Essay/Term paper: Computability - sales goals

Essay, term paper, research paper:  Marketing

ComputAbility, 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 individual"s primary

responsibilities were to train new hires in the areas of sales,

product knowledge, company policies and procedures and

computer systems.



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

missing.



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 company"s 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.



RESEARCH



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

company"s 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

established:



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

week



$ 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

client.



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

factors.



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

attendance.



This data will be analyzed at the .05 significance level.



DATA ANALYSIS



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

goals.



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

a zero.



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

extraction.



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

results.



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

conclusions.



Conclusion



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

graphic presentation.



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

pitches.



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.







Harlan, R., Woolfson, Jr., W., (1991). Telesales That

Works. Chicago, Il: Probus Publishing Company.



Stone, B., (1995). Successful Direct Marketing Methods.

Lincolnwood, Il: NTC Business Books.



Zajas, J., Church, O., (1997). Applying

Telecommunications and Technology from a Global

Business Perspective. Binghamton, NY: The Haworth

Press, Inc.