ComputAbility – Sales Goals

“ComputAbility”, a mail-order company, began in 1982 as an authorized reseller of computer software and hardware. ComputAbility offers its clients over 50,000 products and has built its reputation on a foundation of competitive prices and quality service. In August of 1997, Creative Computers, another mail-order company, acquired ComputAbility. The acquisition provided a number of benefits to the company, primarily a larger product selection to offer customers. Currently, ComputAbility employs over 60 people with plans to add 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, which handles all incoming sales calls, the majority of which were from individual consumers.

Creative Computers had started their company the same way but found that 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, ranging from books and seminars to dedicated sales training company programs. Management decided to work with a company that 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 that 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 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 dollars. The following goals show the expectations given to employees during the first 6 months that the training was in place: Calls: 80-120 calls per day; Talk Time: 3.5-4 hours per day; Dollars: $3,000-$28,000 gross profit (determined by months of employment). The company that 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 the 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 the 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 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, 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 that the daily micromanaging 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. In response, management re-evaluated the metrics. After careful planning, the following revisions were established for months 1-3, months 4-6, and months 7-12:

• Calls: 400 per week, 350 per week, and 300 per week
• Talk Time: 1.5-3 hours per week, 3-4 hours per week, and 3-4 hours per week
• $ goals: remained the same

The primary goal of the revision was to give representatives weekly goals instead of daily ones to eliminate micromanaging and reduce stress for employees. Regarding the second issue, management believed that all employees would now be more willing to follow the program. The revised metrics also gave employees more flexibility. Regardless of their length of employment, an employee is performing to expectation if they achieve any one of the metric breakdowns per week. For example, if Employee A has been with the company for two months, their call time is 3.5 hours a day, and their call amount is 300 per week, then the employee is performing to the metrics. Management hoped that this breakdown would address the first issue expressed by employees, which was that the call amount did not allow for relationship building with the client. Telesales representatives need adequate training and compensation to do their job (Harlan & Woolfson, Jr., 1991).

Creative Computers and ComputAbility understand how important a solid training program is to the success of account executives. The Discovery training program is very effective, and 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 employee’s 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 is 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 that 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 average, it is possible for a field salesperson to make five or six calls a day, whereas a telesalesperson 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 telesalesperson (Baier, 1994). Understanding this, the company has no plans of extending its 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 plays 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 hypothesis 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 the age of employment is equal to the mean of sex, which is equal to the mean of education level, which is equal to the mean of the number and/or length of sales calls per month, which is equal to the mean of attendance.

The alternative 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 0.05 significance level.

Data Analysis

Data for the analysis was collected over a five-month period from November 1998 to March 1999. The raw data information is contained in Table 1. The subject sample size was nine sales personnel in active employment in the target timeframe. 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 then subjected to a multiple regression to determine the overall significance of the multiple factors.

The basic outline and formulas for using correlation with multiple regression were outlined in Chapter 13 of Statistical Techniques in Business and Economics by Mason, Lind, and Marchal in 1999. Totals of five-month sales figures for the Adjusted Gross Profit (AGP) from representatives were used as the dependent variable in the analysis. This was a sum of profit from total sales in the timeframe. A five-month timeframe 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 started in February 1998, and the different start dates were noted for all subjects. The workforce 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 startup and have developed a comfort level for their position.

Suggested sales goals are adjusted for the amount of time a particular salesperson has worked in this department. There is a startup suggested sales target that is adjusted on an established schedule. The commissions paid are tied to the ability of a salesperson 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 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 associate’s degree, and received a bachelor’s degree. The level attained was noted with a one, levels not attained were recorded as 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 then 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 then 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 subject 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 Tables 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 (see Graph 4). Negative correlation was witnessed in the scatter plot for APG vs. number of calls (see Graph 3). Graphs 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.

In 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 the 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 to 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 vary 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 a 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 salespeople 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 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 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 begun from the last graph. The criteria that call for good customer qualification and building 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 number of quality conversations developed over time are the important factors. Sales will not improve when activity is based on factors of quantity only.