1. PC Design, Use, and Purchase Relations
Al M. Rashid, Bob Kuhn, Bijan Arbab, and David Kuck
Intel Corporation, 2200 Mission College Blvd, Santa Clara, CA 95054
{al.m.rashid|bob.kuhn|bijan.arbab|david.kuck}@intel.com
Abstract—For 25 years, industry standard benchmarks have
proliferated, attempting to approximate user activities. This has
helped drive the success of PCs to commodity levels by charac-
terizing apps for designers and offering performance information
for users. However, the many new configurations of each PC
release cycle often leave users unsure about how to choose one.
This paper takes a different approach, with tools based on new
metrics to analyze real usage by millions of people. Our goal is
to develop a methodology for deeper understanding of usage that
can help designers satisfy users. These metrics demonstrate that
usages are uniformly different between high- and low-end CPU-
based systems, regardless of why a user bought a given system.
We outline how this data can be used to partition markets and
make more effective hardware (HW) and software (SW) design
decisions tailoring systems for prospective markets.
I. INTRODUCTION
Computers are among the most complex devices ever
invented, and yet in the past 30 years affordable price, small
form factor, user-oriented software, and the rise of the internet
have enabled their transition from rare scientific and business
tools to ubiquitous commodity devices [1], [2], [3] used widely
by consumers. However, one may question how well-matched
personal computer (PC) systems are to their users, and whether
or not people over-buy or under-buy PCs. In short, how well
do people choose and then use personal computers?
Unlike HPC or server workload characterization, where
many users share a system, PCs and tablets are mostly indi-
vidually owned and used commodity products. Non-technical
factors are very important to buyers (style, appearance), but
overheating, battery life, response time and even weight are
driven by technical tradeoffs. Designers need to choose the
right combination of microprocessor, ram, disk, network inter-
faces, etc. to support the owners’ software and usage needs. As
an industry, many companies need to cooperate and compete to
provide 350M individuals per year with a personally exciting
new PC at a low price.
The ubiquity of computers supports hundreds of suppliers
around the world, each using similar basic technology to build
many models and SKUs with similarities to each other, and
to those of other suppliers. Many products, some with tiny
differences, make a buyer’s choices very rich and yet quite
confusing in detail. The case for PCs can be seen on any
computer company’s website. For example, Dell Computer
offers PCs for “home” use in three high level categories:
“home and home office”, “ultimate experience”, and “high-
performance gaming.” The apparent contradictions between
ubiquity and complexity present challenges in explaining how
effectively computer-based products are designed by experts,
then chosen and used by untrained people around the world.
Available product choice has been shown to increase the
ability to match individual preferences to outcomes [4], and
provide personal autonomy and well-being [5], but excessive
choice leads to confusion and choice deferment [6]. The
quandary between wanting more options and then finding that
hard to deal with is referred to as the Paradox of Choice [7].
These decisions are important to at least three groups:
1) Questions of concern to PC hardware and solutions
vendors include: a) user needs, b) the choices (models
or SKUs) to offer, to meet various user needs, and c)
the expected demand for each SKU. Planning and
design decisions for next generation systems require
feedback about the intended use vs. actual use of
various types of systems.
2) Consumers’ purchasing decision processes are also
complex [8], [9], since purchasing a particular model
might be influenced by: current fads, prior experience
(or lack thereof), cost, brand quality, and intended
use.
3) IT purchasers face a range of similar issues in choos-
ing appropriate SKUs for various types of corporate
users [10], [11].
This paper focuses on a quantitative two part analysis to
characterize the overall process of choosing and using PCs.
First we explore a dataset of users’ self-reports collected from
a large online electronics store to understand user intentions.
We then present a comprehensive study of large scale logged
behavioral data from PC users worldwide. We compare mea-
sured CPU usage of various PC types (models) to understand
if people generally over-buy or under-buy. Intel PC CPUs are
branded with increasing capability as Celeron/ Pentium, i3,
i5, and i7. We analyze how people choose a less powerful
(e.g. i3-based) or more powerful (e.g. i7-based) model, and
whether they use the systems accordingly. The analyses gen-
erate insights that can be used to improve the definition and
design of new systems. Our methodology and findings seem
generalizable to other PC and tablet categorizations.
Contributions. This is a position paper based on massive
amounts of unprecedented behavioral data from a diverse set
of world-wide PC users. Four research questions are posed and
discussed; one about how people choose a new PC, two about
how they use their systems, and one about how well-informed
designers are about user needs.
First, we show a summary of self-reported data about
intentions and demography from online reviews to demonstrate
the potential of understanding factors influencing PC purchase
decisions. In much more detail, we then study anonymous
logged behavioral data from millions of PC users, to explore
how consumer PC purchasing decisions align with various
aspects of their actual use. This spans the user experience0000–0000/00$00.00 c 2015 IEEE. IISWC 2015.

2. External
stimulus
Cognitive
response
Intention Behavior
External
variables
Perceived
usefulness
Perceived
ease of use
Behavioral
intention
Adopt Actual
use
i7
i5
i3
Fig. 1. Technology Acceptance Model (TAM) [12], which builds on Theory
of Planned Behavior (TPB) [13] .
(UX), workload characterizations (WC) that capture the UX,
and HW/SW codesign (HSC) that exploits the WC.
We build on existing theories and studies where applicable,
and introduce new behavioral metrics where necessary. Our
goal is to find an overall procedure that links UX to WC to
HSC, and improves UX for the next iteration. Many details
remain to be understood, but by tackling this entire process
we are able to discuss some underlying principles and how
they are related to current practices:
1) Without sufficiently understanding user experiences
and needs, a system design can be redundant and
confusing to buyers. Currently, industry standard
benchmarks and marketing personas only represent
real users intuitively.
