Organizations increasingly rely on customer data to personalize products, improve marketing, and create new sources of revenue. The readings show that customer profiling can improve customer experiences and organizational performance, but it also raises concerns surrounding privacy, transparency, consent, and trust (morey2015?; gabel2024?). Companies that collect extensive customer information without clearly communicating how it will be used risk damaging customer relationships and losing long term access to valuable data.
Organizations are finding new ways to use and monetize customer information. Retailers are becoming advertising platforms by using first party purchase data to deliver targeted promotions and advertisements. At the same time, AI companies are developing personalized digital assistants that learn users preferences, values, and behaviors. Both developments depend on the creation of increasingly detailed customer profile.
The Northwind customer analysis demonstrates how routine transaction data can reveal a customers category preferences, spending patterns, and purchasing activity over time. Organizations should therefore treat customer profiling as a responsibility rather than simply a technical opportunity. Businesses should clearly explain their data practices, provide customers with meaningful control, limit profiling to legitimate purposes, and deliver recognizable value in exchange for personal information.
Customer data has become an important strategic resource because it allows organizations to understand customer preferences, predict behavior, personalize services, and improve business decisions. Companies collect this information through websites, applications, connected products, loyalty programs, purchase histories, and other customer interactions. When these individual pieces of information are combined, organizations can create detailed profiles that predict what customers may want or do in the future.
Morey, Forbath, and Schoop divide customer information into three categories: self-reported data, digital exhaust, and profiling data (morey2015?). Self reported data includes information that customers intentionally provide, such as an email address or demographic information. Digital exhaust is created through normal technology use and may include browsing history, location, or communication activity. Profiling data is produced when organizations combine and analyze information to make predictions about a person’s interests and behaviors.
The reading argues that profiling data is especially valuable and sensitive because it includes conclusions that organizations create about customers rather than information customers directly provide. Although consumers are generally aware that companies collect information, they often do not understand the exact types of information being captured. The article reports that consumers are concerned about misuse even when they are poorly informed about their complete data footprints.
The authors argue that companies should not approach customer data though secrecy or minimal regulatory compliance (morey2015?). Organizations should instead build transparency, control, and value into their data strategies. Customers are more willing to provide information when they understand how it is used, receive a clear benefit, and trust the organization to protect it. Trust therefore becomes a competitive advantage because trusted businesses may receive greater and more sustained access to customer data.
Customer profiling creates a conflict between organizational value and individual privacy. Businesses benefit from understanding customer behavior because profiles can improve recommendations, promotions, product design, and advertising. Customers may also benefit when personalization saves time, reduces costs, or improves their experience. However, profiling becomes more concerning when customers do not understand that it is occurring or when organizations use information in ways that primarily benefit the company.
The transparency and trust reading explains that customers place different values on different types of data. Customers generally place less value on basic information that they intentionally provide and greater value on digital exhaust and profiles generated through analytics (morey2015?). Customer expectations also depend on how organizations use the data. People may accept data collection when it directly improves a product or service, but they expect more value when their information is used for targeted advertising and the greatest value when information is sold to third parties.
Disney’s MagicBand system demonstrates a relatively transparent exchange. Disney collects extensive information about visitors’ movements and purchases, but the system also provides customers with benefits such as park access, hotel entry, reservations, and simplified purchases. Disney explains the system’s data practices during registration, allowing customers to better understand the exchange. Pandora similarly collects stated preferences and listening behavior to personalize music selections. In both cases, profiling supports an identifiable service benefit (morey2015?).
Other examples show the consequences of profiling without sufficient transparency. Target caused public concern after its analysis of shopping behavior allowed it to identify customers who were likely to be pregnant, sometimes before those customers had shared that information with others. The concern was not simply that Target possessed purchase information. The company used ordinary purchases to infer sensitive personal information that customers had not knowingly provided (morey2015?).
The growth of retail media further expands the use of profiling. Retailers such as Amazon, Walmart, Target, and other companies use first-party transaction data to deliver sponsored search results, digital coupons, and advertisements (gabel2024?). Retailers may understand purchasing behavior more accurately than outside advertising platforms because they can observe actual transactions. This creates a major business opportunity, but it also makes customer purchase histories increasingly valuable for purposes beyond completing a sale.
Retail media also introduces transparency challenges because retailers may both execute advertising campaigns and report their performance (gabel2024?). Advertisers may not be able to independently observe the impressions purchased, the retailer’s margins, or the attribution methods used to connect advertisements with sales. The reading argues that retail media platforms need transparent measurement and experimentation capabilities so organizations can credibly demonstrate the effectiveness of their advertising activity.
Recent research from the Federal Trade Commission demonstrates that the conflict between personalization and privacy continues to affect major digital platforms. In their 2024 examination of social media and video streaming companies, the FTC found that many platforms collected and retained extensive amounts of user information in order to support targeted advertising and other revenue generating activities. The report concluded that these businesses incentives were frequently in tension with user privacy and recommended stronger data minimization, retention, transparency, and consumer control practices. this supports the arguments from the other readings that organizations should not collect information simply because it may become valuable later. Customer profiling should instead be limited to clearly defines purposes that customers understand and reasonably expect (book?).
The article about personalized AI companions presents another form of profiling. Companies want conversational AI systems to learn users’ personalities, preferences, values, and communication styles so interactions feel more personal. These systems may provide companionship and emotional support, but personalization also requires continuous collection and interpretation of user behavior. An AI designed to reflect a specific worldview may reinforce a user’s existing beliefs rather than expose the person to balanced information. This creates the risk of highly personalized echo chambers.
The article concerning corporate “trust words” provides an additional warning. Companies that repeatedly use words such as ethical, integrity, and responsibility do not automatically become trustworthy. The research described in the article found that the use of these terms was associated with several negative outcomes, although the study did not establish causation. The implication is that organizations cannot replace transparent behavior with reassuring language. Trust must be supported through actual policies, controls, and accountable decisions.
Together, the readings suggest that customer profiling is not automatically harmful. The ethical and strategic concern depends on what data is collected, what is inferred, how the profile is used, whether the customer understands the process, and whether the exchange creates fair value. Profiling can strengthen customer relationships when it produces useful services and respects customer choices. It can damage those relationships when it is hidden, intrusive, manipulative, or primarily designed to extract value from customers.
To demonstrate how transaction data can produce a customer profile, this section examines Hanari Carnes, customer ID ‘HANAR’. Northwind does not contain the same sensetive personal information collected by modern digital platforms. Its transaction records can still reveal purchasing preferences, spending levels, order frequency, category interests, and changing behavior over time.
Query identifies customer and provides basic account information.
customer_info <- dbGetQuery(
mydb,
'
SELECT
CustomerID,
CompanyName,
City,
Country
FROM Customers
WHERE CustomerID = "HANAR"
'
)
customer_info CustomerID CompanyName City Country
1 HANAR Hanari Carnes Rio de Janeiro BrazilThis query confirms that ‘HANAR’ represents Hanari Carnes, located in Rio de Janeiro, Brazil. This information provides the basic context for analyzing the customers transactions.
