By Caroline Glackin, PhD and Murat Adivar, PhD

The study applies predictive analytics to data from the 2018 National Salesforce Survey commissioned by the Direct Selling Association. It identifies key factors in salesforce motivation and performance theories studied within Expectancy Theory (Vroom 1964), Herzberg’s (1959) Two Factor Theory, and the job demands and resources model (JD-R) (Bakker and Demerouti 2007). Academic researchers dedicate substantial efforts to studying salesforce motivation and performance, but the power of machine learning has not been applied to direct sellers as independent representatives until now.

By employing supervised learning algorithms for predictive analytics to create models for (1) recruits and (2) existing independent representatives, the study finds that the most crucial factors align with Vroom’s Expectancy Theory (1964), which posits that motivation is influenced by the belief that effort will lead to desired outcomes. For recruits, the key predictors of selling success include allocated time for direct selling, ability to find new customers, gender, and adopting direct selling as a career. Time invested, experience of direct selling, recruitment, tenure, and use of technology are most predictive for top-producing direct sellers. Direct Selling Organizations (DSO) can customize their recruitment, training, and incentives to maximize sales performance by having such analysis for their organizations. This work has the potential to improve the landscape for recruitment, retention, and success.

Literature

DSOs foster an entrepreneurial culture and mindset through entrepreneurial training, tools, and professional networking (Peterson and Wotruba 1996). Crittenden, Crittenden, and Ajjan (2019) state, “Independent distributors are backed by established brands who provide them…quality products, marketing tools, business education, and a wealth of digital resources for professional and personal development.”

Representatives are DSOs’ go-to-market strategy and channel, and revenues depend on successful recruitment, retention, and performance. Because no sales experience is required and recruitment occurs primarily through an independent contractor salesforce, successful selection is essential. With a low entry cost for starter kits (average of $199 per Gamse, 2016), training, mentoring, and performance incentives matter.

DSOs focus on fostering entrepreneurial cultures and offering transparency in the company-distributor-customer dynamic (Fleming 2017). Traditional attitudinal variables like organizational commitment, expectations, satisfaction, and motivation matter (Kim and Machanda 2021). Non-pecuniary benefits, income, personal networks, and autonomy and others influence sellers (Coughlan et al. 2016, Wotruba and Tyagi 1992). These add to the broader sales performance literature.

DSOs have unique brands and cultures, which they share with sellers. This collaborative culture supports networks and promotes teamwork, coaching, and mentoring, rather than fostering competitive sales environments (Bhattacharya and Mehta 2000; Biggart 1989; Crittenden and Crittenden 2004; Lan 2016; Merrilees and Miller 1999). Representatives’ lives are intertwined with the brand and company, finding more than just a job but a community and worldview (Biggart, 1989). They often join for the products and stay for the supportive environment (Coughlan et al. 2016). Research suggests that meeting realistic expectations is more important than overselling for job satisfaction (Wotruba and Tyagi 1992).

The factors identified are like those for salesforces and entrepreneurs. This is logical, considering that direct selling representatives operate as self-employed micro-entrepreneurs with intricately linked relationships to their direct selling companies.

The underlying theoretical frameworks on needs, motivations, and expectations in the literature suggest determinants to examine. While machine learning remains agnostic to theory, classic research continues to add value to the discussion and is incorporated into the framework.

Sales Performance Determinants

We developed a taxonomy to identify key factors influencing direct selling representative performance, such as education, tenure, and experience. Without forming specific hypotheses, our evidence-driven approach highlights determinants like expectations, social benefits, commitment, organizational traits, and personal factors that impact salesforce motivation and performance. See Figure 1 for details.

Figure 1
Salesforce Performance Determinants from the Literature

Methodology

We created two classification models (Model I and Model II) to predict the sales performance of direct selling salespeople based on the predictors with the highest degree of accuracy (see also Glackin and Adivar 2023). Sales performance is assessed by the salesperson’s reported annual direct selling business net income.

