Keyword

Sales Force, Forecasting, Sales Efficiency and Sale Force Size.

Introduction

Each year businesses across the globe spend huge amounts on sales force investment. The sales force is the engine that drives revenue of organizations and represents a significant investment for most organization. Sales force constitutes one of an organization’s most productive and most expensive assets and has a major impact on overall performance of the sales organization. Sales force sizing affects profitably of a sales organization by affecting both revenues and costs Pankaj, (2019).

In daily life, a businessperson deals with different transaction in terms of selling and purchasing of goods and services. In these transactions, the second one persuades the first person. Therefore, selling may be defined as persuading people to satisfy the want of first one. The person, who does this act, is called as the sales force or salesperson, the result of this action as sales, while these activities of the person, are supervised and controlled by sales-management (Azizi et al, 2012). Salespersons provide key link in adding value for customers, in finding new opportunities, in influencing future intentions and in gaining referrals. Moreover, it is argued that salesperson serves as the major determinant of whether or not consumers receive a brand-marketing message (Boles et al 2000).

Salespersons are the most important marketing tools in the interface between the company and its customers. Added that top-quality salespersons that maximizes revenues from current existing customers, systematically identifies, and manages new prospects well are critical. This will allow a business entity to grow faster than its competitors will. As such, top-quality salespersons may offer substantial business performance improvement in today’s increasingly competitive environment Brent, (2014)

Sales force constitutes one of an organization’s most productive and most expensive assets. The sales force is a sales generator as well as a cost generator. Sales force sizing affects profitably of sales organizations by affecting both revenues and costs. While the cost impact is immediate, the sales impact is often much longer term. The sales force size affects customers, salespeople and the overall sales organization.

A sales force that is of the right size is challenged and motivated, but not overworked, connects with customers effectively, sales compensation costs are reasonable and sales as well as profitability of the organization is strong

Many internal and external forces can cause a company to expand its sales force size. Internal forces include launching a new product, entering a new market, adopting a new selling process and strategy of expanding customer base, prospects and sales leads to increased sales and market share. External forces include changes in customer base, its competitor set and its environment. Similarly, many internal and external forces can cause a company to contract its sales force. Internal factors include a merger/acquisition or changes in marketing strategy. External forces include changes in the buying process, shrinking markets, market consolidation, economy downturn, a new sales channel or sales force technology that enhances field productivity Pankaj, (2019)

Sales organizations have used many subjective and objective measures to sales performance. Objective measures, in subjective measures, sales organizations focus on how hard sales people have worked in terms of the overall amount of efforts or time that sales people devote trying to achieve sales goals. While in sales organizations monitor and reward their sales people with sales revenue or output measure Kumar et al., (2015).

Strength of sales force of a particular company affect for the highest possible sales of that company. Therefore, a mechanism that has the ability to increase the sales force productivity is a need as well as a challenge. An innovative approach to increase sales force engagement in company sales process is a vital. Sales force productivity can be increased by allowing real time access to enterprise data like customer contact information, sales history, pricing and products on mobile devices for sales force. The major changes that affect sales functions are globalization, increased customer focus and increasing Dassanayake et al, (2018).

Despite the insightful knowledge the information systems research and sales literature has generated, no studies have thoroughly examined the effect of sale force forecasting on salesperson efficiency. In fact, Marshall, Greg W., William C. Moncrief, and Felicia G. Lassk (1999) state that “very little research has been devoted to investigating the impact of technology on individual salesperson effectiveness” and “future research needs to be directed toward understanding the sale force forecasting in sales man efficiency.

Objectives of the study

• To investigate the effect of training and development on sale force efficiency in the Beer distributors

• To examine the effect of compensation and bonus on the sales force in adding value for customers, identifying new opportunities, and gaining referrals

• To examine sales force capability on sale efficiency of in Brewery Distributor

Theoretical Literature

Sales Forecasting Theory

Sales forecasting involves predicting future sales levels based on historical data, market trends, and other relevant factors. By accurately forecasting sales, a sales team can better allocate resources, set realistic targets, and optimize their strategies for maximum efficiency Robert et al, (2003).

Resource Allocation Theory

Efficient sales force forecasting enables better resource allocation within a sales team. By forecasting sales accurately, organizations can allocate their sales force resources effectively, ensuring that each salesperson is assigned the right tasks and territories to maximize their productivity Robert et al, (2003).