The first analysis identifies the product categories that generate the most revenue from Hanari Carnes. Query joins ‘Orders’, ‘Order Details’, ‘Products’, and ‘Categories’ tables. It then uses ‘GROUP BY’ to organize the transactions by category.
category_profile <- dbGetQuery(
mydb,
'
SELECT
c.CategoryName,
COUNT(*) AS purchase_lines,
SUM(od.Quantity) AS total_units,
ROUND(
SUM(od.UnitPrice * od.Quantity * (1 - od.Discount)),
2
) AS total_revenue,
ROUND(
AVG(od.Discount) * 100,
2
) AS avg_discount_percent
FROM Orders AS o
INNER JOIN "Order Details" AS od
ON o.OrderID = od.OrderID
INNER JOIN Products AS p
ON od.ProductID = p.ProductID
INNER JOIN Categories AS c
ON p.CategoryID = c.CategoryID
WHERE o.CustomerID = "HANAR"
GROUP BY c.CategoryName
HAVING
SUM(od.UnitPrice * od.Quantity * (1 - od.Discount)) > 0
ORDER BY total_revenue DESC;
'
)
category_profile CategoryName purchase_lines total_units total_revenue avg_discount_percent
1 Beverages 979 24462 977299.9 0.04
2 Confections 1073 27473 699676.5 0.00
3 Meat/Poultry 502 12949 657680.6 0.00
4 Dairy Products 809 20922 594148.5 0.04
5 Condiments 970 24945 573695.6 0.07
6 Seafood 957 24882 510611.7 0.02
7 Produce 415 10689 347259.2 0.04
8 Grains/Cereals 555 13884 277496.7 0.07ggplot(
data = category_profile,
mapping = aes(
x = reorder(CategoryName, total_revenue),
y = total_revenue
)
) +
geom_col(fill = "pink") +
coord_flip() +
scale_y_continuous(
labels = scales::label_dollar()
) +
labs(
title = "Hanari Carnes Spending by Product Category",
subtitle = "Revenue includes quantity, unit price, and order-line discounts",
x = "Product Category",
y = "Total Revenue"
) +
theme_minimal()
Evaluates when Hanari Carnes purchases products and which categories contribute to spending during each period. The query groups purchases by year, quarter, and product category.
quarterly_category_profile <- dbGetQuery(
mydb,
'
SELECT
strftime("%Y", o.OrderDate) AS order_year,
CASE
WHEN CAST(strftime("%m", o.OrderDate) AS INTEGER)
BETWEEN 1 AND 3 THEN "Q1"
WHEN CAST(strftime("%m", o.OrderDate) AS INTEGER)
BETWEEN 4 AND 6 THEN "Q2"
WHEN CAST(strftime("%m", o.OrderDate) AS INTEGER)
BETWEEN 7 AND 9 THEN "Q3"
ELSE "Q4"
END AS order_quarter,
c.CategoryName,
SUM(od.Quantity) AS total_units,
ROUND(
SUM(od.UnitPrice * od.Quantity * (1 - od.Discount)),
2
) AS total_revenue
FROM Orders AS o
INNER JOIN "Order Details" AS od
ON o.OrderID = od.OrderID
INNER JOIN Products AS p
ON od.ProductID = p.ProductID
INNER JOIN Categories AS c
ON p.CategoryID = c.CategoryID
WHERE o.CustomerID = "HANAR"
GROUP BY
order_year,
order_quarter,
c.CategoryName
HAVING
SUM(od.UnitPrice * od.Quantity * (1 - od.Discount)) > 0
ORDER BY
order_year,
order_quarter,
total_revenue DESC
'
)
quarterly_category_profile order_year order_quarter CategoryName total_units total_revenue
1 2012 Q3 Beverages 1065 47636.00
2 2012 Q3 Confections 1144 34483.75
3 2012 Q3 Dairy Products 1006 27186.70
4 2012 Q3 Condiments 1039 23875.20
5 2012 Q3 Meat/Poultry 437 23080.81
6 2012 Q3 Seafood 831 14390.17
7 2012 Q3 Produce 426 12058.70
8 2012 Q3 Grains/Cereals 487 8377.00
9 2012 Q4 Beverages 424 18961.75
10 2012 Q4 Dairy Products 443 13017.60
11 2012 Q4 Meat/Poultry 238 12053.90
12 2012 Q4 Condiments 489 11089.95
13 2012 Q4 Seafood 513 10518.94
14 2012 Q4 Confections 454 9221.47
15 2012 Q4 Produce 152 5249.05
16 2012 Q4 Grains/Cereals 176 2803.50
17 2013 Q1 Condiments 241 5489.50
18 2013 Q1 Produce 155 4298.70
19 2013 Q1 Seafood 174 3293.00
20 2013 Q1 Meat/Poultry 63 1812.60
21 2013 Q1 Dairy Products 68 1459.00
22 2013 Q1 Confections 96 1299.30
23 2013 Q1 Beverages 97 1260.00
24 2013 Q1 Grains/Cereals 64 935.50
25 2013 Q2 Beverages 194 12492.50
26 2013 Q2 Confections 267 6866.36
27 2013 Q2 Condiments 232 5320.30
28 2013 Q2 Dairy Products 162 5120.70
29 2013 Q2 Meat/Poultry 86 4523.98
30 2013 Q2 Produce 89 3859.85
31 2013 Q2 Seafood 209 3339.41
32 2013 Q2 Grains/Cereals 78 1281.00
33 2013 Q4 Beverages 497 34869.00
34 2013 Q4 Meat/Poultry 332 19303.46
35 2013 Q4 Dairy Products 571 17243.40
36 2013 Q4 Confections 582 14632.59
37 2013 Q4 Condiments 609 14406.90
38 2013 Q4 Seafood 564 13882.34
39 2013 Q4 Produce 218 7883.40
40 2013 Q4 Grains/Cereals 279 5605.50
41 2014 Q1 Meat/Poultry 319 16445.35
42 2014 Q1 Beverages 421 16391.25
43 2014 Q1 Condiments 526 13236.85
44 2014 Q1 Seafood 522 10539.19
45 2014 Q1 Confections 582 9707.48
46 2014 Q1 Dairy Products 344 8942.20
47 2014 Q1 Produce 253 7314.50
48 2014 Q1 Grains/Cereals 198 3299.75
49 2014 Q2 Beverages 599 29580.75
50 2014 Q2 Condiments 900 22369.60
51 2014 Q2 Confections 807 18890.81
52 2014 Q2 Meat/Poultry 358 16822.22
53 2014 Q2 Produce 355 12642.80
54 2014 Q2 Seafood 658 11963.62
55 2014 Q2 Dairy Products 392 11517.00
56 2014 Q2 Grains/Cereals 413 8480.25
57 2014 Q3 Beverages 872 45555.50
58 2014 Q3 Dairy Products 892 25734.20
59 2014 Q3 Meat/Poultry 456 23613.87
60 2014 Q3 Condiments 925 21935.60
61 2014 Q3 Confections 796 20928.98
62 2014 Q3 Seafood 839 18398.88
63 2014 Q3 Produce 503 16382.30
64 2014 Q3 Grains/Cereals 587 13092.75
65 2014 Q4 Beverages 167 15501.00
66 2014 Q4 Confections 251 7917.10
67 2014 Q4 Meat/Poultry 180 7718.34
68 2014 Q4 Condiments 307 7020.80
69 2014 Q4 Dairy Products 258 5427.50
70 2014 Q4 Seafood 209 3945.24
71 2014 Q4 Grains/Cereals 190 3129.25
72 2014 Q4 Produce 18 954.00
73 2015 Q2 Beverages 290 8725.00
74 2015 Q2 Meat/Poultry 112 8180.00
75 2015 Q2 Confections 210 6205.16
76 2015 Q2 Seafood 267 5938.19
77 2015 Q2 Condiments 300 5844.25
78 2015 Q2 Dairy Products 120 3784.80
79 2015 Q2 Produce 90 3266.40
80 2015 Q2 Grains/Cereals 141 2484.00
