The artificial intelligence of the platform uses NLP (Natural Language Processing) to recognize the presence of praise, positive, negative or conflicts in communications between employees.
The information contained in text messages is extremely sensitive to the business of our customers. With this awareness, we deliberately designed the platform in such a way that all text analytics are implemented as a classification. The system has a task: to understand whether the text contains a set of high-level features that can give useful information about the performance of the individual employee of the company, about their contribution to a healthy atmosphere in the team, about the actual (not nominal) place of the employee in the business processes of the enterprise.
The platform is designed in such a way that text messages are not stored in the platform, so they cannot be read or stolen, they only exist in the information systems of the customer's company. It is absolutely safe to trust the platform to “read” the text.
We have developed and patented a two-step procedure for working with text. Thanks to it, we can use already trained classifiers or train new ones even without the text itself.
Praising an employee in a message's informative part is most often aimed at expressing, in an explicit or implicit form, gratitude for a job well done.
Praise is a complex action. It is part of the positive subset and serves as an assessment of achievements, a powerful motivational driver and a feedback form at the same time.
Praise is a marker that reflects respect for the employee's personality and the work done by him, impartiality, and lack of arrogance on the management's part. And on the subordinates' part, this is an indicator of the level of work comfort and satisfaction with the company's top management. Praise can be expressed to a group of employees or the whole company. And then it is a significant marker of a healthy team environment.
Examples of praise:
We are the best team and our sepulchres will conquer the market!
Jason, you're a great sysop, and a great DevOps as well.
Thank you for the presentation! Everyone listened with absorbed attention.
You can receive metrics with praise analysis using our Reports API.
For each block of metrics the platform calculates:
The Number of received emails with praise
The number of incoming emails of an employee with praise without taking into account mailings and automatic notifications.
The Share of received emails with praise
The share of incoming emails with praise, excluding mailings and automatic notifications.
The Number of sent emails with praise
The number of outcoming emails of the employee with praise, excluding emails that are marked as automatic.
The Share of sent emails with praise
The share of outcoming emails with praise, excluding mailings and automatic notifications.
The Average response time to emails with praise
The average response time of an employee to incoming emails with praise when taking into account only those emails that the employee responded to.
Weekly calculation: all outgoing emails of the user for the week are taken. For each of these emails, it is checked which of them were responses to the incoming previous email with praise. For each message that is a response, the difference in HH:MM:SS is taken between the moment of sending the response and the moment of receiving the message. For a week, the median of all the values obtained is taken. Emails that are marked as automatic are excluded from the calculation.
The calculation for more than a week: the median of weekly values.
The Percent of responses to emails with praise
The percentage of incoming emails with praise that the employee has responded to.
Weekly calculation: all incoming emails with praise of the user are taken into account. For each of these emails, it is checked whether there was a response to this email. This gives a +1 response message.
Sent emails that are marked as automatic are excluded from the list of response emails.
The metric is considered as "the number of response emails" / "the total number of emails with praise received".
The calculation for a period longer than a week: the median of weekly values.