2) Despite point 1, users’ experiences in the past 20
years have led to good, market- segmented use of
existing systems (Section V). However, better under-
standing the process will allow improving it.
3) To focus the PC design process on actual uses (2.),
HSC should include real workload measurements (and
user-query responses) anticipating distinct user expe-
riences. Specific application combinations (clusters)
should be chosen for each PC model being designed,
to reflect user-experience aspirations, WW geograph-
ical usage realities, etc.
4) We discuss off-line approaches to deriving design
workloads from globally measured user activities.
II. RESEARCH QUESTIONS AND RELATED WORK
We begin our exploration of how PC usage aligns with
users’ decisions to buy particular types of systems by posing
a few overarching research questions.
RQ1-Choose. How do people choose a new PC?
Literature in psychology, information science, economics,
and marketing is replete with models and studies indicating
how people choose or adopt technology. Ajzen’s theory of
planned behavior (TPB) [13] is one of the most studied relevant
frameworks that influenced and was adapted into other models,
including Technology Acceptance Model (TAM) (Fig. 1) [12],
and MATH [2]. These models suggest that users’ (behavioral)
intention (BI) to use best predicts the actual system use. TAM
suggests that perceived usefulness and ease of use determine
intention to use.
Studies employing TPB reveal that cost, influences from
friends and family and other sources, pleasure derived by
using PCs, and even status gains influence BI, in addition to
various types of perceived utility. These determinants can be
recognized through motivation theory as well [14]. Some can
be put under “extrinsic motivation”: achievement of a specific
goal, e.g., the social and utilitarian outcomes, while others,
e.g. hedonistic ones belong to “intrinsic motivation”: “pleasure
and satisfaction derived from a specific behavior.” Further,
experience (post-use beliefs) and expectations (pre-use beliefs)
were shown to influence BI [15]. We have added a dotted box
in Fig. 1 indicating adoption/purchase (implicit in the original
TAM), as we focus on understanding both factors affecting
adoption and actual use in this paper.
We summarize the italicized topics above as influencers of
PC choice that are not necessarily exclusive of one another.
1) Financial cost: Buyer’s willingness and affordability
of buying and operating a given PC system.
2) Peer advice: Acquaintances, salespeople, ads, and
websites offer advice to prospective PC buyers.
3) Aspiration: How the user thinks she would use the
system and benefit from it.
4) Experience: Prior usage experience can shape aspira-
tion. Further, pleasure of use and importance of social
status may vary with experience.
Prior work provides valuable insights about RQ1, but few
precise studies of users’ decision-making process for choices
of complex systems like PCs exist. We attempt to gain insight
by studying a self-report dataset (Section III).
RQ2-Use. Do people generally use and benefit from what
they pay for?
Our goal is to understand how distinct capabilities and
characteristics of computing devices lead to different types
of uses. It is obvious that supercomputer users and tablet
users compute different things and use their computers in
different ways. But is there any difference between what tablet
and PC users do, or between low and high end PC usage?
Our hypothesis is that people generally use and benefit from
what they pay for, but since computers are complex, this is
not obvious. Folklore sometimes suggests that manufacturers
hype technology that is not useful (i.e. is not used), people
waste money on unused technology, and even that further PC
progress is unnecessary because we have reached the end of
the value road. Consider two PC purchases with different capa-
bilities and price points. Both enable new potential consumer
entertainment, creativity, connectivity, and productivity, but at
different levels. The question we then ask is: how easy, wide
ranging, and likely to be used is the full spectrum of benefits
to each buyer (over a large sample population)? And finally,
can our analyses yield important data for enriching PC UX?
RQ3-Peers. Are there commonalities of use on similar
systems?
If the purchasers of a specific SKU intend to use it the
same way, it is reasonable to expect similar types of post-
purchase actual usage. Conversely, similar patterns of usage on
a particular system type, suggest that users bought the system
with similar types of usage plans. As prior work suggests,
intended usage is only one determinant of purchase and actual

3. use [16]. Further, a specific type of system might be suitable
for multiple usage types, e.g. a gaming laptop may have
everything a professional photographer wants, resulting in both
game and professional photo editing use. So we cannot expect
either a uniform or an exclusive usage on a type of system.
RQ3 and questions about actual usage can best be tackled
by logged behavioral data. As recent research on mobile phone
usage suggests, self-reports on actual use, even a simple piece
of information like usage duration, can be imprecise and inac-
curate [17]. We address this by analyzing large scale logged
behavioral data toward workload characterization (WC).
Beauvisage [18] monitored and analyzed logged PC usage
data of 2,730 French users, and [19] studied 36 PC users of
various PC and desktop systems. We extend and build on their
work in a number of ways including a large user-base; unlike
small focus groups we collect anonymous and less intrusive
usage data from millions of worldwide users with varying
grades of systems. We also incorporate an online reviews
dataset from users who are disjoint from our logged data to see
if the two sources tell similar stories. We show some results
of user clustering relative to SW usage.
RQ4-Design. How well designed are systems for market
satisfaction and coverage?
To understand usage details one must analyze many users
world-wide. Only intuitive knowledge is available from mar-
keting studies or in-depth interviews of a few hundred users.
We extend [20] where the authors interview home computer
users to understand usage; [21] where the application sequence
is clustered to glean usage patterns; and [22] showing that the
male-female computer use attitude gap is declining.
Market satisfaction and coverage are dealt with by multiple
OEMs, each producing more PCs than users can cope with.
Each offers many SKUs with generic descriptions intended to
convey some idea of their intended use. They also resort to
speed and size statistics, which offer little insight and often
confuse users who have no idea of what their apps really
require. So products fail to find markets, users fail to find the
right PC, and benchmarks proliferate, hoping to give insight
[23], [24]. But positive answers to RQ4 require major efforts
to measure, analyze and act on the results.