81 2015 Q4 Beverages 542 19365.50
82 2015 Q4 Meat/Poultry 221 14062.53
83 2015 Q4 Dairy Products 471 13605.90
84 2015 Q4 Seafood 412 10992.80
85 2015 Q4 Condiments 481 10991.35
86 2015 Q4 Confections 424 10135.61
87 2015 Q4 Grains/Cereals 210 3564.75
88 2015 Q4 Produce 102 3424.70
89 2016 Q1 Confections 634 17111.50
90 2016 Q1 Condiments 642 16115.80
91 2016 Q1 Beverages 262 14244.00
92 2016 Q1 Seafood 533 13242.61
93 2016 Q1 Dairy Products 423 11335.30
94 2016 Q1 Meat/Poultry 200 9909.77
95 2016 Q1 Grains/Cereals 392 8546.25
96 2016 Q1 Produce 188 4750.30
97 2016 Q2 Confections 608 17172.86
98 2016 Q2 Beverages 523 15520.75
99 2016 Q2 Meat/Poultry 296 15295.88
100 2016 Q2 Dairy Products 308 8458.60
101 2016 Q2 Condiments 293 7529.70
102 2016 Q2 Seafood 347 6537.29
103 2016 Q2 Grains/Cereals 295 5686.50
104 2016 Q2 Produce 78 3255.65
105 2016 Q3 Beverages 487 15915.80
106 2016 Q3 Meat/Poultry 223 12946.70
107 2016 Q3 Confections 637 11048.82
108 2016 Q3 Condiments 467 10915.90
109 2016 Q3 Dairy Products 332 8347.20
110 2016 Q3 Seafood 397 8154.03
111 2016 Q3 Produce 250 6712.45
112 2016 Q3 Grains/Cereals 191 4400.00
113 2016 Q4 Condiments 324 8281.60
114 2016 Q4 Beverages 284 7527.00
115 2016 Q4 Dairy Products 250 7037.40
116 2016 Q4 Confections 245 5766.72
117 2016 Q4 Seafood 331 5530.66
118 2016 Q4 Meat/Poultry 105 5377.06
119 2016 Q4 Grains/Cereals 226 4932.00
120 2016 Q4 Produce 101 3216.60
121 2017 Q1 Beverages 731 29026.50
122 2017 Q1 Meat/Poultry 337 19587.80
123 2017 Q1 Dairy Products 519 16128.50
124 2017 Q1 Condiments 620 14873.65
125 2017 Q1 Confections 631 14011.07
126 2017 Q1 Seafood 600 12453.73
127 2017 Q1 Produce 291 9278.80
128 2017 Q1 Grains/Cereals 328 7609.50
129 2017 Q2 Beverages 711 24771.35
130 2017 Q2 Meat/Poultry 444 19915.89
131 2017 Q2 Confections 606 19367.40
132 2017 Q2 Dairy Products 571 16096.05
133 2017 Q2 Condiments 588 13473.67
134 2017 Q2 Seafood 505 9817.66
135 2017 Q2 Produce 180 7016.35
136 2017 Q2 Grains/Cereals 367 6536.00
137 2017 Q3 Meat/Poultry 175 8956.52
138 2017 Q3 Confections 187 5806.32
139 2017 Q3 Condiments 122 3820.20
140 2017 Q3 Seafood 175 2922.95
141 2017 Q3 Produce 50 2479.35
142 2017 Q3 Beverages 88 2047.00
143 2017 Q3 Grains/Cereals 78 1664.50
144 2017 Q3 Dairy Products 51 1528.00
145 2017 Q4 Dairy Products 393 11324.05
146 2017 Q4 Meat/Poultry 166 8896.26
147 2017 Q4 Beverages 215 7928.00
148 2017 Q4 Confections 249 5713.04
149 2017 Q4 Condiments 251 5377.90
150 2017 Q4 Grains/Cereals 170 4033.00
151 2017 Q4 Seafood 227 3894.34
152 2017 Q4 Produce 22 263.00
153 2018 Q1 Beverages 739 41436.50
154 2018 Q1 Dairy Products 583 18876.50
155 2018 Q1 Confections 598 16108.56
156 2018 Q1 Condiments 761 16046.40
157 2018 Q1 Meat/Poultry 328 14589.38
158 2018 Q1 Seafood 689 12466.13
159 2018 Q1 Produce 219 9268.35
160 2018 Q1 Grains/Cereals 288 6159.40
161 2018 Q2 Beverages 538 19574.50
162 2018 Q2 Seafood 602 12888.18
163 2018 Q2 Meat/Poultry 231 12697.31
164 2018 Q2 Confections 511 12553.40
165 2018 Q2 Dairy Products 372 11615.10
166 2018 Q2 Condiments 374 8791.90
167 2018 Q2 Produce 137 4987.80
168 2018 Q2 Grains/Cereals 223 3401.25
169 2018 Q3 Confections 945 26047.42
170 2018 Q3 Beverages 664 22667.50
171 2018 Q3 Condiments 650 14903.00
172 2018 Q3 Dairy Products 531 13820.80
173 2018 Q3 Meat/Poultry 189 11393.62
174 2018 Q3 Produce 351 11068.90
175 2018 Q3 Seafood 599 10994.88
176 2018 Q3 Grains/Cereals 247 4432.50
177 2018 Q4 Beverages 1049 41862.75
178 2018 Q4 Meat/Poultry 640 35307.49
179 2018 Q4 Confections 1080 27520.03
180 2018 Q4 Dairy Products 943 26055.30
181 2018 Q4 Condiments 1095 24660.65
182 2018 Q4 Produce 461 16589.20
183 2018 Q4 Seafood 908 16420.05
184 2018 Q4 Grains/Cereals 531 11177.00
185 2019 Q1 Meat/Poultry 329 18067.10
186 2019 Q1 Beverages 385 18050.00
187 2019 Q1 Dairy Products 563 16528.20
188 2019 Q1 Confections 442 9850.35
189 2019 Q1 Grains/Cereals 444 9553.75
190 2019 Q1 Seafood 409 9428.65
191 2019 Q1 Condiments 331 8369.80
192 2019 Q1 Produce 120 1906.10
193 2019 Q2 Confections 289 6987.70
194 2019 Q2 Seafood 237 6275.51
195 2019 Q2 Beverages 314 6196.75
196 2019 Q2 Meat/Poultry 144 5846.19
197 2019 Q2 Condiments 221 4247.90
198 2019 Q2 Dairy Products 120 3481.50
199 2019 Q2 Grains/Cereals 110 2802.50
200 2019 Q2 Produce 27 627.75
201 2019 Q3 Beverages 335 14182.50
202 2019 Q3 Meat/Poultry 178 10442.50
203 2019 Q3 Confections 441 10100.25
204 2019 Q3 Dairy Products 302 9224.40
205 2019 Q3 Seafood 347 7162.38
206 2019 Q3 Produce 214 6800.95
207 2019 Q3 Condiments 288 6414.45
208 2019 Q3 Grains/Cereals 312 6100.00
209 2019 Q4 Beverages 402 14588.75
210 2019 Q4 Meat/Poultry 235 11144.94
211 2019 Q4 Condiments 405 10373.05
212 2019 Q4 Confections 509 9046.74
213 2019 Q4 Dairy Products 281 8182.10
214 2019 Q4 Grains/Cereals 269 5303.00
215 2019 Q4 Seafood 309 5230.25
216 2019 Q4 Produce 145 4624.55
217 2020 Q1 Confections 540 14576.13
218 2020 Q1 Seafood 610 12236.54
219 2020 Q1 Meat/Poultry 272 12211.71
220 2020 Q1 Condiments 366 8535.80
221 2020 Q1 Beverages 327 7405.50
222 2020 Q1 Produce 233 6994.60
223 2020 Q1 Grains/Cereals 304 5381.50
224 2020 Q1 Dairy Products 230 5100.60
225 2020 Q2 Seafood 218 5396.52
226 2020 Q2 Confections 195 4587.25
227 2020 Q2 Beverages 157 3564.50
228 2020 Q2 Condiments 129 2297.75
229 2020 Q2 Dairy Products 58 1902.90
230 2020 Q2 Meat/Poultry 32 768.00
231 2020 Q2 Produce 25 733.50
232 2020 Q2 Grains/Cereals 33 519.00
233 2020 Q4 Beverages 801 30243.50
234 2020 Q4 Condiments 895 22649.90
235 2020 Q4 Confections 737 22209.50
236 2020 Q4 Dairy Products 673 19522.80
237 2020 Q4 Meat/Poultry 343 18407.44
