How do both indicators work?
Comparison between both features to understand differences and similarities.
Characteristic | Levels | Disengagement Detector |
Model | Gamification model based on the company's user ranking. | Probabilistic predictive model according to individual behavior. |
Categorías (lo que ve el usuario) | Levels: 5 Legend (the best outstanding) 4 Hero, 3 Shining, 2 Rising, 1 Embarking (the least outstanding) | States: - Engaged, - Disengaged, - Not data |
User and scope | The scores of all users are compared to establish a ranking within the company. | The behavior of each user is taken individually. It does not compare with the rest of the users. |
Internal calculation | Score based on the amount of activity on StarMeUp, in a period, taking importance weights into account. | Calculates the individual behavior based on the average activity and the frequency of access to StarMeUp, in a period. |
Data for calculation | Access to StarMeUp web and mobile, have a complete profile, sent stars, likes, comments, and shared moments. Feedback sent and requested, objectives and progress. | Frequency of accessing the product, sending stars, posting moments, sharing likes, comments and celebratory messages. |
Periodicity of the calculation | Automatic weekly calculation. | Automatic weekly calculation. |
Time frame | The last 30 days of activity of all users on StarMeUp are considered. | The last 90 days of user behavior are considered (60 days of history Vs the last 30 days). |
Scarcity | There are limited seats available for each level. These are completed according to the ranking of user participation. | There are no quotas for each state, each user is taken individually. |
Eligible users | A user's level can be calculated from day 1 of using StarMeUp. | Detection can be done only if the user has been using StarMeUp for more than 3 months and has enough activity to calculate behavioral averages. |
Scenarios that we could find
Given a specific user we can find different scenarios. Here we detail some of them for a better understanding and interpretation.
Scenarios | Levels | Disengagement Detector | Comments |
1 | High ( ej. 3, 4, 5) | Engaged | - The level of the user is relatively high in relation to the rest of the company, and also, - StarMeUp detected that this user maintained or increased their average activity, their average access frequency, or both. |
2 | High ( ej. 3, 4, 5) | Disengaged | - Although the level of the user is relatively high in relation to the rest of the company, - StarMeUp detected that this user decreased their average activity, their average access frequency, or both. |
3 | Low ( ej. 1, 2) | Engaged | - Although the level of the user is relatively low in relation to the rest of the company, - StarMeUp detected that this user maintained or increased their average activity, their average access frequency, or both. |
4 | Low ( ej. 1, 2) | Disengaged | - The level of the user is relatively low in relation to the rest of the company, and also - StarMeUp detected that this user decreased their average activity, their average access frequency, or both. |
Conclusion
In Levels, the activity in StarMeUp of all the company's users is compared to establish a ranking. On the other hand, the Disengagement Detector takes the individual behavior of each user to determine if their activity or average access has decreased, maintained or increased.
Both indicators are useful but are not sufficient by themselves to draw absolute conclusions; instead, they are presented as insights to facilitate more effective 1:1 conversations between leaders and collaborators. It is essential that leaders use these indicators as a basis to delve into the context of each user, which will allow them to achieve a better interpretation of each particular case.