This paper discusses high-level summaries of measure-
ments, based on detailed SW process data and HW performance
counters. For design purposes (HSC), low-level HW operation
details can be used, including memory hierarchy traffic, SSD
usage, energy, and thermal information, and each of these can
be related to individual application .exes. Thus, SW interactions
with the system can be pinpointed, and in some cases SW
improvements may be made during the design process.
III. FINDINGS FROM A SELF-REPORTED DATASET
This section introduces a self-reported dataset about
Windows-laptops from a major US retailer’s website (June
2014), in order to see a) what we can learn from users’ self-
reports about RQ1 and RQ2, and b) if the self-reports align
with the findings from the logged behavioral data. The site
included product reviews and numerical (star) ratings plus
reviewers’ self-descriptions and intended usage via standard
Business Professional
Comfortable with Technology
Basic Web & Email
Parent with Kids
Budget Conscious
Student
Retired
Social Media Enthusiast
Gamer
Number Cruncher
Technology beginner
Process large graphics or video
Technology Guru
36%
34%
29%
31%
27%
34%
41%
32%
29%
58%
58%
51%
25%
17%
20%
25%
26%
30%
22%
24%
23%
22%
19%
14%
13%
25%
i7 i3 or loweri5
UserAttribute
CPU Type
Fig. 2. Planned use vs. purchase. Users’ self-identified attributes vs. the
systems they recently bought shown as a mosaic plot. The attributes can be
viewed as expected user needs. Simply put, a cell under i7 column, as an
example, is shaded blue if it is bigger than all other cells in the same column,
and shaded red for vice versa. The height of each row indicates the relative
proportion of users identified with the attribute. It seems that overall users
are able to translate their needs into the right system component, e.g., high
performance needs correlate well with i7.
phrases related to the product’s genre, e.g. printer reviewers
may use “usage: once a week,” “primary use: business user,”
“Expertise: Beginner,” “gender: female,” etc.
The dataset includes information about laptops, e.g. CPU
type, screen-size, etc., and the reviewers. We used 3,600
reviews and about 15,000 self-selected user attributes to cre-
ate a contingency table [25] by tallying the attribute counts
partitioned by CPU types (discarding low frequency < 20
attributes). Most reviewers had just bought systems and started
to use them, so their comments likely reflect early usage or
planned usage based on prior systems usage.
The rows of Fig. 2, a mosaic plot [25], show self-identified
attributes, some related to demography, e.g., “Retired,” and
others to intended usage, e.g., “Process large graphics or
video.” The columns correspond to CPU types. Each row is a
distribution totaling 100%. The first row shows 584 reviewers
self-identified as “Business professional,” and 17% and 36%
gave feedback on systems based on i3 (or lower) and i7 CPUs,
respectively. Fig. 2 also shows population inference. Pearson
residuals to invalidate that there is no relationship between the
corresponding row and column of a box are shown. Values
> |2| standard deviation show significance at 95% confidence
level. So a blue box indicates that it is significantly larger than
others in that column; red boxes show the opposite.
Fig. 3 is a biplot [26], a scatter plot-like portrayal on
mapped dimensions showing the structure of the data sum-
marized in Fig. 2, generated using correspondence analysis
[26] of contingency table data. Fig. 3 lines show positions
and directions of CPU types from the origin (0, 0); angles
between lines approximate correlations. The lengths indicate
variances, so i5 has the least variation. Thus, i3 and i5 are
most correlated based on user attribute (dots) frequency. The
intersection of a perpendicular from a dot to a line is the value
along the direction of the CPU type. Therefore, “Technology
Guru (TechGuru)” has the highest association with i7, but
not with i5, since the intersection on the i5 line is on the

4. Fig. 3. Correspondence analysis biplot showing user attributes and CPU
types. The horizontal axis accounts for most (˜80%) of data variations.
opposite side of its natural direction. Biplots often show data
of > 2 dimensions in two dimensions, so it is useful to know
how much information was lost due to the approximation.
Total variances of the two dimensions in Fig. 3 show a good
approximation in this case.
Fig. 3 has several RQ1 clues. The highest Fig. 2 value in
the i3 column is in the “budget conscious” row, and in Fig. 3,
i3 and budget-conscious are spatially the closest. So the 30%
of budget conscious who bought i3 seem driven by price. 43%
of the budget conscious were motivated to buy i5, perhaps due
to discounts or buying up a bit. The 26% i7 may be due to
varying levels of affordability and budget consciousness.
Anticipated usage may be driven by prior use of a device
type (tablet vs. PC) or branded components, e.g. i3 vs. i7
CPU. We analyze this by categorizing the attributes “Num-
ber cruncher,” “Process large graphics (LargeGraphVid),”
“Gamer,” and “Tech Guru/Power user” as “pro users,” the
most likely buyers of “pro-edition” software. In Fig. 3 these
descriptions are closest to i7. “Business professionals” seem
to prefer i5 and i7, but little i3: i3 is the farthest from this
attribute in Fig. 3 and has the lowest third column value in Fig.
2, perhaps due to the notion that business professionals need
capable systems. “Basic web surfing & emails” and “parents
with kids” (buying for kids?) appear not to require a powerful
CPU, and are closest to i3, “retirees” and “social media” types
are closest to i5, while “students” are at the center.
Users who bought systems more central in Fig. 3 might
have been influenced by peers, model availability, experience,
etc. Many reviewers may not have a precise use in mind, and
may not buy what they really need. Their needs can be exposed
and quantified by measuring what they do when they use their
system; RQ2 and RQ3 are the basis for what follows.
