SELECT CategoryName,
COUNT(*) AS purchase_count
FROM Orders
INNER JOIN "Order Details" AS od
ON Orders.OrderID = od.OrderID
INNER JOIN Products
ON od.ProductID = Products.ProductID
INNER JOIN Categories
ON Products.CategoryID=Categories.CategoryID
WHERE Orders.CustomerID='QUICK'
GROUP BY CategoryNameAnalysis Report Three - Privacy and Customer Profiling
Executive Summary
The Harvard Business Review article encourages organizations to “lead the change underway” by shifting their approach to collecting customer data and establishing customer relationships. If organizations want customers to continue sharing more of their personal information, they need to clearly explain why their data is being collected, what for, and what customers receive in return. Doing so helps build a stronger relationship between the customer and the organization, where trust develops over time and both sides benefit. (Morey, Forbath, and Schoop 2015) The Northwind analysis showed the value of looking at more than one type of customer data. By combining purchase frequency and total spending, Northwind is able to build a more complete customer profile that supports better decision making.
Introduction
Customer profiling has become an important part of how organizations better understand consumer behavior and remain competitive, especially in the retail industry. By analyzing customer data, organizations can better understand their customers needs and preferences, allowing them to provide more personalized products, services, and marketing. (Degutis et al. 2023)
As technologies that rely on personal data continue to develop and become part of everyday life, consumers understand that organizations are collecting information about them. Many feel comfortable willingly sharing that information when they receive value in return, whether that be through products and services that make their lives easier, save them money, or create more personalized experiences. What many consumers still lack, however, is a clear understanding of exactly what information is being collected, how its being used, and who ultimately benefits from it. As a result, consumers increasingly expect organizations to be transparent about their data collection practices before trusting them with their personal information. Building that trust has become an essential part of effective customer profiling.
Privacy vs Profiling
In the past, organizations were limited in how much they could learn about their customers. Today, thanks to digitization, the data generated by consumers through digital technologies gives organizations access to information including shopping habits, location, and online activity, allowing them to understand their customers on a much more personal level. While this creates new opportunities for organizations to better understand and serve their customers, it also creates greater opportunities for misuse, security breaches, and other privacy concerns. (Morey, Forbath, and Schoop 2015)
The Harvard Business Review article suggests that organizations hoping to earn consumers trust need to reevaluate their approach to data privacy. Instead of only meeting minimum legal requirements, organizations should educate consumers about their data collection practices, give them more control over the data they choose to share, and clearly explain the value customers receive in return. (Morey, Forbath, and Schoop 2015)
This emphasis on trust is also supported by outside research by Degutis et al., who focused on consumers willingness to share their personal information from a reciprocity-based social exchange perspective. The authors found that consumers were more willing to share their personal information when they trusted the organization and believed they would receive value in return. Their findings also showed that this perceived trust in the organization actually had a greater influence on consumers’ willingness to share personal information than just the value they expected to receive for it. (Degutis et al. 2023)
Research by Bleier, Goldfarb, and Tucker reinforces the idea that organizations can benefit by making privacy a priority. The authors explain that organizations that make privacy a priority and respond to consumers’ privacy concerns can distinguish themselves from competitors, positioning them more favorably in the market. DuckDuckGo is one example discussed in the article. The company differentiated itself from other search engines by choosing not to collect or share users personal information. Its privacy-focused approach helped DuckDuckGo stand out, so much so that even Google now offers it as a default search engine option in many markets. (Bleier, Goldfarb, and Tucker 2020)
Profiling Example
To build a customer profile, I wanted to analyze a customer with a large purchase history. QUICK was one of Northwinds top customers with 199 orders placed, allowing for a more complete analysis of the customer’s purchasing habits.
Visualization One
The first visualization compares the number of purchases QUICK made in each product category. Confections had the highest number of purchases while Produce had the least. Based on this information, it would seem as though Confections would be the best category to target with future promotions because it represents the customer’s most frequent purchases.
ggplot(data = myquery1,
aes(y=CategoryName,
x=purchase_count)) +
geom_col(fill="lightblue") +
labs(
title="QUICK's Product Purchases by Category",
x= "Number of Product Purchases",
y= "Product Category"
)
Visualization Two
The second visualization compares QUICK’s total spending in each product category. Even though Confections was purchased the most, QUICK spent the most money in the Beverages category. By looking at both data visualizations, we now have a more complete picture of the customer’s purchasing behavior. While Confections may initially seem like the best category to target based on purchase frequency, total spending suggests that Beverages may actually be the better choice.
SELECT CategoryName,
SUM(od.UnitPrice*od.Quantity) AS total_spent
FROM Orders
INNER JOIN "Order Details" AS od
ON Orders.OrderID = od.OrderID
INNER JOIN Products
ON od.ProductID = Products.ProductID
INNER JOIN Categories
ON Products.CategoryID = Categories.CategoryID
WHERE Orders.CustomerID = 'QUICK'
GROUP BY CategoryNameggplot(data = myquery2,
aes(y=CategoryName, x=total_spent)) +
geom_col(fill="pink") +
labs(
title="QUICK's Spending by Category",
x= "Total Spending",
y= "Product Category"
)
Recommendations for Industry
To improve customer profiling efforts, I’d recommend that organizations start small by only collecting the information they need and continue building the customer profile over time. As customers become more familiar with the organization and understand the value they receive in return, they will become more comfortable sharing additional data. Organizations should also be transparent throughout that process and give customers access to their personal information so they can decide what they feel comfortable sharing.
I’d also recommend that organizations use the information they collect to improve the customer experience. The analysis of QUICK showed that looking at more than one type of customer data can change the conclusions organizations make about their customers. When organizations use that information responsibly and customers can recognize the value in sharing it, their trust in the organization will grow, ultimately making them more willing to share additional information in the future.
References
Bleier, Alexander, Avi Goldfarb, and Catherine E Tucker. 2020. “Consumer Privacy and the Future of Data-Based Innovation and Marketing.” Available at SSRN 3570156.
Degutis, Mindaugas, Sigitas Urbonavičius, Linda D Hollebeek, and Johan Anselmsson. 2023. “Consumers’ Willingness to Disclose Their Personal Data in e-Commerce: A Reciprocity-Based Social Exchange Perspective.” Journal of Retailing and Consumer Services 74: 103385.
Morey, Timothy, Theodore Forbath, and Allison Schoop. 2015. “Customer Data: Designing for Transparency and Trust.” Harvard Business Review 93 (5): 96–105.