store chain and popular regional fast-food chain, too – and as the saying goes, the proof is in the pudding. 

To learn more about Cadient Decision Point, visit

Jim Buchanan

Jim has been involved in the talent acquisition industry for over fifteen years and has seen both changes and advancements in the recruiting process. The dream of Cadient was conceived several years ago as he saw a great opportunity in the distributed hourly hiring sector.