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"What I wanted to do was create an efficient market where
regular people could compete
with big business ... it was a little bit of an experiment."
— Pierre Omidyar, eBay founder
eBay Analytics:
Innovation Inspired by Opportunity
Abstract
The eBay of 2010 is preparing itself for increasing global
competition. This case
emphasizes alignment between business strategy, innovation,
analytics and IT
investments. An analytics focus on streamlining innovation
through experimentation is
supported through a unique data warehousing architecture that
virtualizes on-demand,
innovation ‘sandboxes.’ Key personnel have been hired to
support the strategy, and
marketplace goals have been established to guide experimental
designs. To support
experimentation, eBay’s Teradata Enterprise Data Warehouse
(EDW) enables self-
service virtual data marts that are cost effective, they can be
realized faster than
traditional data marts, and they support streamlined transfer of
new discoveries into
production. eBay refers to the approach as ‘Analytics as a
Service.’ In addition, a new
area of analytics, called ‘deep analytics,’ is supported by a

dedicated Teradata appliance.
Deep analytics at eBay focus on scrutinizing huge web log data
volumes for doing special
types of analysis. For eBay leaders like Vice President of
Analytics Bob Page, the new
thrust will require nurturing an organizational “culture of
experimentation.” Page’s team
faces challenges like determining appropriate incentives to
encourage experimentation
leading to innovation, establishing experimentation standards
and governance processes
and identifying approaches for evaluating ‘return on innovation
investment.’
This case was prepared by Michael Goul, Professor and Chair,
Department of Information
Systems, W. P. Carey School of Business, Arizona State Univer
sity. This case is free for use for
educational purposes. Copyright 2011 by Michael Goul and the
Arizona Board of Regents.

2
eBay Analytics: Innovation Inspired by Opportunity
Introduction
eBay was founded only fifteen years ago by twenty-eight year
old software developer
Pierre Omidyar in his living room. Echo Bay was the name of
Omidyar’s consulting
firm; the name was shortened to eBay, and the company began
to grow. Stanford MBA
Jeffrey Skoll joined in 1996, and eBay went public in 1998. By
then, both Omidyar and
Skoll were billionaires. In 2009, the total worth of goods sold
on eBay was near $60
billion - $2000 per second. By August 2010, eBay was faring
well through the recession.
Matt Jarzemsky of the Dow Jones Newswires reported that
Moody’s had upgraded eBay;
citing strong performance and liquidity:
“Moody's Investors Service gave a one-notch upgrade to eBay
Inc. (EBAY),
citing the e-commerce giant's strong operating performance and
promising
prospects. The company has seen performance improve of late
as it works
through a three-year restructuring aimed at reviving its
marketplace unit with an
increasing focus on fixed-price sales, which now account for
more listings than
auctions. It reported last month its latest quarter profit climbed
a higher-than-

expected 26% as revenue rose, though it tempered its 2010
guidance because of
the stronger dollar. It noted the company has very low financial
leverage and
solid liquidity, with eBay's strong cash flow position enhanced
by a $1.84 billion
credit facility maturing in 2012. The ratings outlook is stable,
reflecting its
expectation the company will maintain its strong market
positions in e-
commerce. EBay's shares were down 0.4% at $22.66 in recent
trading as the
broader market slumped on disappointing U.S. jobless claims
data.”1
eBay’s worldwide competition started heating up in 2010. One
analyst suggested on July
6, 2010 that eBay actually needed to start an Amazon-style
fulfillment center if it wants
to remain competitive and shed its ‘web 1.0 image.’2 Another
analyst had a broader
focus and cast Rakutan as a major emerging competitor to both
eBay and Amazon.
Rakutan, Japan’s largest auction site (64 million registered
members, sales of $3.2 billion
in 2009 and some 50 million products listed by more than
33,000 sellers) acquired
Buy.com for $250 million.3 Why did Rakutan buy a U.S.
company? Buy.com had 14
million registered members, it has an operation in the UK, and
the press release from
Rakutan on the day of the purchase read, ‘Buy.com represents a
unique opportunity to
accelerate the notion of borderless ecommerce in both
directions – from East to West and

from West to East.’ Another potential competitor is
Taobao.com. Taobao announced a
deal in May 10, 2010 with Softbank Corporation’s Yahoo Japan
claiming that it is aiming
to create the world’s largest market place.4 Taobao is by far
the biggest online shopping
site in China—with 87% of merchandise volume sold online as
of the fourth quarter of
2009. $30.5 billion of goods were sold through Taobao in that
year, about half the $57.2
billion in transactions on eBay's marketplaces in the same year.
Taobao expects
transactions to double this year. In discussing the Yahoo Japan
collaboration, Softbank’s
Masayoshi Son said, “After we join our strengths together we
will definitely beat eBay.
In the future, the Asia market will be very important. If you
can't be No. 1 in Asia, you
won't be No. 1 in the world."
3
eBay’s faced stiff competition before, but from the beginning, it
has thrived by staying
true to its mission of being a company that pioneers
communities built on commerce,
sustained by trust and inspired by opportunity. Employees of
the eBay of 2010 are
increasingly inspired by opportunities discovered through what
has recently been referred
to as ‘smart business experimentation.’5 Led by CEO John
Donahoe, he recently
reinforced the importance of experimentation for purposes of

innovation as follows:
“We want to be an innovator. We want to make it easy to list,
easy to sell, and
easy to buy. Our new iPhone app is a perfect example. In the
past, it has been
cumbersome to post goods for sale. As a result, some goods
that could have
been resold ended up in landfill. With eBay's iPhone app, my
mother can go
into her closet, take photos of a coat she's ready to pass on,
describe it and list it
in less than a minute. The same will be true with mobile
payments. We will
make it easier for people to buy and sell wherever they are…
We also support
innovations that help the efficiency of our operations.”6
A New Addition to the Team
How will eBay continue its leadership across multiple areas of
commerce innovation?
According to scholar Tom Davenport, the company’s evolved an
organizational
capability to test-and-learn. Davenport and many others have
extolled eBay’s successes
in developing this capability as embodied in the company’s
Experimentation Platform:
“Companies that want testing to be a reliable, effective element
of their decision
making need to create an infrastructure to make that happen.
They need training