IV. LOGGED BEHAVIORAL DATA
The PC usage data we analyze are generated from an
anonymous data collection project run jointly by Intel and its
PC OEM partners. The DCA (data collection and analysis)
project has built and used a tool set to understand user
experience and issues (including performance), user needs, and
how people use their computers, with a goal of improving
product design. Anonymous behavioral data is collected from
user systems whose owners explicitly opt in. No personally
identifiable information are collected. The collector is a low
impact (< 0.5% CPU usage) Windows service, probing hard-
ware counters (e.g., status registers) and OS APIs every 5
seconds. Most of the data is then summarized daily before
transmitting to servers.
Currently, about 15 million systems worldwide have been
sending structured data, amounting to about 30TB in rela-
tional databases. Captured information includes system type,
geo location (at the country level), CPU type, CPU usage,
temperature, battery, on-off behavior, application usage, etc.
We extracted a dataset to understand how people
use PCs by taking a stratified random sample of 0.5
million users worldwide, as summarized in Table I.
TABLE I. PROPERTIES OF THE DATASET.
#of users 500,000
#of exe’s 30,000
#of records 2.5B
Systems by CPU
We use the terms
users and systems
interchangeably
here. All users
selected used
Windows 7
operating system
(OS). We avoided
Windows 8x-based
systems, which
lacked enough usage history at the time. Except when we
analyze longitudinal usage, we focus on a usage time-window
of Jan-Mar, 2013. The systems had CPUs that spanned
multiple generations.
Application categories. The raw logged data contains ap-
plications as executable (exe) names, e.g., skype.exe. We
classified thousands of exe’s into a few high level categories
to simplify analyses. Table II shows exe categorization, de-
scription, and a few example applications in each category.
Note that each application may fit several of the categories,
however, we use a best-match approach. The benefit of this
categorical approach is to enable high level observations over
a large volume of data. Fig. 4 quickly shows that PC usage
in US and Europe are more similar than that of China, which
uses more games.
V. METHODOLOGY AND ANALYSIS
PC usage can be viewed as an interplay between three
domains: the hardware (HW) used, the software (SW) used, and
Application
Category
Description Most popular apps
Communication Communication: VOIP,
instant messengers, email
Skype, MSN/windows live messenger,
outlook, qq
Office Productivity :Spreadsheets,
word processor, financial,
engineering
MS Word, Excel, Acrobat reader,
powerpoint, utocad
Media
Consumption
Entertainment: Audio-video
playback
Windows media player, vlc player, itunes
Game Entertainment: Apps for
gaming
Solitaire, League of legends, WOW, World
of tanks
Utility Productivity: Backup,
archiving, tuning, print
Winrar, webcam, dropbox
Network Apps Communication: Peer-to-
peer, remote desktop, FTP
Utorrent, teamviewer, vpn
Media Edit Creativity: Audio-video
editing
Moviemaker, Photoshop, Picasa, youtube
converters
IT Productivity: Software
development, databases
SQL server, VMware, visual studio
Table IV. PC use relative to RQ2 and RQ3.Table IV. PC use relative to RQ2 and RQ3.
TABLE II. APPLICATION CATEGORIES EXPLAINED. NOTE THAT OS
PROCESSES AND ANTI-VIRUS ARE NOT SHOWN, SINCE THEY ARE
SELF-EXPLANATORY.

5. Media Edit
Media
Consumption
Anti-Virus
Game
Web
Office Commu-
nication
Network Apps Utility
IT System/
Other
Fig. 4. Usage commonalities and differences by geos. Application usage
between US and Europe are more similar than China.
the users of computers (user). We can define PC usage metrics
based on each of the domains alone, or their combinations. Fig.
5 defines three levels per domain to make measurements and
interpretations meaningful. From this point of view, computers
operate at level 3 of the combined HW/SW/user domains. Only
privacy-insensitive data is collected under the “user” domain.
Assuming that all PCs are used for entertainment, cre-
ativity, communication, and productivity, the focus on each
category (Table II) and the degree of engagement with each
activity can be highly variable. Our analysis is based first,
and most straightforwardly on time metrics. How much time
a system is on, how much time various applications run,
how much time a user spends looking at which foreground
applications, etc. These give some indication of a day in the
life of a user/PC combination. More deeply, it is important to
understand what kinds of uses are made of PCs and why. This
includes grouping users by type of applications used, time-
used for particular user-discretionary apps, and correlation with
PC model: do high-end models get more or less use than
less capable systems? Also we examine the extreme use of
systems, in that some users do a lot of everything. And are
there differences between the types of apps used per area -
e.g. media editing can be na¨ıve or black belt, as can game
playing, etc. Understanding how the apps are used gives an
indication of various users’ depth of computer use.
Next, we define 7 behavioral metrics and show a user-
clustering approach. We discuss the implications in terms of
a) User experience (UX) b) Workload characterization (WC)
c) Hardware-software co-design (including reliability) (HSC).
Because the space of domains and app categories is so
rich, each metric presentation is much abbreviated from a full
discussion, and these metrics are only representatives of a
fuller set.
High level
demographics
Low level
demographics
System type
System models
Overall usage
SW categories
Personal
Identification
Individual
Executables
System
components
User Software Hardware
Sex, age, geo, personna
Income, occupation, interests
Name, physical/email address
Game, communication, etc.
Starcraft.exe, skype.exe, etc.
laptop, desktop, convertible
Model #, SKU #
CPU, memory, disk, screen
L1
L2
L3
Model Choice 1
Model Choice 2
Model Choice 1
Model Choice 2
Generation i Generation i+1
Satisfied
Satisfied
Unsatisfied
Productivity, entertainment, etc.
Fig. 5. Analyzable space: domains and levels with examples.
A. M1: SYSTEM ON TIME
This metric measures the total time each system is kept
powered on during the quarter analyzed. In domain space, this
is a HW/user metric with level 1 user-geographic information.
Observations: Fig. 6 shows that system on time usually
increases with CPU model and that there are overall 2x
differences in magnitude across the world. Note the similarities
between the US and Canada across CPU types on one hand,
and Japan and Korea on the other, as well as their differences
in M1. Assuming that system on time is related to how long
people use their systems (later metrics), this metric helps
explore usage differences of various system types.
