ComputAbility – Sales Goals 1607 Essay Example

by specialized sales training companies. However, the management chose to work together with a company that had developed its own sales training program.

Before implementing the sales training program called "Discovery," Creative Computers' top management underwent training to assess its value. After extensive research, the company chose to adopt "Discovery" and enlisted the creators of the program to train internal trainers and select corporate sales representatives. Subsequently, Creative Computers' trainers now administer the "Discovery" program for both existing and new employees.

The training program includes five courses, each with one to three modules. The modules cover techniques for cold calling, understanding company needs, developing client relationships, and managing accounts and time. During the first 6 months of the training, representatives were given goals in three areas: number of calls, talk time, and dollar amount. The goals were as follows: Calls: 80-120 calls per day, Talk Time: 3.5-4 hours per day, Dollars: $3000 - $28000 gross profit (based on months of employment). The metrics for calls and talk time were developed by the company Discovery, while the dollar goals were determined by ComputAbility. Discovery has been in place for about 9 months.

The metrics at ComputAbility have resulted in issues, particularly with the required number of calls for sales representatives. Representatives have informed management that these goals are unrealistic and impede the establishment of client relationships. Consequently, some representatives cease to follow the program after a few weeks on the job.

It is crucial for the company to evaluate the effectiveness of the Discovery program and determine if the provided metrics are realistic. This evaluation is challenging because not all sales representatives comply fully with the program, and other factors

like length of employment can affect their performance. To assess the effectiveness of Discovery, researching established sales training programs and techniques, analyzing existing sales numbers in relation to metrics, and considering other contributing factors are important.

Research

According to Zajas and Church (1997), telesales refers to offering goods and/or services over various electronic media such as phone, fax, television, computer (p.227).

Telesales offers several advantages, including cost-effective personal contact, the ability to adapt to customer needs, and the flexibility to adjust sales campaigns. When integrated into a company's overall marketing strategy, telesales can improve sales efficiency and increase profits (Stone, 1995). Successful telesales requires managers who excel at motivating and guiding others to sell effectively over the phone. Managers lacking experience in telesales may encounter various challenges such as setting unrealistic goals, employing high-pressure tactics, utilizing rigid and impractical scripts, overlooking burnout issues, neglecting systematic information collection, and committing excessive resources without proper testing (Harlan, Woolfson, Jr., 1991, p.8).

ComputAbility has faced challenges with the established "Discovery" metrics, and it is important for management to know who developed them and how to measure their effectiveness. To ensure that unrealistic goals are not set, a telesales manager should personally test every new program by making calls and keeping statistics as benchmarks. Developing rapport with the team and listening to both work-related and non work-related problems is crucial for an effective telesales manager, as it helps prevent potential burnout. Additionally, a manager needs to be able to recognize when boredom or frustration arises in the job. After a few months into the Discovery program, many of the sales representatives, known as Account Executives at Computability, were becoming increasingly frustrated.

During

a meeting, managers identified the cause of job-related stress to be daily micro managing of numbers and people. This realization led them to understand that their unrealistic goals were contributing to the stress. As a response, management decided to reassess the metrics and establish revised goals.

The revised goals were as follows: for months 1-3, there would be 400 calls per week; for months 4-6, 350 calls per week; and for months 7-12, 300 calls per week. Talk time goals also changed: for months 1-3, it would be 1.5-3 hours per week; for months 4-6 and 7-12, it would be 3-4 hours per week. However, the financial goals remained unchanged.

The main objective behind these revisions was to provide representatives with weekly goals instead of daily ones in order to eliminate micro managing and reduce employee stress. Management believed that by doing so, employees would be more willing to adhere to the program and that the revised metrics would offer greater flexibility.

The employee's performance is satisfactory if they are meeting any of the weekly metric breakdowns, regardless of how long they have been employed. For instance, Employee A, who has been with the company for 2 months, has a daily call time of 3.5 hours and handles 300 calls per week. This employee's performance meets the expected metrics. The purpose of breaking down the metrics in this way is to address employees' concern that the call volume hindered their ability to foster client relationships.

Telesales representatives need adequate training and compensation to effectively perform their job (Harlan, Woolfson, Jr., 1991). Both Creative Computers and ComputAbility understand the importance of a robust training program for their account executives.

The Discovery training program is highly successful, although its metrics may require adjustment. However, it is essential for the company to acknowledge that relying solely on the Discovery training program does not ensure representative success. There are additional vital factors to take into account.

