HR analytics: how to apply the 360 ​​method correctly

Today, over 90 percent of Fortune 500 companies use 360 ​​performance reviews to assess employee competencies. This method of analysis has gained popularity, as it allows you to get a balanced understanding of a person, avoiding subjective opinions (as far as possible in such a difficult area as working with people). The research results are based on the opinions of the manager, colleagues, subordinates, as well as on the self-esteem of the employee himself. It is important to understand that 360 analysis is based on the company’s values ​​and competencies, that is, it does not affect business results (what and how much was achieved), but how it was done.

The 360 ​​performance review method is also used in X5 Retail Group. Today we are going to tell you about BigData X5’s best practices for in-depth HR analytics.

Obviously, the accuracy of such a method, although it increases by averaging different opinions, still depends on the openness and enthusiasm with which people fill out the questionnaires, on their understanding of the scale, on the strength of the team, the atmosphere in the team, and much more.

An important aspect of the operation of such a system is interaction with filling out questionnaires. If a person thoughtlessly gives everyone fives, they need to work with him, explain the importance of the process. In Russia, there is a certain attitude towards grades on the basis of a five-point scale, according to which a C-student is a so-so character, a good one is normal, but an excellent student is someone who works well, that’s praise. Losers remain for the second year, and indeed “there are not many of them, and I have never seen them in our company” – this is how managers usually answer about their team. “Somewhere,” but not here. So, if you think that an employee is good, you give him a four, because a C … well, there is a C, and if you are friends, then you can put an five – no hesitation. This leads to a skewed assessment, to a high percentage of fives in the survey, which degenerates into almost two-point: with fours and fives.

Educating assessors is a slow and sad (well, not always sad) process, which includes explanations: how the instrument works; how to correctly assess a person, without striking either admiration following the results of one single interaction, or negative after one rude letter; what a grading scale looks like, different from the one used in school; an overview of the typical mistakes of reviewers, and so on. It is very important to relax people, to get away from their perception of the process as yet another boring tool, to get rid of the fear that the assessment will affect the financial results of a colleague. It is fundamentally important here not to make hasty personnel decisions and not to adjust the command staff in fresh tracks.

The formulation of the analytical process from the point of view of an HR employee is well described in the text from Avito, which I strongly recommend to read. The guys observed a strong bias towards good, the number of fours (“above expectations”) was similar to the number of triples (“meets expectations”). We also encountered arguments about “good and evil”, although we used a scale of our own design.

Further, the voices were divided. Either it is a strong friendly team, or one of two things. Therefore, we quickly launched a second review on another team,

and were convinced that sometimes data with high variance can be obtained without additional work on clarifying the scale and calibrating the estimates. That is, it is necessary to work with people and take into account the organic propensity for “objective” assessments. Or maybe this is a disagreement within the team, which, generally speaking, is also useful to know.

The 360 ​​score is generally used for two purposes: employee development and performance analysis. It is important to understand that the output may differ depending on the level of preparedness and openness of those who provide the feedback. When we create a tool for empowering employee development, it is important for us to provide anonymous feedback from various sources in order to help him understand his strengths and weaknesses, pump skills, develop missing qualities. The survey focuses on competencies or behaviors closely related to the performance of job responsibilities and the values ​​of the organization. When we launch such a tool, we must make it clear to the participants that we not we will use the results for personnel decisions. Our story will be about using the 360 ​​review method to develop employees.

Employee development data is needed to assess strengths and areas for development, not to make decisions about bonuses / talent changes. It is also important for a company to understand how a person’s values ​​correlate with those of the company. 360 results are always shared with the employee and their manager.

360 survey scores and results are a treasure trove of data that can be used to provide insight and analytics. This data is needed to calculate “correction” factors that will help to obtain a more reliable result, as well as for clustering employees by competencies, skills, compiling a “profile” of individual teams and much more. All these calculations require additional power and frameworks, which we decided to move into a separate microservice. Thus, we have logically separated the part that the user sees (from the HR department) from the “analytical” part, on which all additional analytical calculations are performed. This approach allows these services to be developed independently, and provides an opportunity for additional separation of computations. The analytical service does not have its own database, all calculations are made based on the data that is in the database of the main service and interacts using the REST-API.

The analytical service is a separate server written in Flask, and the main service is implemented in NodeJS with a PostgreSQL database. This undoubtedly complex interaction scheme is presented below:

Consider an example of evaluating surveys in other teams, let’s call them team A and team B. Imagine a situation that in team A the employees are friendly, treat each other well, and, accordingly, the average score can be quite high. In contrast to Team A, suppose Team B is made up of more critical people who honestly give high marks only to those employees who actually perform well.

