Figure 5 shows the critical difference diagram for the post hoc Nemenyi test for the unweighted dataset following a significant Friedman test. An horizontal line that crosses two or more classifier lines means that the mean performances of those features are not statistically different. In summary, we conclude that the LR and LGBM classifiers have highest predictive power of the turnover intention. Beyond predictive performance, we are interested in determining the main features behind turnover.
- However, despite the fact that some of these papers use the same datasets, there is no consensus around the best models.
- Involuntary turnover is the termination of employment by the employer, whereas voluntary turnover is the departure of the employee by their own decision.
- Logistic regression and LightGBM rank as the top two performing models.
- Second, following 63 we use structural causal models (SCM) 47 to equip the partial dependence plot (PDP) 22 with causal inference properties.
This rating classifies customers as detractors (0-6), passives (7-8), or promoters (9-10). We argue that country-specific traits, from location to internal politics, will affect the type of industries that developed nationally. For example, countries with limited natural resources will prioritize non-commodity-intensive industries. Similarly, individual-specific attributes will determine the type of work that an individual can perform. 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. Further, the figures show the critical difference diagrams for the post hoc Nemenyi test, thus answering the question whether there is any statistical difference among them.
A parallel and growing line of research focuses on predicting individual desire or want (i.e., intent or intention) over time using graphical and deep learning models. These approaches require sequential data detailed per individual. The adopted models allow to account for temporal dependencies within and across individuals for identifying patterns of intent. Intention models have been used, for example, to predict driving routes for drivers 55, online consumer habits 58, 59, and even for suggesting email 54 and chat bot responses 52. Our survey data has a static nature, and therefore we cannot directly compare with those models, which would be appropriate for longitudinal survey data.
Keywords
Finally, TABNET has intermediate performances, but it is two orders of magnitude slower than its competitors. Employee turnover rate calculates the percentage of employees who leave a company within a given period. Losing employees is normal for every company, but a high turnover number can indicate workplace problems and dissatisfaction. Customer retention rate is the percentage of customers a company keeps over time, directly reflecting customer loyalty and business stability. You can also calculate your voluntary turnover as a percentage of all turnover.
Aligning Teams and Strategies with Business Goals
To this end, we build on the explainable AI (XAI) research 28, in particular XAI for tabular data 49, for extracting from ML models a ranking of the features used for making (accurate) predictions. ML models can either explain and present in understandable terms the logic of their predictions (white-boxes) or they can be obscure or too complex for human understanding (black-boxes). The k-nearest neighbor, logistic regression, and decision trees models we use are white-box models. For the latter group, we use the built-in model-specific methods for feature importance. First, we device our own ranking procedure to aggregate each feature’s importance across many fold. Second, following 63 we use structural causal models (SCM) 47 to equip the partial dependence plot (PDP) 22 with causal inference properties.
Customer Complaint Resolution Time
It’s a key metric for evaluating the operational efficiency and financial performance of a company. To conclude, we believe that further interdisciplinary research like this paper can be beneficial for tackling employee turnover. One possible extension would be to collect country’s national statistics to avoid selection bias in survey data or, alternatively, to align the weights of the data to a finer granularity level. Another extension would be to carry out the causal claim tests using a causal graph derived entirely from the data using causal discovery algorithms. In fact, an interesting combination of these two extensions would be to use methods for causal discovery that can account for shifts in the distribution of the data (see, e.g., 41 and 44).
Tracking these quantitative metrics is essential for optimizing email campaigns and improving customer engagement. By leveraging data-driven insights, businesses can fine-tune strategies, optimize spending, and maximize results, ensuring every marketing effort contributes to measurable success. Gross profit margin is the percentage of the company’s revenue that remains after deducting the direct expenses such as labour and materials. It helps businesses allocate resources wisely by identifying the most profitable products and ultimately optimizing investments.
Time to fill
The items and themes along with employee contextual information reported in GEEI capture these determinants. The presented approach brings determinants of voluntary turnover to the surface. Where the logistic regression results in a turnover probability at the individual level, the decision tree makes it possible to ascertain employee groups that are at risk for turnover. With the data set-based approach, each company can, immediately, ascertain their own turnover risk.
