netrashetty
Netra Shetty
Fabrik Inc. is a manufacturer of external hard drives and digital content management software and services. Fabrik is the third largest supplier of external storage products in North America.[1] It is headquartered in San Mateo, with offices in Santa Ana and Culver City, California.
The company sells external hard drives and online backup software in a bundled product for consumers, small business users and audio/video (A/V) content creation professionals.[2] The company also hosts the free online digital content management and sharing site, Joggle.[3][4][5][6][7][8]
Fabrik’s product lines include SimpleTech, G-Technology, Joggle and Fabrik Ultimate Backup.
dispersion item sold for $55.00; the low-dispersion item sold for $96.50.
We have seen that surveys can provide measures of unobservable information. Both Vt
and the SDt generate results that are consistent with what we would expect from the relation
between item values, information dispersion in the auctions, and prices in a CV setting. The
next section describes the data used to correct for potential bias in these survey measures.
5 Participant Background
The survey asked about background characteristics of the respondents. Their responses are
summarized in Table 5. The Örst set of responses in the table shows the number of yes and
no responses for each auction in my sample, so those who responded to multiple auctions
9Figure 2: High dispersion item description (Vhighsd = $318:81)
were counted multiple times. Half of all responses were from those who were familiar with
computers. Half of all responses were also from those who had been shopping for computers
and those who had looked at an online computer auction before. Of those that had recently
bought a computer, most had bought their computer through a retail outlet. The next set
of responses in the table show the number of people who recently bought 0, 1, or 2 or more
computers, respectively. The survey respondents were then asked how many online auctions
they had entered (0, 1, 2 through 5, and 6 or more were the respondentsípossible choices).
Those who had entered auctions were then asked whether all, none, or some of those auctions
were on eBay, and whether they had won all, some, or none of those auctions. The majority
of respondents had not bought a computer in the last six months. Most people had not
entered an online auction, including most of those who had looked at online auctions. Those
who had entered auctions tended to have done so more than once, favored eBay auctions,
and had won some of those auctions.
I ran a least squares regression of individual responses Xi;t on the background characteristics to determine how valuations di§ered between di§erent types of respondents. The
10Table 2: Summary statistics of survey respondentsíbackgrounds
Background questions Responses
(10,350 observations) no yes
familiar w/computers 5140 5202 - -
shopped for computer in last 6 months 4923 5427 - -
bought computer via auction - 403 - -
bought computer via retail - 2988 - -
bought computer via wholesale - 967 - -
looked at online computer auction 5762 4588 - -
looked at eBay computer auction 6449 3881 - -
0 1 2+ -
# computers bought last 6 months 6836 2471 1033 -
0 1 2-5 6+
# online computer auctions entered 7428 750 1271 828
none some all -
- on eBay 4751 960 1509 ...
- won on eBay 5338 1558 324 ...
11results are summarized in Table 5. The largest di§erences in valuations were correlated
with di§erences in the respondentsífamiliarity with computers, recent purchases, and their
familiarity with eBay auctions. Participants who were less experienced on these dimensions
tended to value items more highly. Although a number of the coe¢ cients are statistically
signiÖcant, their magnitude relative to the average of Vt
is low, and the overall explanatory
power reáected by the R-squared statistic is low. Averaging over the responses of di§erent
types of respondents will probably not result in large di§erences from adjusting the mean
for the di§erent types, but we will allow for this possibility in the bias correction process in
Section 6.
6 Bias Correction
Vt and SDt may be biased measures of the true CV (or average PV), denoted vt
, and the
dispersion of information facing auction participants, denoted xjv;t
This section proposes a .
bias correction method which exploits the background data collected on the survey respondents.
On average, 20% of the responses for each auction in my sample came from respondents
who had won all or some of the eBay online computer auctions in which they had entered.
I designate their responses as ìexperiencedîresponses (subscripted by e), and designate the
rest of the responses as ìinexperiencedîresponses (subscripted by a).
I model and estimate the potential bias as follows. I treat the valuations Xi;t from my
survey respondents as potentially biased draws of signals xi;t that the auction participants
draw about vt
Thus, Xi;t are drawn from a potentially di§erent distribution than the one .
that the auction participants face. I model the responses from my inexperienced respondents,
denoted Xa;i;t
, as draws from a distribution whose mean may di§er from vt by a shift factor
0
and a scale factor
1 and whose variance may be di§erent as well: Xa;i;t v (
0 +
1vt
;
2
xjv;a;t
.(
I assume that the experienced survey respondents more closely resemble the auction participants. I model their responses as being drawn from a distribution whose mean only di§ers
from vt by a shift factor 0 and whose variance may be di§erent: Xe;i;t v (0 + vt
;
2
xjv;e;t
.(
An unbiased estimate of vt can then be written as
1. How long have your organization implemented the market segment process?
2. In your opinion, what made the company decide to adopt marketing strategies on a more comprehensive manner, why?
3. What are the possible factors UK telecoms will consider before planning such marketing pattern for satisfying mobile users?
4. As an employee, what were the problems you encountered while on the process of serving quality market stance to customers?
5. How did you deal with the marketing problems if there is any?
6. Now that the company has applied market segments and certain market models, is the outcome successful? Explain
7. Based on the current status of marketing value in telecom organization, what are the benefits of implementing better market segments?
8. Do you prefer market segmentation method that is traditional market communication in the telecom industry? Why?
9. Are you satisfied with marketing quality as well as services provided by the company?
10. What do you think are the areas for market improvement for telecom companies to incur enough marketing service channels?
Resource
The resources deemed for the realization of the study can come from related books and certain publications mostly underlying to the support resource materials ideally as basis for the literature studies of the study. The knowledge and information to be integrated and evaluated for validity and reliability can be resources directed to emerald insight journals and articles as well as from the Questia Library.
