Analyzing Starbucks datasets

Photo by Darke Lv on Unsplash

I love coffee and what is better than a coffee in the morning?
Starbucks is one of the most popular cafes worldwide! And here we have some sample data of their rewards mobile app and we are going to dive deep into it and analyze it to gain insights on customers buying behavior and what offers are the most attractive to them.

In the datasets, we have there are three types of offers that can be sent: buy-one-get-one (BOGO), discount, and informational. In a BOGO offer, a user needs to spend a certain amount to get a reward equal to that threshold amount. In a discount, a user gains a reward equal to a fraction of the amount spent. In an informational offer, there is no reward, but neither is there a required amount that the user is expected to spend. Offers can be delivered via multiple channels.
The basic task is to use the data to identify which groups of people are most responsive to each type of offer, and how best to present each type of offer.

So here we have some questions to ask :

• How people make purchasing decisions and how those decisions are influenced by promotional offers.
• What is the customer's income?
• What is the offer most completed?

First, of our analysis let’s check the shape of our datasets

Now we will explore and clean the portfolio dataset

We can see here there are three types of offers BOGO(buy one get one), discount, and informational offers.

Here I have separated the channel they are using for these offers

In the transcript dataset, we can see what customer reaction to these offers

We see here in the plot that 45% have received the offer,34% offer viewed it 18%, and only 20% have completed the offer.

For the Profile dataset

number of missing values

Here I noticed that gender and income have missing data in the same pattern and after investigating, they also have the age of 118 which is not real of course so it’s wrong data I decided to drop these nulls with the wrong age value.

Here we can see the gender with the income we notice that males are more in this data.

gender income

Insights and recommendations:

• The average age here is between 50 and 60 they might want to do more attractive offers for other ages.
• Male customers are 57.2% of all customers.
• We notice that a lot of customers has become a member in 2017 there might be a huge change here in 2017.
• The average income for the customers is 61843.
• The most completed offer is a discount offer.

These insights can be used to target new customers and to customize the marketing for the current customer based on their demographic and behavioral data.