Marketing Cloud, Trailhead...
Dublin based Cpl is Ireland’s largest recruitment agency and a global provider of staffing, recruitment, training and outsourcing services across a range of roles and industry sectors, from technology to the legal sector. It has been in business for 29 years and revenue in the last full year topped €0.5bn. Cpl’s goal is to power business growth in future workplaces through providing new innovative talent solutions built around its clients.
“By embracing artificial intelligence, we can deliver better outcomes for our candidates, clients, and the business.”
Cpl is a Trailblazer and an innovator in the recruiting industry
Recruiting is a continually evolving industry. In order to support company growth, Cpl needed to be able to quickly identify new ways of fulfilling their customers’ needs for top talent.
“The recruitment industry is extremely competitive,” said Sweeney. “You’re only ever as good as yesterday’s placement, so we need to stay at the forefront by quickly finding our clients the best possible candidates every time.”
Cpl has been successfully tracking all its candidates, clients and job orders via Salesforce since 2010. However, Cpl was ready to take this data to the next level and leverage intelligent predictions. Over the past two years, Cpl has implemented machine learning and deep learning solutions on this rich Salesforce database to improve business insights and client delivery.
Cpl transforms recruitment with an AI-enabled app
Cpl receives about 40,000 CVs every month, which is a lot of information to rely solely on manual review and keyword searching. With over 400 consultants, Cpl was excited to bring AI into the hands of its employees to improve efficiency and provide an additional tool to help find candidates they may not ordinarily identify.
Cpl has built a candidate evaluation app which evaluates its history of successful and unsuccessful applicants to better assess new candidates, which helps build a stronger shortlist faster.
The initial focus Einstein was to look at a candidates likelihood for placement while focusing on recently applied high scoring candidates that had not been contacted since their application.
“Within hours of receiving a CV, we can highlight indicators of success. Given the high volume of CVs that we receive, this massively improves consultant productivity and the quality of service for our clients.”
By applying machine learning to eight years of recruitment data, Cpl can begin to help predict candidates’ suitability for different vacancies. “The recruitment industry is evolving with AI capabilities increasingly widespread,” said Sweeney. “We’re combining the talent of our consultants with the efficiency of smart technology to deliver better results for both employers and job-seekers.”
The second iteration of Einstein tested by Cpl gives users more capability to influence the model outcome and quickly score the latest data. This has opened up new functions in the business to leverage AI insights.
Einstein Predictions reveal insights related to punctual invoice payments
Another area Cpl wanted to better understand was around the likelihood for an invoice to get paid on time. To date, Cpl has used standard industry Dunning processes to track and chase future payments with the goal of achieving payment at or as close as possible to the invoice due date.
With the nature and amount of money involved in Contingent staffing, the payment date can make an extraordinary impact on the Group’s financial performance and wellbeing. Improving cashflow in this area enables Cpl to continually reinvest in its technology and client solutions.
Cpl staff have previous business knowledge around which clients might pay late but were looking for a way to provide quicker identification of potential delays so that early intervention could reduce the payment cycle.
Cpl began running Einstein Prediction Builder around invoice payments and found some expected and unexpected discoveries. Einstein revealed how much impact a customer including a purchase order for invoicing can have. It also identified different results that the invoice day of the week had on payment receipt. Cpl is now using Einstein Prediction Builder on all open invoices parallel to the standard cash collection processes with the goal of further improving collection targets by early identification and intervention.
In addition, Cpl is leveraging Einstein Analytics to analyze and visualize the financial data coming through Salesforce and external systems.
Einstein Prediction Builder can also be used to implement different pricing mechanisms based on objective analysis of a Client’s performance in payment timings and help assess future sales targets.
Identifying which jobs are most likely to have a successful placement
Another key part of the recruiting industry is the importance of job order completion rate. The challenges around recruiting are similar to a sales cycle, some client jobs will complete the journey with successful placement and some will fall through. The likelihood of an existing permanent job order being filled ranges from single digit percentage likelihood to greater than 75 percent completion.
This is a pain point for Cpl as revenue is 100% reliant on a successful job placement. It has been difficult to immediately identify what constitutes a good job order and how to automatically predict those traits based on the data associated with that job at the present time.
Cpl knew it was important to adjust its strategy to increase completion rates. By using Einstein Prediction Builder, Cpl has been able to rank jobs by score, which allows prioritization of jobs with a higher likelihood to have a successful placement. Based on this, key items can be discussed with clients to help improve fill rate.