These metrics not only help business owners and entrepreneurs but also allow teams to evaluate processes, identify areas of improvement, and optimize strategies to drive growth. Effectory ran in 2018 the Global Employee Engagement Index (GEEI) survey, a labor market questionnaire that covered a sample of 18,322 employees from 56 countries. The target question (or target variable, the one to be predicted) is the intention of the respondent to leave the organization within the next three months.

• Effectory ran in 2018 the Global Employee Engagement Index (GEEI) survey, a labor market questionnaire that covered a sample of 18,322 employees from 56 countries.
• Conversion rate is a crucial performance metric that measures the percentage of website visitors or leads who complete a desired action, such as purchase or signup.
• The customer churn rate is the percentage of customers who stop using a business’s services over a specific period of time.
• A higher ROA means the company is more efficient at using its assets to make a profit, while a lower ROA shows underutilized assets.
• For example, individuals with higher education, where education is among the attributes in I, can apply to a wider range of industries than an individual with lower levels of educational attainment.
• There is always something that the HR department can do – and a personalized approach to understand each employee’s intrinsic and extrinsic motivators is necessary.

Predicting and explaining employee turnover intention

• In summary, we conclude that the LR and LGBM classifiers have highest predictive power of the turnover intention.
• Similarly, individual-specific attributes will determine the type of work that an individual can perform.
• It can be classified as voluntary, when it is the employee who decides to terminate the working relationship, or involuntary, when it is the employer who decides 33.
• The work has focused mostly on what factors influence and predict employee turnover 27.
• Comparing your numbers to industry averages can help you get a clearer view of how your business is performing and where improvements are required.

The output of the analysis showed that the themes Sustainable Employability, Employership, and Attendance Stability are within the top-five determinants for both LR and LGBM. From the XAI strand of research, we also adopted partial dependency plots, but with a stronger conclusion than correlation/importance. The third perspective, in fact, is a novel causal approach in support of policy interventions which is rooted in causal structural models.

The output confirms those from the second perspective, where highly ranked themes showed PDPs with higher variability than lower ranked themes. The value added from the third perspective here is that we quantify the magnitude and direction for the causal claim \(T \rightarrow Y\). Sales and Revenue metrics play a crucial role in evaluating a company’s ability to generate income.

Aligning Teams and Strategies with Business Goals

By leveraging time-tracking tools like Clockdiary, businesses can simplify productivity tracking, making it easier to manage time efficiently and boost overall performance. With the right insights at your fingertips, predicting voluntary turnover you can make smarter business decisions, streamline processes, and drive long-term success. Every business has limited resources, whether it’s budget, talent, or time. Business metrics should do more than just track numbers, they should guide your decisions and drive real improvements.

Net Sales Revenue

By analyzing this business metric, organizations can streamline their hiring process, attract top talent faster, and gain a competitive edge in the talent market. Brand awareness measures how well customers recognize and recall a business. Strong brand recognition leads to enhanced customer trust and sales growth. To assess the effectiveness of your website content and user experience, businesses track Bounce Rate. It is the percentage of visitors who leave a website without interacting. Net Promoter Score is a metric that measures how likely customers are to recommend your service or business on a scale of 0 to 10.

Overall performances of each classifier improve over the theme dataset. Elapsed times also increase due to the larger dimensionality of the dataset. LGBM and LR are the best classifiers for both the unweighted and the weighted datasets.

We had the opportunity to analyze a unique cross-national survey of employee turnover intention, covering 30 European countries. The analytical methodologies adopted followed three perspectives. The first perspective is from the human resource predictive analytics, and it consisted of the comparison of state-of-the-art machine learning predictive models. Logistic Regression (LR) and LightGBM (LGBM) resulted the top performing models. The second perspective is from the eXplainable AI (XAI), consisting in the ranking of the determinants (themes and items) of turnover intention by resorting to feature importance of the predictive models. Moreover, a novel composition of feature importance rankings from repeated cross-validation was devised, consisting of critical difference diagrams.

Regularly Review and Improve Your Metrics

High rates can disrupt productivity, lower morale, and signal job dissatisfaction. By monitoring this business metric, businesses can address potential problems proactively and create a healthier work environment. ROMI is a key marketing metric that helps businesses evaluate the profitability of their marketing campaigns. By using this metric, businesses can make data-driven decisions and allocate marketing budgets more effectively to maximize returns. The customer churn rate is the percentage of customers who stop using a business’s services over a specific period of time.

Similarly, tracking customer success KPIs like churn rate can help pinpoint potential service issues early, allowing companies to fix them before they negatively impact overall business performance. Customer lifetime value (CLV) is a key metric that measures the total revenue a business can expect from a customer throughout the time they remain a customer. By knowing the potential revenue from each customer, companies can enhance marketing strategies, personalize customer experiences, and optimize marketing budgets. Tracking the right business metrics is essential for measuring business performance and improving operational efficiency.

Similarly, this line of work does not agree on the top data-driving factors behind employee turnover either. For instance, 2 identifies overtime as the main driver while 24 identifies it to be the salary level. First, rather than reporting feature importance on a final model, we do so across many folds for the same model, which gives a more robust view on each feature’s importance within a specific model. Second, we go beyond the limited correlation-based analysis 3 by incorporating causality into our feature importance analysis. Tracking key efficiency metrics, such as employee productivity and operational costs, helps businesses fine-tune their operations and minimize disruptions.

By getting a clear view of your goals, you will be able to determine which performance metrics will provide the real value. When employees are happy, they’re more productive and less likely to leave the job, which benefits your business. However, these surveys are not just about keeping the employees satisfied but also about creating a positive workplace culture that attracts and retains top talent. For example, reducing Time to Fill by 10 days for high-demand roles can significantly lower recruitment expenses and minimize lost productivity.

Monthly Recurring Revenue (MRR)

A McKinsey survey found that companies using business metrics and KPIs in decision-making experience 16% higher revenue growth. Predictive models are built from survey data (questionnaires) and/or from data about workers’ history and performances (roles covered, working times, productivity). Given its sensitive information, detailed data on actual and intended turnover is difficult to obtain.

The GEEI survey includes 303 to 323 respondents per country, with the exception of Germany which has 1342 respondents. We sampled 323 German respondents stratifying by the target variable. Thus, the datasets have an approximately uniform distribution per country.

We repeat the procedure on a non-top-ranked theme for both models, namely the Adaptability theme (the capability to adapt to changes), to see how the PDPs compare. In the case of the LGBM, the PDP is essentially flat and implies a potential non-causal relationship between this theme and employee turnover intention. For the LR, however, we see a non-flat yet narrower PDP, which also seems to support a potential non-causal link. This might be due again to the non-linearity in the data, where the more flexible model (LGBM) can better capture the effects in the changes of T than the less flexible one (LR) that can tend to overestimate them. Our first objective is to compare the predictive performances of a few state-of-the-art machine learning classifiers on both the datasets, which, as observed, are quite imbalanced 9.