Campaign metadata for all campaigns run for the Customer Journey study. This dataset gives the length of time for which a campaign runs. So, any coupons received as part of a campaign are valid within the dates contained in this dataset.

campaign_descriptions

Format

A data frame with 27 rows and 4 variables

  • campaign_id: Uniquely identifies each campaign; Ranges 1-27

  • campaign_type: Type of campaign (Type A, Type B, Type C)

  • start_date: Start date of campaign

  • end_date: End date of campaign

Source

84.51°, Customer Journey study, http://www.8451.com/area51/

Value

campaign_descriptions

a tibble

Examples

# full data set campaign_descriptions
#> # A tibble: 27 x 4 #> campaign_id campaign_type start_date end_date #> <chr> <ord> <date> <date> #> 1 1 Type B 2017-03-03 2017-04-09 #> 2 2 Type B 2017-03-08 2017-04-09 #> 3 3 Type C 2017-03-13 2017-05-08 #> 4 4 Type B 2017-03-29 2017-04-30 #> 5 5 Type B 2017-04-03 2017-05-07 #> 6 6 Type C 2017-04-19 2017-05-21 #> 7 7 Type B 2017-04-24 2017-05-28 #> 8 8 Type A 2017-05-08 2017-06-25 #> 9 9 Type B 2017-05-31 2017-07-02 #> 10 10 Type B 2017-06-28 2017-07-30 #> # … with 17 more rows
# Join product campaign metadata to campaign_table dataset require("dplyr")
#> Loading required package: dplyr
#> #> Attaching package: ‘dplyr’
#> The following object is masked from ‘package:testthat’: #> #> matches
#> The following objects are masked from ‘package:stats’: #> #> filter, lag
#> The following objects are masked from ‘package:base’: #> #> intersect, setdiff, setequal, union
campaigns %>% left_join(campaign_descriptions, "campaign_id")
#> # A tibble: 6,589 x 5 #> campaign_id household_id campaign_type start_date end_date #> <chr> <chr> <ord> <date> <date> #> 1 1 105 Type B 2017-03-03 2017-04-09 #> 2 1 1238 Type B 2017-03-03 2017-04-09 #> 3 1 1258 Type B 2017-03-03 2017-04-09 #> 4 1 1483 Type B 2017-03-03 2017-04-09 #> 5 1 2200 Type B 2017-03-03 2017-04-09 #> 6 1 293 Type B 2017-03-03 2017-04-09 #> 7 1 529 Type B 2017-03-03 2017-04-09 #> 8 1 536 Type B 2017-03-03 2017-04-09 #> 9 1 568 Type B 2017-03-03 2017-04-09 #> 10 1 630 Type B 2017-03-03 2017-04-09 #> # … with 6,579 more rows