Basic work with WindsoraiR

Goal

The goal here is to outline in a couple of paragraphs and few lines of code some simple ways in which we can use the Windsor.ai API and R package windsoraiR to gain insights into marketing campaign performance across channels like Google and Facebook. The nice thing about Windsor.ai is that you can have all of your marketing channels aggregating in a single place and then access all data at once using this package. Of course, once the data is in R you can do much more than the examples below, and work on analysis, predictions or dashboards.

Getting data from Facebook and Google ads into R

After we create an account at Windsor.ai and obtain an API key, collecting our data from Windsor to R is as easy as:

library(windsoraiR)
my_data <- 
  windsor_fetch(
  api_key = "your api key",
  date_preset = "last_7d",
  fields = c("source", "campaign", "clicks",
             "medium", "sessions", "spend")
)

Lets take a peek at the data we just downloaded to get a better idea about the structure and type of information included.

str(my_data)
#> 'data.frame':    1675 obs. of  6 variables:
#>  $ data.campaign: chr  "Website visits - May 25, 2019" "(ID)<00_mat>[id-cat]{eb}: mattress" "(ID)<00_mat>[emma mattress]{eb}: emma mattress" "Kampanja #1" ...
#>  $ data.clicks  : int  5 0 0 0 0 0 0 0 0 0 ...
#>  $ data.spend   : chr  "15.7900000000000001003" "0" "0" "0" ...
#>  $ data.medium  : chr  "Unknown" "Unknown" "Unknown" "Unknown" ...
#>  $ data.source  : chr  "linkedin" "google" "google" "google" ...
#>  $ googlesheets : chr  "'spreadsheet_id'" "'spreadsheet_id'" "'spreadsheet_id'" "'spreadsheet_id'" ...

Analyzing our Facebook and Google ad campaign data

First, lets try to find the campaigns with most clicks. To do this, we’ll filter only those rows that have clicks, then group the dataset by campaign (data.campaign column) and sum up the click count per campaign.

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(ggplot2)

top_10 <- 
  my_data %>% 
  filter(data.clicks > 0) %>% 
  group_by(data.campaign) %>% 
  summarise(n_clicks = sum(data.clicks)) %>% 
  ungroup %>% 
  arrange(desc(n_clicks)) %>% 
  slice_head(n = 10)

knitr::kable(top_10)

data.campaign	n_clicks
AO_UAC_app_install	202116
AO_Smart_Shopping_Running_Jogging	119181
AO_Dynamic_Remarketing_Cart_Abandoners	70238
city_display_awareness_hyd_april_2021	54666
summer_opco_men_reach_april2021	53662
AO_Search_Brand_Exact	53513
AO_Dynamic_Remarketing_Cart_Abandoners_Website	45808
AO_UAC_app_install_Hyd	36544
AO_Smart_Shopping_Yoga	31994
AO_UAC_app_install_chennai	30174

Thereafter we can quickly visualize our data using ggplot2

ggplot(top_10, aes(x = n_clicks, y = data.campaign)) +
  geom_col()

We can gain further insight by grouping by other variables add mapping them to the plot aesthetics. For example, in this case we might want to keep track of the data source (linkedin, google, …). So we’ll modify our magrittr chain to add another grouping variable before tallying the clicks.

top_10 <- 
  my_data %>% 
  filter(data.clicks > 0) %>% 
  group_by(data.source, data.campaign) %>% 
  summarise(n_clicks = sum(data.clicks)) %>% 
  ungroup %>% 
  arrange(desc(n_clicks)) %>% 
  slice_head(n = 10)
#> `summarise()` has grouped output by 'data.source'. You can override using the `.groups` argument.

knitr::kable(top_10)

data.source	data.campaign	n_clicks
google	AO_UAC_app_install	202116
google	AO_Smart_Shopping_Running_Jogging	119181
google	AO_Dynamic_Remarketing_Cart_Abandoners	70238
google	city_display_awareness_hyd_april_2021	54666
google	summer_opco_men_reach_april2021	53662
google	AO_Search_Brand_Exact	53513
google	AO_Dynamic_Remarketing_Cart_Abandoners_Website	45808
google	AO_UAC_app_install_Hyd	36544
google	AO_Smart_Shopping_Yoga	31994
google	AO_UAC_app_install_chennai	30174

ggplot(top_10, aes(x = n_clicks, y = data.campaign, fill = data.source)) +
  geom_col()

In this case the vast majority of data (all of it for the top 10 ad campaigns) comes from Google, so only this source is labeled on the graph.

We can apply the same type of data manipulation and plotting to check the data.spend values.

my_data %>%
  filter(data.clicks > 0) %>%
  group_by(data.campaign) %>%
  summarise(sum_spend = sum(as.numeric(data.spend))) %>%
  ungroup %>%
  arrange(desc(sum_spend)) %>%
  slice_head(n = 10) %>%
  ggplot(aes(x = sum_spend, y = data.campaign)) +
  geom_col()

Finally, for a direct comparison, we can aggregate both the clicks and spending per ad campaign and plot them jointly:

library(tidyr)

my_data %>%
  filter(data.clicks > 0) %>%
  group_by(data.campaign) %>%
  summarise(n_clicks = sum(data.clicks), sum_spend = sum(as.numeric(data.spend))) %>%
  arrange(desc(sum_spend)) %>%
  slice_head(n = 10) %>%
  pivot_longer(cols = c("n_clicks", "sum_spend"), names_to = "aggreg", values_to = "values") %>%  
  ggplot(aes(x = values, y = data.campaign, fill = aggreg)) +
  geom_col() +
  facet_wrap("aggreg", ncol = 2) + 
  theme(legend.position="bottom")