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Most common journeys

Source: R/path-diagnostics.R
top_paths.Rd

Most common journeys

Usage

top_paths(paths, n = 10, sep = " > ", converting_only = FALSE)

Arguments

paths

An mm_paths object from build_paths().

n

Number of distinct channel sequences to return. Each row aggregates every journey that followed that sequence, so the journeys column will normally sum to far more than n.

sep

Separator between channels in the rendered path string.

converting_only

Restrict to converting journeys? Defaults to FALSE, because the commonest non-converting journeys are usually the more interesting half.

Value

A data frame with one row per distinct channel sequence: path, journeys (how many journeys followed it), conversions and conversion_rate. Ordered by descending frequency, then alphabetically.

See also

as_channel_paths(), path_diagnostics()

Examples

data(mm_events)
paths <- build_paths(mm_events, id = "customer_id", channel = "channel",
                     timestamp = "timestamp", conversion = "conversion")
top_paths(paths, n = 8)
#>                       path journeys conversions conversion_rate
#> 1                   social      203          97       0.4778325
#> 2                  display      201          79       0.3930348
#> 3           organic_search      172          81       0.4709302
#> 4    display > paid_search      164          74       0.4512195
#> 5     social > paid_search      158          65       0.4113924
#> 6              paid_search      155          81       0.5225806
#> 7 display > organic_search      102          48       0.4705882
#> 8                    email      102          46       0.4509804