When hiring a salesperson, it is crucial for a manager to review their work references as attitude can be shown through habits like promptness, attendance, and completing job assignments (Zajas, Church, 1997). To succeed in telesales, an individual must possess certain desirable traits. An account executive should have a pleasant and trustworthy voice that is easily understandable and enthusiastic. Additionally, telesales representatives ought to be friendly and have a willingness to assist others, even when faced with rude, insensitive, or vulgar callers.

The representative should have confidence and be able to cope with rejection and pressure without becoming defensive. The key quality that the representative must possess is being a good listener, which involves being able to empathize, interpret underlying messages, and analyze what they hear. It is important for them to have a good understanding of the product in order to handle common customer inquiries. This knowledge is gained through the training program. Account Executives should also have the ability to sit for extended periods, often in small cubicles.

According to Harlan, Woolfson, Jr. (1991), individuals with prior experience in a quality telesales program and familiarity with the product are more likely to succeed. When comparing telesales to field sales, it becomes apparent that the pure ratios heavily favor telesales. On average, a field sales person can only make five or six calls per day, whereas a telesales person can have over one

hundred contacts per day.

If five salespersons were added for every one telesales person, the same contact level could be achieved in field sales (Baier, 1994). The company understands this and does not plan to expand its sales force from inside to outside. Computability's success relies on their corporate account executives. Computability is uncertain about which factors contribute to the employees' ability to increase sales profits and which factors are most important. These factors include length of employment, sex, education level, number and length of sales calls per month, and attendance. The first step in determining the most significant factors is to state the null and alternative hypotheses. The null hypothesis suggests that there is a relationship between the improvement in adjusted gross profit from sales and the influence of the aforementioned factors.

Ho: The mean of age of employment, sex, education level, number and/or length of sales calls per month, and attendance are all equal. The alternate hypothesis states that there is no relationship between the improvement in adjusted gross profit from sales and the influence from the mentioned factors. H1: There is a difference in the means of age of employment, sex, education level, number and/or length of sales calls per month, and attendance. This data will be examined at a significance level of 0.05.

Data Analysis

Data for analysis was gathered from November 1998 to March 1999 over a period of five months.

Table 1 contains the raw data information. The subject sample size consisted of nine sales personnel who were actively employed during the target time frame. The independent variables examined were 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. A correlation analysis was conducted on each variable to determine its significance in relation to the adjusted gross profit generated.

The variables underwent a multiple regression analysis to determine the overall significance of the multiple factors. The procedure for using correlation with multiple regression was explained in Chapter 13 of Statistical Techniques in Business and Economics (Mason, Lind and Marchal, 1999). In the analysis, the dependent variable was the Adjusted Gross Profit (AGP) obtained from representatives' five-month sales figures. The AGP represented the total profit from sales during that period.

The decision to use a five-month timeframe for this report was made considering the data and deadline. We considered the duration of subjects' sales experience within this division, taking into account that the division was established in February 1998 with varying start dates for each person. The program has been running for 15 months, and participants have spent an average of 11.8 months in it, indicating a stable workforce. Certain individuals have been part of the program since its beginning and have effectively adapted to their roles.

The sales goals for sales persons in this department are adjusted based on their tenure. Initially, a start-up sales target is given and then updated regularly. Commissions depend on the sales person's ability to meet their personal profit goals. To indicate gender, a dummy variable was used: females were assigned a value of zero and males were assigned a value of one.

The education levels were categorized using dummy variables, with high school, associates degree, and bachelors degree represented as one for attainment and zero for non-attainment. Phone data was

used to gather details on two more factors. The first factor was the average daily call volume made by each salesperson. The recorded data consisted of individual daily call frequencies, which were averaged to determine the overall average for each salesperson.

The second set of data contained the average duration of each call, measured in minutes. Each individual call was timed and recorded in seconds, then the total time was averaged to determine the mean. Finally, the mean value was converted into a format that represents time in minutes.

For analysis, the average monthly attendance of the subjects was considered. The number of days worked was compared to the total available days to determine absenteeism patterns. All data analysis would be evaluated at a significance level of .05. This level was selected to accept critical values with a 95% significance for business purposes. The raw data was included in a spreadsheet labeled Table 2, using a Windows Excel format.

The information was organized using two computerized formats in order to present the same information in two different ways for easy comparison of data extraction. Table 3 presents the correlation statistics done in Windows Excel for data analysis. Table 4 showcases the same information carried out using the Windows Excel Megastat program.

Both programs yielded similar results, but the researcher found the Megastat presentation to be more easily understandable. The Megastat program provided critical values for the sample size, making it easier to compare information. The findings indicated a significant correlation between adjusted gross profit and months employed, as well as between adjusted gross profit and call length. However, there was only a minor correlation between adjusted gross profit and

the education level of the participants. Given the limited sample size and the correlation results, the education category was excluded from the final analysis.