programs to hone competencies, software to structure and
analyze the tests, a
means of capturing learning, a process for deciding when to
repeat tests, and a
central organization to provide expert support for all the
above.”7
In 2010, efforts to reinforce the central organization to provide
the expert support
mentioned by Davenport came in the form of new eBay hires.
In late May, company
spokesperson Johnna Hoff was interviewed by
AuctionBytes.com and announced that
technological innovation and hiring the right team had been
keys to recent progress:
"Our focus on the technology front has been strengthening
eBay's foundation
and taking steps to set the stage for future innovation. We have
taken time to lay
the groundwork, hiring a world-class tech team, building an
experimentation
platform, launching the Garden by eBay, and listening to
customers. From that,
we've driven early successes that eBay users have told us
perform well: the
‘More like this’ feature; and Mobile are examples - and there is
much more to
come."8
Hoff’s mention of hiring a world-class tech team was right on
target. The new V.P. of
Analytics, Bob Page, had been the Senior Director of Analytics

at Yahoo! since October
of 2004. He blogged an announcement of his arrival at eBay on
March 20, 2010:9
4
Hello eBay!
Thanks for the emails and tweets around my time off, it was
short but sweet.
While it would have been great to take more time to
decompress, I knew what
was ahead — and felt like a kid on Christmas Eve. I didn’t want
to wait, because
…
I’ve joined eBay.
eBay has many fabulous analytical tools already, both
commercial and home-
grown, for lots of different kinds of analysis. In addition, they
are on a road to
build out a whole new class of analytic capabilities based on
Hadoop. They
recently reorganized the data initiatives and groups to form a
team that re-
focuses the “many standalone tools” mindset to a “platform” for
analytics. This
holistic vision, and the “central data, distributed analysis”
mindset aligns so

well with my thinking and interests that I had to make the jump.
As much as I
love what Yahoo! is doing with analytics, the opportunity at
eBay was too
compelling to pass up. I mean, come on .. it’s the world’s
largest online
marketplace!
My discussions with the eBay leadership team told me two
important things.
First, they are ready to make significant investments in data
capabilities to drive
the next generation of eBay. Second, the new leadership over
the last couple of
years is bringing a change to the business, where the company
will be much
more technology- and innovation-driven than it has been in the
past. Many of
the leadership hires in the last 18 months are a testament to
that. And I like to
think I am another proof point.
Having cool technology and a leadership team that understands
the value of
data is a great start. But the icing on the cake is the level of
data and analytics
talent within eBay. It is, in a word, staggering. I am truly
humbled by the
opportunity to work with a group of this caliber.
Shortly after arriving at eBay, Page was a member of
FutureWeb’s panel session on web
analytics and offered what, to many, was a surprising appraisal
of the world of data
warehousing and business intelligence when he stated:

“Analytics are going to get worse before they get better. We
have so much data
and means to collect it, and we produce endless reports. What’s
going to happen
is decision makers are going to say, ‘I have a hypothesis. I’ve
decided what I
want to do with X, Y and Z,’ then they go find the data that
backs up their
position. Having folks that are ethical about the data and what
it means will be
important.”10
In a later interview, Page elaborated. In leading eBay analytics
forward, he discussed
how one step is to create the right platform, and another very
important step is to build
the right experimentation culture. Experimentation requires
developing a hypothesis,
designing an appropriate test of that hypothesis (one that has
appropriate controls),
executing the test, and then ensuring results that improve the
bottom line performance are
made a part of the business. An ‘equitable’ mindset on the part
of experimenters is a
key. If tests are contrived or are conducted to simply confirm
preconceived or desired
outcomes and opinions, the scientific nature of the inquiry goes
out the door. Page states,
5
“When you generate hundreds of metrics – you aren’t doing

analysis, you are just
justifying your argument – throwing data at this will just
confuse matters more.”11
Developing a test-and-learn organizational capability means
investing in platform to
enable it - and at the same time, one must lay the foundation for
a culture that extols
scientifically sound experimentation. Regarding infrastructure,
traditional investments in
data warehouse architectures don’t often translate to solid test-
and-learn platforms. Data
warehouses were originally designed for reporting and OLAP,
so extending them to a
test-and-learn capability represents significant challenges. The
nature of experimentation
requires advanced analytics – and that is where rethinking the
nature of data warehousing
has created a new set of decisions that organizations have to
make. Philip Russom’s
TDWI Checklist Report: Data Requirements for Advanced
Analytics, explains:
“Organizations will face challenges as they move into advanced
analytics. Many
don’t understand that reporting and analytics are different
practices, often with
different data requirements. Many have designed a data
warehouse to fulfill the
requirements of reporting and online analytic processing
(OLAP), and they will
soon need to expand the warehouse (or complement it with
analytic databases)
to fulfill the data requirements of advanced analytics, whether
query-based or
predictive. One of the most critical design and architecture

decisions adopters of
advanced analytics must make is whether to store analytic data
in a data
warehouse or in a stand-alone analytic database. There are
three options: 1)
Analytics processed within the EDW, 2) Analytic sandboxes and
3) Analytic
databases outside the EDW.”12
In considering Russom’s options, a major issue is that the
analytic tools based on data
mining technologies are often optimized for data dumps in the
form of flat files or
denormalized tables, so generating a very large flat file is core
to data preparation for
many mining algorithms. Many claim that organizations with
an existing, true EDW
(enterprise data warehouse) are likely better able to make the
extensions necessary to
enable test-and-learn. In this context, an EDW is thought of as
one that is all-
encompassing; it is a repository for data that can be leveraged
by many departments, it is
capable of handling many different workloads concurrently, and
it manages data that has
been captured in different types of data structures and models.
Many organizations have
an EDW, others perceive EDW as a model to aspire to, and they
are working towards it.
This is because the contrast is often a proliferation of data
marts (an option derogatorily
referred to as ‘spreadmarts’) that are often slow because they
require frequent
independent processing for functions like transformation,
cleansing, modeling, etc. Many

architectures composed of what is often referred to as ‘rogue
data marts’ have to be
brought under central management in order to maintain their
accuracy and to minimize
prohibitive administrative and overhead costs. However,
conventional debates over
EDW and data mart architectural approaches are being
reconsidered given the push
towards advanced analytics and test-and-learn capability
development. Along with the
move towards advanced analytics, the addition of data
warehouse appliances to
organizations’ infrastructure possibilities has served to further
muddle the historic debate,
and appliances extend the options for retrofitting EDWs for
advanced analytics.
As Russom indicates, adding analytics to an existing
architecture can be done in several
6
ways. Analytics processed within an EDW are often stated as a
preferred option, but
because of the structure of data required by current analytical
tool suites, the processing
of the flat files required is inconsistent with most EDW
structures. In addition, there
could be significant performance drags to the EDW workload as
advanced analytics
usage is scaled. Often, flat files are often better off sent to
some sort of a secondary
repository, but the cost is that there are some latency and

