Behave4 is a world pioneer management consulting firm that applies Behavioral Economics to provide private and public organizations with a deeper knowledge of why employees and customers/users behave the way they do.
Our mission is to show companies novel methods for understanding people’s preferences and behaviors through the application of Behavioral Economics techniques and to provide them with scientific-based solutions.
Behave4 is the result of merging business and science from a fully pragmatic perspective. Our founders build a perfect team due to their multidisciplinary profiles (business strategists, marketing, and behavioral economists) including an unusual hybrid of business and scientific expertise.
Thanks to the interaction and knowledge generated, our MAIN framework emerges, where first we Measure, then Analyze and finally intervene. No intervention without proper measurement!
Boosting Agile Performance
We’ve applied it for example to an IT large Corp that was struggling to maximize the performance of teams (squads) using an agile framework. Why do some squads perform better than others? Faced with the same task, what are the elements that make one team function and deliver extraordinarily, whereas another team struggles, wastes time, and obtains only mediocre results?
To answer these questions, we assessed a representative sample of their employees using our Behave4 Diagnosis Platform.
The behavioral economics assessment collected 30+ variables for each person and squad, including risk aversion, loss aversion, social preferences, etc. Our analysis showed that those teams that were formed by more cooperative and long-run oriented people had better performance.
Based on these results, we recommended several interventions related to those key behavioral predictors (cooperativeness and long-run orientation), focused on people rotation, hiring, coaching/training, and organizational environment modification. If fully implemented, our analysis suggests that these interventions could increase the average squad performance by up to 30%.
Behavioral Decoding of the Top Seller
HRIs Optimization can also be applied to predict individual, rather than team, indicators. Let’s see an application to individual KPI. A Multinational Call-Center Corp. wanted to maximize individual sales and reduce the strong disparities between sellers. Which behaviors differentiate top sellers from poor sellers? Why are some sellers brilliant while others suffer to get a single sale, even having the same “hard skills” set?
To tackle these questions, we assessed a whole sales department using Behave4 Diagnosis for a total of 28 behavioral variables. Our analysis showed that the “top seller” was found to be behaviorally defined as risk neutral, loss averse, long-run oriented, socially efficient, and low in numerical ability.
On the other hand, the “poor seller” was found to be behaviorally defined as irrational about risks, immune to losses, short-run oriented, concerned about social comparisons, and high in numerical ability.
Our recommendation was to include training programs during the onboarding process (improving decision-making under risk and potential losses), introduce incentives and programs of group recognition to foster long-run orientation and reduce within-department social comparisons, and set training programs for people who are “too mathematical” to improve their Emotional Intelligence and their capacity to empathize by phone.
We recently applied our knowledge about HR Data with a large-scale company. They were retrieving data of their employees using a 360º questionnaire. The employees answered items regarding 19 different Organizational skills like teamwork, innovation, and commercial management. A 360º questionnaire is an exceptionally useful resource because employees not only answer the items about themselves but other colleagues (ranging in the organization hierarchic) answer the items about them. The utility of this kind of methodology is huge only if you can exploit all the information that underlies data.
Therefore, we worked following two main objectives:
1) First, our client needed to assess the reliability of the data, and 2) in case the questionnaire was solid, we wanted to yield many insights as we could to improve the general company performance.
To check the reliability and the questionnaire robustness, we performed several analyses including, consistency, factorial (exploratory and confirmatory), missing values, and asymmetry analysis. After we were sure the questionnaire was reliable at all levels, we conducted some more challenging analyses. In short, we were able to predict the optimal number of responses needed per employee (splitting by evaluator role), we noticed that not all items made sense for certain role groups, and we concluded that centrality measures (we have drawn the formal and informal organizational network from the responses) were strong predictors of promotion to higher responsibility positions. To sum up, HR Data analysis has been proved to bring organizations several valuable insights to improve performance.
In this case, particularly, our client will be able to reduce substantially the time spent in future assessments, improving not only the data quality but saving valuable resources. Besides, we also have found a strong KPI that can be used to promote their employees to higher roles (centrality measures).