Word Count: 3428ComputAbility, a mail-order company, began in 1982. An
authorized reseller of computer software and hardware,
ComputAbility offers their clients over 50,000 products.
The company has built their reputation on a foundation of
competitive prices and quality service. In August of 1997,
Creative Computers, also a mail-order company, acquired
ComputAbility. The acquisition provided a number of
benefits to the company, primarily a larger product
selection to offer to customers.
Currently, ComputAbility employs 60 + people with plans
of adding on 20 to 30 more sales representatives and
support staff during the next year. Prior to February of
1998, all of the sales representatives were in the inbound
division. This division handles all incoming sales calls.
Majorities of these calls are from individual consumers.
Creative Computers had started their company the same
way, but found the growth potential was in the business
sector. In February of 1998, ComputAbility started their
corporate sales division, an area already underway at
Creative. This division of the company was created to
develop relationships with business clients, and become the
primary way of increasing company profit. Computability
added a dedicated trainer to the staff at the same time the
corporate division was started. This individuals primary
responsibilities were to train new hires in the areas of sales,
product knowledge, company policies and procedures and
Although there was a solid training program in place,
including ongoing new product training from manufacturers,
the company was not profiting at an acceptable rate.
ComputAbility experienced a decrease in sales and profits
during the first year after the acquisition. The expectation
was that the acquisition should have provided the tools
necessary to increase sales. So what could be the
problem? Although ComputAbility sales representatives
now had more tools available to them, something was still
Creative Computers decided to test a sales training
program for the corporate sales division. There are a
number of sales training tools available. Tools range from
books and seminars to dedicated sales training company
programs. Management decided to work with a company
who had developed a sales training program. The initial
step was for top management to go through the training to
see if it was worth the time and money investment. After
extensive research, the sales training program, from this
point forward called “Discovery”, was adopted. Creative
Computers hired the company who developed
“Discovery,” to train the companys internal trainers and
select corporate sales representatives. After the initial
training, the company trainers conduct Discovery for all
remaining and new employees.
The training program consists of five courses, each
containing one to three modules. The modules focus on
techniques for cold calling, probing the company needs,
developing client relationships, and account and time
management. Representatives are given metrics (daily
goals) in the following areas; number of calls, talk time
(amount of time the representative spends on the
telephone), and dollar. The following goals show the
expectations given to the employees during the first 6
months the training was in place:
Calls: 80-120 calls per day
Talk Time: 3.5-4 hours per day
Dollars: $3000 – $28000 gross profit
(determined by months of employment)
The company who created Discovery developed the
metrics of calls and talk time. The dollar goals were
determined by ComputAbility.
Discovery has been in place for approximately 9 months.
ComputAbility has experienced a few issues regarding the
metrics. The first issue deals with the number of calls the
sales representatives are required to make. Representatives
have expressed to management that the goals are not
realistic and do not allow for development of client
relationships. As a result of the first issue, the company is
finding that not all representatives are following the
program. This typically occurs after a few weeks on the
job. At this point, the company needs to analyze if the
Discovery program is effective; are the metrics given
realistic? In addition, the determination needs to be made if
Discovery is followed, it leads to the representatives
success. This is very difficult to analyze because as
mentioned earlier, not every corporate sales representative
is thoroughly following the program. It is also important to
measure other factors that may be hindering their
performance or assisting in their success, such as length of
employment. The best way to determine the effectiveness
of the Discovery program is to research proven sales
training programs and techniques, analyze existing sales
numbers in relation to the metrics and weigh additional
factors that may influence the end result.
Telesales is the offering of goods and/or services by the
telephone, fax, television, computer, or other electronic
media (Zajas, Church, 1997, p.227). Telesales has several
advantages such as low cost personal contact, flexibility in
responding to customer needs, and flexibility in adjusting
the sales campaign. When telesales is integrated into a
companys total marketing process by qualifying leads,
increasing response from catalogs and direct mail, and
maintaining contact with direct marketers most valuable
asset, their customer base, it can increase sales efficiency
and profits (Stone, 1995).
Telesales requires managers who are effective at getting
others to market and sell effectively over the telephone.
