Security Services Provider

This realization brought Marcy, the CHRO of a Security Services Provider, to us at ThriveVance. This organization relies on nearly 9000 agents to build trust with its subscribers and sell its array of security products, meaning the company has to hire hundreds of new agents each year. Marcy and the team are now utilizing big data and artificial intelligence in their recruiting and management development efforts to accomplish that efficiently and cost-effectively. To identify the characteristics of top performers, they collect and analyze data on their existing agents, including performance data, customer records, and training information combined with the outside experts' views regarding productivity, career ambition, customer service, adaptability, and sales ability. This information drives AI-enabled interviews that generate questions and check candidates' responses against a collection of answers to determine the best matches on the characteristics that matter most.

Marcy has significantly improved the organization’s ability to identify, recruit, and retain great talent at a dramatically lower cost using new technology and big data. For example, the company has increased its 18-month retained-agent ratio to 95% while cutting close to $15 million in costs and keeping pace with the overwhelming demand for new agents.

Part 2:

Our client Marcy, the CHRO of a leading provider of security products and services, provides a case in point. In this security services company, subscriber service representatives handle more than 3 million calls and cases each year, managing all process aspects. Providing excellent customer service means making the processing more convenient, faster, accurate, and less costly.

Accordingly, leadership has taken steps to ensure that service representatives are supported by machine-learning algorithms that use artificial intelligence to handle cases more accurately and efficiently.

In deploying these AI-enabled tools, we engaged the technology team to collaborate closely with the service representatives to design and train its AI model, ensuring that the AI learns to think like an experienced rep. The company's risk management uses the model to assess risk. Still, they can adjust its risk estimates with an explanation that feeds back to the AI so that the AI model can be continually updated and improved. Lastly, AI directly takes on lower-value tasks such as fraud detection and prevention, allowing the rest of the team to focus more intensely on connecting with and providing guidance to subscribers. This approach is more satisfying for people and better leverages their capabilities, delivering results on multiple fronts.