Goal Setting Theory

Sales force forecasting is closely tied to goal setting. By setting realistic and achievable sales targets based on accurate forecasts, sales teams can stay motivated and focused on achieving their objectives. Clear goals derived from sales forecasts help in improving sales force efficiency

Sale force and sale force forecasting

Sales Force is the team of employees responsible for the sales on a company. They represent the company’s direct contact with customers and they are the company’s main resource consumers, therefore they are directly related with the company’s profitability. Their impact on the company is increasing as markets become more competitive and customers are demanding more attention to their needs (Filipe, 2008).

One of the most important reasons companies invest in SFF is to increase the efficiency and productivity of the sales staff (Erffmeyer and Johnson 2001). Sale force forecasting time consuming, but important, tasks such as scheduling sales appointments, sending follow-up letters and emails, tracking contacts and updating sale opportunities. Sales automation applications also enable salespeople to quickly generate estimates - and speedily turn these estimates into proposals, quotes and then orders when a deal is signed. In addition, the sales team has immediate access to order information, and can proactively alert customers to an order's arrival or delay. In addition, if a customer calls with a question about their order, this information is at the salesperson's fingertips, a step that saves time, improves the entire customer experience and increases the value of the sales professional in the eyes of the customer Ronald and Rapp, (2005).

Sales force in any company big or small, manufacturing or service, is charged with generating product sales from assigned customers in different independent territories. However, the evolving selling environment today is much more complex, demanding significant changes in performance indicators, compensation, control and attainment of goal (Okeke, 2014). The sales force industry has grown exponentially since the mid 1990s (Rahman, et al, 2015). Organisations are desperately seeking qualified people, while they are feeling the effects of downsizing, industry growth, voluntary attrition, and the onset of new product lines. There is a major effort to hire sales people or to retain those already employed. Performance evaluation criteria are characterized as among the most crucial factors used to determine a sales person’s salary or wage, promotion, or termination (Caruth and Handlogten, 1997). The purpose of sales force evaluation can never be underestimated if organisations wish to actualise their objectives of achieving customer satisfaction and loyalty. Sale force evaluation is simply measuring goals with results (Jobber and Lancaster, 2009)

Sales force offers powerful tools for sales forecasting, essential for effective sales management. However, achieving accurate forecasts can be challenging. Many sales leaders invest significant time in estimating future sales, often finding their predictions inaccurate.

Proper use and understanding of Sales force’s forecasting tools are crucial for generating reliable and precise sales performance estimates. Using these tools wisely can significantly enhance the accuracy of sales forecasts, benefiting the overall sales strategy and in this article, it is better you through my insights on how to improve your Sales force sales forecasting. However, be aware that there is no one-size-fits-all solution. Just as weather forecast accuracy varies by location, in business, the precision of forecasts can differ based on your industry and business model.

Sale force efficiency evaluation

Sales organizations have used many subjective and objective measures to sales efficiency. In subjective measures, sales organizations focus on how hard sales people have worked in terms of the overall amount of efforts or time that sales people devote trying to achieve sales goals. While in sales organizations monitor and reward their sales people with sales revenue or output measure. Such output based measures of efficiency are simple and easy to implement, but ad-hoc rather than systematic and tend to reward sales people's past behaviour while ignoring future profit potential. There are various aspects of a salesperson's efficiency, which are not accounted for by current sales efficiency. For instance, prospecting for new customers may require extra efforts or collecting market information that does not result in immediate sales. Such qualitative elements are more likely to be reflected in long-run profit Kumar et al., (2015)

Sales force efficiency evaluation is the last function of the sales management process (Kerin, Hartley and Rudelius, 2011). It is the interval when sales management correlate objectives with efficiency and profit. It helps in the sales manager in identifying and sieving or fishing out dull and low performing salesperson from smart and high performing ones; and taking adequate measures to praise, reward and promote the astute and diligent salesperson and also, either advising, educating, guiding, training, punishing and totally removing the lackadaisical one (Jobber, Lancaster and Jamieson, 2004). Sales force evaluation is assessing result of sales force activities with the results (Jobber and Lancaster, 2009).

