Collecting information and candidate engagement, especially when you are a global recruiter can be challenging in terms of time, resource and management.
In this case study we will explore how BMA Group used elay automation services to develop an automated candidate pre-screening engine to capture leads and process more than four-thousand candidates per month.
BMA Group is an HR consulting firm that helps businesses attract, engage, and retain employees. With offices in Puerto Rico, Florida, Dominican Republic, Trinidad & Tobago, Costa Rica, Panamá, Perú, and Madrid, they serve a wide range of clients (including Fortune 100 companies).
BMA Group needed to automate their outbound candidate attraction processes along with candidate pre-screening functions and increase the number of candidates in Vincere at the same time.
They needed a lead capturing mechanism that could gather automate the application process, upload all of the information onto Vincere and qualify the candidate.
BMA Kickstarted their automation journey with elay by designing a chatbot to help with the pre-screening and qualifying of applicants at large scales and uploading applicant information into their Vincere.io CRM.
BMA Group also used elay’s social integration capabilities to share their chatbots on platforms such as Meta and LinkedIn to create applicant funnels in new verticals.
To achieve this, BMA Group adopted a conversational approach to talent acquisition. Elay’s team built a lead generation and qualification chatbot to gather candidate information, and more importantly, ask qualification questions to assess the candidate’s fit for the position they are interested in.
Using the Elay’s automation service to facilitate human interactions feels counterintuitive. But for BMA Group, the results have been game changing!
First, they were able to achieve a higher conversion rate than their standard forms that BMA group had previously used, and reducing the overall cost per lead of their campaigns.
Second, by incorporating qualification questions into the chatbot flow, they were able to qualify candidate at a larger scale and use conditional branching to identify candidates fit for a position to accelerate their application process.