238 2020 Q4 Seafood 870 15181.84
239 2020 Q4 Produce 432 11597.70
240 2020 Q4 Grains/Cereals 416 9347.00
241 2021 Q1 Dairy Products 394 11955.60
242 2021 Q1 Seafood 418 8962.77
243 2021 Q1 Meat/Poultry 245 7738.20
244 2021 Q1 Confections 328 6640.27
245 2021 Q1 Produce 223 6599.45
246 2021 Q1 Condiments 236 4583.95
247 2021 Q1 Grains/Cereals 216 4319.75
248 2021 Q1 Beverages 263 4168.75
249 2021 Q2 Dairy Products 622 20897.50
250 2021 Q2 Condiments 825 20475.45
251 2021 Q2 Confections 890 18878.99
252 2021 Q2 Beverages 543 17667.25
253 2021 Q2 Meat/Poultry 362 16413.11
254 2021 Q2 Seafood 675 14536.04
255 2021 Q2 Produce 311 10690.25
256 2021 Q2 Grains/Cereals 531 10490.75
257 2021 Q3 Beverages 800 29511.75
258 2021 Q3 Confections 927 25381.97
259 2021 Q3 Seafood 878 18509.15
260 2021 Q3 Dairy Products 596 17594.30
261 2021 Q3 Condiments 793 16551.85
262 2021 Q3 Meat/Poultry 386 15631.88
263 2021 Q3 Produce 412 11036.65
264 2021 Q3 Grains/Cereals 244 5716.75
265 2021 Q4 Meat/Poultry 484 25538.27
266 2021 Q4 Beverages 759 24582.50
267 2021 Q4 Confections 745 23453.73
268 2021 Q4 Seafood 701 15820.73
269 2021 Q4 Condiments 661 13972.65
270 2021 Q4 Produce 297 10799.30
271 2021 Q4 Dairy Products 370 10138.40
272 2021 Q4 Grains/Cereals 467 9375.25
273 2022 Q1 Confections 697 22566.30
274 2022 Q1 Dairy Products 678 21571.00
275 2022 Q1 Beverages 455 19367.75
276 2022 Q1 Seafood 751 16655.00
277 2022 Q1 Condiments 544 11773.40
278 2022 Q1 Grains/Cereals 496 9800.00
279 2022 Q1 Meat/Poultry 227 7814.20
280 2022 Q1 Produce 278 7725.50
281 2022 Q2 Beverages 442 18593.75
282 2022 Q2 Meat/Poultry 242 12506.37
283 2022 Q2 Dairy Products 368 10775.30
284 2022 Q2 Condiments 438 9521.00
285 2022 Q2 Confections 429 9257.48
286 2022 Q2 Seafood 417 8588.60
287 2022 Q2 Produce 250 7860.90
288 2022 Q2 Grains/Cereals 187 3912.75
289 2022 Q3 Beverages 1257 49109.00
290 2022 Q3 Confections 1473 38342.78
291 2022 Q3 Meat/Poultry 655 37400.93
292 2022 Q3 Seafood 1257 27166.65
293 2022 Q3 Dairy Products 952 26853.20
294 2022 Q3 Condiments 1171 25129.10
295 2022 Q3 Produce 524 18064.00
296 2022 Q3 Grains/Cereals 686 14010.75
297 2022 Q4 Beverages 856 33878.50
298 2022 Q4 Dairy Products 637 18626.30
299 2022 Q4 Confections 791 17290.72
300 2022 Q4 Meat/Poultry 314 15330.99
301 2022 Q4 Seafood 630 14055.85
302 2022 Q4 Condiments 603 12742.95
303 2022 Q4 Produce 359 11212.35
304 2022 Q4 Grains/Cereals 325 4740.00
305 2023 Q1 Confections 822 24413.16
306 2023 Q1 Beverages 765 20809.00
307 2023 Q1 Condiments 864 20484.50
308 2023 Q1 Seafood 900 20337.60
309 2023 Q1 Meat/Poultry 317 18509.39
310 2023 Q1 Dairy Products 727 16956.20
311 2023 Q1 Produce 459 14502.05
312 2023 Q1 Grains/Cereals 384 7499.25
313 2023 Q2 Beverages 253 17357.00
314 2023 Q2 Confections 358 11049.19
315 2023 Q2 Meat/Poultry 157 6181.00
316 2023 Q2 Condiments 266 6049.55
317 2023 Q2 Dairy Products 174 4703.80
318 2023 Q2 Grains/Cereals 164 3107.50
319 2023 Q2 Produce 50 1280.00
320 2023 Q2 Seafood 97 1213.09
321 2023 Q3 Confections 826 23002.99
322 2023 Q3 Beverages 678 18925.25
323 2023 Q3 Meat/Poultry 356 17337.78
324 2023 Q3 Condiments 692 16456.85
325 2023 Q3 Seafood 670 12578.22
326 2023 Q3 Dairy Products 454 12404.90
327 2023 Q3 Produce 187 6744.55
328 2023 Q3 Grains/Cereals 277 6117.25
329 2023 Q4 Meat/Poultry 125 9355.13
330 2023 Q4 Beverages 145 8592.00
331 2023 Q4 Produce 159 5758.10
332 2023 Q4 Condiments 188 4560.80
333 2023 Q4 Confections 133 3403.75
334 2023 Q4 Seafood 192 3263.38
335 2023 Q4 Grains/Cereals 63 1185.00
336 2023 Q4 Dairy Products 49 612.50
337 2024 Q1 Beverages 539 30711.50
338 2024 Q1 Dairy Products 482 14912.00
339 2024 Q1 Confections 537 11082.80
340 2024 Q1 Meat/Poultry 225 10439.40
341 2024 Q1 Condiments 381 9196.10
342 2024 Q1 Seafood 434 9021.56
343 2024 Q1 Produce 140 5727.75
344 2024 Q1 Grains/Cereals 278 5678.25
345 2024 Q2 Beverages 796 39043.75
346 2024 Q2 Confections 1136 30645.41
347 2024 Q2 Meat/Poultry 400 25280.28
348 2024 Q2 Dairy Products 789 20621.80
349 2024 Q2 Seafood 911 18779.54
350 2024 Q2 Condiments 860 18773.95
351 2024 Q2 Produce 397 13638.75
352 2024 Q2 Grains/Cereals 534 10325.00
353 2024 Q3 Beverages 519 19739.25
354 2024 Q3 Condiments 506 11338.05
355 2024 Q3 Dairy Products 414 10807.80
356 2024 Q3 Confections 424 10384.47
357 2024 Q3 Seafood 493 9762.63
358 2024 Q3 Produce 219 6482.05
359 2024 Q3 Meat/Poultry 172 6355.50
360 2024 Q3 Grains/Cereals 183 3589.50
361 2024 Q4 Confections 799 19787.67
362 2024 Q4 Meat/Poultry 408 18951.47
363 2024 Q4 Dairy Products 676 18947.20
364 2024 Q4 Seafood 906 18298.73
365 2024 Q4 Beverages 889 16997.00
366 2024 Q4 Condiments 689 14871.50
367 2024 Q4 Produce 374 13592.50
368 2024 Q4 Grains/Cereals 615 12527.75
369 2025 Q1 Beverages 323 21154.50
370 2025 Q1 Seafood 441 9626.18
371 2025 Q1 Confections 461 8221.14
372 2025 Q1 Dairy Products 310 8166.40
373 2025 Q1 Condiments 357 7954.60
374 2025 Q1 Meat/Poultry 205 7518.03
375 2025 Q1 Produce 165 6038.80
376 2025 Q1 Grains/Cereals 187 4463.25quarterly_category_profile <- quarterly_category_profile |>
mutate(
quarter_number = case_when(
order_quarter == "Q1" ~ 1,
order_quarter == "Q2" ~ 2,
order_quarter == "Q3" ~ 3,
order_quarter == "Q4" ~ 4
),
period = paste(order_year, order_quarter)
) |>
arrange(
order_year,
quarter_number
) |>
mutate(
period = factor(
period,
levels = unique(period)
)
)
quarterly_category_profile order_year order_quarter CategoryName total_units total_revenue
1 2012 Q3 Beverages 1065 47636.00
2 2012 Q3 Confections 1144 34483.75
3 2012 Q3 Dairy Products 1006 27186.70
4 2012 Q3 Condiments 1039 23875.20