Sales force Size Calculation: Various Approaches

The sales force is a critical component to the overall success of the sales organization’s goals and objectives. Sales force sizing focuses on sales force deployment decisions that are related to determining selling effort needed to cover sales accounts adequately. Based on the desired amount of selling efforts, the size of the sales force is calculated and territories are designed to ensure proper coverage of accounts and to provide each salesperson with a reasonable opportunity for success. Deciding on the proper size of the sales force is a strategic management issue because it has an important impact on the sales organization’s revenues and profits. A properly sized selling organization assures that customers and prospects receive appropriate coverage, the sales organization’s products get proper representation, the sales force is stretched but not overworked and the organization makes an appropriate investment in its sales resources. Appropriately, sized sales force maximizes the economic return on investment of selling resource Madhani, (2013).

Determining the most appropriate sales force size is dependent on a number of factors, such as stages of business life cycle, the use of selling partners, the return on investment for new salespeople, productivity of sales people and turnover of the sales staff. Sales organizations use a number of analytical tools to determine the appropriate sales force size, including the following:

Breakdown approach

This approach determines the appropriate sales force size by using the forecasted sales to calculate the average sales per salesperson. This approach is easy to develop; however, it is weak conceptually. The concept underlying the calculations is that sales determine the number of salespeople needed.

Sales force size = Forecasted sales/Average sales per salesperson

Workload approach

This method determines the total selling effort needed to adequately cover the sales organization’s market and then calculate the average selling effort per salesperson to find the appropriate sales force size for the desired selling effort. This approach is relatively simple to develop.

Sales force size = Total selling effort needed/ Average selling effort per salesperson

Incremental approach

This incremental approach states that a new salesperson should be added until the gross profit on new business is equal to the cost of deploying another salesperson. Early in the growth stage, when uncertainty is low, a sales organization adds sales employees until the increased contribution equals the incremental cost. This approach is rigorous for calculating sales force size as it quantifies the important relationships between sales force size, sales and costs. However, this approach is difficult to develop, and it cannot be used for new sales force where historical data and accurate judgments are not possible.

Factors affecting sales force forecasting

There are several factors that being described by Stup (2003) towards the success of the employees" performance. The factors are such as physical work environment, equipment, meaningful work, performance expectation, and feedback on performance, reward for good or bad system, standard operating procedures, knowledge, skills and attitudes. The most widely described determinants of employees ‘performance are the following (Marshall and Johnston 2010, Stup 2003).

Training and Development:

Producing the best available product or service is not enough – it has to be sold. If companies are to survive they must attach the utmost importance to training their field sales force, not just pay lip service to the concept. Top management must be totally committed to training and authorise sufficient investment for this to occur. They must also accept that the benefits derived from sales training may not be immediate; they take time to show through (Jobber and Lancaster, 2009).

Compensation and Bonus

Compensation is not considered a motivational factor but it is central to an employee’s morale. Management has to balance the level of compensation for the employees with the operation’s ability to meet its bottom line. Being the highest paying employer will make it easier to find staff but it may also put the company out of business Chesser (2016).

According to Chesser (2016) Benefits are part of a total compensation package. Employee meals in the restaurant industry, “friends and family” rates in the lodging industry, and complimentary entry of staff and their family to the amusement park are all examples of nonmonetary benefits common in the hospitality industry. Benefits with a direct monetary value, such as paid vacation or sick leave and health insurance are also offered to hospitality employees.

Rangarajan et al. (2005) investigated the impact of sales force automation on technology-related stress, effort, and technology usage among salespeople. Outsourcing the sales force is another important factors for developing a business unit (Rapp, 2009); Ross Jr. et al. (2005) discussed whether we must set up our own sales force or outsource it. Investigated the relationship between salesperson performance and understanding of customer decision making. Widmier et al. (2002) studied the effects of infusing technology into personal selling. Vlachos et al. (2010) investigated sales force reactions to corporate social responsibility and finally Venkatesh et al. (2003) investigated the user acceptance of information technology.

Sales force would be the key factor for business survival, especially in highly competitive markets. If the firm wants profit to keep growing, they must rely on the sales people to promote products launched to the customers. Therefore, the salespeople have to make great effort to collect information and identify potential customers in other to reach the selling goals and obtain the necessary profit for the firm to survive. Santrock (2008) opined personality as a dynamic and organized set of characteristics possessed by a person that uniquely influences his or her cognitions, motivations, and behaviours in various situations. Personality arises from within the individual and remains consistent throughout life.