Table 5 displays the adjusted sample information, excluding educational data, which underwent correlation analysis with minimal variation in reported results. The adjusted information underwent regression analysis and analysis of variance, with the findings presented in Tables 7 and 8. A p value of .15 was recorded, indicating a satisfactory examination of the variables. Graph 1 illustrates the positive correlation between APG and months of employment, while Graph 4 demonstrates the relationship with call length.

The scatter plot for APG vs number of calls (see Graph 3) showed a negative correlation. Graph 2 and 4, however, did not show any clear direction. The analysis of the information in the tables mentioned will be addressed in the next section, specifically the conclusions.

Conclusion

In other parts of this paper, we have explored the factors that contribute to a salesperson's ability to increase profit.

We have conducted external research to identify the key factors that have the greatest impact on boosting sales and gross profits. We have organized the data we collected and identified relevant statistical methods to guide our decision-making. In the next section, we will interpret the results from the statistical analysis and present the data through various numerical and graphical formats. To begin, we will analyze the data presented in the correlation matrix located in table 2 of the appendix.

The matrix consisted of seven parameters. To compare them with our critical value, we decided to exclude the level of achieved education due to low correlation analysis results. Subsequently, the data was removed and a second correlation

matrix was conducted. This second matrix displayed similar values to the first one and still indicated strong relationships in other areas. Out of the six remaining parameters, the correlation between adjusted gross profit and number of months employed, as well as the average length of a phone call, had the strongest correlation with the critical value at .05 of .666. Additionally, there was a significant correlation between the average length of a phone call and the average number of calls made. This suggests that the quantity and quality of phone calls have a stronger correlation than simply increasing the number of phone calls to generate sales.

According to past studies, the belief that making more calls will result in increased sales is not supported by our findings. Instead, our research suggests that building a strong rapport and product knowledge based on experience and engaging in quality conversations with potential clients is more effective than making a large number of brief and impersonal sales pitches. The regression analysis in table 5 confirms these interpretations by demonstrating the relationship between all variables and the significant quantitative strength of each relationship. This regression data enables us to reject the null hypothesis and support our alternative hypothesis.

All the variables show different effects on the ability to increase adjusted gross profits, and they are not equal to each other. Looking at the regression analysis table, the coefficient of multiple determination (R squared) indicates that 86 percent of the variation in adjusted gross profit is explained by a combination of all the variables studied. The significant factors in this study are the length of time employed at the company and the length

of phone calls. It is important to note that this study was conducted with a limited sample size due to time constraints. However, the high correlation rates and significant relationships suggest that the results would remain relatively unchanged if the sample size were increased.

The p-value in the analysis of variance table, which is recorded as .1513, provides further confirmation. Comparing this to the .863 coefficient of multiple determination on the regression analysis table, it reinforces the decision to reject the null hypothesis with reasonable confidence in avoiding a type 2 error. To visually represent the results, we have created scatter plots to illustrate the relationships between various variables. The first scatter diagram depicts the relationship between adjusted gross income and the number of months employed with Computability.

There is a notable correlation between these variables. The presence of long-standing salespeople suggests that a level of comfort is formed over time, leading to improved sales. Establishing a stable sales force is a key factor in enhancing profits. Gender of the salesperson had no significant impact on the results.

This indicates that the job is suitable for individuals of any gender, regardless of their other talents. The graph illustrates that there is no connection between these two factors. Another significant correlation was observed between the average number of calls and the adjusted gross profits, but it was negative. This implies that boosting sales would not be enhanced by increasing the number of sales calls; instead, it might have a detrimental impact.

If a salesperson is solely evaluated based on the number of calls they make, they may demonstrate a reduced willingness to properly qualify customers and provide appropriate solutions to

their needs. This is because their primary focus is on meeting the call quotas set by management. However, this approach can ultimately harm sales and profitability in the long run. The following graph illustrates the connection between the average duration of calls and the adjusted gross profit, further reinforcing the earlier point. The criteria that emphasize effective customer qualification and relationship building will result in a positive correlation with increased sales performance.

Building a strong relationship with clients takes time, and one effective way to qualify and establish trust is through longer phone conversations. The more time spent on the phone, the higher the likelihood of making sales. However, when examining the monthly attendance compared to adjusted gross profit, there appears to be minimal direct correlation.

It is important to note that not coming to work means not making any calls. However, simply being present at work does not guarantee success. The success of the program relies on having a quality attitude, rather than focusing on quantity. In conclusion, the key factors are the expertise developed over time and the quality conversations that are fostered over time.

Sales cannot be improved by focusing solely on quantity-based factors.