Managers with limited telesales experience are susceptible
to a number of problems: establishing unrealistic goals,
pushing high pressure tactics, writing inflexible, unworkable
scripts, failing to recognize or cope with burnout, neglecting
to collect information systematically, and committing too
many resources before testing a concept (Harlan,
Woolfson, Jr., 1991, p.8). ComputAbility has experienced
some of the above problems by relying on the established
“Discovery” metrics. Who developed them? How does
management know they are measurable? A telesales
manager should test every new program by personally
making calls and keeping the statistics to use as
benchmarks to ensure that unrealistic goals are not set
(Harlan, Woolfson, 1991).
An effective telesales manager must have patience and
develop enough rapport with their team to listen to
problems that are both work and non work related, in
order to prevent possible burnout. A manager needs to
sense when boredom or frustration with the job sets in. A
few months into the Discovery program, many of the sales
representatives (titled Account Executives at
Computability) were becoming frustrated. Managers called
a meeting to determine the cause and found the daily micro
managing of the numbers and hence the people, was adding
to the stress of the job. This is when the first issue of
unrealistic goals, was discovered. What management did in
response to this was to re-evaluate the metrics.
After careful planning, the following revisions were
Months 1-3 Months 4-6 Months 7-12
Calls: 400 week 350 week 300 week
Talk Time: 1.5-3 hrs week 3-4 hours week 3-4 hours
$ goals: remained the same
The primary goal of the revision was to give the
representatives weekly goals instead of daily to eliminate
the micro managing and in turn result in less stress for the
employees. In relation to the second issue, management felt
all employees would now be more willing to follow the
program. The revised metrics also gave employees more
flexibility. Regardless of the length of employment, the
employee is performing to expectation if they are achieving
any one of the metric breakdowns per week.
Example: Employee A has been with the company for 2
months, call time is 3.5 hours a day and call amount is 300
a week. The employee is performing to the metrics.
What management hoped to achieve with this breakdown
relates to the first issue expressed by employees that the
call amount did not allow for relationship building with the
Telesales representatives need adequate training and
compensation to do the job (Harlan, Woolfson, Jr., 1991).
Creative Computers and ComputAbility understand how
important a solid training program is to the success of the
account executives. The Discovery training program is very
effective. The metrics simply need revision. However, it is
critical for the company to realize that the Discovery
training program is not the “total solution” to making a
representative successful. There are other essential factors.
It is crucial for a manager to look at a potential employees
work references before hiring a salesperson, as attitude can
be demonstrated by habits such as promptness,
attendance, and completion of job assignments (Zajas,
Church, 1997). In order to excel in telesales, a person must
have several desired traits. An account executive needs to
have a voice that sounds pleasant, trustworthy, and
pleasing to the ear, is easily understood, and enthusiastic.
Telesales representatives should be friendly and have an
interest in helping others, even when callers are rude,
unfeeling, or obscene. They should be confident and have
the ability to handle rejection and operate under pressure
without getting defensive. The most important characteristic
the representative needs to possess is to be a good listener.
This includes the ability to empathize, read between the
lines, and analyze what they hear. Product knowledge is
essential to enable them to handle routine customer
questions. This product knowledge is acquired through the
training program. Account Executives need to be able to sit
for long periods, often in small cubes. Those who have had
experience in a quality telesales program and have
experience with the product have the best background for
success (Harlan, Woolfson, Jr. 1991).
If one were to compare telesales to field sales, it is evident
that the pure ratios favor telesales. On the average, it is
possible for a field sales person to make five or six calls a
day whereas a telesales person can make over one
hundred contacts a day. If the same contact level were to
be achieved in field sales, five salespersons would have to
be added for every one telesales person (Baier, 1994).
Understanding this, the company has no plans of extended
it sales force from inside to outside.
The success of Computability depends on the success of
their corporate account executives. Computability is unsure
at this time which of the following factors play a role in the
employees abilities to increase sales profits and which
factors are most significant: length of employment, sex,
education level, number and/or length of sales calls per
month, and attendance.
The first step in determining which factors are most
significant is to state the null hypotheses and alternative
hypotheses. The null hypotheses states there is a
relationship between the improvement in adjusted gross
profit from sales and the influence from the above named
Ho: The mean of age of
employment is equal to the mean
of sex is equal to the mean of
education level is equal to the
mean of number and/or length of
sales calls per month is equal to
the mean of attendance.
