Instacart Marketing Strategy
I performed an analysis of customer order behavior to inform a targeted marketing strategy involving prices, types of products, and customer profiling.
OVERVIEW:
In this project, I analyzed online grocery store sales data from Instacart to inform a targeted marketing strategy. I combined data sets about orders, products, and (fictional) customers and used Python to uncover information and sales patterns and customer behavior. I also created some data visualizations to illustrate my findings. These results were used to answer various business questions and create recommendations regarding marketing strategy.
PURPOSE & CONTEXT:
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Instacart is an online grocery store that operates through an app and provides open-source data about their sales.
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For a portfolio project, I analyzed their data to uncover information about their sales patterns and customer behavior.
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I used the results of my analysis to create customer segments and suggest relevant targeted marketing strategies.
DATA:
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"The Instacart Online Grocery Shopping Dataset 2017"
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Data Dictionary
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Customer data, falsified by CareerFoundry for purpose of project
TOOLS:
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Python libraries:
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pandas
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NumPy
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Matplotlib
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seaborn
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SciPy
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PROCESS:
In order to answer business questions regarding sales and customers, I derived new variables and performed some aggregations. I then created visualizations to illustrate some of my findings. To answer marketing questions, I created some customer profiles. I then analyzed the ordering behavior of these different groups and suggested some targeted marketing strategies. Finally, I created a report in Excel with my results and recommendations to be sent to the hypothetical stakeholders.
In this case study, I will only provide a couple examples of my results and recommendations. However, my GitHub contains the Excel report as well as my code.
EXAMPLE RESULT & RECOMMENDATION:
What are the busiest days of the week and hours of the day (in order to schedule ads for times with fewer orders)?
Saturday and Sunday are the busiest days of the week, and the busiest hours of the day are from 9AM-4PM. Therefore, more ads should be scheduled after 4PM and before 9AM on weekdays.
EXAMPLE RESULT & RECOMMENDATION:
How does ordering behavior differ between different types of customers? → In which region(s) do young parents tend to place the most orders?
Young parents tend to place the most orders in the Midwest and South regions. This is a good demographic to target since these customers have much less time to spend at a grocery store, so they would see a greater benefit from ordering grocery delivery with Instacart. Ads should target young parents in the Northeast and West in order to increase customers and number of orders in those regions.