Discussion: Part of M1 is daily system on/off frequency,
which is useful in determining device reliability. Substantial
differences between field-measured and factory-measured val-
ues led to important HSC-based changes in reliability models
for chip design [27]. An M1 limitation is that system on
time includes time when the user is not engaged with the
system, either interactively or non-interactively [18]. So, M1
does not accurately reflect “usage,” but higher-end systems
having higher on-times suggests longer use. Other metrics will
help explain this observation.
B. M2: FOREGROUND APP CATEGORY TIME (FACT)
This is the total time a user interacts with the application
window visible over other windows. The x axis of Fig.
7 shows application categories. Foreground application time
captures applications with which users are active, and ignores
all other (user-passive) applications, including the background
processes (OS, virus check, etc.) that run automatically. In
domain space, this is a HW/SW metric with level 2 SW. We
define computer use through the application categories used,
and how much they are used. This metric captures the duration
of the user interactions per category. The varying usage details
for individual applications or categories by experienced vs.
novice users are discussed in Metric 5.
Observations: Fig. 7 shows this metric by CPU types and
by grouping foreground applications into the categories of
Table II. Web browser time accounts for nearly half of all
usage, so it is shown in the inset. Fig. 7 shows that i7-based
systems are used more for serious creativity or productivity
TotalSystemOnTime(Hours)
Geo
i7
i3
i5
Fig. 6. System on time by CPU type, broken down by geo. Error bars are
1 standard errors from the averages. Overall i7>i5>i3.

6. 0
10
20
30
40
50
60
Communication Office Media
Consumption
Game Utility Network Apps Media Edit IT
InteractionTime(Hrs)/System
i3 i5 i7
Commu
nication
Office Media
Consump
Game Utility Network
Apps
Media
Edit
IT
WebWeb
153
141
134
Fig. 7. App interaction time by SKUs. Individual applications are grouped
by Table II categories. Note that office, communication, media edit, and IT
apps are used more on higher-end CPU-based systems.
(see caption), while i3-based systems are much-used for enter-
tainment, such as media consumption, browsing, etc. i5-based
systems fall in between.
Discussion: Fig. 7 shows UX differences in foreground
application time across CPUs. These differences arise taking
all systems together; some i3-based system use approaches
certain i7-based system use, and vice versa. From the WC and
HSC viewpoints, designers can extract distinctions in device
needs per PC model, e.g. what NIC and support SW are needed
for the web and communications apps to be run. Furthermore,
UX enhancements can be provided by informing users of better
settings through online usage monitoring. Similarly, home
router advice can be derived from home network usage.
C. M3: APPLICATION CPU USAGE (ACU)
ACU is the CPU-centric expression of “work” (bits, ops,
instructions, transactions, images, benchmark units, etc.) com-
puted per application. ACCU (app-category CPU-usage) is the
sum of work across all apps in a category (Fig. 8). In domain
space, these are HW/SW metrics with level 2 SW. Two CPUs
with different capabilities i.e. component bandwidths (BWs)
generally require different amounts of time to complete a given
application doing the same task. Lower capability CPU sys-
tems generally use more time. Therefore, behavioral analysis
of two users (systems) shows a CPU time difference, even if
both are doing the same thing, using the same workload.
To account for this, we normalize the capabilities of the
CPUs. The BW capabilities of a CPU only have value to a user
when an application makes use of them. The BW used is the
computational capacity (C work-units/sec) of the CPU for the
application. For example, if CCP U1
=100 and CCP U2
=125 for
a given workload, and they cause CPUs to run for TCP U1 =12.5
and TCP U2 =10 seconds, respectively, their throughputs are
both 1250. We express ACU in thousands of operations [KOp]:
ACUappi
= TCP Uj ,appi
× CCP Uj
(1)
We express CPU capacities as benchmark scores for the pub-
licly available PassMark [28] scores, simply because complete
data is available for all the CPUs in our dataset.
Observations: Fig. 8 shows that for all app categories but
games, media consumption and the web, i7 ACCU exceeds
Anti.Virus_CPM
Communication_CPM , mean_Communication_CPM= mean(Communication_CPM), se_Communication_CPM = sd(Communication_CPM) / sqrt(N)
Office_CPM , mean_Office_CPM= mean(Office_CPM), se_Office_CPM = sd(Office_CPM) / sqrt(N)
Media_Consumption_CPM, mean_Media_Consumption_CPM= mean(Media_Consumption_CPM), se_Media_Consumption_CPM = sd(Media_Consumption
Game_CPM , mean_Game_CPM= mean(Game_CPM), se_Game_CPM = sd(Game_CPM) / sqrt(N)
Utility_CPM , mean_Utility_CPM= mean(Utility_CPM), se_Utility_CPM = sd(Utility_CPM) / sqrt(N)
Network.Apps.CPM , mean_Network.Apps.CPM= mean(Network.Apps.CPM), se_Network.Apps.CPM = sd(Network.Apps.CPM) / sqrt(N)
Media_Edit_CPM , mean_Media_Edit_CPM= mean(Media_Edit_CPM), se_Media_Edit_CPM = sd(Media_Edit_CPM) / sqrt(N)
0
10K
20K
30K
40K
50K
60K
70K
80K
90K
100K
i3 i5 i7 i3 i5 i7 i3 i5 i7 i3 i5 i7 i3 i5 i7 i3 i5 i7 i3 i5 i7 i3 i5 i7
Commun
ication
Office Media
Consumption
Game Utility Network
Apps
Media
Edit
IT
CPUUsage(KOps)
240K
260K
280K
300K
320K
340K
i3 i5 i7
Web
Fig. 8. Average ACCU per system. Error bars, too tiny to be visible, represent
standard error of means. Note the similarities with trends in Figure 7.
that of other CPUs. Since ACU depends on the applications
run, M3 captures how the user is using the system, showing
which applications are most used and how much CPU-work
each category uses. Relatively high game and media use on i5
indicates higher likelihood of entertainment focus.