5 2012 Q3 Meat/Poultry 437 23080.81
6 2012 Q3 Seafood 831 14390.17
7 2012 Q3 Produce 426 12058.70
8 2012 Q3 Grains/Cereals 487 8377.00
9 2012 Q4 Beverages 424 18961.75
10 2012 Q4 Dairy Products 443 13017.60
11 2012 Q4 Meat/Poultry 238 12053.90
12 2012 Q4 Condiments 489 11089.95
13 2012 Q4 Seafood 513 10518.94
14 2012 Q4 Confections 454 9221.47
15 2012 Q4 Produce 152 5249.05
16 2012 Q4 Grains/Cereals 176 2803.50
17 2013 Q1 Condiments 241 5489.50
18 2013 Q1 Produce 155 4298.70
19 2013 Q1 Seafood 174 3293.00
20 2013 Q1 Meat/Poultry 63 1812.60
21 2013 Q1 Dairy Products 68 1459.00
22 2013 Q1 Confections 96 1299.30
23 2013 Q1 Beverages 97 1260.00
24 2013 Q1 Grains/Cereals 64 935.50
25 2013 Q2 Beverages 194 12492.50
26 2013 Q2 Confections 267 6866.36
27 2013 Q2 Condiments 232 5320.30
28 2013 Q2 Dairy Products 162 5120.70
29 2013 Q2 Meat/Poultry 86 4523.98
30 2013 Q2 Produce 89 3859.85
31 2013 Q2 Seafood 209 3339.41
32 2013 Q2 Grains/Cereals 78 1281.00
33 2013 Q4 Beverages 497 34869.00
34 2013 Q4 Meat/Poultry 332 19303.46
35 2013 Q4 Dairy Products 571 17243.40
36 2013 Q4 Confections 582 14632.59
37 2013 Q4 Condiments 609 14406.90
38 2013 Q4 Seafood 564 13882.34
39 2013 Q4 Produce 218 7883.40
40 2013 Q4 Grains/Cereals 279 5605.50
41 2014 Q1 Meat/Poultry 319 16445.35
42 2014 Q1 Beverages 421 16391.25
43 2014 Q1 Condiments 526 13236.85
44 2014 Q1 Seafood 522 10539.19
45 2014 Q1 Confections 582 9707.48
46 2014 Q1 Dairy Products 344 8942.20
47 2014 Q1 Produce 253 7314.50
48 2014 Q1 Grains/Cereals 198 3299.75
49 2014 Q2 Beverages 599 29580.75
50 2014 Q2 Condiments 900 22369.60
51 2014 Q2 Confections 807 18890.81
52 2014 Q2 Meat/Poultry 358 16822.22
53 2014 Q2 Produce 355 12642.80
54 2014 Q2 Seafood 658 11963.62
55 2014 Q2 Dairy Products 392 11517.00
56 2014 Q2 Grains/Cereals 413 8480.25
57 2014 Q3 Beverages 872 45555.50
58 2014 Q3 Dairy Products 892 25734.20
59 2014 Q3 Meat/Poultry 456 23613.87
60 2014 Q3 Condiments 925 21935.60
61 2014 Q3 Confections 796 20928.98
62 2014 Q3 Seafood 839 18398.88
63 2014 Q3 Produce 503 16382.30
64 2014 Q3 Grains/Cereals 587 13092.75
65 2014 Q4 Beverages 167 15501.00
66 2014 Q4 Confections 251 7917.10
67 2014 Q4 Meat/Poultry 180 7718.34
68 2014 Q4 Condiments 307 7020.80
69 2014 Q4 Dairy Products 258 5427.50
70 2014 Q4 Seafood 209 3945.24
71 2014 Q4 Grains/Cereals 190 3129.25
72 2014 Q4 Produce 18 954.00
73 2015 Q2 Beverages 290 8725.00
74 2015 Q2 Meat/Poultry 112 8180.00
75 2015 Q2 Confections 210 6205.16
76 2015 Q2 Seafood 267 5938.19
77 2015 Q2 Condiments 300 5844.25
78 2015 Q2 Dairy Products 120 3784.80
79 2015 Q2 Produce 90 3266.40
80 2015 Q2 Grains/Cereals 141 2484.00
81 2015 Q4 Beverages 542 19365.50
82 2015 Q4 Meat/Poultry 221 14062.53
83 2015 Q4 Dairy Products 471 13605.90
84 2015 Q4 Seafood 412 10992.80
85 2015 Q4 Condiments 481 10991.35
86 2015 Q4 Confections 424 10135.61
87 2015 Q4 Grains/Cereals 210 3564.75
88 2015 Q4 Produce 102 3424.70
89 2016 Q1 Confections 634 17111.50
90 2016 Q1 Condiments 642 16115.80
91 2016 Q1 Beverages 262 14244.00
92 2016 Q1 Seafood 533 13242.61
93 2016 Q1 Dairy Products 423 11335.30
94 2016 Q1 Meat/Poultry 200 9909.77
95 2016 Q1 Grains/Cereals 392 8546.25
96 2016 Q1 Produce 188 4750.30
97 2016 Q2 Confections 608 17172.86
98 2016 Q2 Beverages 523 15520.75
99 2016 Q2 Meat/Poultry 296 15295.88
100 2016 Q2 Dairy Products 308 8458.60
101 2016 Q2 Condiments 293 7529.70
102 2016 Q2 Seafood 347 6537.29
103 2016 Q2 Grains/Cereals 295 5686.50
104 2016 Q2 Produce 78 3255.65
105 2016 Q3 Beverages 487 15915.80
106 2016 Q3 Meat/Poultry 223 12946.70
107 2016 Q3 Confections 637 11048.82
108 2016 Q3 Condiments 467 10915.90
109 2016 Q3 Dairy Products 332 8347.20
110 2016 Q3 Seafood 397 8154.03
111 2016 Q3 Produce 250 6712.45
112 2016 Q3 Grains/Cereals 191 4400.00
113 2016 Q4 Condiments 324 8281.60
114 2016 Q4 Beverages 284 7527.00
115 2016 Q4 Dairy Products 250 7037.40
116 2016 Q4 Confections 245 5766.72
117 2016 Q4 Seafood 331 5530.66
118 2016 Q4 Meat/Poultry 105 5377.06
119 2016 Q4 Grains/Cereals 226 4932.00
120 2016 Q4 Produce 101 3216.60
121 2017 Q1 Beverages 731 29026.50
122 2017 Q1 Meat/Poultry 337 19587.80
123 2017 Q1 Dairy Products 519 16128.50
124 2017 Q1 Condiments 620 14873.65
125 2017 Q1 Confections 631 14011.07
126 2017 Q1 Seafood 600 12453.73
127 2017 Q1 Produce 291 9278.80
128 2017 Q1 Grains/Cereals 328 7609.50
129 2017 Q2 Beverages 711 24771.35
130 2017 Q2 Meat/Poultry 444 19915.89
131 2017 Q2 Confections 606 19367.40
132 2017 Q2 Dairy Products 571 16096.05
133 2017 Q2 Condiments 588 13473.67
134 2017 Q2 Seafood 505 9817.66
135 2017 Q2 Produce 180 7016.35
136 2017 Q2 Grains/Cereals 367 6536.00
137 2017 Q3 Meat/Poultry 175 8956.52
138 2017 Q3 Confections 187 5806.32
139 2017 Q3 Condiments 122 3820.20
140 2017 Q3 Seafood 175 2922.95
141 2017 Q3 Produce 50 2479.35
142 2017 Q3 Beverages 88 2047.00
143 2017 Q3 Grains/Cereals 78 1664.50
144 2017 Q3 Dairy Products 51 1528.00
145 2017 Q4 Dairy Products 393 11324.05
146 2017 Q4 Meat/Poultry 166 8896.26
147 2017 Q4 Beverages 215 7928.00
148 2017 Q4 Confections 249 5713.04
149 2017 Q4 Condiments 251 5377.90
150 2017 Q4 Grains/Cereals 170 4033.00
151 2017 Q4 Seafood 227 3894.34
152 2017 Q4 Produce 22 263.00
153 2018 Q1 Beverages 739 41436.50
154 2018 Q1 Dairy Products 583 18876.50
155 2018 Q1 Confections 598 16108.56
156 2018 Q1 Condiments 761 16046.40
157 2018 Q1 Meat/Poultry 328 14589.38
158 2018 Q1 Seafood 689 12466.13
159 2018 Q1 Produce 219 9268.35
160 2018 Q1 Grains/Cereals 288 6159.40
161 2018 Q2 Beverages 538 19574.50
162 2018 Q2 Seafood 602 12888.18
163 2018 Q2 Meat/Poultry 231 12697.31
