Webhelp's A.I. powered People First platform reduced employee resignation risk by 40%. With a ninefold increase in employees across 50+ countries, Webhelp utilized AI and machine learning to personalize support, enhancing employee well-being and achieving impressive results in retention rates.
Let's break out the top ten key points
Webhelp's People First platform, powered by AI and developed in collaboration with Gobeyond Partners, successfully reduced employee resignation risk by a staggering 40% among the targeted population.
Webhelp, a multi-national business process outsourcing company, experienced a ninefold increase in employees over the past 10 years, operating across 50+ countries and 190 sites.
With a revenue of over €1.6 billion in 2020, Webhelp is an industry leader in Europe and continues to expand its global presence.
The People First platform leverages machine learning and data analysis, utilizing contextual data like absenteeism, performance, scheduling, and employee surveys to generate predictive scores for each employee.
Dataiku, an advanced analytics and machine learning platform, played a crucial role in powering the People First platform, providing a collaborative and efficient solution.
The initial pilot of the People First platform showcased a remarkable 50% increase in data team efficiency, streamlining data management tasks and accelerating decision-making processes.
An impressive 80% of managers have embraced the People First platform, leveraging its capabilities to improve employee well-being and retention rates.
By implementing the People First platform, Webhelp has demonstrated a proven return on investment, leading to its continuation and expansion across multiple countries.
The success of the People First platform has allowed the Webhelp team to reduce the adaptation time for new countries from five days to just half a day, showcasing the scalability and efficiency of the solution.
Webhelp's future plans include globalizing the People First model, rolling it out in additional countries, integrating additional contextual factors, and developing a parallel model focusing on employees' first 90 days on the job.
Next steps for Webhelp
We can see that A.I. in Human Resources is making jobs, not taking them but what are the next steps?
The next logical project for Webhelp could be to expand the People First platform to include proactive employee development and career progression initiatives. By leveraging AI and machine learning, they can identify individual employee strengths, interests, and growth opportunities, enabling personalized career planning and fostering employee engagement and long-term retention.
How you can get started
To start a similar project focused on leveraging AI and machine learning to improve employee retention, a company can follow these three simple steps:
1. Define Objectives and Key Metrics:
Clearly define the project's objectives, such as reducing employee turnover or improving employee satisfaction. Identify the key metrics that will be used to measure success, such as attrition rates, employee engagement scores, or feedback from satisfaction surveys. Establishing clear goals and metrics will provide a solid foundation for the project.
2. Gather and Analyze Relevant Data:
Collect and analyze relevant employee data to gain insights into factors influencing attrition rates. This data may include employee performance records, absenteeism rates, survey responses, or other relevant HR metrics. Utilize AI and machine learning tools, such as Dataiku, to build predictive models that can identify patterns and indicators of potential attrition risks.
3. Develop a Personalized Support System:
Use the insights gained from data analysis to develop a personalized support system for employees. This system may involve regular check-ins, targeted interventions, or customized development plans tailored to individual employee needs. Leverage AI and machine learning algorithms to automate and optimize these processes, ensuring scalability and efficiency.
Remember, implementing such a project requires collaboration between HR, data science, and relevant stakeholders. It's important to continuously monitor and refine the system based on feedback and results, aiming for an iterative and data-driven approach to improve employee retention over time.
