1. Who are your customers?
Welcome back! My name is Sara and I will be your instructor for this chapter where we'll get to know Divvy's customers.
2. Investigating "Who"
Do you know who your customers are? Learning about them provides valuable information toward successful and continued engagement. As a data explorer, investigating "Who" is a foundational best practice and a great way to continue analyzing data using Tableau.
3. Divvy dataset: trips table
Let's start by reviewing the customer characteristics that are available in the dataset. In the data from the trips table, there are several fields that could bring insight in to who is making the most of the Divvy bike service.
4. Divvy dataset: trips table
The fields that are of interest to us are usertype, gender, and birthyear.
5. User types
There are two general groups that use the Divvy bike service: subscribers, who are likely to be commuters, and non-subscribers, called customers, who are often tourists. More personal information is shared by riders with Divvy when a subscription service is purchased. As a result, the dataset contains their gender and birthyear. For the non-subscribers, in the absence of that ongoing relationship, those fields do not contain data and are reflected as null values in the dataset.
Note that it's possible for subscribers to cancel their subscription and become customers. In that case, the Gender and Birthyear information will be kept, meaning that some customers will have values for these fields.
6. Missing values
Because we know the reason behind the missing information, we can easily retitle those labels within Tableau and increase the available insights from the data.
7. Example
For example, in this table you can see that for subscribers the Gender and Birthyear information is known. Customers, however, don't have an ongoing relationship with the company and aren't asked to provide this information. Note that there is one customer here for who we do have Gender and Birthyear. This is due to the fact that they were a subscriber before, and shared that data with Divvy when subscribing.
Because we know the reason for these Null values, it makes sense to add that information to the data. Let's replace the Null values with the label Day Pass Riders, indicating that these users don't have an ongoing relationship with Divvy and just rented a bike for one day.
8. Let's practice!
Slicing and dicing the trips by fields that describe the type of user or provide demographic information will bring insights to life. Let's get started!