164 2018 Q2 Confections 511 12553.40
165 2018 Q2 Dairy Products 372 11615.10
166 2018 Q2 Condiments 374 8791.90
167 2018 Q2 Produce 137 4987.80
168 2018 Q2 Grains/Cereals 223 3401.25
169 2018 Q3 Confections 945 26047.42
170 2018 Q3 Beverages 664 22667.50
171 2018 Q3 Condiments 650 14903.00
172 2018 Q3 Dairy Products 531 13820.80
173 2018 Q3 Meat/Poultry 189 11393.62
174 2018 Q3 Produce 351 11068.90
175 2018 Q3 Seafood 599 10994.88
176 2018 Q3 Grains/Cereals 247 4432.50
177 2018 Q4 Beverages 1049 41862.75
178 2018 Q4 Meat/Poultry 640 35307.49
179 2018 Q4 Confections 1080 27520.03
180 2018 Q4 Dairy Products 943 26055.30
181 2018 Q4 Condiments 1095 24660.65
182 2018 Q4 Produce 461 16589.20
183 2018 Q4 Seafood 908 16420.05
184 2018 Q4 Grains/Cereals 531 11177.00
185 2019 Q1 Meat/Poultry 329 18067.10
186 2019 Q1 Beverages 385 18050.00
187 2019 Q1 Dairy Products 563 16528.20
188 2019 Q1 Confections 442 9850.35
189 2019 Q1 Grains/Cereals 444 9553.75
190 2019 Q1 Seafood 409 9428.65
191 2019 Q1 Condiments 331 8369.80
192 2019 Q1 Produce 120 1906.10
193 2019 Q2 Confections 289 6987.70
194 2019 Q2 Seafood 237 6275.51
195 2019 Q2 Beverages 314 6196.75
196 2019 Q2 Meat/Poultry 144 5846.19
197 2019 Q2 Condiments 221 4247.90
198 2019 Q2 Dairy Products 120 3481.50
199 2019 Q2 Grains/Cereals 110 2802.50
200 2019 Q2 Produce 27 627.75
201 2019 Q3 Beverages 335 14182.50
202 2019 Q3 Meat/Poultry 178 10442.50
203 2019 Q3 Confections 441 10100.25
204 2019 Q3 Dairy Products 302 9224.40
205 2019 Q3 Seafood 347 7162.38
206 2019 Q3 Produce 214 6800.95
207 2019 Q3 Condiments 288 6414.45
208 2019 Q3 Grains/Cereals 312 6100.00
209 2019 Q4 Beverages 402 14588.75
210 2019 Q4 Meat/Poultry 235 11144.94
211 2019 Q4 Condiments 405 10373.05
212 2019 Q4 Confections 509 9046.74
213 2019 Q4 Dairy Products 281 8182.10
214 2019 Q4 Grains/Cereals 269 5303.00
215 2019 Q4 Seafood 309 5230.25
216 2019 Q4 Produce 145 4624.55
217 2020 Q1 Confections 540 14576.13
218 2020 Q1 Seafood 610 12236.54
219 2020 Q1 Meat/Poultry 272 12211.71
220 2020 Q1 Condiments 366 8535.80
221 2020 Q1 Beverages 327 7405.50
222 2020 Q1 Produce 233 6994.60
223 2020 Q1 Grains/Cereals 304 5381.50
224 2020 Q1 Dairy Products 230 5100.60
225 2020 Q2 Seafood 218 5396.52
226 2020 Q2 Confections 195 4587.25
227 2020 Q2 Beverages 157 3564.50
228 2020 Q2 Condiments 129 2297.75
229 2020 Q2 Dairy Products 58 1902.90
230 2020 Q2 Meat/Poultry 32 768.00
231 2020 Q2 Produce 25 733.50
232 2020 Q2 Grains/Cereals 33 519.00
233 2020 Q4 Beverages 801 30243.50
234 2020 Q4 Condiments 895 22649.90
235 2020 Q4 Confections 737 22209.50
236 2020 Q4 Dairy Products 673 19522.80
237 2020 Q4 Meat/Poultry 343 18407.44
238 2020 Q4 Seafood 870 15181.84
239 2020 Q4 Produce 432 11597.70
240 2020 Q4 Grains/Cereals 416 9347.00
241 2021 Q1 Dairy Products 394 11955.60
242 2021 Q1 Seafood 418 8962.77
243 2021 Q1 Meat/Poultry 245 7738.20
244 2021 Q1 Confections 328 6640.27
245 2021 Q1 Produce 223 6599.45
246 2021 Q1 Condiments 236 4583.95
247 2021 Q1 Grains/Cereals 216 4319.75
248 2021 Q1 Beverages 263 4168.75
249 2021 Q2 Dairy Products 622 20897.50
250 2021 Q2 Condiments 825 20475.45
251 2021 Q2 Confections 890 18878.99
252 2021 Q2 Beverages 543 17667.25
253 2021 Q2 Meat/Poultry 362 16413.11
254 2021 Q2 Seafood 675 14536.04
255 2021 Q2 Produce 311 10690.25
256 2021 Q2 Grains/Cereals 531 10490.75
257 2021 Q3 Beverages 800 29511.75
258 2021 Q3 Confections 927 25381.97
259 2021 Q3 Seafood 878 18509.15
260 2021 Q3 Dairy Products 596 17594.30
261 2021 Q3 Condiments 793 16551.85
262 2021 Q3 Meat/Poultry 386 15631.88
263 2021 Q3 Produce 412 11036.65
264 2021 Q3 Grains/Cereals 244 5716.75
265 2021 Q4 Meat/Poultry 484 25538.27
266 2021 Q4 Beverages 759 24582.50
267 2021 Q4 Confections 745 23453.73
268 2021 Q4 Seafood 701 15820.73
269 2021 Q4 Condiments 661 13972.65
270 2021 Q4 Produce 297 10799.30
271 2021 Q4 Dairy Products 370 10138.40
272 2021 Q4 Grains/Cereals 467 9375.25
273 2022 Q1 Confections 697 22566.30
274 2022 Q1 Dairy Products 678 21571.00
275 2022 Q1 Beverages 455 19367.75
276 2022 Q1 Seafood 751 16655.00
277 2022 Q1 Condiments 544 11773.40
278 2022 Q1 Grains/Cereals 496 9800.00
279 2022 Q1 Meat/Poultry 227 7814.20
280 2022 Q1 Produce 278 7725.50
281 2022 Q2 Beverages 442 18593.75
282 2022 Q2 Meat/Poultry 242 12506.37
283 2022 Q2 Dairy Products 368 10775.30
284 2022 Q2 Condiments 438 9521.00
285 2022 Q2 Confections 429 9257.48
286 2022 Q2 Seafood 417 8588.60
287 2022 Q2 Produce 250 7860.90
288 2022 Q2 Grains/Cereals 187 3912.75
289 2022 Q3 Beverages 1257 49109.00
290 2022 Q3 Confections 1473 38342.78
291 2022 Q3 Meat/Poultry 655 37400.93
292 2022 Q3 Seafood 1257 27166.65
293 2022 Q3 Dairy Products 952 26853.20
294 2022 Q3 Condiments 1171 25129.10
295 2022 Q3 Produce 524 18064.00
296 2022 Q3 Grains/Cereals 686 14010.75
297 2022 Q4 Beverages 856 33878.50
298 2022 Q4 Dairy Products 637 18626.30
299 2022 Q4 Confections 791 17290.72
300 2022 Q4 Meat/Poultry 314 15330.99
301 2022 Q4 Seafood 630 14055.85
302 2022 Q4 Condiments 603 12742.95
303 2022 Q4 Produce 359 11212.35
304 2022 Q4 Grains/Cereals 325 4740.00
305 2023 Q1 Confections 822 24413.16
306 2023 Q1 Beverages 765 20809.00
307 2023 Q1 Condiments 864 20484.50
308 2023 Q1 Seafood 900 20337.60
309 2023 Q1 Meat/Poultry 317 18509.39
310 2023 Q1 Dairy Products 727 16956.20
311 2023 Q1 Produce 459 14502.05
312 2023 Q1 Grains/Cereals 384 7499.25
313 2023 Q2 Beverages 253 17357.00
314 2023 Q2 Confections 358 11049.19
315 2023 Q2 Meat/Poultry 157 6181.00
316 2023 Q2 Condiments 266 6049.55
317 2023 Q2 Dairy Products 174 4703.80
318 2023 Q2 Grains/Cereals 164 3107.50
319 2023 Q2 Produce 50 1280.00