Sales force capability

There are few recent studies focusing on sales capabilities, such as Krush et al. (2013), who studied this capability and firm performance in marketing dashboards and sense making. Siahtiri, O’Cass, and Ngo (2014) studied marketing capabilities and sales capabilities on customer-centric performance and brand performance in the B2B market. Finally, Guenzi et al. (2016) developed a model to measure sales capabilities. According to Krush et al. (2013), the sales capability and marketing dashboards’ efforts of a business unit directly and positively influence its sense making. Sales capability works acquiring customer information (Vorhies & Morgan, 2005) that, when used with marketing dashboards, which are a technology that allows access to important marketing metrics and initiatives (Krauss, 2005), help companies to construct market knowledge (Krush et al., 2013).

Research Methodology

Research Design

The research design chosen for this study was research descriptive and exploratory design method. It comprises of using well-structured questionnaire administered to the respondents in order to gather some vital, accurate and up to date information needed for the research work.

Source of Data

The primary data for this research collected from questionnaires and interview from distributor’s managers. The Secondary data was obtained from written documents of the distributor’s office human resource management.

Sampling

Population of the Study

The population of this study comprises of 36 staff of BGI Ethiopia distributor office, Dashen distributor office, Bedele distributor office in Mizan Aman town. The organisations were chosen because of their high level of customer attraction and retention

Sampling Procedure and Technique

This study used nonprobability sampling design, non-probability sampling, this study used purposive probability sampling to conduct the interview, it is used based on a judgment of researcher to get more reliable information concerning the topic among 3 managers to exist, a personal interview is conducted manger to get more relevant information. Census survey was used to collect primary data (questionnaire) from three staff of distributors selected purposively.

Source of data

The Analysis utilized both primary and secondary data to come up with the findings and recommendations contained in this study.

Questionnaire survey

Primary data were collected using questionnaires administered to respondents who are involved in sale force forecasting functions for business efficiency. Both closed and open-ended questions were used to ensure efficient and convenient collection of the qualitative and quantitative data. Second and third objectives answered through questionnaire.

Data Analysis

Quantitative Data Analysis

Quantitative data from the questionnaire were examined for errors & non-responses, coded and responses captured in the SPSS version 24. This was then analysed using statistical methods. The data were analysed using descriptive and inferential statistics by Statistical Package of Social Science (SPSS) version –26. Quantitative data collected were analysed using descriptive statistical techniques, which were mean, and standard deviation.

Result and Discussion

Regression Analysis

Before regression analysis, testing assumptions is important to ensure the validity and reliability of the regression analysis results. These assumptions provide the foundation for the statistical tests used in regression analysis and help in drawing accurate inferences from the data. If someone chooses to analyze data using linear regression, part of the process includes testing to ensure that the data the person wants to analyze can actually be analysed using linear regression. Therefore, it is necessary to do this because it is only appropriate to use line deflection if the required data "exceeds" the basic guesses required to reverse the line to give a valid result. Before joining regression analysis, it is essential to test assumptions of multiple linear regression analysis Model (Keith, 2006; Pall ant, 2005). Therefore, each assumption result was done as discussed below.

Multicollinearity

Multicollinearity occurs when two or more of the independent variables are highly correlated that certain mathematical operations are not possible. The correlation between independent variables was such that multicollinearity is not a concern because multicollinearity will be created while results of the correlation coefficients are above 0.80 and to be considered-very high (Hair et al. 2006).

The Table 1 above shows there are two common procedures for evaluating collinearity, including tolerance and variance inflation factor (VIF) (Pallant, 2007). The statistics was absence of multicollinearity while VIF is less than ten, and tolerance value of greater than 0.10 but less than one (Robert Ho, 2006). Accordingly, as indicated in table above, the collinearity statistics analysis of variance inflation factors (VIF) value ranges from 1.082 to 1.1203 and Tolerance value ranging with 0.832 to 0.922. Therefore, there is no problem of multicollinearity.

Table 1 Coefficients A
Model	Collinearity Statistics
		Tolerance	VIF
1	Constant
	Compensation	0.832	1.203
	Training and development	0.918	1.089
	Sales force personality	0.922	1.085
	Sales force’s capability	0.853	1.173

Normality test

Normality is one of three assumptions for multivariate analysis. Regression assumes normality between the variables under analysis (Hair et al., 2010). Normality can be defined as, the shape of the data distribution for an individual metric variable and its correspondence to the normal distribution, the benchmark for statistical methods (Hair et al., 2010). This assumption is important for hypothesis testing and constructing confidence intervals. The figure below shows the normality test of this study.