The alternate hypothesis states there is not a relationship
between the improvement in adjusted gross profit from
sales and the influence from the above named factors.
H1: There is a difference
between the means of age of
employment, sex, education
level, number and/or length of
sales calls per month, and
This data will be analyzed at the .05 significance level.
Data for analysis was collected over a five-month period
from November 1998 to March 1999. The raw data
information is contained in Table 1. Subject sample size
was nine sales personnel in active employment in the target
time frame. Independent variables included length of
employment, sex, education level, average number of calls,
average length of a sales call and the average monthly
attendance record for each subject. Each variable was
subjected to a correlation analysis to determine the level of
significance to the adjusted gross profit generated. The
variables were than subjected to a multiple regression to
determine the overall significance of the multiple factors.
The basic outline and formulas for using the correlation with
multiple regression was outlined in Chapter 13 of Statistical
Techniques in Business and Economics, by Mason, Lind
and Marchal, 1999.
Totals of five-month sales figures for the Adjusted Gross
Profit (AGP) from representatives were used as the
dependant variable in the analysis. This was a sum of profit
from total sales in the time frame. A five-month time frame
was chosen because of the sample information available
and the deadline for this report.
A factor for consideration was the total months employed
in a sales position in this division. The division was started
in February of 1998 and the different start dates were
noted for all subjects. The work force is relatively stable as
suggested by the mean number of months in the program of
11.8. The whole program is 15 months old. Some
participants have been with the program since the start up
and have developed a comfort level for their position.
Suggested sales goals are adjusted for the amount of time a
particular sales person has worked in this department.
There is a start up suggested sales target that is adjusted on
an established schedule. The commissions paid are tied to
the ability of a sales person to reach their individual profit
A dummy variable was used to record the sex of the
individual. The female was recorded as a zero and the male
was recorded as a one.
Education was a factor with the highest level achieved in
formal education noted. A dummy variable was assigned to
three levels finished high school, received an associates
degree, and received a batchlers degree. The level attained
was noted with a one, levels not attained were recorded as
Phone data was analyzed to collect information for the next
two factors. The first was the average number of daily calls.
There is a suggested quota of 80 calls per day and the
individual daily call frequency for each salesperson was
noted and than averaged for the recorded data. The
second group of data was the daily average call length, in
minutes, for each call. Individual calls are timed by seconds
and recorded. The mean of the total time was computed.
The daily average was than adjusted to a format in minutes.
The final factor for analysis was the average monthly
attendance of the subjects. Actual days worked were
recorded against total days available and the total averaged
to establish a pattern of absenteeism.
All data analysis would be subjected to a significance level
of .05. This level was chosen as the critical values would be
accepted at the 95% significance for business use.
The raw data was included in a Windows, Excel format on
a spreadsheet marked Table 2.
The information was correlated by two computerized
formats. This was done to display the same information in
two different formats for comparison of ease of data
Table 3 shows the correlation statistics completed in
Windows Excel, data analysis. Table 4 shows the same
information conducted by the Windows Excel, Megastat
program. The different programs gave the same results
however this researcher found the Megastat presentation
easier to comprehend. The Megastat program included
critical values for the sample size so comparison
information was readily available.
The results showed significant correlation between adjusted
gross profit and months employed. Adjusted gross profit
and call length also showed a significant correlation.
There was very little correlation between adjusted gross
profit and the education of the subjects. Due to the limited
sample size and the correlation results the education
category was eliminated from the final analysis.
The adjusted sample information is shown in Table 5. This
information omits the educational data and was subjected
to a correlation analysis with little difference in reported
The adjusted information was subjected to a regression
analysis and an analysis of variance. The results are shown
in Table 7 and 8. A low p value of .15 was recorded
suggesting an acceptable analysis of the variables.
Scatter plot charts were constructed to show the positive
correlation for APG vs months of employment, see Graph
1 and call length in Graph 4. Negative correlation was
witnessed in the scatter plot for APG vs number of calls
see Graph 3. Graph 2 and 4 showed no real direction.