320 2023 Q2 Seafood 97 1213.09
321 2023 Q3 Confections 826 23002.99
322 2023 Q3 Beverages 678 18925.25
323 2023 Q3 Meat/Poultry 356 17337.78
324 2023 Q3 Condiments 692 16456.85
325 2023 Q3 Seafood 670 12578.22
326 2023 Q3 Dairy Products 454 12404.90
327 2023 Q3 Produce 187 6744.55
328 2023 Q3 Grains/Cereals 277 6117.25
329 2023 Q4 Meat/Poultry 125 9355.13
330 2023 Q4 Beverages 145 8592.00
331 2023 Q4 Produce 159 5758.10
332 2023 Q4 Condiments 188 4560.80
333 2023 Q4 Confections 133 3403.75
334 2023 Q4 Seafood 192 3263.38
335 2023 Q4 Grains/Cereals 63 1185.00
336 2023 Q4 Dairy Products 49 612.50
337 2024 Q1 Beverages 539 30711.50
338 2024 Q1 Dairy Products 482 14912.00
339 2024 Q1 Confections 537 11082.80
340 2024 Q1 Meat/Poultry 225 10439.40
341 2024 Q1 Condiments 381 9196.10
342 2024 Q1 Seafood 434 9021.56
343 2024 Q1 Produce 140 5727.75
344 2024 Q1 Grains/Cereals 278 5678.25
345 2024 Q2 Beverages 796 39043.75
346 2024 Q2 Confections 1136 30645.41
347 2024 Q2 Meat/Poultry 400 25280.28
348 2024 Q2 Dairy Products 789 20621.80
349 2024 Q2 Seafood 911 18779.54
350 2024 Q2 Condiments 860 18773.95
351 2024 Q2 Produce 397 13638.75
352 2024 Q2 Grains/Cereals 534 10325.00
353 2024 Q3 Beverages 519 19739.25
354 2024 Q3 Condiments 506 11338.05
355 2024 Q3 Dairy Products 414 10807.80
356 2024 Q3 Confections 424 10384.47
357 2024 Q3 Seafood 493 9762.63
358 2024 Q3 Produce 219 6482.05
359 2024 Q3 Meat/Poultry 172 6355.50
360 2024 Q3 Grains/Cereals 183 3589.50
361 2024 Q4 Confections 799 19787.67
362 2024 Q4 Meat/Poultry 408 18951.47
363 2024 Q4 Dairy Products 676 18947.20
364 2024 Q4 Seafood 906 18298.73
365 2024 Q4 Beverages 889 16997.00
366 2024 Q4 Condiments 689 14871.50
367 2024 Q4 Produce 374 13592.50
368 2024 Q4 Grains/Cereals 615 12527.75
369 2025 Q1 Beverages 323 21154.50
370 2025 Q1 Seafood 441 9626.18
371 2025 Q1 Confections 461 8221.14
372 2025 Q1 Dairy Products 310 8166.40
373 2025 Q1 Condiments 357 7954.60
374 2025 Q1 Meat/Poultry 205 7518.03
375 2025 Q1 Produce 165 6038.80
376 2025 Q1 Grains/Cereals 187 4463.25
quarter_number period
1 3 2012 Q3
2 3 2012 Q3
3 3 2012 Q3
4 3 2012 Q3
5 3 2012 Q3
6 3 2012 Q3
7 3 2012 Q3
8 3 2012 Q3
9 4 2012 Q4
10 4 2012 Q4
11 4 2012 Q4
12 4 2012 Q4
13 4 2012 Q4
14 4 2012 Q4
15 4 2012 Q4
16 4 2012 Q4
17 1 2013 Q1
18 1 2013 Q1
19 1 2013 Q1
20 1 2013 Q1
21 1 2013 Q1
22 1 2013 Q1
23 1 2013 Q1
24 1 2013 Q1
25 2 2013 Q2
26 2 2013 Q2
27 2 2013 Q2
28 2 2013 Q2
29 2 2013 Q2
30 2 2013 Q2
31 2 2013 Q2
32 2 2013 Q2
33 4 2013 Q4
34 4 2013 Q4
35 4 2013 Q4
36 4 2013 Q4
37 4 2013 Q4
38 4 2013 Q4
39 4 2013 Q4
40 4 2013 Q4
41 1 2014 Q1
42 1 2014 Q1
43 1 2014 Q1
44 1 2014 Q1
45 1 2014 Q1
46 1 2014 Q1
47 1 2014 Q1
48 1 2014 Q1
49 2 2014 Q2
50 2 2014 Q2
51 2 2014 Q2
52 2 2014 Q2
53 2 2014 Q2
54 2 2014 Q2
55 2 2014 Q2
56 2 2014 Q2
57 3 2014 Q3
58 3 2014 Q3
59 3 2014 Q3
60 3 2014 Q3
61 3 2014 Q3
62 3 2014 Q3
63 3 2014 Q3
64 3 2014 Q3
65 4 2014 Q4
66 4 2014 Q4
67 4 2014 Q4
68 4 2014 Q4
69 4 2014 Q4
70 4 2014 Q4
71 4 2014 Q4
72 4 2014 Q4
73 2 2015 Q2
74 2 2015 Q2
75 2 2015 Q2
76 2 2015 Q2
77 2 2015 Q2
78 2 2015 Q2
79 2 2015 Q2
80 2 2015 Q2
81 4 2015 Q4
82 4 2015 Q4
83 4 2015 Q4
84 4 2015 Q4
85 4 2015 Q4
86 4 2015 Q4
87 4 2015 Q4
88 4 2015 Q4
89 1 2016 Q1
90 1 2016 Q1
91 1 2016 Q1
92 1 2016 Q1
93 1 2016 Q1
94 1 2016 Q1
95 1 2016 Q1
96 1 2016 Q1
97 2 2016 Q2
98 2 2016 Q2
99 2 2016 Q2
100 2 2016 Q2
101 2 2016 Q2
102 2 2016 Q2
103 2 2016 Q2
104 2 2016 Q2
105 3 2016 Q3
106 3 2016 Q3
107 3 2016 Q3
108 3 2016 Q3
109 3 2016 Q3
110 3 2016 Q3
111 3 2016 Q3
112 3 2016 Q3
113 4 2016 Q4
114 4 2016 Q4
115 4 2016 Q4
116 4 2016 Q4
117 4 2016 Q4
118 4 2016 Q4
119 4 2016 Q4
120 4 2016 Q4
121 1 2017 Q1
122 1 2017 Q1
123 1 2017 Q1
124 1 2017 Q1
125 1 2017 Q1
126 1 2017 Q1
127 1 2017 Q1
128 1 2017 Q1
129 2 2017 Q2
130 2 2017 Q2
131 2 2017 Q2
132 2 2017 Q2
133 2 2017 Q2
134 2 2017 Q2
135 2 2017 Q2
136 2 2017 Q2
137 3 2017 Q3
138 3 2017 Q3
139 3 2017 Q3
140 3 2017 Q3
141 3 2017 Q3
142 3 2017 Q3
143 3 2017 Q3
144 3 2017 Q3
145 4 2017 Q4
146 4 2017 Q4
147 4 2017 Q4
148 4 2017 Q4
149 4 2017 Q4
150 4 2017 Q4
151 4 2017 Q4
152 4 2017 Q4
153 1 2018 Q1
154 1 2018 Q1
155 1 2018 Q1
156 1 2018 Q1
157 1 2018 Q1
158 1 2018 Q1
159 1 2018 Q1
160 1 2018 Q1
161 2 2018 Q2
162 2 2018 Q2
163 2 2018 Q2
164 2 2018 Q2
165 2 2018 Q2
166 2 2018 Q2
167 2 2018 Q2
168 2 2018 Q2
169 3 2018 Q3
170 3 2018 Q3
171 3 2018 Q3
172 3 2018 Q3
173 3 2018 Q3
174 3 2018 Q3
175 3 2018 Q3
176 3 2018 Q3
177 4 2018 Q4
178 4 2018 Q4
179 4 2018 Q4
180 4 2018 Q4
181 4 2018 Q4
182 4 2018 Q4
183 4 2018 Q4
184 4 2018 Q4
185 1 2019 Q1
186 1 2019 Q1
187 1 2019 Q1
188 1 2019 Q1
189 1 2019 Q1
190 1 2019 Q1
191 1 2019 Q1
192 1 2019 Q1
193 2 2019 Q2
194 2 2019 Q2
195 2 2019 Q2
196 2 2019 Q2
197 2 2019 Q2
198 2 2019 Q2
199 2 2019 Q2
200 2 2019 Q2
201 3 2019 Q3
202 3 2019 Q3
203 3 2019 Q3
204 3 2019 Q3
205 3 2019 Q3
206 3 2019 Q3
207 3 2019 Q3
208 3 2019 Q3
209 4 2019 Q4
210 4 2019 Q4
211 4 2019 Q4
212 4 2019 Q4
213 4 2019 Q4
214 4 2019 Q4
215 4 2019 Q4
216 4 2019 Q4
217 1 2020 Q1
218 1 2020 Q1
219 1 2020 Q1
220 1 2020 Q1
221 1 2020 Q1
222 1 2020 Q1
223 1 2020 Q1
224 1 2020 Q1
225 2 2020 Q2
226 2 2020 Q2
227 2 2020 Q2
228 2 2020 Q2
229 2 2020 Q2
230 2 2020 Q2
231 2 2020 Q2
232 2 2020 Q2
233 4 2020 Q4
234 4 2020 Q4
235 4 2020 Q4
236 4 2020 Q4
237 4 2020 Q4
238 4 2020 Q4
239 4 2020 Q4
240 4 2020 Q4
241 1 2021 Q1
242 1 2021 Q1
243 1 2021 Q1
244 1 2021 Q1
245 1 2021 Q1
246 1 2021 Q1
247 1 2021 Q1
248 1 2021 Q1
249 2 2021 Q2
250 2 2021 Q2
251 2 2021 Q2
252 2 2021 Q2
253 2 2021 Q2
254 2 2021 Q2
255 2 2021 Q2
256 2 2021 Q2
257 3 2021 Q3
258 3 2021 Q3
259 3 2021 Q3
