I am Efehan Danisman and working as Customer Relations Specialist at Turkish Airlines. My main duties are analyzing feedback data(from various channels) in order to improve overall customer experience along with improving customer relations processes. I also coordinate social media customer relations which is also a data-intensive process. Hence data is heart and mind of those processes and harnessing its potential in a better way is my main aim.
Since we are diving into tidyverse environment this week, I chose this video to have an introduction. Speech starts from the basics of tidyverse and its packages from dplyr to manipulate and wrangle data to visualize them. It introduces pipe (%>%), ggplot and its capabilities, tibble;another version of dataframe, spreading key-value pairs and transforming data with dplyr. I used some of those capabilities until now though need more experience to feel competent on them. I also watched video about package “Glue” that is a package to ease advanced data manipulation with regular expressions.
We receive lots of free text feedback data from customers. Sometimes you need data for the super specific feedback subjects which you do not categorize. However text mining using R could be helpful to find the insight we are looking for. This book looks slightly advanced but would have an added value to my work.
I am using R for data visualization and feel confident play with visuals using ggplot and htmlwidgets for limited interactivity such rbokeh and plotly. However using Shiny apps and building interactive reports would take my work to next level while presenting insights to my colleagues.
Some customer feedback data are scattered around online. We can not get the data to a data frame and analyze it in a systematic way. Web scraping can play a role here to make sense out of them. Even though I personally tried, could not reach success previously. At the end of this programme, learning web scraping would be useful for me.