The graphs above show the histogram that is a plot of how often possible values occurred. It's one way to see if there is anything strange in your data any extreme values or all the scores piled up on one side. Paramount, normality through histogram the study used a histogram plot indicating normality of residuals. It produced a bell-shaped curve that shows the normal distribution of the series. In this study, the figure above shows a bell shaped distribution of the residuals. The figure shows that X-axis shows the residuals, whereas Y-axis represents the density of the data set. Therefore, this histogram plot confirms there is no normality in this study.

Linearity

The presence of outliers can be detected from the Scatterplot. In the Scatterplot of the standardised residuals, the residuals were found roughly rectangular distributed, with most of the scores concentrated in the centre. This would indicate no major deviations from normality. As noted by Tabachnick and Fidell (2001), cases with Cook"s Distance values larger than 1 are a potential problem outliers. In our case the maximum value for Cook"s Distance is 0.101, suggesting no major problems.

The figure above shows that the scatterplot of residuals displays no big differences in the spread of the residual, as we look from the left to the right in the above figure. This result advocates that the relationship that is being predicted is linear; consequently, the assumption is satisfied.

Heteroscedasticity

Heteroscedasticity Test Homoscedasticity is the extent to which the data values for the dependent and independent variables have equal variances (Field 2009). At each level of the predictor variables, the variance of the residual terms should be constant. This just means that the residuals at each level of the predictors should have the same variance, therefore checking for this assumption is helpful for the fitness of the regression model. Hair et al. (2006) indicated that Homoscedasticity relates to the assumptions that dependent variable explaining equal levels of variance across the range of independent variables. Hair (2006), argue the test of homoscedasticity is required because the variance of the dependent variable being explained in the dependence association could not be focus in simply a limited range of the independent values.

Homoscedasticity test spread of residuals randomly distributed variance or homogeneity of variance, which is constant across the linear model, and as a result, homoscedasticity is not violated Consistent with Hail (2006), this study tested the homoscedasticity for metric variables using scatter plot. Scatter plots of consistent residual was conducted for all the variables and the results from the data. In effect, the scatter plot showed that the pattern of data points does not contain any exact patterns and thus had not violated the assumption (no discernible patterns of residuals were indicated).

Multiple Linear Regression Analysis

Multiple linear regressions were conducted in order to decide the explanatory power of the independent variables (compensation, training and development, Sales force personality and Sales force’s capability) and dependent variable Efficacy of distributors. The Table 2 below shows the model summary of the regression analysis.

Table 2 Regression Result
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate	Durbin-Watson
1	.851a	0.724	0.699	0.09906	2.363

The above regression model presents how much of the variance in the measure of CBHI Utilization is explained by the underlying independent variables. Furthermore, to explain R, R2, adjusted R2 and Durbin–Watson in details:

R: Indicates the value of the multiple correlation coefficients between the predictors and the result, with a range from 0 to 1, a larger value indicating a larger correlation and 1 representing an equation that completely predicts the observed value (Pedhazur, 1982). From the model summary (R=.851a) indicated that, the linear combination of the four independent variables (Sales force’s capability, Sales force personality, Training and development, Compensation) strongly predicted the dependent variable (Dependent Variable: Efficacy of distributors).

R Square (R²): Indicates the proportion of variance that can be explained in the dependent variable by the linear combination of the independent variables. In another word, R² is evaluates how much of the variability in the outcome is accounted for by the predictors. The values of R² also range from 0 to 1 (Pedhazur, 1982). Efficacy of distributors’ Sales force’s capability, Sales force personality, Training and development, Compensation explains 72.4 % of the variance in Efficacy of distributors’ and the remaining 27.6% is explained by extraneous variables, which have not been included in this regression model.

Adjusted R Square (R2): The adjusted R2 gives some suggestion of how well the model generalizes and its value to be the same, or extremely close to the value of R2. That means it adjusts the value of R2 to more correctly represent the population under study (Pedhazur, 1982). The difference for the final model is small (in fact the difference between R2 and Adjusted R2 is (0.724− 0.699 = 0.025) which is about 2.5%. This reduction means that if the model were derived from the population rather than a sample it would account for approximately 2.5% less variance in the conclusion.