260 3 2021 Q3
261 3 2021 Q3
262 3 2021 Q3
263 3 2021 Q3
264 3 2021 Q3
265 4 2021 Q4
266 4 2021 Q4
267 4 2021 Q4
268 4 2021 Q4
269 4 2021 Q4
270 4 2021 Q4
271 4 2021 Q4
272 4 2021 Q4
273 1 2022 Q1
274 1 2022 Q1
275 1 2022 Q1
276 1 2022 Q1
277 1 2022 Q1
278 1 2022 Q1
279 1 2022 Q1
280 1 2022 Q1
281 2 2022 Q2
282 2 2022 Q2
283 2 2022 Q2
284 2 2022 Q2
285 2 2022 Q2
286 2 2022 Q2
287 2 2022 Q2
288 2 2022 Q2
289 3 2022 Q3
290 3 2022 Q3
291 3 2022 Q3
292 3 2022 Q3
293 3 2022 Q3
294 3 2022 Q3
295 3 2022 Q3
296 3 2022 Q3
297 4 2022 Q4
298 4 2022 Q4
299 4 2022 Q4
300 4 2022 Q4
301 4 2022 Q4
302 4 2022 Q4
303 4 2022 Q4
304 4 2022 Q4
305 1 2023 Q1
306 1 2023 Q1
307 1 2023 Q1
308 1 2023 Q1
309 1 2023 Q1
310 1 2023 Q1
311 1 2023 Q1
312 1 2023 Q1
313 2 2023 Q2
314 2 2023 Q2
315 2 2023 Q2
316 2 2023 Q2
317 2 2023 Q2
318 2 2023 Q2
319 2 2023 Q2
320 2 2023 Q2
321 3 2023 Q3
322 3 2023 Q3
323 3 2023 Q3
324 3 2023 Q3
325 3 2023 Q3
326 3 2023 Q3
327 3 2023 Q3
328 3 2023 Q3
329 4 2023 Q4
330 4 2023 Q4
331 4 2023 Q4
332 4 2023 Q4
333 4 2023 Q4
334 4 2023 Q4
335 4 2023 Q4
336 4 2023 Q4
337 1 2024 Q1
338 1 2024 Q1
339 1 2024 Q1
340 1 2024 Q1
341 1 2024 Q1
342 1 2024 Q1
343 1 2024 Q1
344 1 2024 Q1
345 2 2024 Q2
346 2 2024 Q2
347 2 2024 Q2
348 2 2024 Q2
349 2 2024 Q2
350 2 2024 Q2
351 2 2024 Q2
352 2 2024 Q2
353 3 2024 Q3
354 3 2024 Q3
355 3 2024 Q3
356 3 2024 Q3
357 3 2024 Q3
358 3 2024 Q3
359 3 2024 Q3
360 3 2024 Q3
361 4 2024 Q4
362 4 2024 Q4
363 4 2024 Q4
364 4 2024 Q4
365 4 2024 Q4
366 4 2024 Q4
367 4 2024 Q4
368 4 2024 Q4
369 1 2025 Q1
370 1 2025 Q1
371 1 2025 Q1
372 1 2025 Q1
373 1 2025 Q1
374 1 2025 Q1
375 1 2025 Q1
376 1 2025 Q1ggplot(
data = quarterly_category_profile,
mapping = aes(
x = period,
y = total_revenue,
fill = CategoryName
)
) +
geom_col() +
scale_y_continuous(
labels = scales::label_dollar()
) +
labs(
title = "Hanari Carnes Purchasing Activity Over Time",
subtitle = "Quarterly revenue separated by product category",
x = "Order Period",
y = "Total Revenue",
fill = "Product Category"
) +
theme_minimal() +
theme(
axis.text.x = element_text(
angle = 45,
hjust = 1
)
)
Organizations should first practice transparency by clearly explaining what information they collect, how they create customer profiles, and how those profiles influence customer experiences (morey2015?). Companies should not depend only on lengthy privacy policies or terms and conditions. Data explanations should be understanable and presented when information is collected or used.
Organizations should give customers meaningful control over their information. Customers should be able to review privacy settings, make choices regarding targeted ads, correct inaccurate information, and withdraw access when appropriate. Providing control reduces the imbalance between the organization creating the profile and the person represented by it.
Customer profiling should provide recognizable value. Customers are more likely to accept data collection when it produces better recommendations, greater convenience, improved service, or financial savings. Organizations should be able to explain the relationship between the information collected and the customer benefit delivered.
Organizations should follow data minimization principles (book?). A company should not collect information simply because it may become useful later. It should identify a purpose and limit collection to information needed for that purpose. Derived information should recieve special attention because profiles may reveal conclusions that customers never directly disclosed.
Companies operating media platforms should create transparent and independent measurement processes (gabel2024?). Retailers should clearly explain how advertisements are targeted, how sales are attributed to campaigns, and how performance is measured. Separating campaign execution from evaluation can increase accountability and advertiser confidence.
Organizations that are developing AI systems should prevent personalization from becoming manipulation. AI systems should not use detailed profiles to exploit emotional dependence or reinforce only the opinions users already hold. Companies should establish boundaries for personalization and regularly evaluate whether automated systems produce biases, unsafe, or misleading interactions.
Finally, organizations should demonstrate trustworthiness through behavior rather than promotional language. Teams like: ethical, responsible, and trustworthy are only meaningful when supported by clear policies, secure systems, accountable leadership, and consistent treatment of customer information. In a data driven economy, customer trust is not simply a public relations objective. Its necessary for maintaining long term success to the information that makes effective personalization possible.
(book?){ftc staff report finds large social media and video streaming companies have engaged in vast surveillance of users with lax privacy controls and inadequate safeguards for kids and teens_2024, url={https://www.ftc.gov/news-events/news/press-releases/2024/09/ftc-staff-report-finds-large-social-media-video-streaming-companies-have-engaged-vast-surveillance?utm_source=chatgpt.com}, journal={Federal Trade Commission}, year={2024}, month=sept }
(gabel2024?){gabel_duncan_timoshenko_2024, title={How Retailers Became Ad Platforms}, author={Gabel, Sebastian and Duncan, Duncan and Timoshenko, Artem}, year={2024}, month=june }
(morey2015?){morey_forbath_schoop_2015, title={CUSTOMER DATA: DESIGNING FOR TRANSPARENCY AND TRUST}, author={Morey, Timothy and Forbath, Theo and Schoop, Allison}, year={2015}, month=may }