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:
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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.
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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.
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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.
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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:
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What are the busiest days of the week and hours of the day (in order to schedule ads for times with fewer orders)?
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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.
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EXAMPLE RESULT & RECOMMENDATION:
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How does ordering behavior differ between different types of customers? → In which region(s) do young parents tend to place the most orders?
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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.
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