Durbin-Watson: The Durbin–Watson statistic expresses that whether the supposition of independent errors is acceptable or not. As the conservative rule suggested that, values less than 1 or greater than 3 should definitely raise alarm bells (Field, 2005). So that the desired result is when the value is closer to 3, and for this data, the value is 2.363, which is so moderate to 3 that the assumption has almost certainly been met.

The ANOVA results from Table 3 demonstrate the goodness of fit of the model. More precisely, ANOVA results shows that the non-significance of the regression model. So, the results infer that the model is significant or good fit at F (29.454), p =0.000 that indicates the combined effect of factors that resulted in the study area. As point out in the above table, the p-value is less < 0.05 i.e. 0.000 which indicates the variation explained by the model is not due to possibility. Therefore, the above ANOVA table shows the suitability of the model

Table 3 Analysis of Anova
Model		Sum of Squares	Df	Mean Square	F	Sig.
1	Regression	1.156	4	0.289	29.454	.000b
	Residual	0.442	45	0.01
	Total	1.598	49

In the Table 4 above, the regression analysis constant beta value (β) and the p-value of the variables to study the significance of the hypothesis. Coefficients P values are less than 0.05 (p<0.05), (0.010, 0.00, 0.000, 0.02,) Compensation, Training and development, Sales force personality, Sales force’s capability are respectively highly significant that that Efficacy of distributors in study area. This implies that the other four independent variables have a significant relationship with the dependent variable.

Table 4 The Regression Coefficient
Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta
1	Constant	1.116	0.248		4.498	0
	Compensation	0.071	0.026	0.232	2.699	0.01
	Training and development	0.12	0.026	0.376	4.6	0
	Sales force personality	0.288	0.046	0.511	6.258	0
	Sales force’s capability	0.104	0.031	0.285	3.362	0.002

Based on these results, the regression equation that predicts the effectiveness of distributors is based on the linear combination of compensation, training and development, sales force p'ersonality, and sales force’s capability.

Y= β0+ β1X1+ β2X2+ β3X3+ β4X4+e

Y=1.114+0.237 X1+0.376X2+0.511X3++.0.285X4+e

Where:

• Y= Efficacy of distributors (Dependent Variable)

• β0= Intercept β1, β2, β3, and β4, = Coefficients of the line

• X1= Compensation

• X2= Training and development

• X3= Sales force personality

• X4= Sales force’s capability

• e= Sampling error

Based on these results, the regression equation that predicts Efficacy of distributors based on the linear combination, training and development, sales force personality, and sales force’s capability. This result indicates, first, the intercept is 1.116 when all independent variables have a value of zero.

Discussion of result

The first hypothesis which states, the compensation system for sales force has the positive significant effect on the Efficacy of distributors is supported; because the P-value is less than 0.05, hence compensation system has the significant effect on Efficacy of distributors. study supported by previous studies Pankaj, (2013) stated that compensation is the most important reward used to motivate salespeople. A properly designed and deployed sales compensation plan should drive superior performance, thereby achieving and even surpassing sales targets without exceeding the compensation budget. Gunu, (2010) To build a competitive sales force a company must pay at least the going market wage for different types of sales people. To be sure it can afford a specific type of salesperson, the company should estimate when the job description is written how valuable such a sales person will be.

Training and development has positive influence on sale force forecasting.

Training and development programs play a crucial role in enhancing employee performance and organizational effectiveness. By equipping employees with the knowledge, skills, and motivation needed to excel in their roles, organizations can drive innovation, productivity, and competitive advantage. Training is the act of providing individuals with targeted skills, knowledge, and competences in order to improve their performance as well as productivity within their existing job responsibilities. Structured learning activities and experiences are implemented with the objective of enhancing job-related skills and capacities (Noe, 2017). Development activities encompass a range of strategies, such as mentorship, coaching, work rotation, and formal schooling (Dessler, 2019). it is undeniable fact that in recent times many organizations have come to realization the importance of the role of training and development as it increases the organization staff efficiency, skills and productivity.

