View source: R/identify_usage_segments.R
identify_usage_segments | R Documentation |
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'.
identify_usage_segments(
data,
metric = NULL,
metric_str = NULL,
version = "12w",
return = "data"
)
data |
A data frame with a Person query containing the metric to be
classified. The data frame must include a |
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 |
version |
A string indicating the version of the classification to be
used. Valid options are |
return |
A string indicating what to return from the function. Valid options are:
|
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
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
# 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"
)
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