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Zluri Application Usage and User Usage_v1.0
Zluri Application Usage and User Usage Explained
Customer Facing Documentation
Revision Summary
Disclaimer
This documentation, titled "Zluri Application Usage and User Usage Explained", provides a high-level overview of the logic, calculations, and essential considerations involved in identifying Zluri Application Usage and User Usage. We have made a conscious effort to exclude specific proprietary and confidential information to protect the interests of Zluri and its stakeholders.
The content herein is intended to serve as a general guide for our customers to understand better the conceptual framework of Zluri Application Usage and User Usage. It does not disclose specific details of the algorithms, methodologies, or any proprietary information used in the calculation process.
Readers are advised that the information provided in this documentation is for informational purposes only. It does not constitute a warranty as Zluri's Application Usage and User Usage calculations are subject to updates and improvements, and Zluri reserves the right to modify the methodologies as needed.
For any questions, clarifications, or further details, please contact the Zluri support team.
Thank you for your understanding and cooperation.
Sincerely,
Team Zluri
Overview
Zluri's scoring system provides valuable insights into employees' applications and user usage within an organization. This document breaks down the context behind the scoring, weight distribution, and calculations considered for measuring 'application usage' and 'user usage' within organizations using the Zluri platform.
Application Usage Scoring
Zluri considers Average Volume (AV), Average Activity Days (AD), and Percentage of Users (PU) as the three primary factors to measure usage scoring for applications. Also, it assigns a Weight Distribution percentage defining the relative importance or contribution of each factor (AV, AD & PU) in the overall calculation of the final application usage score.
Definitions and Examples
1. Average Volume (AV)
Average volume of activity fetched per user per month.
Definition: Frequency of user activity fetching for a specific application per month. This can be sign-in logs, activity logs, etc.
Example Scenario: For instance, if a user signs up thrice a month with five activities tracked in the activity log, Zluri calculates these counts based on the proprietary technical logic and assigns a predefined AV score to the calculated number.
2. Average Activity Days (AD)
Average number of activity days for a month.
Definition: Number of days any user is active in a month. This can range from 1 day to 31 days a month, depending on the month.
Example Scenario: For instance, if a user signs up thrice a month with five activities in one day, Zluri calculates the days' count and assigns a predefined AD score to the calculated number.
3. Percentage of Users (PU)
Percentage of users who use this app (adoption rate).
Definition: Adoption rate indicating how many users from the organization are using a specific application.
Example Scenario: For instance, if there are 100 users in the organization, and 50 use the application, Zluri calculates the percentage and assigns a predefined PU score to the calculated percentage.
Final Application Usage Score
The total Application Usage score is generated by combining AV, AD, and PU, also factoring in the assigned Weight Distribution percentage, which defines the relative importance or contribution of each calculated factor.
Refer to the Weight Distribution section for a detailed understanding.
User Usage Scoring
Zluri considers Monthly Count (MC), Mean of Monthly Count, Distinct Days (DD), and Mean of Distinct Days as the four primary factors to measure usage scoring for users. Also, assigns a Weight Distribution percentage defining the relative importance or contribution of each factor (MC, MeanMC, DD & MeanDD) in the overall calculation of the final user usage score.
Definitions and Examples
1. Monthly Count (MC)
Definition: Number of activities a user has in a month.
Example Scenario: For instance, if the number of activities for a user is more than or equal to one, Zluri calculates the activity count and assigns a predefined MC score to the calculated count.
2. Mean of Monthly Count
Definition: Average monthly count in the past three months.
Example Scenario: For instance, if the average number of activities for a user in the past three months is more than or equal to one, Zluri calculates the average activity count and assigns a predefined Mean of Monthly Count score to the calculated count.
3. Distinct Days (DD)
Definition: Number of days of activity received for a user in the past month.
Example Scenario: For instance, if the number of days of activity received for a user is more than or equal to one, Zluri calculates the days count and assigns a predefined DD score to the calculated count.
4. Mean of Distinct Days
Definition: Number of average distinct days in the past three months.
Example Scenario: For instance, if the average number of distinct days in the past three months for a user is more than or equal to one, Zluri calculates the average days count and assigns a predefined Mean of Distinct Days score to the calculated count.
Final User Usage Score
The total User Usage score is generated by combining MC, Mean of MC, DD and Mean of DD, also factoring the assigned Weight Distribution percentage, which defines the relative importance or contribution of each calculated factor.
Refer to the Weight Distribution section for a detailed understanding.
Weight Distribution
Weight distribution in the context of Zluri's Application Usage and User Usage scoring system refers to the relative importance or significance of each factor in the overall calculation of the final usage score. The assigned percentage of weight distribution values determines how much influence each factor has on the final score.
Application Usage Weight Distribution
For Application Usage, three factors are considered: AV, AD, and PU. The weights assigned to each factor reflect the perceived importance of each aspect in assessing application usage within an organization.
AV: The percentage value assigned represents the importance of the frequency of application use.
AD: The percentage value assigned reflects the significance of user activity days on an application.
PU: The percentage value assigned reflects the contribution organizational adoption rate.
User Usage Weight Distribution
For User Usage, four factors are considered: MC, Mean of MC, DD, and Mean of DD. The weights assigned to each factor reflect the perceived importance of each aspect in assessing user usage within an organization.
MC: The percentage value assigned reflects the importance of the user's engagement level within a specific timeframe.
Mean of MC: The percentage value assigned reflects the significance of assessing the user's historical engagement.
DD: The percentage value assigned reflects the essence of assessing the consistency of user activity.
Mean of DD: The percentage value assigned reflects the significance of assessing the historical consistency of user engagement.
