Page 18 - Azerbaijan State University of Economics

THE JOURNAL OF ECONOMIC SCIENCES: THEORY AND PRACTICE, V.82, # 2, 2025, pp. 4-31

N, Manjiang X, Bakhtawar A, Mufti M, Khan M. 2024]. Analysing data from learning
management systems, performance reviews, and employee feedback enables HR
professionals to evaluate training effectiveness and make informed, data-driven decisions.
Furthermore, research has delved into the usage of predictive analytics and
personalisation of algorithms to improve learning experiences based on the specific needs,
preferences, and learning styles of employees. Using these advanced tools and techniques,
organisations can design and implement highly targeted and participative training
programs. This customised approach not only aligns with personal development goals of
employees but also promotes improved skill acquisition and knowledge retention, hence
adding to the overall efficacy of training programmes [Silva A. 2023; Mushtaq N,
Manjiang X, Bakhtawar A, Mufti M, Khan M. 2024].

Employee Recognition and Reward Framework
The scholarly literature on HR analytics advocates the importance of using data-
driven methods to design and implement effective and fair compensation structures
that not only help in retaining the top talent while supporting organisational objectives
[King KG. 2016; Lakshmi PM, Pratap PS. 2016; Kapoor B, Kabra Y. 2014; Diez F,
Bussin M, Lee V. 2019] in their study also articulated the perspective that the use of
data analytics optimises the compensation framework, hence addressing the dynamic
and diversified needs of the employees.

It has been found that data-driven methodologies allow organisations to fix
compensation packages that are competitive with market rates and fairly reflect
employees' profiles, knowledge, skills, and contributions [Mushtaq N, Manjiang X,
Bakhtawar A, Mufti M, Khan M. 2024]. This alignment fosters mutual respect and
transparency, thereby increasing employee satisfaction and in turn strengthens talent
acquisition and retention efforts.

Moreover, the analysis of employee data, including preferences, benefits utilisation
patterns, and satisfaction levels, yields valid inputs for customising the pay structure
according to the needs and contributions of employees. Organisations can assess the gaps,
eliminate redundancies, and reframe the benefit programs that are both cost-efficient and
impactful. For example, customising employee healthcare, welfare, or retirement plans
according to the demographic data or feedback may improve their engagement and loyalty
[Mushtaq N, Manjiang X, Bakhtawar A, Mufti M, Khan M. 2024; Dunderdale N. 2017].

Including HR analytics in determining the pay structure enables organisations to make
well-informed, data-driven decisions that are beneficial for both employees and the
organisation, especially in today's dynamic business environment. Moreover, scholars
also laid emphasis on the critical role of data analytics techniques in identifying and

18