Marketing strategy for online grocery stores
Objective
Instacart is an online grocery store that operates through an app. Instacart already has very good sales but wants to uncover more information about its sales patterns. The data analyst’s task is to perform an initial data and exploratory analysis of some of their data to derive insights and suggest strategies for better segmentation based on the provided criteria.
Data set
Skills
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Python
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pandas,
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numpy,
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matplotlib.pyplot,
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seaborn
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Jupyter anaconda
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Data wrangling
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Data merging
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Deriving variables
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Grouping data
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Aggregating data
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Reporting in Excel
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Population flows
Stakeholders requirements
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The sales team needs to know the busiest days of the week and hours of the day (i.e., the days and times with the most orders) to schedule ads when there are fewer orders.
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They also want to know whether people spend the most money at particular times of the day, which might inform the type of products they advertise.
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Instacart has many products with different price tags. Marketing and sales want specific price range groupings to help direct their efforts.
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Are there certain types of products that are more popular than others? The marketing and sales teams want to know which departments have the highest frequency of product orders.
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The marketing and sales teams are particularly interested in their system's different types of customers and how their ordering behaviours differ.
Conclusion
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The busiest days for the ad campaign are Saturdays and Sundays.
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.Busy hours for purchase orders are from 9 AM to 5 PM. During these hours, advertising/marketing is recommended.
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The high-price range products require an additional ad campaign/marketing to increase sales order counts.
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4Departments 4 and 16 have received the highest orders. The least purchase orders are received in the department 10,11,2, 21 and 8
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