The following are some of the different ways of gathering and presenting reward intelligence:

1. Surveys: Surveys are a useful way to gather information about reward practices and preferences within an organization. Surveys can be designed to collect data on a wide range of topics, including employee satisfaction with their current rewards, preferences for types of rewards, and the perceived value of different rewards.
2. Market benchmarking: Market benchmarking involves comparing an organization’s rewards practices and compensation levels to those of similar organizations in the same industry or geographic area (Cappa et al. 2019). This information can be gathered from publicly available sources, such as industry reports, or through direct contact with other organizations.
3. Employee focus groups: Employee focus groups are a useful way to gather information about reward practices and preferences within an organization. During these sessions, employees are invited to discuss their views and opinions on a range of rewards-related topics.
4. Data analysis: Data analysis is a key component of reward intelligence gathering (Bıyık et al. 2022). By analysing compensation and benefits data, organizations can gain insights into their rewards practices, including the distribution of rewards and the impact of different rewards on employee engagement and motivation.
5. Presentation of findings: The findings of reward intelligence gathering efforts should be presented in a clear and concise manner, using a combination of visual aids, such as graphs and charts, and written summaries. The presentation should be designed to be easily accessible and understandable to all stakeholders, including employees, managers, and senior executives.

The key to success is to use a combination of these methods to gather a comprehensive picture of reward practices and preferences, and to present the findings in a clear and actionable manner that supports the development of effective compensation and benefits strategies.

References

Bıyık, E. et al. 2022. Learning reward functions from diverse sources of human feedback: Optimally integrating demonstrations and preferences. The International Journal of Robotics Research, 41(1), pp.45-67.

Cappa, F., Rosso, F. and Hayes, D., 2019. Monetary and social rewards for crowdsourcing. Sustainability, 11(10), p.2834.