SELECT Categories.CategoryName,
SUM("Order Details".UnitPrice *
"Order Details".Quantity *
(1 - "Order Details".Discount)) AS TotalRevenue
FROM Customers
INNER JOIN Orders
ON Customers.CustomerID = Orders.CustomerID
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 Customers.CustomerID = "HANAR"
GROUP BY Categories.CategoryNameAnalysis Report Three - Privacy and Customer Profiling
Executive Summary
Many organizations collect customer information such as purchase histories, browsing activity, and demographic data to better understand customer behavior and improve business decisions. This information can be used to personalize advertisements, recommend products, improve inventory planning, and provide better customer service. With that being said, collecting more customer data also creates privacy concerns about whether customers understand how their information is being used. Morey, Forbath, and Schoop explain that organizations can build customer trust by being transparent about the data they collect, giving customers control over their information, and providing value in return (Morey, Forbath, and Schoop 2015).
This report builds on the ideas from What’s Your Data Strategy? by examining how organizations can use customer data to improve business decisions while also protecting customer privacy. It also looks at transparency, customer trust, and retail advertising platforms while creating a customer profile for Hanari Carnes (HANAR) using the Northwind database (DalleMule and Davenport 2017).The visualizations demonstrate how grouping customer transactions can identify purchasing patterns that support better business decisions. Organizations should continue using customer profiling to improve decision-making, but they should also be transparent about how customer information is collected and used while giving customers greater control over their personal data.
Introduction
Companies go the distance to collect customer information because it helps them understand customer behavior, allowing them to make better business decisions. Purchase history shows what customers buy, as well as how often they make purchases and how they responded to promotions. This information allows for improved advertising, recommending products, and helps manage inventory, while providing better customer service. Morey, Forbath, and Schoop explain that customers are willing to share their information to companies when organizations are transparent about what data they collect and provide value in return. They also point out that different types of customer data have different levels of sensitivity, meaning organizations should think about not only collecting data, but also making sure customers understand how it is being used (Morey, Forbath, and Schoop 2015).
As customer profiling continues to grow, organizations are finding new ways to use customer information to improve their business. Retailers now use purchase histories to support advertising platforms, while technology companies use customer data to better understand people’s preferences and behaviors. With that being said, collecting more customer information also increases concerns about privacy and how that information is used. Organizations should continue using customer profiling to improve decision-making while making sure they are transparent with customers and protect their personal information throughout the process (Gabel, Simester, and Timoshenko 2024).
Privacy vs Profiling
Morey, Forbath, and Schoop explain that companies should not assume they have unlimited permission to use customer data simply because they collect it. Customers are more likely to trust an organization when it clearly explains what information is being collected, why it is needed, and how it will be used. The authors also explain that giving customers the ability to view, correct, or delete their information can help improve trust while also creating more accurate customer profiles. With that being said, organizations should focus on building long-term customer trust rather than simply collecting as much information as possible (Morey, Forbath, and Schoop 2015).
The article How Retailers Became Ad Platforms shows how retailers are using customer purchase histories to pull in another source of revenue with targeted advertising. Since retailers can connect advertisements linked to recent customer purchases, this allows retailers to understand which advertisements are successful and which customers are likely to buy certain products. While valuable opportunities are created for businesses, it also comes with privacy concerns because many customers don’t realize their purchase history is being used as advertising profiles. With this, organizations should be open about what customer information is collected and how it is being used for during customer profiling to benefit both the business and the customer(Gabel, Simester, and Timoshenko 2024).
Profiling Example
The customer selected for this analysis is Hanari Carnes, represented in Northwind by the CustomerID HANAR. The purpose of this analysis is to identify purchasing patterns using the customer’s recorded transactions. By looking at the products purchased, the amount spent, and the categories ordered most often, a customer profile can be created that shows what Hanari Carnes purchases most frequently. The analysis only uses information related to purchasing behavior, including products, categories, quantities, prices, and discounts, while excluding personal contact information. This follows the principle of data minimization discussed in the readings because only the information needed to answer the business question is included.
Visualization One
The Northwind database shows how customer data can be used to better understand purchasing behavior through grouping and analysis. The first visualization groups Hanari Carnes’ purchases by product category to show which categories generate the most revenue. The second visualization takes the analysis one step further by identifying the products purchased most often by the customer. Together, these visualizations demonstrate how grouping transaction data can reveal purchasing patterns that would be difficult to recognize by looking at individual orders. Organizations can use this type of information to improve inventory planning, create more personalized marketing strategies, and better understand customer preferences while still using only the data needed to answer the business question.
ggplot(data = liveprofile,
aes(x = CategoryName,
y = TotalRevenue)) +
geom_col() +
coord_flip() +
theme_minimal() +
labs(
title = "Individual Hanari Carnes Purchases by Category",
x = "Product Category",
y = "Total Revenue",
caption = "Source: Northwind SQLite"
)
Visualization Two
Recommendations for Industry
Organizations should use customer profiling to improve business decisions rather than simply collecting as much customer data as possible. Leaders should identify how customer data will be used, and from there, decide if it can be used for improving recommendations, managing inventory, detecting fraud, or providing better customer service. Companies should be completely transparent about how customer information is collected and used, specifically when purchase histories are used for advertising and customer profiling. Morey, Forbath, and Schoop explain that organizations build stronger customer trust when they clearly communicate their data practices and provide customers with value in return (Morey, Forbath, and Schoop 2015). Businesses should give customers more control over their personal information by giving them access to correct, delete, or limit how their data is used. Organizations should review customer profiles regularly to make sure they are accurate and supported by available data instead of just treating them as permanent facts. The Northwind analysis demonstrated how customer purchasing patterns can provide valuable business insights, though organizations should avoid assumptions that go beyond what the data actually shows. Lastly, businesses should continue to balance the benefits of customer profiling and customer transparency, privacy, and responsible data practices so they can improve decision-making whale maintaining customer trust and satisfaction.
References
DalleMule, Leandro, and Thomas H Davenport. 2017. “What’s Your Data Strategy.” Harvard Business Review 95 (3): 112–21.
Gabel, Sebastian, Duncan Simester, and Artem Timoshenko. 2024. “How Retailers Became Ad Platforms.” Harvard Business Review.
Morey, Timothy, Theodore Forbath, and Allison Schoop. 2015. “Customer Data: Designing for Transparency and Trust.” Harvard Business Review 93 (5): 96–105.