Logistic regression and LightGBM rank as the top two performing models. We investigate on the importance of the predictive features for these two models, as a means to rank the determinants of turnover intention. Further, we overcome the traditional correlation-based analysis of turnover intention by a novel causality-based approach to support potential policy interventions. The survey includes sets of questions (called items) organized by themes that link an employee’s working environment to her willingness to leave her work. Our objective is to train accurate predictive models, and to extract from the best ones the most important features with a focus on such items and themes. This allows the potential employer/policy maker to better understand intended turnover and to identify areas of improvement within the organization to curtail actual employee turnover.
The design and validation of the GEEI questionnaire followed the approach of 18. After reviewing the social science literature, the designers defined the relevant themes, and items for each theme. Then they ran a pilot study in order to validate psychometric properties of questions to assess their internal consistency, and to test convergent and discriminant validityFootnote 5 of questions. We note that this is not the first paper to approach employee turnover from a causality perspective, but, to the best of our knowledge, it is the first to do so using SCM. Other papers such as 25 and 48 use causal graphs as conceptual tools to illustrate their views on the features behind employee turnover.
By monitoring business metrics, organizations can make data-driven decisions, optimize operations, and stay on top of industry trends. Comparing your business performance metrics to industry benchmarks gives you a competitive edge. If your conversion rate falls below the industry average, it’s a clear signal that there’s a need for improvement. By closely analyzing competitor performance and market trends, businesses can refine their strategies, improve their products, and strengthen their overall market position. Revenue growth rate is the percentage increase in a company’s revenue over a period. It is a key metric to measure success, allowing businesses to evaluate their expansion and financial health.
- Many organizations do not realize the wealth of data at their fingertips, especially if they engage their people with employee recognition.
- We assigned a weight to each instance in our datasets proportional to the workforce in the country of the employee.
- Tracking these quantitative metrics is essential for optimizing email campaigns and improving customer engagement.
- 8, for example, the Motivation, Vitality, and Attendance Stability themes are grouped together.
- This paper contributes to the existing literature by applying and testing the latest in ML techniques to a unique, relevant survey data for turnover intention.
Under our approach, we are able to test causal claims around drivers of turnover intention. The right business metrics can make all the difference in driving success, but tracking too many can lead to confusion. Instead of monitoring every possible key metric, narrow them down to a selected few that have a real impact on your business.
Turnover intention is an employee’s reported willingness to leave her organization within a given period of time and is often used for studying actual employee turnover. Since employee turnover can have a detrimental impact on business and the labor market at large, it is important to understand the determinants of such a choice. We describe and analyze a unique European-wide survey on employee turnover intention. A few baselines and state-of-the-art classification models are compared as per predictive performances.
The direction of the response scale is uniform throughout all the items 50. For a respondent, a score from 0 to 10 is also assigned to a theme as the average score of the items of the theme. Social Media Engagement tracks interactions like likes, shares, and comments on social media platforms.
By analyzing MRR, businesses can spot growth opportunities, forecast future income, and make strategic decisions. The break-even point is when a company’s total revenue is exactly equal to its total expenses, meaning there is no profit or loss. It is a critical business metric for businesses as it helps them calculate how much they need to sell before they start making a profit. See the cost of implementation and if it has any direct impact on your voluntary turnover rate. The issue of staff turnover has been the subject of growing interest in many organizations around the world.
Financial metrics are measurable numbers that provide insights into a company’s financial performance. According to a study by US Bank, 82% of businesses fail due to cash flow issues and poor financial management. This highlights the benefits of tracking metrics like gross profit margin, net income, and ROI, which provide a clear picture of profitability, expenses, and overall financial health. By monitoring these performance metrics, businesses can make smarter decisions, prevent financial setbacks, and build a foundation for long-term growth. As predicting voluntary turnover a natural question, one may wonder how the performance would change if the datasets were weighted to reflect the workforce of each country. We collected the employment figures for all the countries in our training dataset for 2018, which was when the survey was carried out.
Voluntary turnover is a normal occurrence, as employees seek new opportunities or leave because they are unsatisfied with the current role for a multitude of reasons. A centralized business metrics dashboard ensures you always have key data at your fingertips. Tools like Google Data Studio and Databox allow you to visualize different business metrics in real time. By integrating data from multiple sources, businesses can quickly assess progress and respond to trends.
Leave a Reply