Analysis of the information compiled in the mentioned
tables will be handled in the next section on the
I other portions of this paper we have discussed what
factors play a role in the salesperson’s abilities to increase
profit. We have collected outside research to determine
which factors are most significant in influencing an increase
of sales and gross profits. We have outlined the collected
data and the statistical methods we feel are relevant to give
us some direction to base some decisions. The following
section will interpret the data results the statistical analysis
and display that data in multiple forms of numerical and
We start with an analysis of the data displayed in the
correlation matrix in the appendix marked table 2. There
were seven different parameters used in this matrix. In
comparing these parameters to our established critical value
it was decided to exclude the level of achieved education
based on the low numerical results in the correlation
analysis. The data was removed and a second correlation
matrix was performed which showed little difference in
values from the first group and still showed strong
relationships in other areas.
Of the six remaining parameters the significant relationships
of adjusted gross profit to number of months employed and
the average length of a phone call turned out to have the
strongest correlation in the comparison to the critical value
at .05 of .666. The relationship of the average length of a
phone call to the average number of calls made was also
significant suggesting that the quantity and quality of the
phone calls are better correlated than a relationship
between increasing the number of phone calls and sales that
can be generated. This idea disagrees with what past
studies have suggested to management. That is the idea that
the more calls made will lead to increased sales. Our
findings suggest that developing a comfort level and
product expertise based on time in grade and developing a
high quality conversation with prospective clients is more
effective than high volume, short length, impersonal sales
The regression analysis lends confirmation to these
interpretations. In table 5 the regression analysis shows the
relationship of all the variables and the significant
quantifiable strength of each relationship. This regression
data allows us reject the null hypothesis and not reject our
alternate hypothesis. All the variables are not equal to each
other and show different effects on the ability to increase
adjusted gross profits.
If one looks at the coefficient of multiple determination
shown as R squared on the regression analysis table it
indicates that 86 percent of the variation in the adjusted
gross profit is due to a combination of all the variations
studied. The length of time employed at the company and
the length of phone calls are the significant factors. It should
be noted that this study was conducted with a rather limited
sample size due to constraints of time. The high correlation
rates substantiated with a significant percent of relationship
should suggest that the results would very little if the sample
size were increased.
There is further confirmation when one looks at the p-value
in the analysis of variance table. The recorded p-value is
.1513. When this is compared to the value of .863
coefficient of multiple determination on the regression
analysis table one can see that it reinforces the decision to
reject the null hypothesis with a fair confidence of not
having a type 2 error.
To display the results in a more pictorial presentation we
have outlined in scatter plot design the relationships of the
different variables. The first scatter diagram shows the
adjusted gross income vs the amount of months employed
with Computability. There is a strong positive relationship
between these two variables. The fact that the salespersons
have been in place for a fair time frame leads one to
speculate that there is some sort of comfort level that
develops over time and helps to improve sales. The
development of a stable sales force is a significant way to
improve sales and profits.
The effects of the sex of the salesperson had no real
correlation. This suggests that the job is suited to any
person with other talents and is not a gender-based
attribute. The graph shows there is a zero correlation
between these two variables.
The average number of calls vs the adjusted gross profits
yielded another strong correlation but in a negative
direction. This would suggest that improvements in sales
would not benefit from increasing the number of sales calls
but might have the opposite effect. If a salesperson is only
judged by the volume of calls made they may project a
limited willingness to get to properly qualify the customer
and offer the correct solution to a need because they are
more interested in meeting management’s call quotes. This
may actually hurt sales and profits over the long term.
The next graph showing the relationship of the average
length of calls vs the adjusted gross profit supports the
theme began from the last graph. The criteria that call for
good customer qualification and a building of a relationship
with that customer will be reflected by a positive correlation
to sales improvement. This relationship will take some time.
A longer phone conversation can help to qualify better and
build the trust needed to assist in repeated sales volume.
The longer you are on the phone, the greater the chance
you will have to sell something to the client.
The last graph looks at the monthly attendance vs adjusted
gross profit and one can see little relationship on a direct
basis. It should be noted that if you do not come to work
you would not make any calls. However just being at work
will not guarantee you success. The success of the program
is dependent on the attitude of quality not quantity.
In summary, the amount of expertise developed over time
and the amount of quality conversations developed over
time are the important factors. Sales will not improve when
activity is based on factors of quantity only.
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