Discussion: Notice the similar upward trend with CPU type
as shown for CPU on time (Fig. 6) and foreground application
time (Fig. 7). More capable systems do even more work than
less capable ones. The i5 spike for games, unlike the flat Fig.
7 profile may indicate that while user times are similar, the i5
cpu spends more time in complex games.
Capacity normalizations other than Passmark will change
figure magnitudes but are unlikely to change this paper’s
conclusions, which depend on ranking inequalities rather than
absolute magnitudes. For more accurate magnitudes, appropri-
ate benchmarks could be used for normalization in specific app
areas. A system model allowing varying BW values should
be used to deal with cache or cpu saturated cases for HSC.
This could capture relative saturations and intensities for all
important HW nodes in a system [29].
D. M4: ACU RANK ORDER (ACURO)
This estimates similarities between ACU rank orders
(ACUROs) among CPU types. We rank ordered the top 100
applications in each app category by CPU usage. This produces
three ordered lists (one per CPU type) per application category.
We then compute Kendall’s tau coefficient for each pair of lists
to show ACCU similarity among CPUs.
Observations: Fig. 9 shows that i3 and i7 are the least
correlated, and i5 usage is more similar to i3 than i7 - a high
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Office Media Edit IT Games Web Media
Consumption
RankCorrelation(Kendall'sTau)
(i3, i5) (i5, i7) (i3, i7)
Fig. 9. Pairwise rank correlation considering top 100 applications in each
app categories.

7. Photoshop
Mencoder
handbrakecli
ffmpeg
moviemaker
lightroom
sketchup
illustrator
adobe premiere pro
coreldrw
SolidWorks (3D CAD)
afterfx
picasa3
avsvideoconverterhst
indesign
trueimagehomeserv
potencoder
adobe premiere elem
revit
VideoStudio (Ulead)
i7 i3 i5
Fig. 10. Top 20 media editing applications for i7, and their ranks for i3 and
i5. Legend at the bottom: line thickness ∝ ACU/system of the left column of
a pair of columns ({i7, i3}, {i3, i5}). Therefore, the thickness of the two sets
of lines shown here correspond to ACU on i7- and i3-based systems.
value means similarity not magnitude of use. Fig. 10 is an
example of ACURO, showing top 20 apps for the Media Edit
category, for i7 usage (on left).
Discussion: Fig. 9 shows that IT usage is least correlated
on i3 and i7, whereas web use and media consumption have
closer correlations. Fig. 10 shows how the top 20 media editing
ACUROs change. For example, Adobe Photoshop is top on all
CPUs, but ACU is much lower on i3 than i7. However, Picasa,
a simple app is 13th on i7, 10th on i5 and 5th on i3. These
crossovers are reflected in the Editing correlations of Fig. 9. In
developing HSC workloads, these details must be considered
for each persona-specific design target.
E. M5: PREMIUM-APP ACUS
Fig. 11 shows ACU (as in M3) vs. installed base % for
four applications. Installed base % is the percent of systems
on which a given application is installed. This is a HW/SW
metric, with level 3 SW (individual applications, not categories
as with earlier metrics).
Observations: This metric shows that users of high end
systems and low end systems form distinct clusters. Some
applications are run much more on i3 than i7, and vice
versa. Examining the applications shows clearly that the i7
applications require more computing resources and are more
comprehensive in scope. For example, productivity applica-
tions (Excel and Acrobat) are installed on 2X to 4X more
i7 systems than i3, and their ACU per i7 system is about
7X that of i3. On the other hand, for a low-level enabler
(Chrome browser), installed base and ACU are both similar
and somewhat inverted in order from i3 to i7.
Discussion: For every application category, we have ob-
served a general trend of migration in premium application
use, Figures 10 and 11. On i3, less-complex SW is used more
than on i7, and more-complex applications are used more on
i7 than i3. In every category, some applications get high use
on all systems, but premium application migration to high-
end CPUs seems universal across applications categories. In
general, premium application CPU usage indicates a better user
experience on high-end PCs than on low-end ones.
Metrics 4-6 form a WC tool set for selecting apps to
combine as HSC inputs. These considerations appear to be
far from the industry-standard benchmarks plus marketing
intuition usually applied as design inputs. The benefits of
Metrics 4-6 include exhausting the user base, currency (e.g.
latest quarter), and measurement objectivity. They offer ben-
efits to microprocessor designers, system architects and SW
developers. Fig. 11 can also be the basis for informing users
of potentially useful apps.
F. M6: EXTREME-USER ACCUS
We define a user as “extreme” if her ACCU exceeds that
of most other users. We choose a percentile, p, say the 70-
th percentile as the threshold, and note p-th percentile ACCU
in various categories of applications (excluding anti-virus and
OS processes). A user is extreme if her ACCU exceeds the
p-th percentile ACCU in all application categories. In domain
space, this is a HW/user metric; all SW is included, specific
categories are not expressed by the metric.
Observations: In principle, we can imagine the existence
of extreme users who use most types of applications compar-
atively more intensely than other users do. Fig. 12 shows that
i7’s have the highest fraction of extreme users, and that they
are found more commonly on higher end PCs than lower.
Discussion: Other metrics have shown that i7 users are
likely to get more out of their systems than i3 users in various
ways. Extreme use combines all application categories and
shows that deviations above average use in all categories are
much more common for i7 than i3 users. The ratio is about
twice as many for the 50th percentile, with higher ratios for
higher percentiles (Fig. 12). Our interpretation is that the
higher model CPU one uses, the more engaged one is with
one’s PC. M6 has specific benefits in UX design, as it identifies
a class of users that truly use everything a PC offers. By
varying the parameters defining extreme use, designers can
identify sizeable subsets with narrower needs for specific HSC
studies.