potential accuracy issues. (It
should be noted that many vendors are working on altering
EDW capabilities to enable
this preferred option through an approach called in-database
analytics where the tool
suites and the EDW are more tightly coupled - but these
developments have not yet been
widely deployed.) Another approach is referred to as ‘analytic
sandboxes.’ Here, an
analytic database is set up somewhere inside the EDW, and the
analytic tool suite user
can work within the sandbox - with the advantage that there is
little drag to the overall
workload of the EDW. Finally, a repository outside the EDW
for analytics, or the
creation of specialized analytics databases, is an option. At one
extreme, this approach
can deteriorate into the spreadmart situation, but then again,
creating these repositories
can be helpful if the analytics have high and unpredictable
workloads. eBay has worked
through these options as it has built-out a world-class test-and-
learn infrastructure.
eBay’s Experimentation Platform
eBay’s Experimentation Platform has been developed in
collaboration with Teradata
Corporation. Oliver Ratzesberger, senior director of
architecture and operations for
eBay, led the original move from an Oracle-based system to a
Teradata system in 2002.
CIOZone’s Mel Duvall sized up the history of the switch as
follows:
“EBay overhauled its data warehouse and analytics capabilities

in 2002, moving
from an Oracle-based system to a Teradata system. Ratzesberger
said the move
at the time was based on necessity—daily data loads were
exceeding 24 hours
and the system simply could not produce results in satisfactory
time frames.
While the Teradata upgrade helped overcome capacity
limitations, it was still
difficult for central IT to keep up with demands by business
units for new data
marts to perform analytics. About two years ago, the idea was
conceived to
create a self-help service, where individual business units could
use a Web-
portal to load their own data into the Teradata warehouse. Once
loaded, they
could then begin performing their own analysis using standard
tools from such
vendors as SAS, Microstrategy, Cognos and/or Excel. Business
units can also
bring in outside data from third parties using the Web portal.
The end result is
that instead of taking weeks for central IT to process a request,
a business unit
can upload their own data and quickly begin getting results.
There literally are
some business units who have an idea on Monday, and results
by Wednesday.
The idea of allowing business units to tap directly into the data
warehouse in this
manner did cause some uneasiness within the IT ranks. As
safeguards, the
virtual data marts are set up in sandboxes, what eBay calls
PETs, short for
prototype environments. Time limits of 90 days are set on the

use of the PETs,
preventing storage space from being indefinitely tied up, and
individual business
units are limited to the number of PETs they can run. If a
prototype is deemed
to be a success and of continuing value, the business unit can
request that it be
brought into full production. Since the parameters of the data
mart are already
known, the prototypes can be brought into production in a
fraction of the time
than if they had started from scratch.”13
More recently, eBay and Teradata announced their extension of
their joint engineering
agreement in order to further improve eBay’s Experimentation
Platform as per the
following press release:
“’eBay continues to operate at the forefront of data warehousing
architecture
and vision, and this collaboration with Teradata helps ensure
eBay.com
processes data quickly and efficiently with new breakthrough
analytical
capabilities. eBay is leveraging advanced analytics
technologies at a massive
scale in providing the best experience possible for our users,’
said James
Barrese, vice president of technology at eBay. ‘The eBay team
is well-known
for its use of analytics for competitive advantage and for

thought leadership in
the data warehousing space,’ said Scott Gnau, chief
development officer,
Teradata Corporation. ‘We are happy to extend our engineering
collaboration
with eBay to include next-generation analytics across a broad
range of
structured and semi-structured data."14
That collaboration has already resulted in an infrastructure that
can quickly spin out what
are referred to as ‘virtual data marts.’ These are akin to the
analytic sandboxes that
Russom included as an alternative to extending the EDW to
accommodate analytic
workloads. These virtual spaces combine views and
aggregations on the EDW.
Additional data can be added by mart users. The sandboxes are
instantaneously created
using self–service interfaces that are configured to manage a
mart where some
permissions, quotas, etc. have been already established, but
where an end-user can
request the data to be loaded and has control over certain other
mart parameters. Figure
One is an example interface to an eBay virtual data mart, and
Figure Two shows the
process:
Figure One: Virtual Data Mart Self-Service Interface at eBay
7

Figure Two: The Process for Creating Virtual Data Marts
The virtual data mart exists in a partition separate from the
EDW, and in the Teradata
EDW, it relies on the workload management software to deliver
with assurance on many
virtual data mart service level agreements at once. Teradata’s
approach to supporting
analytics facilitates the variety of architectural configurations
Russom has identified as
shown in Figure Three:
Figure Three: Teradata Options for Extending EDWs for
Analytics
8
Many have referred to eBay’s data mart approach by the phrase
‘analytics as a service’
(some abbreviate this as ‘AaaS’). The approach is also referred
to as ‘agile analytics,’
but in that usage, it is important to clarify it’s the
infrastructure, and the ability to quickly

9
perform analytics that are reflected in the use of the term agile,
not the development
methodology per se. Oliver Ratzesberger of eBay blogged
about the infrastructure on
xlmpp.com as follows:
“Think of it as providing utility computing for analytics to
anyone within or
even outside your organization. Analytics as a Service is not
limited to a single
database or software, but the ability to turn a general-purpose
analytical platform
into a shared utility for an enterprise. It's an advanced self-
service model that
allows groups to leverage virtual systems for their individual
data processing
needs, at a cost that undercuts any data mart or cascaded
systems
implementation. In addition the biggest advantage to the user
groups of such
services is that ALL other organizational data is automatically
at their
immediate disposition. The real beauty of this concept is that,
the more virtual
analytical systems you deploy, the better the overall scalability
and the higher
the cost savings. With dozens or hundreds of virtual systems,
chances are that
more and more of them leverage processing at different times
and frequencies,
one of the main selling points of virtualization in the first place.

All of this is
only possible if you are able to implement a highly resilient
infrastructure that
delivers availability and can handle virtually any workload -
known or unknown
- optimized or not, even and especially 'bad' workload. The
platform has to be
able to tightly control dozens to hundreds of virtual partitions,
with variable and
workload dependent prioritization schemes, hard or soft limits,
any form of
mixed workload, batch and streaming data feeds. On any given
day our systems
process models and queries that have never been seen before.
For us, Analytics
is a Service is becoming more and more a utility computing
platform that
enables agile prototyping for the Business. Analytics as a
Service - or PET - as
we internally call them, have become THE win-win for the
organization.15
The notion of virtual data marts, sandboxes and PETs has
conjured up the historical
debate about the virtues of relying on a federated data mart
approach vs. a completely
centralized enterprise data warehouse approach to provide an
organization’s business
intelligence capability. The debate became renown as staunch
allies of the two different
approaches had historically lined up steadfastly behind each
option: Ralph Kimball and
Bill Inmon. Inmon, the person credited with being the founder
of data warehousing, in
consistent fashion to his passion for EDW, recently lashed out