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Zluri Application Usage and User Usage Explained
Customer Facing Documentation
Revision Summary
Disclaimer
This documentation, titled "Zluri Application Usage and User Usage Explained", provides a high-level overview of the logic, calculations, and essential considerations involved in identifying Zluri Application Usage and User Usage. We have made a conscious effort to exclude specific proprietary and confidential information to protect the interests of Zluri and its stakeholders.
The content herein is intended to serve as a general guide for our customers to understand better the conceptual framework of Zluri Application Usage and User Usage. It does not disclose specific details of the algorithms, methodologies, or any proprietary information used in the calculation process.
Readers are advised that the information provided in this documentation is for informational purposes only. It does not constitute a warranty as Zluri's Application Usage and User Usage calculations are subject to updates and improvements, and Zluri reserves the right to modify the methodologies as needed.
For any questions, clarifications, or further details, please contact the Zluri support team.
Thank you for your understanding and cooperation.
Sincerely,
Team Zluri
Overview
Zluri's scoring system provides valuable insights into employees' applications and user usage within an organization. This document breaks down the context behind the scoring, weight distribution, and calculations considered for measuring 'application usage' and 'user usage' within organizations using the Zluri platform.
Application Usage Scoring
Zluri considers Average Volume (AV), Average Activity Days (AD), and Percentage of Users (PU) as the three primary factors to measure usage scoring for applications. Also, it assigns a Weight Distribution percentage defining the relative importance or contribution of each factor (AV, AD & PU) in the overall calculation of the final application usage score.
Definitions and Examples
1. Average Volume (AV)
Average volume of activity fetched per user per month.
Definition: Frequency of user activity fetching for a specific application per month. This can be sign-in logs, activity logs, etc.
Example Scenario: For instance, if a user signs up thrice a month with five activities tracked in the activity log, Zluri calculates these counts based on the proprietary technical logic and assigns a predefined AV score to the calculated number.
2. Average Activity Days (AD)
Average number of activity days for a month.
Definition: Number of days any user is active in a month. This can range from 1 day to 31 days a month, depending on the month.
Example Scenario: For instance, if a user signs up thrice a month with five activities in one day, Zluri calculates the days' count and assigns a predefined AD score to the calculated number.
3. Percentage of Users (PU)
Percentage of users who use this app (adoption rate).
Definition: Adoption rate indicating how many users from the organization are using a specific application.
Example Scenario: For instance, if there are 100 users in the organization, and 50 use the application, Zluri calculates the percentage and assigns a predefined PU score to the calculated percentage.
Final Application Usage Score
The total Application Usage score is generated by combining AV, AD, and PU, also factoring in the assigned Weight Distribution percentage, which defines the relative importance or contribution of each calculated factor.
Refer to the Weight Distribution section for a detailed understanding.
User Usage Scoring
Zluri considers Monthly Count (MC), Mean of Monthly Count, Distinct Days (DD), and Mean of Distinct Days as the four primary factors to measure usage scoring for users. Also, assigns a Weight Distribution percentage defining the relative importance or contribution of each factor (MC, MeanMC, DD & MeanDD) in the overall calculation of the final user usage score.
Definitions and Examples
1. Monthly Count (MC)
Definition: Number of activities a user has in a month.
Example Scenario: For instance, if the number of activities for a user is more than or equal to one, Zluri calculates the activity count and assigns a predefined MC score to the calculated count.
2. Mean of Monthly Count
Definition: Average monthly count in the past three months.
Example Scenario: For instance, if the average number of activities for a user in the past three months is more than or equal to one, Zluri calculates the average activity count and assigns a predefined Mean of Monthly Count score to the calculated count.
3. Distinct Days (DD)
Definition: Number of days of activity received for a user in the past month.
Example Scenario: For instance, if the number of days of activity received for a user is more than or equal to one, Zluri calculates the days count and assigns a predefined DD score to the calculated count.
4. Mean of Distinct Days
Definition: Number of average distinct days in the past three months.
Example Scenario: For instance, if the average number of distinct days in the past three months for a user is more than or equal to one, Zluri calculates the average days count and assigns a predefined Mean of Distinct Days score to the calculated count.
Final User Usage Score
The total User Usage score is generated by combining MC, Mean of MC, DD and Mean of DD, also factoring the assigned Weight Distribution percentage, which defines the relative importance or contribution of each calculated factor.
Refer to the Weight Distribution section for a detailed understanding.
Weight Distribution
Weight distribution in the context of Zluri's Application Usage and User Usage scoring system refers to the relative importance or significance of each factor in the overall calculation of the final usage score. The assigned percentage of weight distribution values determines how much influence each factor has on the final score.
Application Usage Weight Distribution
For Application Usage, three factors are considered: AV, AD, and PU. The weights assigned to each factor reflect the perceived importance of each aspect in assessing application usage within an organization.
AV: The percentage value assigned represents the importance of the frequency of application use.
AD: The percentage value assigned reflects the significance of user activity days on an application.
PU: The percentage value assigned reflects the contribution organizational adoption rate.
User Usage Weight Distribution
For User Usage, four factors are considered: MC, Mean of MC, DD, and Mean of DD. The weights assigned to each factor reflect the perceived importance of each aspect in assessing user usage within an organization.
MC: The percentage value assigned reflects the importance of the user's engagement level within a specific timeframe.
Mean of MC: The percentage value assigned reflects the significance of assessing the user's historical engagement.
DD: The percentage value assigned reflects the essence of assessing the consistency of user activity.
Mean of DD: The percentage value assigned reflects the significance of assessing the historical consistency of user engagement.
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Sorry! We couldn't be helpful
Thank you for your feedback
Feedback sent
We appreciate your effort and will try to fix the article