identify_usage_segments: Identify Usage Segments based on a metric

In vivainsights: Analyze and Visualize Data from 'Microsoft Viva Insights'

Identify Usage Segments based on a metric

Description

This function identifies users into usage segments based on their usage volume and consistency. The segments 'Power Users', 'Habitual Users', 'Novice Users', 'Low Users', and 'Non-users' are created. There are two versions, one based on a rolling 12-week average (version = "12w") and the other on a rolling 4-week average (version = "4w"). While a main use case is for Copilot metrics e.g. 'Total_Copilot_actions', this function can be applied to other metrics, such as 'Chats_sent'.

Usage

identify_usage_segments(
  data,
  metric = NULL,
  metric_str = NULL,
  version = "12w",
  return = "data"
)

Arguments

data	A data frame with a Person query containing the metric to be classified. The data frame must include a PersonId column and a MetricDate column.
metric	A string representing the name of the metric column to be classified. This parameter is used when a single column represents the metric.
metric_str	A character vector representing the names of multiple columns to be aggregated for calculating a target metric, using row sum for aggregation. This is used when metric is not provided.
version	A string indicating the version of the classification to be used. Valid options are "12w" for a 12-week rolling average and "4w" for a 4-week rolling average. Defaults to "12w".
return	A string indicating what to return from the function. Valid options are: "data": Returns the data frame with usage segments. "plot": Returns a plot of the usage segments.

Details

There are two versions of usage segments, one based on a rolling 12-week period (version = "12w") and the other on a rolling 4-week period (version = "4w"). This function assumes that in the input dataset is grouped at the weekly level by the MetricDate column.

The definitions of the segments as per the 12-week definition are as follows:

• Power User: Averaging 15+ weekly actions and any actions in at least 9 out of past 12 weeks

• Habitual User: Any action in at least 9 out of past 12 weeks

• Novice User: Averaging at least one action over the last 12 weeks

• Low User: Any action in the past 12 weeks

• Non-user: No actions in the past 12 weeks

The definitions of the segments as per the 4-week definition are as follows:

• Power User: Averaging 15+ weekly actions and any actions in at least 4 out of past 4 weeks

• Habitual User: Any action in at least 4 out of past 4 weeks

• Novice User: Averaging at least one action over the last 4 weeks

• Low User: Any action in the past 4 weeks

• Non-user: No actions in the past 4 weeks

Value

Depending on the return parameter, either a data frame with usage segments or a plot visualizing the segments over time. If "data" is passed to return, the following additional columns are appended:

• IsHabit12w: Indicates whether the user has a habit based on the 12-week rolling average.

• IsHabit4w: Indicates whether the user has a habit based on the 4-week rolling average.

• UsageSegments_12w: The usage segment classification based on the 12-week rolling average.

• UsageSegments_4w: The usage segment classification based on the 4-week rolling average.

@import slider slide_dbl

Examples

# Example usage with a single metric column
identify_usage_segments(
  data = pq_data,
  metric = "Emails_sent",
  version = "12w",
  return = "plot"
)

# Example usage with multiple metric columns
identify_usage_segments(
  data = pq_data,
  metric_str = c(
    "Copilot_actions_taken_in_Teams",
    "Copilot_actions_taken_in_Outlook",
    "Copilot_actions_taken_in_Excel",
    "Copilot_actions_taken_in_Word",
    "Copilot_actions_taken_in_Powerpoint"
  ),
  version = "4w",
  return = "plot"
)

vivainsights documentation built on June 8, 2025, 1:13 p.m.