at data mart virtualization
or what he referred to as ‘virtual data warehousing’:
“Have you ever been to a fair with the game where a mechanical
gopher pops
out of a hole and it is your job to whack it when it appears?
Once you whack the
gopher, it is only a short amount of time before it reemerges
from another hole.
This is a good game for 4-year-olds, but it is very frustrating
for adults. A
virtual data warehouse is like this carnival game. I believe
virtual data
warehouses are inane. Just when you think this incredibly inane
idea has died
and just when someone has delivered it what should have been a
deathly blow,
out it pops again from another hole. The virtual data warehouse
just won’t die,
no matter how hard or how many times it gets whacked.16
This analysis led James Kobelius, an analyst with Forrester
Research, to pen an article
titled, “Inmon’s Virtiolic Slap at Virtual Data Warehousing
Does Not Withstand
Scrutiny.” He refers to an overall data federation approach and
defines it as follows:
“Data federation is any on-demand approach that queries
information objects
10

from one or more sources; applies various integration functions
to the results;
maps the results to a source-agnostic semantic-abstraction
model; and delivers
the results to requesters. Nothing in the scoping of data
federation necessarily
requires the multi-source aggregation and joining that Inmon
puts at the heart of
‘virtual DW.’ For starters, his definition of virtual data
warehousing is oddly
vague and questionably narrow: ‘a virtual data warehouse
occurs when a query
runs around to a lot of databases and does a distributed query.’
Essentially,
Inmon defines ‘virtual DW’ as the ability to a) farm out a query
to be serviced in
parallel by two or more distributed databases, b) aggregate and
join results from
those databases, and c) deliver a unified result set to the
requester. That’s an
important query pattern, but not the only one that should be
supported under
(pick your quasi-synonym) data federation, data virtualization,
or enterprise
information integration (EII) architectures. My basic objection
to Inmon’s line
of discussion is that he treats data federation as mutually
exclusive from the
enterprise DW (EDW), when in fact they are highly
complementary approaches,
not just in theory but in real-world deployments. Yes, data
federation can be
deployed as an alternative to traditional EDWs, providing direct
interactive
access to online transactional processing (OLTP) data stores.

However, data
federation can also coexist with, extend, virtualize, and enrich
EDWs...”17
One thing is clear from Kobelius’ discussion is that the classic
textbook debate regarding
data marts vs. EDWs is now entering a new, much more
complex chapter. With
advanced analytics garnering significant interest from the
business side of the house,
eBay’s infrastructure is ahead of its time – and the architecture
is likely going to be
scrutinized not just from the technical elegance of the solution
(although it is very
sophisticated), but the proof is also in the massive scaling of the
business experiments
that can be conducted - and the ability of those experiments to
drive business value.
eBay’s ‘Extreme’ Extension
eBay’s Experimentation Platform delivers on extending an
EDW to efficiently and
effectively support advanced analytics – and a lot of those
analytics are being done at the
same time by many decision makers throughout eBay’s many
functional areas. Appendix
One shows the architecture of eBay’s analytics core. Because
virtual data marts are
contained in the EDW, the cost of creating new data marts for
each department/unit that
might need to be doing analytics is drastically reduced. A new
data mart’s total cost of
ownership can easily exceed $500K. The biggest cost drivers
are maintaining the
separate databases, doing the weekly/daily/hourly data

transfers, dealing with data
inconsistencies, managing data redundancy and increased
complexity, and of course,
dealing with what might be referred to as ‘time to market’ for
new analytics needs. Over
85% of eBay’s analytics workload is new and unknown – this
workload is not routine
report generation or predictable OLAP query loads. eBay
leaders often say, “The metrics
you know are cheap; the ones you don’t know are expensive, but
they’re also high in
potential ROI”18 eBay’s virtual data marts can be offered at no
cost to the business.
In 2010, a new project started in eBay’s collaboration with
Teradata and began with
eBay's purchase of the Teradata Extreme Data Appliance.
Starting at six petabytes in
size, the Teradata appliance can analyze and store 30 terabytes
of data each day. The
project will focus on site analytics through web logs, a layer
deeper than clickstream data
[Reuters, May 6, 2010]. This newest collaboration represents
an architectural addition to
eBay’s Experimental Platform in the form of a separate
analytics data mart. Analysts
have conjectured that the appliance provides a needed
complement to eBay’s EDW for
high volume data that, by its very nature, is less valuable on a
per-unit storage basis than
is the data in the EDW. That data will also be subject to
minimal concurrency issues as
it is dedicated to a certain type of analysis. The value in this

additional data lies in the
new behavior or operational insight that eBay might gain by
analyzing it – this is often
referred to as ‘deep analytics.’ Analytics sandboxes within the
EDW, on the other hand,
are focused on deriving enormous value from the integration of
cross-functional business
data sets accessed by enterprise-wide users. Figure Four shows
the relationship between
an EDW and a dedicated high volume data appliance on several
important dimensions.
Figure Four: EDW and Data Warehouse Appliance
Complementarities
Experimentation 101
eBay’s Experimentation Platform’s success depends on the
innovations its services are
instrumental in co-producing. There have been relatively few
comprehensive guidelines
published to help guide experimentation for online settings that
embody the ideals of
scientific inquiry. One exception is Kohavi, et al. who focus on
web-based controlled
experiments, the context relevant to eBay’s online business.19
Following are lessons
from their research:
1. Analysis: Sloppy analysis cannot be overcome by good
intentions.

2. Mining Data: Appropriate benchmarks for experiments are
important. Better
methods/approaches discovered in rich analysis using machine
learning and data
mining approaches need to be compared to benchmarks, and
they must show a
statistically significant benefit.
11
3. Speed Matters: If you think a new treatment might be better
than an existing
practice, be sure to determine that the efficiency of the new
treatment is
acceptable in the context of a practical user experience – see to
it that the
12
treatment doesn’t actually decrease performance times.
4. Testing Factor Interactions: Conduct single-factor
experiments for gaining
insights and when making incremental changes; try bold bets
and different
designs for a new feature – test them against each other, and
perturb the winning
version to improve it further; when factors are expected to
interact, full or
fractional factorial designs are appropriate, but limit the number
of values per

factor and assign the same percentages to the treatments as to
the controls.
5. Run Continuous A/A Tests: An A/A test is the null test in
an experiment – it
means to assign users to one of two groups, but expose them to
exactly the same
experiment. A/A tests can be used to collect data for assessing
variability for
power calculations - and to test the experimentation system. A
null hypothesis
should be rejected about 5% of the time when a 95% confidence
interval is used.
6. Automate Ramp-Up and Abort: Gradually increase the
percentage of users
assigned to treatment(s). An experimental system that analyzes
the experiment in
near real-time can shut down a Treatment if it’s significantly
underperforming
relative to the Control. An auto-abort reduces the risk of
exposing many users to
errors; this enables the organization to make bolder bets and
innovate faster. Of
course, these apply to online vs. offline studies.
7. Determine the Minimum Sample Size: This is aided by the
A/A test. A common
mistake is to run underpowered experiments.
8. Assign 50% of Users to a Treatment: The authors
recommend using 50% of users
see the variant in an A/B test. This needs to be translated to the
running time for
the experiment.