How do we compare two employees from Team A and Team B? To compare employees from different teams, we use a special “team” calibration to get an employee’s score relative to the average score in his team. You can’t do without a formula here.

Suppose we have employee x with a score of 0.9 from team A, whose average score is 0.85, and there is employee y with a score of 0.65 from team B, whose average score is 0.5. After subtracting the average score of the teams, we get the “calibrated” scores for employees:

Thus, we see that employee y has a calibrated score higher than the calibrated score of employee x.

The same example applies to in-team normalization. All employees are different and tend to rate their colleagues differently as well. Let’s say there is employee x who treats all colleagues very well and gives everyone an average score of 0.8, and there is employee y who looks more critically at others and gives an average of 0.5 to other employees. When employees x and y rate employee z, they can rate him equally good (or equally bad), but in their own system of values, therefore, when averaging the average score within the team, we subtract the average of each employee, which is calculated from historical data. Suppose employee x rated employee z at 0.9, and employee y at 0.7, the average score will be

However, if we subtract the average historical scores of the authors, then we get

After this calibration, we get a metric that takes into account the “value system” of each employee and, therefore, is more “honest”.

The important thing is that we can weigh the ratings from the reviewers with different coefficients when defining a person’s profile. There is ample evidence that leaders tend to be more accurate and impartial in assessing people (in fact, this is also why they ended up where they were), most likely due to more experience.

The default values ​​of the weights are 0.25, that is, in the current version we do not give preference to any of the categories of respondents, but as it was said in one old anecdote, “the tool is there”.

In other words, having collected estimates calibrated by authors, we try to drive them into a global “coordinate system” in order to be able to extract the correct insights from the data. Otherwise, due to biased assessments, we can discover some amazing regularity that really does not exist, and what good, we will begin to develop the employee in the direction opposite to his profile.

May we succeed, and we have compiled vectors that represent the employee’s competency profile. Moreover, there are vectors received from colleagues, managers, subordinates and self-esteem. We collect all this into a cube (to be precise, a parallelepiped, but further I will use the term cube by analogy with OLAP cubes).

But now, by dissecting the cube along different axes, we can obtain various analytical dependencies. For example, let’s fix the competence and see its distribution throughout the organization as a whole or cross-team within the organization. Or take the far-right column of ratings from managers and look internally at the variance of ratings to see if there are any surprising discoveries.

Developing this logic, one can obtain diagrams of comparison of employees, both within the team and belonging to different departments, the so-called cobweb; or, on the same diagram, you can give the average values ​​of competencies in a team and understand for a specific person where he is knocked out relative to the team and in which direction you can take another team instead of the one in which the employee is located and compare its average competencies with the competencies of a person. Why, if you swing, you can compare the team with respect to another team, that’s what a fun game can come out.

You can also analyze clusters of certain types within the organization to find people who, perhaps, are effective communicators, or experts who are known for their deep approach to problem solving.

Analytically simpler finds are also possible, although no less interesting. In particular, the high variance in the ratings of one of the employees when polled among colleagues may indicate a polar perception of his colleagues.

What if the variance is high when comparing ratings from colleagues and from a manager? Do colleagues and the manager assess the employee very differently? Perhaps here you can wonder what kind of leader he is, and if he is too strict with the members of his team (well, or vice versa, uncritical). Or draw a conclusion about the fundamental superobjectivity of managers in the organization, if a similar pattern repeats for other teams.

A high number of missing evaluations for any of the employees is likely to indicate that the person has little involvement in interactions with colleagues. At the same time, for some teams in X5, this is quite a modus operandi, and there is nothing surprising here, but it is obvious that for some teams this will serve as an indicator of the need for changes in the process of work.

In the future, we want to form more subtle questions in a research form in order to eliminate biases in ratings at this stage, avoiding manual work with service users and endless explanations of how to choose the right ratings and what they mean. We have several ideas, they are in the process of verification, and we will definitely share the results with you. We also want to apply more cunning techniques to the data cube, in addition to cuts along the axes and clustering. Here we try various autoencoders, linear and non-linear, looking for cross-links between views along different coordinate axes. In general, there is a lot of work, the data is naughty, and the system setup is not easy 🙂


Evgeny Makarov
Valery Babushkin
Svyatoslav Oreshin
Daniil Pavlyuchenko
Evgeny Molodkin

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