A supervised learning approach in which the outcome is the independent sales representative’s financial performance, dependent on some direct seller-specific inputs is the least biased, strongest method available (Glackin and Adivar 2023; Habel et al. 2024). To attain the goal using supervised learning, we employ the 2018 DSA Salesforce Survey dataset of 6,941 direct selling representatives, including a range of personal factors, experiences, attitudes, and interests. DSOs among the 122 members of the Direct Selling Association distributed the survey. The survey queries on the respondent’s gender, desire to dedicate time to direct selling, capacity to find new clients, motives to become and remain representatives, and numerous other topics. The sample for this study is a subset of the entire dataset of 8,714 entries to incorporate active sales representatives who answered the question regarding net income from direct selling and did not respond “don’t know.”

The survey question regarding net income from direct selling is crucial, as it is used to gauge the direct seller’s financial success (performance) of direct sellers. This question in the survey could be answered with one of 15 income levels between $0 and $400,000. Our exploratory analysis indicates that DSOs should identify the characteristics of direct sellers who earn more than $10,000 annually in net income, which results from data clustering and as the accepted norm for “business builders” in direct selling. Even though this is a modest annual net income for a salesperson, it is a crucial level of productivity for the channel. The direct selling channel generated more than $30 billion in annual revenue, according to an approximate extrapolation of industry sales statistics predicting $40.1 billion in revenue in 2020 from the DSA 2021 Growth and Outlook Report. We construct our target response (“Above$10,000”) as a binary variable by combining 15 levels of the categorical variable.

Utilizing domain expertise and current literature, we tested variables that are readily available to DSOs and can predict the financial success of independent salesforces before and after recruitment. Using the variable selection and dimension reduction tools of our data analytics platform, we excluded the tested predictors that had no measured effect on the models’ precision. We combined the recorded states of the dependent variable into two levels as another dimension reduction strategy. Using best practices, we divided the dataset into training and validation sets, compared machine learning methods on the validation set, and raised the target-level accuracy of the selected classification algorithm by adjusting the classification probability threshold.

In this study, we utilize supervised learning to predict high-performing independent sales representatives before and after recruitment. Our supervised learning task is building classification models that predict the class of categorical responses. The data analysis platform is JMP Pro (by SAS) and requires the selection of suitable machine learning algorithms for classification. We employ the following classification techniques and evaluate probable independent variables to build two classification models: Decision Trees, Boosted Tree, Bootstrap Forest, Logistic Regression, and K-Nearest Neighbors. For superior performance in models, the independent variables with the strongest predictive contribution are employed as predictor variables.

Model I – Classification Model for Recruits

We used multiple supervised learning approaches (i.e., Decision Trees, Logistic Regression, Naive Bayes, K-Nearest Neighbors, and Bootstrap Forest) to identify the optimal model for predicting the success of an applicant based on data knowable at recruitment. We explored 16 independent and 14 underlying variables for model building. Model I intended to predict the direct seller recruits that were most likely to earn an annual net income of more than $10,000 from their efforts. Decision (classification) Trees with a particular profit matrix (connected with the goal response) produced the highest classification accuracy. In this study, 78% of validation data at the target level “Above10K=1” were correctly categorized by Model I with the set probability threshold value of 0.21.

These values can be interpreted as follows: In a scenario where a DSO employs this classification model to choose the top 20% of new representatives expected to net more than $10,000 annually from direct selling, the organization will receive 2.5 more target-level applicants than with “no model baseline.”

Model II: Classification Model – Active Sales Representatives

In this part, we create a prediction model and identify the most relevant elements influencing the financial performance of current (active) direct salespeople utilizing determinants that are only available once a representative is an active independent representative. This model included 35 independent variables with their comprehensive underlying variables.

The outputs are described in Table 2.

The indication column “Above10K” is the response to which our analysis is directed. The estimation of the model’s accuracy is based on the misclassification rate across the validation set. Bootstrap Forest and Boosted Tree outperform other supervised learning algorithms with an 88% overall accuracy value. Nevertheless, in terms of target-level accuracy using the default classification probability threshold (i.e., 0.5), Bootstrap Forests performed best with a classification accuracy rate of 50.4% at the target level. We refer to this Bootstrap Model as Model II.

By applying such a model, the direct selling company will have a 3.66 times greater probability of predicting candidates at the desired level than if there were no model.

To interpret these numbers, assume that a DSO uses this model to estimate the top 20% of existing representatives anticipated to net more than $10,000 annually through direct selling. In this situation, the company will have a 3.66 times greater probability of predicting candidates at the desired level than if there were no model.

