Analysis Report Three - Privacy and Customer Profiling

Executive Summary

In the highly digital age we live it, we share information about ourselves with major corporations everyday. Whether you shop online or in stores, complete the check out process or abandon a full cart, or simply use navigation apps on your phone, you release extensive amounts of consumer data everyday. This data can have many uses for the companies collecting it, however not all of these uses benefit the consumer. Which begs the question, why do consumers allow companies to collect their data and how is it being used? While there are infinite possibilities with the large pools of data major corporations collect, there is three main categories of use for customer data: 1)Improving products and services for consumers, 2) Marketing and advertising, and 3) Generating revenue for the company through selling data. In this paper we will talk about each one of these applications of consumer data, as well as how this affects the consumer and the business entity, lastly giving a recommendation of how a company can effectively use this information to support it’s clients.

Introduction

Consumer data is something we give away to businesses daily. Knowing this, the question is why? Why would a consumer provide personal information to a company with no promise of reaping it’s benefits? The answer is that they don’t. Studies conducted by the Harvard Business Review found that more often than not consumers are blissfully unaware of the massive amounts of information they are providing to these organizations, citing only “17% of people realize they are sharing their IP Address” and “25% realize when they are sharing their location” (Morey, Forbath, and Schoop 2015). Understanding these statistics explains both consumer privacy concerns, as well as how these companies get away with collecting information that consumers do not know they are revealing.

When most consumers think about the data they share with companies they think of age, gender, and socioeconomic statues. They are willing to share these things as they believe the companies use them to create customer profiles, therefore allowing better understand their target audience. While this is one common use of consumer data, this is just the tip of the iceberg. Companies have the ability to use data received from customers in a variety of ways, some of which are highly questionable even though they are legally allowed. Collecting and selling customer data to other brands is one of the most controversial forms of data usage, even though many major companies like FaceBook get away with it. With rising concerns over privacy and transparency, customers question what benefit they reap from this transaction. This is where tradeoff comes into play. In order to create personalized social media algorithms much the ones provided by TikTok or FaceBook the companies need data to make informed predictions about what content you are interested in viewing. However, along with the curated content, these apps are able to deciper what types of ads you are most likely to engage with, some companies like FaceBook/Instagram taking it even a step further and selling these organizations your information.

Privacy vs Profiling

While there are many uses for customer data, we have reached a pivotal point in history where it is up to companies to either work towards self regulation and transparency or potentially face consequences as laws surrounding digital privacy and customer profiling gain more and more traction. While certain data is inevitably collected because of it’s readily available, it is the responsibility of any ethical organization to not only provide customers with option for data collect, but tell them where and how this data is being used. By giving customers transparency and choice companies strengthen their relationship with consumers, as well as protect the business legally.

Currently companies are faced with yet another tough decision, how to implement this data to benefit everyone involved. While using data to better product development may be a longer process than selling data, this strategy promotes authenticity and encourages customers to continue providing data to companies in a time where access is everything. On the opposite side of the spectrum we have surveillance pricing, which in short uses customer data to adjust costs to allow companies to make the most profit possible from an individual consumer. This strategy is highly controversial and only benefits the company and a very small portion of consumers that aren’t flagged as likely consumers of that specific product. This data strategy directly benifits the companies profit margins by marking prices up as high as the algorithms deem acceptable by each consumer, but diminishes trust with consumers and raises questions about legality of data collection, some research even pointing to surveillance pricing as “a loss in total consumer welfare” (Fruits et al. 2020). While this may seem discriminatory and far off, many major million dollar corporations have begun implementing surveillance pricing into their data strategy, including household names like Uber and Walmart.

While the intended purpose of surveillance pricing is to “customize pricing and maximize revenue” (Voss 2025), there are alternative data strategies that promote ethical use and a consumer first mindset that can also further a businesses revenue stream. Mint Mobile is one organization that highlights how a company can transperanty collect and sell data, while benefiting the customer and promoting customer well fair. As Timothy Morey explains, Mint utilized data many would consider sensitive to help users find cards that allow them to make transactions fee free. “Mint receives a com‑ mission for the referral from the new-card issuer, and the customer avoids future fees” [morey2015customer]. This is what is considered an exchange of value, much like how we let TikTok monitor our activity to form custom algorithms. While both examples are different types of consumer data strategy, both provide a service to the customers, in turn creating a trust and openness. These data strategies can be tailored to most business operations to provide the customers added value, which in the long run promotes fiscal growth for the organization.

Profiling Example

For my profile I used the company Chop-suey Chinese (whose CompanyID is CHOPS). My first query shows the average freight by Ship Country for Chop-suey Chinese. When I live edit this query it will reflect what countries Chop-suey Chinese gets orders to the most, showing only countries with more than 5 orders, as well as their associated freight. This is relevant as it allows the supplier to better control their distribution channels better predict where Chop-suey Chinese’s product demand is. This allows them to move product around distribution centers and hopefully reduce the freight costs from what is currently reflected. My second query shows the top cities Chop-suey Chinese is placing order to, as well as what shipping partner is completing the delivery. This is relevant as these are repeat orders, as shown by n > 2. Comparing cities with frequent orders and their associated shipping partner not only allows the company to better predict demand, it helps the company monitor it’s shipping partners to ensure quality control for high priority/repeat orders.

SELECT ShipCountry, COUNT(*) AS n, Freight
FROM Orders
WHERE CustomerID =  "CHOPS"
GROUP BY ShipCountry
HAVING n > 5

ggplot(query1, aes(y = ShipCountry, x = n, fill = Freight )) +
  geom_tile(color = "white", linewidth = 0.5) +
  scale_fill_gradient(low = "#e0f2fe", high = "#0369a1") +
   labs(title = "Graph showing Ship Country and Freight HAVING n > 5")

SELECT ShipVia, ShipCity, COUNT(*) AS n
FROM Orders
WHERE CustomerID =  "CHOPS"
GROUP BY ShipCity, ShipVia
HAVING n > 2

ggplot(data = query2,
       aes(y = ShipCity, x = n, fill = ShipVia)) +
  geom_col() +
  labs(title = "Graph showing Ship Cities and Shippers HAVING n > 2")

Recommendations for Industry

Utilizing customer data, distributors have the ability to predict demand better than ever before. By leveraging this data, companies have the ability to make their products and processes better for their clients, which in turn builds trust with clients, therefore allowing them to collect even more information with even less push back.

One suggesting based on my queries would be for Northwinds to track their what countries their clients order to the most, and focus on ensuring their distribution centers in these locations have a high back stock or product more quickly. Additionally tracking the average freight associated with these orders, both before and after they redistribute their resources to show clients how they are working to get them their products as quickly and cheap as possible. Additionally they can track things like what cities are most frequently ordered to and which shipping partners deliver to these locations. These are just a few quality control metrics that major distributors can utilize to show clients why data is important, however there are infinite ways to use data to better business processes. Although, the most important aspect of data collect is the trade-off between customers and the company and how your organization chooses to protrey this process to it’s clients.

References

Fruits, E, JG Hurwitz, GA Manne, J Morris, and A Stapp. 2020. “Directorate for Financial and Enterprise Affairs Competition Committee.” In Global Forum on Competition: Static and Dynamic Effects of Mergers.

Morey, Timothy, Theodore Forbath, and Allison Schoop. 2015. “Customer Data: Designing for Transparency and Trust.” Harvard Business Review 93 (5): 96–105.

Voss, W Gregory. 2025. “Surveillance Pricing and Personal Information.” CPI Antitrust Chronicle, 42–47.