Fig. 11. ACU vs. % of installed base for a few applications by CPU types.

8. 0.2% 0.5%
1.0%
1.0%
1.6%
2.7%
6.6%
8.5%
13.3%
i3 i5 i7 i3 i5 i7 i3 i5 i7
70 %-ile 60 %-ile 50 %-ile
ExtremeUsers'Fraction
CPU usage threshold percentile to define "extreme" Users
Fig. 12. Proportion of “Extreme” users in each type of CPU. A user is
“extreme” if she uses >= 70 (also shown for 60 and 50) percentile of CPU
sec/day in each of the application categories (ACCU) except System/Other and
Antivirus. i7’s seem to have a higher fraction of extreme users. Horizontal lines
are upper and lower control limits.
G. M7: ACU DORMANCY CHANGE
This expresses the change in ACU per day (Metric 3) on
a monthly basis. It can be interpreted as growth in ACU up
to the point of maximum use. It also can show that new users
require time to engage with new systems, unlike repeat users
(not distinguished here). As with Metric 6, this is a HW/user
metric; all level 1 SW is included here. New systems may have
dormant value: use grows, flattens, then begins to drop.
Observations: For all three CPU types, usage rises, ap-
proaching a steady state (Fig. 13). After two years of system
age, however, the direction is inconclusive, since the confi-
dence intervals become much wider due to insufficient data
points. Not all systems have data for each age bucket. i7’s
are used more than i3’s; however, i5’s show the most usage
throughout. These graphs show all CPU time, including OS,
antivirus, etc. In contrast, Metric 3 ignored background SW.
Discussion: While we have not measured it directly, dor-
mancy supports the idea that new users ramp up to the same
level of use on a given system as experienced users. Fig. 13
includes new and repeat users around the world. A plausible
explanation is that new users are a mix, and repeat users
will continue immediately with steady state usage relative to
previously owned systems. New users will require a learning
System Age in Months
CPUKOps/day
i7
i3
i5
Fig. 13. CPU usage (ACU) over time. Error bars are standard errors of
means.
period, and ramp up to levels shown. Additionally, if first
time buyers are more likely to start with i3, Fig. 13 expresses
the learning-curve knee at about one year, whereas likely
experienced repeat buyers of i5 and i7 have a 5-8 months
knee. Separating new from repeat buyers is an important next
step. There is general agreement that the SW on new systems
grows and slows system operation over time [30]; this deserves
deeper examination. Relevant UX periods must be chosen for
WC studies and HSC because designers may want appropriate
usage periods for certain PC models, e.g. distinguishing naive
and experienced users in entry-level systems.
H. Clustering Users toward WC
Fig. 14 illustrates an approach to finding market personas,
groups that we observe form through commonalities of users’
application use, by clustering User/SW pairs across all HW.
Each user first is expressed as a numerical vector of application
usage by the M2 (FACT) metric. All of the users’ vectors
produce a matrix of 500K rows and 12 columns (=app cate-
gories). We then apply a hierarchical clustering algorithm [31]
to produce 15 clusters. That is, each vector starts as a single-
point cluster, and repeatedly two clusters A and B are joined
if the merging cost by Ward’s method [31] is minimum:
CostA,B =
nAnB
nA + nB
mA − mB (2)
where mi is the center of cluster i with ni points in it.
This merging process will continue until 15 specified clusters
remain. 4 such cluster centroids are shown in Fig. 14. Other
clustering insights can be obtained by substituting M3 for M2.
In depth analyses of various clusters is a topic for future work.
Observations: Fig. 14 shows four distinct clusters, covering
about 2/3 of our population. Overall about half of FACT
is web, indicating that if real life workload is of interest,
benchmarks that include this area should be used.
Discussion: To apply these ideas, OEMs would combine
this objective data with subjective judgment constraints that
we do not consider: installed base, competition, growth plans,
product goals (entry vs. high-end), market focus (Fig. 4
indicates geo variations), etc. The Mi could be used to refine
Fig. 14. App use (M2 metric) distribution of 4 of the top user clusters: (a)
Heavy Internet, (b) Pro Office users, (c) Gamers, and (d) Entertainment users.

9. clusters in many ways (geo, CPU types, usage areas, etc.).
Or starting with the Mi, market segments could be included/
excluded, and the target set could be clustered. Iterating
between the Mi and clustering is also possible.
I. Summary
Table III summarizes this section, relating RQ2 and RQ3
to the 7 metrics in terms of the domains defined in Fig. 5.
Metrics
(domain)
RQ2 RQ3
Higher-end models show Common use
observed throughMore Usage More benefit
M1. On time (HW/user) Yes System/ Geo
M2. Foreground time (HW/SW) Yes App category
M3. CPU usage (HW/SW) Yes App category
M4. App correlation (HW/SW) Yes App category
M5. Premium apps (HW/SW/user) Yes Application name
M6. Extreme use (HW/user) Yes App category
M7. Dormancy (HW/user) Partially yes System
TABLE III. PC USE RELATIVE TO RQ2 AND RQ3.
The first 3 metrics relate time and usage to PC models (by
CPU type), partly answering RQ2, in terms of use of each
model. The next 3 metrics relate details of app categories to
use, helping to explain RQ2 in terms of CPU benefits. Finally,
dormancy covers all of RQ2 by showing how CPU benefits
grow over the months of new PC ownership. RQ3 coverage of
each metric is presented in the last column.
