In this analysis we’ll look at Zendesk tickets that have come in for Publish.
We’ll gather the Zendesk tickets from the
dbt_buffer.zendesk_tickets model with the query below.
# define query sql <- " select distinct t.id , date(t.created_at) as created_at , t.subject , t.description as text , t.product , t.topic , t.feature , t.social_account from dbt_buffer.zendesk_tickets t where t.product = 'publish' " # query BQ tickets <- dbGetQuery(con, sql) # filter out tickets with old dates tickets <- tickets %>% filter(created_at != "1970-01-01") # save data saveRDS(tickets, "publish_zendesk_tickets.rds")
We have over 34 thousand tickets for Publish from October 2019 to August 2020. Let’s use the
skimr package to get a brief summary of the dataset.
# skim dataset skim(tickets)
|Number of rows||31924|
|Number of columns||8|
|Column type frequency:|
Variable type: character
Variable type: Date
complete_rate in particular is interesting for the
social_account columns. It is understandable that these values are not always present.
Number of Tickets Over Time
Now let’s plot the number of tickets we’ve gotten over time. We’ll count the number of tickets received each week.
Let’s break this down further by looking at the individual topics.
It’s a bit hard to see the individual topics when they’re all clumped together, so let’s organize them in a different way.
This is more clear. It shows us that payment and technical issues make up the majority of the tickets for Publish. We can summarise the median number of tickets received by topic with the plot below.
Let’s first look at the proportion of Publish tickets that are due to technical issues each month.
Now let’s take a deeper look at the issues that are driving the largest proportion of the ticket volume.
Social accounts, publishing, and composing/scheduling are the features account for most of the technical issue tickets.
Let’s look at the top 10 features and view their trends over time.
It looks like the trends are fairly consistent over time, apart from a couple anomalous spikes. Now let’s look at the social accounts that are causing the most tickets for the
Unsurprisingly, Facebook, Instagram, and LinkedIn are the main culprits. We also want to see how many issues that are coming in about technical issues that are not related to connecting social accounts.
There are around 140 tickets coming in each week on average. Let’s look at some of the individual tickets.
# show individual tickets tickets %>% filter(week >= "2020-01-01" & topic == "tech_issues" & (feature == "publish_publishing" | feature == "publish_social_accounts")) %>% select(subject, text) %>% head(10)
## # A tibble: 10 x 2 ## subject text ## <chr> <chr> ## 1 "Compression" "Hi, I posted a couple posts on IG (… ## 2 "Unable to post to instagram" "I have tried numerous times to post… ## 3 "image not showing" "I used your system to do a post on … ## 4 "Video cards and tiles not displaying … "I have been uploading article links… ## 5 "I keep getting an error message" "My posts aren't posting to IG and I… ## 6 "The buffer app will not post to my In… "The buffer app will not post to my … ## 7 "Julianna Gaita, P.A. Attorneys at Law… "The facebook account says in has ru… ## 8 "Facebook group queue" "We are having issues on queue limit… ## 9 "Post to to contact you" "Hi there! It appears one of my post… ## 10 "Update Failed to Post" "I did the \"retry\" twice (as sugge…
It seems like this sample mostly contains tickets about posting issues.