Marketing Campaign Analysis

BACKGROUND

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As data professionals, we know that your portfolio is key to demonstrate your skillset. To help me in putting together a portfolio, I signed up for Avery Smith's Data Analytics Accelerator program.

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Part of the program will find me taking a dataset and putting together a project that I can add to my portfolio. Up first is the DoorDash Marketing Analysis project. We were given a dataset from DoorDash* and tasked with:

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• Understanding the data

• Finding business opportunities and insights

• Propose any data drive action to optimize the campaign results & generate value to the company

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Overall, the company wants to improve its marketing and determine who has purchased following a campaign.

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*This dataset is for educational purposes only and does not belong to DoorDash. The data is actually from iFood, a Brazilian company that operates similar to DoorDash.

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THE DATA

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The dataset is an Excel file that contains 2 years of data from December 2014 to December 2016. There are 2205 rows and 36 columns of data that provided information about customer demographics, purchasing patterns, and amount spent.

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KEY FINDINGS

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• Over the time series, customers spent $1.2 million and an average of $563.

• Customers with a higher income tended to spend more money.

• Campaign 6 is the most successful campaign to date.

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CAMPAIGN SUCCESS

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To determine which campaign was the most successful, I created a chart by using COUNTIFS to determine if a customer purchased following a campaign. Campaign 6 is the most successful campaign to date.

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CAMPAIGN SUCCESS BY AGE GROUP

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I wanted to dig in a little more and determine the campaign success by age group. Using COUNTIFS, I was able to see the breakdown of customers making a purchase following a campaign. In Campaign 6, those in the 36-50 Age Group made the most purchases following the campaign, followed by those aged 51-65.

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CUSTOMER INCOME VS AMOUNT SPENT

To determine if there was a relationship between a customer's income and the amount they spent on orders, I created a scatterplot. The scatterplot shows a 67.74% correlation between customer income and amount spent. The more a customer makes, the more they tend to spend.

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CUSTOMER INCOME BY AGE GROUP

Given that Campaign 6 was the most successful campaign and those between the ages of 36-50 made a purchase following that campaign, I wanted to do some more digging. So, I decided to look at Customer Income by Age Group. In this chart, we can see that those between the ages of 36-50 have the highest income.

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AMOUNT SPENT BY AGE GROUP

Now that we know the 36-50 Age Group has the highest income, I wanted to see if they spent the most. Turns out those between the ages of 51-65 spent the most, followed by those between the ages of 36-50.

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CUSTOMER COUNT BY AGE GROUP

The company seems to have the most success with the 36-50 and 51-65 age groups. I wanted to see how much these age groups account for the total customers. Turns out, these two age groups account for the majority of the company's customers.

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RECOMMENDATIONS

• Since Campaign 6 was the most success, I would recommend that the company continue to build on that marketing campaign.

• Target those between the ages of 36-50 and 51-60 since they made purchases following Campaign 6, have the highest income, and spent the most.

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