9. Beware of Day of the Week Effects: The day of the week
may impact the running
time of an A/B experiment. At times, it is wise to run an
experiment longer in
order to mitigate day of week impacts.
10. Agree on the Overall Evaluation Criterion (OEC) Up-Front:
Agree on the
evaluative criterion before the experiment is run. OEC’s may
be derived from
organizational objectives, and a single OEC may reflect a
combination of multiple
objectives.
11. Weigh the Feature Maintenance Costs: Running online
experiments can
provide results that can be used as input to company decisions,
and that can have
a dramatic impact on a company’s culture.
In eBay’s web-based business, the amount of data about each
click, rollover, search
parameter or page view time becomes immense when multiplied
by the thousands of
visitors a site could have on a typical busy day. This data has
tremendous value if
actionable information can be unlocked to answer questions
such as:
change their mind?

preferred listing
placements change consumer behavior over a 60-day period?
- and long-term visitor activities affect the site’s
sales figures?
Answering these questions, and others like them, are indicative
of the importance of
analytics to the bottom line. At eBay, they are relevant to
specific goals the company set
for 2010. An employee revealed a picture of a sign in an eBay
building stating 2010
marketplace priorities as shown in Figure Five.20 The Net
Promoter Score shown in the
picture refers to a measure of customer sentiment and word of
mouth. This metric has
been advanced as the “one number you need to grow” by
scholars.21 It is defined as
follows:
“Net Promoter is a customer loyalty metric developed by (and a
registered
trademark of) Fred Reichheld, Bain & Company, and Satmetrix.
The most
important proposed benefits of this method derive from
simplifying and
communicating the objective of creating more "Promoters" and
fewer
"Detractors" -- a concept claimed to be far simpler for
employees to understand
and act on than more complicated, obscure or hard-to-

understand satisfaction
metrics or indices. The Net Promoter Score is obtained by
asking customers a
single question on a 0 to 10 rating scale: ‘How likely is it that
you would
recommend our company to a friend or colleague?’ Based on
their responses,
customers are categorized into one of three groups: Promoters
(9-10 rating),
Passives (7-8 rating), and Detractors (0-6 rating). The
percentage of Detractors
is then subtracted from the percentage of Promoters to obtain a
Net Promoter
score. A score of 75% or above is considered quite high.
Companies are
encouraged to follow this question with an open-ended request
for elaboration,
soliciting the reasons for a customer's rating of that company or
product. These
reasons can then be provided to front-line employees and
management teams for
follow-up action.”22
Figure Five: Revealed Marketplace Goals?
13
Bought Item Velocity (most analysts believe this is a measure
of how quickly things sell)
and other goals and priorities are represented in this photo, and
even if the priorities

aren’t genuine, they provide insights into eBay’s approach to
competing on analytics.
One analyst offered the following interpretations of the items on
eBay’s leaked priority
list:23
• “Market Share - I'm going to guess this is basically saying that
eBay
will grow with e-commerce.
• ‘Retail-like’ trust levels by reducing BBEs and protecting
buyers-
BBEs are Bad Buyer Experiences, kind of like the Amazon ODR
(Order Defect Rate). Inside of here you have all kinds of things
that
can go wrong like (SNAD - Significantly Not As Described,
INR -
Item Not Received, etc.) all of which result in a claim or low
NPS
score, or the dreaded 1/2 stars of death.
• Enhance selection and value in CSA - CSA is the acronym for
the
Clothing Shoes and Accessories (now rebranded Fashion)
category.
• Deliver value across the site - Great deals across the site.
• Scale B2C sellers and improve efficiency - B2C (business to
consumer)
sellers probably refers to the larger merchants that are coming
into the
system like Disney, etc. Scaling them means helping them grow

much larger. Deal of the day is one lever for this, perhaps there
are
more to come.
• Defend C2C seller business - C2C are consumer to consumer
sellers -
a.k.a. 'small sellers' or 'casual sellers'.
• Improve the eBay buyer experience - Self explanatory
• Build our advertising business - I'm assuming this is
something like
AdCommerce and ads on the site and not eBay classifieds (not
part of
marketplaces).
Experimentation in the Trenches
EBay’s Research Lab publishes exemplary experiments and the
innovations they support
(http://labs.ebay.com/erlpublications.html). In the following,
two such experiments are
discussed along with their findings. Both experiments
demonstrate how the test-and-
learn process can impact how eBay interfaces might be
improved.
Experiment One [Duong, Sundaresan, Parikh and Shen (Univ of
Michigan and eBay),
“Modeling Seller Listing Strategies,” 2010]:
Two important questions relevant to eBay priorities discussed
above are: 1) What
sellers’ strategies work best to yield high and profitable sales?
and 2) Can we estimate

how changes in the eBay interfaces impact sellers’ strategies
and thereby make changes
catered to sellers’ needs? For this experiment, the data captured
eBay sellers’ sales
activities over a certain period of time to investigate sellers’
fixed-price product listing
strategies. A dataset was constructed as exemplified in Table
One:
Table One: Example Dataset
14
http://labs.ebay.com/erlpublications.html
15
Each tuple contains descriptive features of an item listed for
sale in the iPod nano
category. Data for a listing includes the seller identification,
the product identification,
the listing’s start and end dates, the listing’s price, the title and
the average shipping cost.
Experimenters first did product clustering to capture and
summarize a seller’s strategy for
selling different products. From this, they determined that eBay
sellers do consistently
rely on certain strategies to decide how to list their inventory on
eBay. Experimenters

