Analysis Report Three - Privacy and Customer Profiling

Author

Olivia Mitchell

Executive Summary

This report examines how businesses use customer data to better understand consumer behavior while balancing privacy concerns. The research shows that customer profiling can improve products, marketing, and customer experiences when companies are transparent about the data they collect and provide value in return (Morey, Forbath, and Schoop 2015). Using the Northwind database, I analyzed one customer’s purchasing behavior to identify buying patterns and demonstrate how businesses can use data to make better decisions. Based on both the research and data analysis, companies should focus on building customer trust by being honest about data collection and using customer information to improve products and services rather than only increasing profits (Ward 2024).

Introduction

Customer data has become one of the most valuable resources for businesses because it helps them better understand their customers and make more informed decisions. Companies use customer data to improve products, personalize marketing, recommend products, and strengthen customer relationships. However, as businesses collect more information, they also face greater responsibility for protecting customer privacy and building trust.

This week’s readings explain that customer profiling can benefit both businesses and consumers when it is used responsibly. Companies should be transparent about what data they collect, explain how it will be used, and provide customers with value in return for sharing their information (Morey, Forbath, and Schoop 2015). The readings also discuss how retailers, social media companies, and artificial intelligence are collecting larger amounts of customer data to create more personalized experiences and new business opportunities (Gabel, Simester, and Timoshenko 2024). While these technologies can improve customer satisfaction, they also raise concerns about privacy, transparency, and ethical data use (Higgins 2025).

In this report, I will use the Northwind database to examine one customer’s purchasing behavior and demonstrate how customer profiling can help businesses better understand buying patterns. I will also discuss recommendations for using customer data in a way that balances business goals with customer trust.

Privacy vs. Profiling

Customer profiling helps businesses understand their customers by collecting data about what they buy and how they interact online. This information helps companies improve products, personalize marketing, and recommend items, but it also raises privacy concerns because many customers do not realize how much data is being collected (Morey, Forbath, and Schoop 2015).

One of the main ideas from this week’s readings is that companies should be honest about what data they collect and why they need it. Customers are more willing to share information when they receive clear benefits, such as better recommendations or a more personalized experience (Morey, Forbath, and Schoop 2015).

TikTok is one example discussed in class because it uses customer data to improve its recommendation algorithm. While users benefit from personalized content, many people still worry about how much information the platform collects.

Dr. Church also discussed Uber, which used customer location data to make assumptions about users’ personal lives. Although the company already had this data, many customers felt uncomfortable with how it was being used. This shows why companies need to use customer data responsibly.

The Wall Street Journal article explains that AI chatbots are becoming more personalized by learning about users over time, but this also creates new privacy concerns because they collect even more personal information (Higgins 2025). Another article found that companies build more trust through honest actions than by simply using words like “ethical” or “trustworthy” in their reports (Ward 2024).

Overall, customer profiling can benefit both businesses and customers, but companies must be transparent, respect customer privacy, and use data in ways that build long-term trust (Morey, Forbath, and Schoop 2015).

Profiling Example

To better understand how customer profiling can support business decisions, I analyzed the purchasing history of HANAR (Hanari Carnes) from the Northwind database. Looking at one customer’s buying behavior allows a business to identify purchasing preferences, spending habits, and opportunities for more personalized marketing. Instead of sending the same promotions to every customer, companies can use purchasing history to better match products with customer interests.

The first visualization shows how much money HANAR spent in each product category. This provides insight into which categories generate the most revenue from this customer and where future marketing efforts may be most effective.

SELECT CategoryName,
       "Order Details".UnitPrice * Quantity AS amount_spent
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 CustomerID = "HANAR"
ggplot(data = myquery1,
       aes(x = amount_spent,
           y = CategoryName)) +
  geom_point(color = "hotpink") +
  labs(
    title = "HANAR Individual Purchases by Category",
    x = "Amount Spent",
    y = "Category"
  )

The second visualization looks at how many purchases HANAR made in each product category. While total spending identifies where the customer spends the most money, purchase frequency shows which categories they buy from most often.

SELECT CategoryName, COUNT (*) AS n
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 CustomerID = "HANAR"
GROUP BY CategoryName
HAVING n > 2
ggplot(data = myquery2,
       aes(x=n, y = CategoryName)) +
  geom_col(fill = "plum") +
  labs(
    title = "HANAR Purchases by Product Category",
    x = "Number of Purchases",
    y = "Category"
  )

Recommendations for Industry

  1. Be transparent about what customer data is collected, why it is collected, and how it will be used.
  2. Use customer profiling responsibly by personalizing recommendations and improving the customer experience instead of only increasing sales.
  3. Give customers control over their personal information by allowing them to manage privacy settings and choose what data they share.
  4. Balance personalization with privacy by collecting only the information that is necessary and protecting customer data through ethical practices.

References

Gabel, Shelle, Duncan Simester, and Artem Timoshenko. 2024. “How Retailers Became Ad Platforms.” Harvard Business Review.
Higgins, Tim. 2025. “Why Tech Billionaires Want Bots to Be Your BFF.” The Wall Street Journal.
Morey, Timothy, Theodore Forbath, and Allison Schoop. 2015. “Customer Data: Designing for Transparency and Trust.” Harvard Business Review, May.
Ward, Lisa. 2024. “Beware When a Company Says It Is Trustworthy.” The Wall Street Journal.