FOR TRANSPORT AND LOGISTIC INDUSTRY

Leverage data to enhance the value of talents

Identify key factors to empower individuals and drive business by objectifying decisions through Talent Intelligence

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The new challenge of the Transport and Logistics sectors:
Leveraging data to enhance the value of talents.

In the transportation and logistics sector, leadership faces several priorities that can impact operations and overall organizational success. Here are some of the key HR challenges for this industry:
  • Operational efficiency management: In the transportation and logistics sector, this point is crucial for profitability and company success. It requires accurate planning, optimal resource utilization, and effective management of delivery times, all while maintaining cost control.
  • Talent shortage: This industry demands a wide range of skills, including shipment management, transport planning, inventory management, logistics, and warehouse operations. However, many companies face a shortage of personnel with the necessary skills to perform these tasks. This can impact the ability of companies to meet transportation demand and effectively manage the supply chain.
  • Adaptation to new technologies: Technologies such as artificial intelligence, the Internet of Things, and automation are transforming logistics, with the introduction of advanced monitoring systems, autonomous vehicles, and traceability solutions. The challenge lies in adapting to and effectively implementing these new technologies, providing proper training to staff, and managing the organizational change that comes with it.
Listen to the voice of colleagues and make objective decisions
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Understand sentiment through data analysis
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Discover the most discussed topics and capture the context
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Transforming data into concrete actions

Through technology, Glickon creates Talent Intelligence tools that can simplify processes, monitor and listen to the voice of every talent, objectify management perceptions, and promote a new data-driven culture within organizations.

Active data

Specific feedback can be collected, and the real collaboration network can be captured through surveys, engagement surveys, and Organizational Network Analysis.

Passive data

Corporate communication can be tracked both internally and externally by connecting Glickon to communication and collaboration tools (email, calendars, chats, etc.).

Predictive data

By leveraging the collected data, it is possible to evaluate organizational risk scenarios and predict phenomena such as turnover, attraction, or attrition.

The main challenges of HR Leadership and beyond

By regularly conducting surveys to measure engagement and the quality of the candidate journey, leadership has the opportunity to listen to and visualize the so-called "vital signs" of individuals, especially the more hidden ones. With predictive analysis, they can understand and implement interventions to reduce resignation rates, hire or train new talents, and enhance overall business performance.

By leveraging data and analyzing the voice of talents, it is possible to gain a detailed understanding of talent behavior, engagement, performance, and trends within the organization. This information enables the identification of areas for improvement in both Candidate and Employee Experience, optimizing talent management strategies, and aligning HR initiatives with business objectives.
Talent acquisition and retention
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Employee engagement
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Turnover prediction
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What Glickon does for you

Glickon collects information both actively (ONA surveys) and passively (emails, calendars, etc.) to provide leaders with a comprehensive view of colleague and candidate engagement, as well as productivity.

To understand, predict, and implement interventions to reduce turnover rates, develop new talents, and enhance company performance, the model is based on three elements: motivation, relationships, and skills, with four focal points: day-to-day (in everyday work), team (in relationships with colleagues), manager (in relationships with managers), and organization (in relationships with the company).

Active Surveys & Passive Data

Glickon aims to measure work and selection experience by utilizing recurring experience/engagement surveys and supporting the analysis of digital interactions through emails, chats, meetings, etc.

Platform & Insights

The platform allows for administering talent surveys autonomously and provides real-time results, analysis, and insights. Glickon is an integrated platform compatible with 170+ HR platforms (ATS and HCM).

Analysis and Reporting

Glickon combines survey data with digital traces from organizations to provide insights on attraction levers, engagement, and productivity. Our AI-based analysis can help establish a clear link between employees and revenue.

Concrete Actions

Transform human resources data into tangible improvements through designed paths to guide actions. You will be able to identify key factors to focus on, in order to develop better experiences throughout the entire talent journey.

Key statistics

-50%

Cost reduction in hiring
(Deloitte, 'HR Technology Disruption')

+15%

Increase in hiring effectiveness
(Talent Board, 'The CX Report')

+30%

Increase in talent retention
(Harvard Business Review)
The voice of leadership
Annalisa Di Stefano
@ DHL Italia
HR Director
The transportation and logistics sector is a strategic industry for the Italian economy, and companies need to be able to compete on an international level. HR executives must be able to develop the skills required by talents to address market challenges and meet the evolving needs of the workforce to enable them to perform at their best.

Glickon White Paper: Uncover the Relationship between Employee Experience and Revenue

High turnover risk in high-performing teams, low trust in leadership, and financial performance. Discover the relationship between Employee Experience and company value in our research.

Listen to candidates and employees to define strategic actions

Help the organization achieve business goals by leveraging
people's talent through data

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