then built a model to accurately predict sellers’ strategies.
From this model, and by using
actual sales figures, the model was calibrated to empirically
demonstrate top-ranked
seller’s strategies across three different product categories. The
experiment highlighted
the model’s capability for capturing sellers’ strategies as shown
by contrasting it with a
semi-random baseline model. Prediction power was enhanced
by incorporating past
listings in the analysis. The overall evaluation criterion was a
combination of sale-
through rate and average revenue per listing.
Experiment Two [Adapted from "Rated Aspect Summarization
of Short Comments. Y. Lu
and C. Zhai of University of Illinois Urbana-Champagne, N.
Sundaresan of eBay. WWW
2009, Madrid, Spain, April 2009. 131-140.]:
Buyers leave feedback comments on eBay describing a
transaction. They rate the
transaction as positive, neutral or negative. Usually, there are a
large number of
comments about a target – maybe hundreds of thousands – and
that number keeps
growing as more and more people keep contributing online.
The key question for this
experiment was: How can we help a customer more easily
digest such a large number of
comments? The experimenters hypothesized that what is called
a ‘rated aspect summary’
would help customers. A rated aspect summary provides a
decomposed view of the
overall ratings for the major aspects so that a customer can gain
different perspectives

towards the target entity. One rationale for the rated aspect
summary is that not all
customers have the same needs and desires from a transaction.
For example, a potential
buyer may be willing to compromise on shipping time but not
on product quality. In this
instance, it isn’t sufficient to just know the overall ratings of a
seller; the buyer would be
better off knowing the ratings of that seller on a specific aspect
– quality. A dataset was
created by collecting feedback comments for 28 eBay sellers
with high feedback scores.
For processing the text of comments, the OpenNLP toolkit (
http://opennlp.sourceforge.net/ ) was used to identify phrases in
the form of a pair of
‘head term’ and ‘modifier used’ categorization. In their dataset,
sellers with high
feedback scores received a large number of comments, 57,055
on average. Buyers tend
to use only a few phrases in each comment – after parsing the
comments, there were
about 1.5 phrases per comment. That preprocessing reduced the
data by about 40%,
eliminating terms like the superlative “AAA+++” which does
not provide much detailed
information on aspects. The next step in their methodology
evaluated alternate aspect
clustering algorithms. They counted as a ‘match’ for a
particular algorithm those
instances where the algorithm’s ‘frequent term’ matched one of
the terms in a human
identified cluster. They then determined the top k clusters –
those with the largest size –
such that aspect coverage at the top k clusters was defined as
the number of aspect
matches within the top k clusters divided by k. They then used

clustering accuracy for
determining those final sets of clusters. They found that an
algorithm called Structured
http://opennlp.sourceforge.net/
Probabalistic Latent Analysis achieved the best performance
(was most consistent with
human generated clusters). Table Two shows a sample result
of a rated aspect
summarization.
Table Two: Sample Result of Rated Aspect Summarization
This experiment was among the first to formally define the
problem of rated aspect
summarization, and it evaluated different general methods for
addressing the problem.
Results show that while aspect clustering is a subjective task for
humans, some
algorithms perform well in matching human counterpart
performance. A follow-on
experiment will be needed to determine if rated aspect
summarization provides eBay
buyers with better information when they consider tradeoffs
between aspects like
delivery times and product quality in customer feedback
summaries.
Experiments and ROII

16
Bob Page reflected on his short time with eBay, and he realized
his blog of March 20th,
2010 that announced his arrival at eBay was right on. He had
been right about the
people, leadership commitment and eBay’s adherence to the
holistic vision that aligned
with his own thinking. That common vision of ‘central data,
distributed analysis’ was
reinforced by the Experimentation Platform and its recent deep
dive analytics extensions.
With over 5000 analysts, the scale was staggering. He knew
that getting a full picture of
ROI required a new way of thinking. He had been picturing
some graphs – and some
kind of an innovation effectiveness curve that he had seen in a
business magazine. He
remembered a quote in the article, “Profitable innovation cannot
be bought. Simply
spending more usually leads to a waste of resources on
increasingly marginal projects.”24
It had him thinking. On the vertical axis of the graph he
recalled was ‘Return on
Innovation Investment’ (ROII), and the horizontal axis was
labeled ‘Innovation
Investment’ ($). There was also a horizontal line reflecting the
cost of capital. There
were two arcs – sort of a before and after snapshot. A major
managerial focus was on
how to shift the curve in the proper directions. He took out his
iPad, loaded his favorite
drawing app, and he put quickly together the graphs shown in
Figure Six.

Figure Six: Innovation Effectiveness Curves
17
In graph A, he reflected the decrease in innovation $ invested
based on a contrast of what
the old Oracle system would have required in upgrades to
handle today’s loads versus
eBay’s investment in the Teradata system and its virtual data
marts (AaaS). In fact, it had
been estimated that a new data mart can be brought online for
between $500K and $1
million, and that AaaS saved eBay from needing to support 100
different data marts
(Ratzesberger, Oliver, 2008 talk at Teradata Partners
Conference). The Teradata system
thereby saved millions. In graph B, he contemplated that even
if the costs of fixing the
Oracle system for advanced analytics and in acquiring the
Teradata system had been the
same, the ability to more quickly get innovations discovered
and into production
represented a payoff for the Teradata approach. Graph C was
more complex, and he
would think more about it later. The issue reflected exactly
what his leadership wanted
him to look into. They posed the question: How can we
account for per experiment ROII
lift to assess any new policies, standards, controls and
incentives we might put into place

to encourage company-wide innovation through
experimentation? He had a feeling for
what the corollary question was: How can we innovate faster,
more and at lower
platform cost? He smiled and knew that answering these two
questions may themselves
require a set of well-designed experiments in order to make
headway.
18
References
1
8
Jarzemsky, M., “Moody’s Upgrades EBay, Cites Strong
Performance, Liquidity,” Dow Jones Newswires,
/19/2010, http://online.wsj.com/article/BT-CO-20100819-
709042.html
2 Ju, Stephen in Savitz, E., “eBay Should Start and Amazon-
Style Fulfillment Center,” News from
arron’s Silicon Valley Bureau, 5/6/2010, B
http://blogs.barrons.com/techtraderdaily/2010/07/06/ebay-
should-get-start-amazon-style-fulfillment-unit-rbc-says/
3 Dawson, Chris, “ Rakutan – Serious Competition for eBay and
Amazon,” TameBay, 5/21/1010,
ttp://tamebay.com/2010/05/rakutan-serious-competition-for-

ebay-amazon.htmlh
4 Chao, L. “Taobao, Yahoo Japan Look for sBay-Style Heft,
5/10/2010, Wall Street Journal,
ttp://online.wsj.com/article/SB100014240527487036747045752
35333773371988.htmlh
5 Davenport, T. “How to Design Smart Business Experiments,
Harvard Business Review, February, 2009,
ttp://custom.hbsp.harvard.edu/b01/en/implicit/p.jhtml?login=SA
SM033009S&pid=R0902Eh
6 King, H. “The View from the C-Suite: eBay CEO John
Donahoe,” GreenBiz.com, 5/17/ 2010,
ttp://www.greenbiz.com/blog/2010/05/17/view-c-suite-ebay-
ceo-john-donahoe?page=fullh
7
Davenport, T. op cit., 2010.
8 Steiner, I. “Is Technological Innovation the Key to eBay’s
Turnaround?” Auction Bytes.com, 5/26/2010,
ttp://www.auctionbytes.com/cab/cab/abn/y10/m05/i26/s01h
9 Page, B., “Hello eBay,” BOBPAGE.NET, 4/20/2010,
http://bobpage.net/2010/03/20/hello-ebay/
10 “Panel Session: The Future of Web Analytics,” FutureWeb,
4/29/2010, http://www.elon.edu/e-
web/predictions/futureweb2010/future_web_analytics.xhtml