The company sells external hard drives and online backup software in a bundled product for consumers, small business users and audio/video (A/V) content creation professionals.[2] The company also hosts the free online digital content management and sharing site, Joggle.[3][4][5][6][7][8]
Fabrik’s product lines include SimpleTech, G-Technology, Joggle and Fabrik Ultimate Backup.
dispersion item sold for $55.00; the low-dispersion item sold for $96.50.
We have seen that surveys can provide measures of unobservable information. Both Vt
and the SDt generate results that are consistent with what we would expect from the relation
between item values, information dispersion in the auctions, and prices in a CV setting. The
next section describes the data used to correct for potential bias in these survey measures.
5 Participant Background
The survey asked about background characteristics of the respondents. Their responses are
summarized in Table 5. The Örst set of responses in the table shows the number of yes and
no responses for each auction in my sample, so those who responded to multiple auctions
9Figure 2: High dispersion item description (Vhighsd = $318:81)
were counted multiple times. Half of all responses were from those who were familiar with
computers. Half of all responses were also from those who had been shopping for computers
and those who had looked at an online computer auction before. Of those that had recently
bought a computer, most had bought their computer through a retail outlet. The next set
of responses in the table show the number of people who recently bought 0, 1, or 2 or more
computers, respectively. The survey respondents were then asked how many online auctions
they had entered (0, 1, 2 through 5, and 6 or more were the respondentsípossible choices).
Those who had entered auctions were then asked whether all, none, or some of those auctions
were on eBay, and whether they had won all, some, or none of those auctions. The majority
of respondents had not bought a computer in the last six months. Most people had not
entered an online auction, including most of those who had looked at online auctions. Those
who had entered auctions tended to have done so more than once, favored eBay auctions,
and had won some of those auctions.
I ran a least squares regression of individual responses Xi;t on the background characteristics to determine how valuations di§ered between di§erent types of respondents. The
10Table 2: Summary statistics of survey respondentsíbackgrounds
Background questions Responses
(10,350 observations) no yes
familiar w/computers 5140 5202 - -
shopped for computer in last 6 months 4923 5427 - -
bought computer via auction - 403 - -
bought computer via retail - 2988 - -
bought computer via wholesale - 967 - -
looked at online computer auction 5762 4588 - -
looked at eBay computer auction 6449 3881 - -
0 1 2+ -
# computers bought last 6 months 6836 2471 1033 -
0 1 2-5 6+
# online computer auctions entered 7428 750 1271 828
none some all -
- on eBay 4751 960 1509 ...
- won on eBay 5338 1558 324 ...
11results are summarized in Table 5. The largest di§erences in valuations were correlated
with di§erences in the respondentsífamiliarity with computers, recent purchases, and their
familiarity with eBay auctions. Participants who were less experienced on these dimensions
tended to value items more highly. Although a number of the coe¢ cients are statistically
signiÖcant, their magnitude relative to the average of Vt
is low, and the overall explanatory
power reáected by the R-squared statistic is low. Averaging over the responses of di§erent
types of respondents will probably not result in large di§erences from adjusting the mean
for the di§erent types, but we will allow for this possibility in the bias correction process in
Section 6.
6 Bias Correction
Vt and SDt may be biased measures of the true CV (or average PV), denoted vt
, and the
dispersion of information facing auction participants, denoted xjv;t
This section proposes a .
bias correction method which exploits the background data collected on the survey respondents.
On average, 20% of the responses for each auction in my sample came from respondents
who had won all or some of the eBay online computer auctions in which they had entered.
I designate their responses as ìexperiencedîresponses (subscripted by e), and designate the
rest of the responses as ìinexperiencedîresponses (subscripted by a).
I model and estimate the potential bias as follows. I treat the valuations Xi;t from my
survey respondents as potentially biased draws of signals xi;t that the auction participants
draw about vt
Thus, Xi;t are drawn from a potentially di§erent distribution than the one .
that the auction participants face. I model the responses from my inexperienced respondents,
denoted Xa;i;t
, as draws from a distribution whose mean may di§er from vt by a shift factor
0
and a scale factor
1 and whose variance may be di§erent as well: Xa;i;t v (
0 +
1vt
;
2
xjv;a;t
.(
I assume that the experienced survey respondents more closely resemble the auction participants. I model their responses as being drawn from a distribution whose mean only di§ers
from vt by a shift factor 0 and whose variance may be di§erent: Xe;i;t v (0 + vt
;
2
xjv;e;t
.(
An unbiased estimate of vt can then be written as
1. How long have your organization implemented the market segment process?
2. In your opinion, what made the company decide to adopt marketing strategies on a more comprehensive manner, why?
3. What are the possible factors UK telecoms will consider before planning such marketing pattern for satisfying mobile users?
4. As an employee, what were the problems you encountered while on the process of serving quality market stance to customers?
5. How did you deal with the marketing problems if there is any?
6. Now that the company has applied market segments and certain market models, is the outcome successful? Explain
7. Based on the current status of marketing value in telecom organization, what are the benefits of implementing better market segments?
8. Do you prefer market segmentation method that is traditional market communication in the telecom industry? Why?
9. Are you satisfied with marketing quality as well as services provided by the company?
10. What do you think are the areas for market improvement for telecom companies to incur enough marketing service channels?
Resource
The resources deemed for the realization of the study can come from related books and certain publications mostly underlying to the support resource materials ideally as basis for the literature studies of the study. The knowledge and information to be integrated and evaluated for validity and reliability can be resources directed to emerald insight journals and articles as well as from the Questia Library.