Sales force personality is the best determinants of sales force efficiency. The study also found that it has positive effect on sales efficiency and the result is positive effect. Sales force would be the key factor for business survival, especially in highly competitive markets. If the firm wants profit to keep growing, they must rely on the sales people to promote products launched to the customers. Therefore, the salespeople have to make great effort to collect information and identify potential customers in other to reach the selling goals and obtain the necessary profit for the firm to survive. Santrock (2008) opined personality as a dynamic and organized set of characteristics possessed by a person that uniquely influences his or her cognitions, motivations, and behaviours in various situations. Personality arises from within the individual and remains consistent throughout life Agodi, (2017).

Sales force capability and sales efficiency

There are few recent studies focusing on sales capabilities, such as Krush et al. (2013), who studied this capability and firm performance in marketing dashboards and sense making. Siahtiri, O’Cass, and Ngo (2014) studied marketing capabilities and sales capabilities on customer-centric performance and brand performance in the B2B market.

Finally, Guenzi et al. (2016) developed a model to measure sales capabilities. Sales capabilities, as a kind of marketing capability, can be developed from strategic orientations (Day, 1994; Morgan et al., 2009). The market orientation with a cultural approach develops market knowledge based on customers and competitors’ information (Narver & Slater, 1990). The salespeople use this information as a resource to develop individual skills (Guo et al., 2018; Mohiuddin Babu et al., 2019)

Conclusion

The study focuses on the relationship between sales force size and efficiency in beer distributors. The greatest increases in profit are obtained through improved sales force efficiency, either through sales force size or district allocation adjustments. Sales force forecasting can be done in three ways: breakdown approach, workload approach, and incremental approach. BGI Ethiopia distributors use a breakdown approach, which calculates their sales force size by dividing the forecasted sales by the average sales per salesperson. The workload approach assumes that all sales people should shoulder equal workloads, considering factors such as customer size, sales volume potential, and travel load. Dashin Beer Ethiopia distributors use a breakdown approach, which calculates their sales force size by dividing the forecasted sales by the average sales per salesperson. The workload approach divides the total work time available per salesperson by tasks, dividing it by selling tasks, non-selling tasks, and transportation tasks. Badele Beer distributors use an incremental approach, which compares the marginal profits and marginal costs associated with each incremental salesperson. This approach quantifies the important relationships between sales force size, sales, and costs, allowing for better decision-making and increased profitability. Overall, the study highlights the importance of sales force size and efficiency in beer distributors.

Compensation and bonus: The results support the hypothesis that the compensation system positively affects the Efficacy of distributors, aligning with previous research emphasizing the importance of motivating salespeople through effective compensation plans.

Training and Development: Training and development program to have a positive influence on sales force forecasting, enhancing employee performance, productivity, and organizational effectiveness.

Sales Force Personality: Personality traits of the sales force are identified as crucial determinants of sales efficiency, emphasizing the significance of understanding and leveraging individual characteristics in driving sales success.

Sales Force Capability: Recent studies highlight the importance of sales capabilities in driving firm performance, emphasizing the role of strategic orientations and market knowledge in developing individual sales skills.

In generally, the results of the regression analysis indicate that compensation, training and development, sales force personality, and sales force’s capability collectively have a significant impact on the Efficacy of distributors. These findings underscore the importance of designing effective compensation plans, investing in training and development initiatives, understanding the role of personality in sales success, and developing sales capabilities for driving organizational performance and sales efficiency in competitive markets.

Recommendations

• Continuously evaluate and adjust compensation structures to ensure they are competitive, motivating, and aligned with organizational goals

• Increase investments in comprehensive training and development programs tailored to enhance distributor efficacy

• Implement personality assessments to better understand the strengths and weaknesses of the sales force

• Develop targeted strategies to enhance sales force capabilities, such as refining sales processes, providing access to market

• Regularly review and adapt sales sale force and training initiatives based on feedback, market trends, and evolving customer preferences to maintain a competitive edge.

• In order to match sales force capabilities with consumer preferences, the organisation should improve its sales promotion methods, regularly assess, and improve them.

• Create thorough training curricula dedicated to improving client interaction strategies, product expertise, and sales abilities.

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Received: 01-Dec-2024, Manuscript No. IJE-25-15690; Editor assigned: 02-Dec-2024, Pre QC No. IJE-25-15690(PQ); Reviewed: 16-Dec-2024, QC No. IJE-25-15690; Revised: 21-Dec-2024, Manuscript No. IJE-25-15690(R); Published: 28-Dec-2024