1
1 Interview with Bob Page, eBay, 8/16/2010.
12 Russom, P. TDWI Checklist Report: Data Requirements for
Advanced Analytics,” 9/2009,
ttp://www.teradata.org/t/analyst-reports/TDWI-checklist-report-
data-requirements-for-advanced-h
analytics/?type=AR
13 Duvall, M. “eBay Looks to Pioneer Analytics as a service,”
CIOZone, retrieved 8/19/2010,
ttp://www.ciozone.com/index.php/Business-Intelligence/EBay-
Looks-To-Pioneer-Analytics-As-A-h
Service.html
14 “eBay and Teradata Collaborate to Develop Deep Data
Analytic
Solution
s,” PRNewswire, 5/6/2010,
ttp://www.prnewswire.com/news-releases/ebayr-and-teradata-
collaborate-to-develop-deep-data-analytic-h
solutions-92978514.html
15 Ratzesberger, O., “Analytics as a Service,” 4/21/2008,

retrived from http://www.xlmpp.com/ on
/31/2010. 8
16 Inmon, B. “The Elusive Virtual Data Warehouse,”
BeyeNetwork, 4/19/2010, http://www.b-eye-
network.com/view/9956
http://online.wsj.com/article/BT-CO-20100819-709042.html
http://tamebay.com/2010/05/rakutan-serious-competition-for-
ebay-amazon.html
http://online.wsj.com/article/SB10001424052748703674704575
235333773371988.html
http://custom.hbsp.harvard.edu/b01/en/implicit/p.jhtml?login=S
ASM033009S&pid=R0902E
http://www.greenbiz.com/blog/2010/05/17/view-c-suite-ebay-
ceo-john-donahoe?page=full
http://www.auctionbytes.com/cab/cab/abn/y10/m05/i26/s01
http://bobpage.net/2010/03/20/hello-ebay/
http://www.elon.edu/e-

http://www.elon.edu/e-
http://www.teradata.org/t/analyst-reports/TDWI-checklist-
report-data-requirements-for-advanced-analytics/?type=AR
http://www.ciozone.com/index.php/Business-Intelligence/EBay-
Looks-To-Pioneer-Analytics-As-A-Service.html
http://www.prnewswire.com/news-releases/ebayr-and-teradata-
collaborate-to-develop-deep-data-analytic-solutions-
92978514.html
http://www.xlmpp.com/
http://www.b-eye-network.com/view/9956
http://www.b-eye-network.com/view/9956
19
17 Kobielus, J, “Inmon’s Vitriolic Slap at ‘Virtual Data
Warehousing’ Does Not Withstand Scrutiny,”
nformation Management, 4/3/2010, I http://www.information-
management.com/blogs/inmon_kobielus_virtual_data_warehousi
ng_challenge-10015212-1.html

1
U
8 Ratzesberger, O. “Agile Enterprise Analytics,” 2010 SMDB
2010 Workshop Keynote Address, Duke
niversity, 2/2010,
http://www.cs.duke.edu/smdb10/_files/toc_data/SMDB/keynote/
keynote.pdf
1
M
9 Kohavi, R., R. Longbotham, D. Sommerfield, R. M. Henne,
“Controlled Experiments on the Web,” Data
ining and Knowledge Discovery, 2/2009,
http://portal.acm.org/citation.cfm?id=1485091
20 ttp://yfrog.com/0943ckjh retrieved 9/1/2010.
Reichheld, F. F. “The One Number You Need to Grow,”
Harvard Business Review, Product #5534,
ecember, 2003, Accessed 12/2010 at
ttp://www.netzkobold.com/uploads/pdfs/the_one_number_you_n

eed_to_grow_reichheld.pdf
21
D
h
22 http://en.wikipedia.org/wiki/Net_Promoter , Accessed
9/2/2010.
23 Wingo, S., “Decoding and Analyzing eBay’s Stated 2010
Marketplace Priorities, 5/25/2010,
ttp://www.istockanalyst.com/article/viewarticle/articleid/41525
58h
24 Kandybin, A. and M. Kihn, “Raising Your Return on
Innovation Investment, s+b, 8/2005,
http://www.strategy-business.com/media/file/rr00007.pdf

http://www.information-
http://www.cs.duke.edu/smdb10/_files/toc_data/SMDB/keynote/
keynote.pdf
http://portal.acm.org/citation.cfm?id=1485091
http://yfrog.com/0943ckj
http://www.netzkobold.com/uploads/pdfs/the_one_number_you_
need_to_grow_reichheld.pdf
http://en.wikipedia.org/wiki/Net_Promoter
http://www.istockanalyst.com/article/viewarticle/articleid/4152

558
http://www.strategy-business.com/media/file/rr00007.pdf
20
Appendix One: eBay’s Experimental Platform Architecture
eBay Analytics Core
Relational Data
XML, name/value, raw
Teradata Teradata
2.2PB 6.6PB
Primary Secondary
Phoenix, AZ Sacramento, CA

Linux Linux
Solaris
2.2PB2.5PB
MPP MPP
MPP/HPC/GridMPP/HPC/Grid
Sun Fire 4xxx
Relational Data
Local Interconnect
MicroStrategy
Wide Area
Interconnect
1000 miles
Solaris
Business

Objec ts Unica SOA/DAL
Ab Initio Informatica UC4 SOA
Data Access
Data Integration
Crystal SAS SQL
Sun Fire 4xxx
Local Interconnect
Golden Gate BES
MAX
MAX
BUSI 604 100 Integrated Discussion Boards 1-4 Rubric

Criteria
Levels of Achievement
Content 70%
Advanced 92-100%
Proficient 84-91%
Developing 1-83%
Not present
Part 1-- Key Term/Concept Reservation
Part 2 -- Main Thread
37 to 40 points
Major Points are supported by all of the following:
Your thread must be placed in the Discussion Board Forum text
box and adhere precisely to the following headings:
· KEY TERM and WHY YOU ARE INTERESTED IN IT
· EXPLANATION OF THE KEY TERM
· MAJOR ARTICLE SUMMARY
· DISCUSSION
· REFERENCES