VI. DISCUSSION
The first two sections discussed many factors that can
influence a PC purchaser. As our analysis is mainly at the
behavioral level, it transcends many of these details, reflecting
some but not others. For example, users spending more time
with rather complex applications may indicate strong support
for CPU and screen factors, but say less about the battery and
form factor importance. The complex interactions among all of
these issues in the buyer’s mind vs. the observed behavior are
challenging to analyze. Our results can be regarded as a bottom
line: whatever motivated the purchase, we presented exactly
how massive numbers of users behaved once they owned a
given system model, using the three research questions.
RQ1. We saw evidence of cost sensitivity (budget con-
sciousness) and aspiration (intended use and goals) in choos-
ing PCs. We also saw cursory evidence of experience, with
self-descriptions such as Technology guru, Comfortable with
technology, etc. Furthermore, Pro users and creation-type
usage intentions were tied with higher-end CPU based systems
(Fig. 3). Conversely, regular users and consumption usage were
generally related to choosing lower-end CPU based PCs.
RQ2 and RQ3. By grouping PCs as less capable and more
capable, the behavioral data analyses showed that overall, the
answer to both RQ2 and RQ3 are yes. Users engaged longer
with higher-end systems, and premium applications ran and
extreme use happened more on higher-end systems.
RQ4. Using the results of these measurements to enhance
workload characterization is summarized in the discussion of
each metric. That there are problems with industry-standard
benchmarks [23], [24] can be inferred from the fact that
as shortcomings have appeared in existing benchmarks, new
ones have proliferated for 20 years. Furthermore, there is no
evidence that benchmarks capture most concepts above. DCA
measurements may enhance existing design procedures at
several levels: market partitioning, system design, and micro-
processor design via new traces. Many derivatives of this work
can be used to inform users of system weaknesses (section
II) or enhancements (section V-E). If the manufacturer of a
“gaming” PC, e.g., learns that “media editing professionals”
use this model as well, it can jump-start UX by bundling both
popular games and media editing software with the PC.
Design Implications and Future Directions
This paper describes several steps toward understanding the
PC purchase decision process and the actual use that follows.
Our central contribution is a methodology based on a set of
behavioral data metrics plus software tools that help explain
usage, and in turn, user aspirations about new purchases. Some
of the implications of our work, recommendations, and ideas
for future work follow.
A. Design improvement implications
1) PC planners and designers need to know if their
designs are used as intended. Logged behavioral data
and metrics presented above can be analyzed to
provide usage-driven product design feedback for the
next generation.
2) Figures 4 and 6 show that usage varies across geos,
so geo-specific solutions may benefit both hardware
and solution vendors, and users.
3) Following the cycle that improving the LHS of WC +
HSC → UX improves the RHS, and that leads to new
WC changes to be measured (WC + HSC → UX →
WC ), it seems clear that current benchmarks and WCs
should be enhanced with more real use data.
4) These methods can be extended to studying the
effectiveness of various parts of a CPU, e.g. vectors
or caches, or the system, e.g. SSD, GPU, or wireless
features. Knowing how much they are used by various
apps and various user personas, leads to HSC of more
effective SKUs, further enhancing UX per SKU.
B. Future Work
To lay a tractable foundation for one aspect of our future
work on the role of PC usage experience in PC purchasing
decisions and use, consider two simplifying assumptions:
1) The user population is fixed.
2) Disruptive technological innovations are absent.
Furthermore, assume there are two PC, tablet, etc. models
to choose from - high and low capability. Fig. 15 tracks this
population over time assuming that for the next generation,
each user either continues using the old system, continues with
a new system of the same type, or transitions to the opposite
type of next generation system. Transitions are labeled satisfied
or unsatisfied (for crossover users). Each box in Fig. 15
corresponds to the adoption box of Fig. 1. The methodology
we propose for analyzing Fig. 15 is to question new buyers,
and then follow up with the kinds of workload characterization
measurements discussed above. This would lead to a feedback
process from measurement to improved methodology, and
generate more insights than presented in this paper.

10. Fig. 15. Overall inter-PC generational flow of PC users.
Our hypothesis is that the overall uniformity of use ob-
served for low and high PCs at time i, leads the fixed
population to become more confident and more homogeneous
in their PC experience and aspirations. Therefore, the primary
driving influence for future PC choice becomes users’ aspira-
tions: achieving life-goals that are tied to PCs. Other factors
like peer advice become less important as the user gains a
rich experience over time and is comfortable tracking com-
puter/communication innovations. Cost-influence also dimin-
ishes as cost/effectiveness typically falls over time, and the user
may defer buying until a system dictated by experience and
aspirations becomes affordable. For this simplified situation we
hypothesize that the unsatisfied user transitions would diminish
substantially after several iterations of Fig. 15. The end-state
of the hypothesis for the simple model is that users buy (want)
what they know they need, and a steady state is reached for
the two PC models.
A real-world model would drop the simplifications above,
add many device choice rows to Fig. 15, and allow for new
users and disruptive devices. We expect that it would tend
to behave as the simple model does, generalized for multiple
choices per generation together with the superimposed “noise”
of new users and disruptive devices. This can explain some of
the anomalies in the data shown earlier. More choices will
generate more “unsatisfied” transitions, unless users clearly
understand the options in terms of their aspirations.
One positive outcome, if this hypothesis is true, is its self-
sustaining technology aspect. Usage helps drive the process of
designing effective PC hardware/software combinations. The
results of 30 years of design are complex systems that everyone
can use effectively, even without training. Manufacturers have
a major responsibility to continue producing new PCs that
are effective for increasingly broad user needs. As business
is driven by money for worthwhile products, this allows
companies to charge competitive prices (not just commodity
prices) for aspiration fulfilling systems while distinguishing
themselves from competitors.
ACKNOWLEDGMENTS
The DCA team: Jamel Tayeb, Sushu Zhang, Layne Mills, Ravi
Mattani, Chansik Im, Alexey Kryukov, Dwarka Nath.
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