SELECT strftime('%Y', OrderDate) AS OrderYear,
COUNT(*) AS n
FROM Orders
WHERE CustomerID = "SAVEA"
GROUP BY OrderYearAnalysis Report Three - Privacy and Customer Profiling
Executive Summary
Companies collect customer data, create customer profiles, and balance the benefits of data collection with customer privacy. The assigned readings explain that businesses collect information from websites, loyalty programs, mobile devices, and other digital technologies to better understand customer behavior. This information helps companies improve products, personalize recommendations, and create new advertising opportunities. However, the readings also show that many customers are concerned about how their personal information is collected and used. Building trust through transparency and giving customers more control over their data can improve long-term relationships. The report also uses the Northwind database to create a customer profile using SQL queries and visualizations. By grouping and summarizing customer purchases, patterns in buying behavior become easier to understand. Overall, the report concludes that organizations should use customer data responsibly, communicate clearly with customers, and protect privacy while still using data to improve business decisions and customer experiences.
Introduction
The readings are focused on the growing importance of customer data and the challenges companies face when collecting and using personal information. The Harvard Business Review article Customer Data: Designing for Transparency and Trust explains that companies can improve products and services by collecting customer data, but they must also be open about what information they collect and how it is used. Customers are more willing to share information when companies are transparent and provide value in return. Another reading explains how retailers such as Amazon and Walmart have expanded beyond selling products by creating retail media platforms that use customer shopping data to sell advertising opportunities. The additional articles discuss how trust cannot simply be created by using positive language or AI personalization. Instead, companies must earn trust through responsible data practices, transparency, and ethical decision-making when handling customer information.
Privacy vs Profiling
The readings show that companies face a difficult challenge when trying to collect enough customer data to create useful customer profiles while still respecting privacy. The Harvard Business Review article explains that businesses collect information from websites, mobile devices, loyalty programs, and connected products to improve products and personalize customer experiences. However, many customers are concerned about how this information is collected and shared. Companies that clearly explain their data practices and allow customers to control their information are more likely to earn customer trust. The retail media article shows that retailers such as Amazon and Walmart use customer shopping data to deliver targeted advertising and create new sources of revenue. While this creates business opportunities, it also raises concerns about transparency because retailers both manage advertising campaigns and measure their success. The readings also suggests that simply using words like “integrity” or “responsibility” does not convince customers that a company is trustworthy. Finally, the AI article shows how companies are designing AI systems to become more personal and conversational, creating additional concerns about privacy and how much personal information users share with technology. Together, these readings show that organizations must balance personalization with honesty, transparency, and respect for customer privacy.
Profiling Example
Create a story around one customer from Northwind. Include code and graphs for a minimum of two well-developed visualizations that profile and explain their purchasing behavior. In your visualizations, you must utilize elements from Practice three. Your queries can be loosly based on the queries I complete in the practice, but they must extend or adapt the practice in interesting ways. I have not included the placeholder blocks this week, but by now you should be able to copy/paste from your other work.
ggplot(data = myquery1,
aes(x = OrderYear, y = n)) +
geom_col(fill = "lightblue") +
labs(title = "Orders Placed by SAVEA Each Year" ,
x = "Year" ,
y = "Number of Orders")
SELECT Categories.CategoryName,
SUM("Order Details".UnitPrice * "Order Details".Quantity) AS TotalSpent
FROM Orders
INNER JOIN "Order Details"
ON Orders.OrderID = "Order Details".OrderID
INNER JOIN Products
ON "Order Details".ProductID = Products.ProductID
INNER JOIN Categories
ON Products.CategoryID = Categories.CategoryID
WHERE Orders.CustomerID = "SAVEA"
GROUP BY Categories.CategoryNameggplot(data = myquery2,
aes(x = TotalSpent, y = CategoryName)) +
geom_col(fill = "lightblue") +
labs(title = "SAVEA Spending by Product Category" ,
x = "Total Spending" ,
y = "Category")
Warning
Remember, the practice covers certain specific concepts. Your grade is based on how well you show mastery of these concepts. Your queries can be loosly based on Practice queries, but they must extend or adapt the practice in interesting ways.
Important
For your required live edit this week, you must start your video with a query and visualization that does not use GROUP BY or aggregation, then adjust it accordingly so that it groups, aggregates and visualizes the grouped data. I demonstrate how to do this in my video about the GROUP BY clause located in the video library. When you render and submit your report you should use your final grouped query that you arrive at when you finish your live edit. Just ask if you have questions about this.
Recommendations for Industry
Organizations should focus on building customer trust instead of simply collecting as much data as possible. The readings suggest that companies should clearly explain what information they collect, why it is collected, and how it benefits customers. Giving customers choices about how their information is used can improve trust and encourage people to continue sharing data. Companies should also protect customer information with strong security practices and regularly review their privacy policies. Businesses using customer profiles for advertising should create transparent reporting systems so customers and business partners understand how data is being used. Companies should avoid relying on marketing language that only claims to be trustworthy and instead demonstrate responsible behavior through their actions. AI companies should also be careful when creating personalized digital assistants because users may become overly dependent on them or share sensitive personal information. Overall, organizations that combine responsible data collection, customer transparency, and ethical decision-making will be more successful at maintaining long-term customer relationships while still benefiting from customer profiling.
References
Getting your citations out of Google Scholar and into your references.bib file takes just a few clicks.
Go to Google Scholar and search for the paper you want to cite (for example: “What’s your data strategy”).
Look directly underneath the search result for the Cite button (it looks like a double quotation mark ”). Click it.
A pop-up window will appear showing standard citation styles (APA, MLA, etc.). At the very bottom of that pop-up, click the link that says BibTeX.
A new page or plain text block will open showing some bibtex code. Paste that block of text into your [references.bib] file.
If you have multiple sources, just keep pasting them one after another down the file. You don’t need commas between the different articles; just separate them with a blank line to keep it clean. You can then cite them like this (DalleMule and Davenport 2017) and they will automatically appear in the references section.
DalleMule, Leandro, and Thomas H Davenport. 2017. “What’s Your Data Strategy.” Harvard Business Review 95 (3): 112–21.