34 to 36 points
Major Points are supported by most of the following:
box and adhere precisely to the following headings and format:
· DISCUSSION
· REFERENCES
1-33 points
Major Points are supported by some of the following:
box and adhere precisely to the following headings and format:
· DISCUSSION
· REFERENCES
0 points
No work submitted.
Or

Duplicate concept reservations will not earn credit under any
circumstance—this includes work submitted in a previous
course
Part 3 – Replies
28 to 30 points
A minimum of 3 substantive and comprehensive replies to other
student postings are submitted, in at least one of the following
ways:
· compare/contrast the findings of others with your research;
· compare how the findings of others relates/adds to the
concepts learned in the required readings; and/or
· share additional empirical knowledge regarding global
business -- or international experiences you may have had --
relative to the postings of others.
25 to 27 points
A minimum of 2 substantive and comprehensive replies to other
ways:

1-24 points
A minimum of 1 substantive and comprehensive reply to other
ways:
0 points
No work submitted.
Or
course
Structure 30%
Advanced 92-100%
Proficient 84-91%
Developing 1-83%
Not present
Posted in Text Box
5 points

All 3 parts of the Forum are posted in the text box.
4 points
2 parts of the Forum are posted in the text box.
1 to 3 points
1 part of the Forum is posted in the text box.
0 points
No parts of the Forum are posted in the text box.
Or
course
Grammar/Spelling
5 points
Proper spelling and grammar are used.
4 points
Between 1–2 spelling and grammar errors are present.
1 to 3 points
Between 3–4 spelling and grammar errors are present.
0 points
More than 4 spelling and grammar errors are present.
Word Count
9 to 10 points
At least 800 words are submitted for Part 2; 450 words are
submitted for Part 3.
8 points

799 to 750 words are submitted for Part 2; 449 to 400 words are
submitted for Part 3.
1 to 7 points
749 to 100 words are submitted for Part 2; 399 to 100 words
are submitted for Part 3.
0 points
Less than 100 words are submitted for Part 2; less than 100
words are submitted for Part 3.
Part 2 References are in APA format
9 to 10 points
All references are in APA format.
8 points
Most references are in APA format.
1 to 7 points
Some references are in APA format.
0 points
No references are in APA format.
Important Notes:
All parts of the DB are posted in the text box -- not as an
attachment -- before or on the due date. In the event formatting
is lost in the text box, you may also attach your Word file to the
text box as evidence of proper formatting.
BUSI 604 Discussion Board Forum 1–4 Instructions

Important Assignment Deadlines:
· Part 1 due by 11:59 p.m. (ET) on Thursday of the first of the
assigned modules/weeks.
· Part 2 due by 11:59 p.m. (ET) on Monday of the second of the
· Part 3 due by 11:59 p.m. (ET) on Sunday of the second of the
· Note: For Module Week 8, Part 3 is due by Friday, 11:59pm
EST, since that is the last day of class. No work is accepted
after the course ends.
Part 1 (no word count)
As you read the chapters assigned to each module/week, you
will find some concepts more interesting and applicable to your
personal or work situation than others. Review the key terms
listed in the assigned chapters; then, submit a preliminary
thread indicating the key term that you wish to reserve for your
thread for that module/week.
Check the discussion board area prior to submitting your
reservation to ensure no one else has chosen that key term. In
the event the key term you wish to explore is already reserved
by someone else, simply move on to another key term in your
readings. Duplicate key terms will not earn credit under any

course. Do not ask the instructor to accept a duplicate key term,
as this is not fair to those who followed these explicit
directions.
Include the exact key term you selected in your thread’s subject
line.
Submit Part 1 to the Discussion Board Forum by 11:59 p.m.
(ET) on Thursday of the first of the assigned modules/weeks.
Part 2 (800 words minimum)
After you have successfully reserved the key term that interests
you the most, research a minimum of 5 recent scholarly articles
that relate to the concept on which you wish to focus your
research. Articles must be found in reputable professional
and/or scholarly journals and/or informational venues that deal
with the content of the course (i.e., not blogs, Wikipedia,
newspapers, etc.). After reading the articles, select the 1 article
that you wish to discuss.
It is highly recommended that you use Liberty University’s
Jerry Falwell Library online resources. A librarian is available
to assist you in all matters pertaining to conducting your
research, including what constitutes a scholarly article. For
more details on what constitutes a scholarly journal (and what

does not), visit Jerry Falwell Library’s "Scholarly Journals –
What Are They?" page.
Your thread must be placed in the Discussion Board Forum
textbox (not as an attachment) and adhere precisely to the
following headings and format:
1. Key Termand Why You Are Interested in It (100 words
minimum)
After reading the textbook, specifically state why you are
interested in conducting further research on this key term (e.g.,
academic curiosity, application to a current issue related to
employment, or any other professional rationale). Include a
substantive reason, not simply a phrase.
2. Explanation of the Key Term(100 words minimum)
Provide a clear and concise overview of the essentials relevant
to understanding this key term.
3. Major Article Summary(300 words minimum)
Using your own words, provide a clear and concise summary of
the article, including the major points and conclusions.
4. Discussion

In your own words, discuss each of the following points:
a. How the cited work relates to your above explanation AND
how it relates specifically to the content of the assigned
module/week. This part of your thread provides evidence that
you have extended your understanding of this key term beyond
the textbook readings. (150 words minimum)
b. How the cited work relates to the other 4 works you
researched. This part of your thread provides evidence that you
have refined your research key term to a coherent and
specialized aspect of the key term, rather than a random
selection of works on the key term. The idea here is to prove
that you have focused your research and that all works cited are
related in some manner to each other rather than simply a
collection of the first 5 results from your Internet search. (150
words minimum)
5. References
A minimum of 5 recent scholarly articles (not textbooks,
Wikipedia, or other popular reading magazines), in current APA
format, must be included and must contain persistent links so
others may have instant access. In the event that formatting is
lost or corrupted when submitting the thread, attach the
Microsoft Word document to your thread as evidence that your
work was completed in the proper format. Access the following
URL from the Jerry Falwell Library for instructions on creating

persistent links: How to Create a Persistent Link.
(ET) on Monday of the second of the assigned modules/weeks.
Part 3 (150 words minimum, per reply)
Additionally, you will reply to a minimum of 3 other
classmates’ threads. Thus, you will have submit substantive
written responses to a minimum of 3 other classmates’ threads.
(ET) on Sunday of the second of the assigned modules/weeks.
· Note: For Module Week 8, Part 3 is due by Friday, 11:59pm
EST, since that is the last day of class. No work is accepted
after the course ends.

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