Results

Recruitment

Using individual data that DSOs may secure before and at recruitment, Predictive Model-I can generate the probability that a new candidate is a preferred prospect because they are appropriate to the target audience of direct sellers who can generate more than $10,000 net annual income from a direct selling business.

Deploying the predictive Model I on a new population can correctly classify almost 78% of candidates to generate a $10,000 annual net income from direct selling. DSOs can apply such a model to identify and recruit the most productive candidates. Even though 88.3% of representatives are part-time, the top performers are interested in devoting their sales time and view direct selling as a career. While only 11% of direct sellers are male, they are more likely to become top performers (34% versus 15%).

We show that if a direct selling company uses Predictive Model I to select the top 20% of new representatives who are predicted to be most likely to generate more than $10,000 annual net income, the company will get 2.5 times more productive candidates than “no model baseline.” Here, no model baseline classifies everybody as belonging to the majority class (e.g., not likely to make more than $10K) in the historical data. While DSOs do not screen recruits, they could use this data to determine the best prospects and design recruitment materials, campaigns, and support systems to optimize performance, as well as creating adaptive learning systems to support incoming recruit success.

If a direct selling company uses Predictive Model I to select the top 20% of new representatives who are predicted to be most likely to generate more than $10,000 annual net income, the company will get 2.5 times more productive candidates than “no model baseline.”

Retention, Growth, and Performance

By using determinants available only after the recruitment of an independent representative, we calculated the likelihood of financial success in Predictive Model II.

This can assist companies to focus on factors that most significantly contribute to the financial success of direct selling representatives to lead to increased company growth and performance. By using a Bootstrap Forest technique, we show that Predictive Model-II can correctly classify 84% of representatives who are more likely to generate more than $10,000 a year. DSOs can develop organizational training and support based on this knowledge.

Predictive Model-II can correctly classify 84% of representatives who are more likely to generate more than $10,000 a year.

Impacts/Discussion

Study outcomes suggest DSOs prioritize recruiting sellers dedicated to building careers (business builders), earning long-term income, and expanding networks. This involves revamping recruitment materials to attract high-quality prospects instead of a broad range of recruits. This approach aims to reduce turnover and resources spent. While ensuring inclusivity, incorporating more male and Latinx representation may enhance sales performance.

DSOs are responsible for retaining and growing their representatives. They must set realistic expectations, create a satisfying environment, encourage product sales, and foster positive experiences within the sales team. Managers should focus on career prospects, recruit customers, grow downlines, and use videoconferencing.

DSOs differ in business models, size, reach, customers, products, missions, and cultures. They can benefit from firm-level predictive analytics and channel-level data insights. However, they should consider the research limitations and improve them with advanced methods.

Dr. Caroline Glackin is the Thomas Family Distinguished Professor of Entrepreneurship at the University of North Carolina Pembroke.

Dr. Murat Adivar is a Professor of Management at Fayetteville State University

References

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By Suzanne Altobello, PhD, Caroline Glackin, PhD, and William G. Collier, PhD

Introduction

Direct selling depends on independent representatives who choose to stay, advocate for the opportunity, and help grow the salesforce. Because representatives are not traditional employees, direct selling organizations (DSOs) often win or lose on perceived fit and meaning between the representative and the company, rather than formal supervision. We examined whether identity-related perceptions, such as commitment, shared values, identification, brand meaning and long-term orientation, help to explain three practical outcomes of income, willingness to recommend the DSO/direct selling to others, and likelihood of continuing as a representative.

This research explores how independent sales representatives integrate into the brand and company, and how to strengthen these relationships. Previous studies in the sales literature have examined salesperson/brand identification with the organization and the congruence between personal values and perceived values of the organization, but have not addressed these factors for contract salespeople. This survey was designed for replication and extension of Gammoh et al. (2014) and engaged direct sellers from two organizations.

We modeled three outcomes that matter to DSOs: gross income from sales of products or services by their independent salesforce to consumers (performance), a direct seller’s likelihood of recommending the opportunity to one’s network (advocacy), and a direct seller’s likelihood of continuing to represent the company over the next year (retention). We tested whether these outcomes were predicted by Social Identity Theory constructs including: organizational commitment, salesperson–DSO values congruence, brand distinctiveness, brand attractiveness, salesperson–brand identification, along with long-term orientation (individual planning; respect for tradition). Figure 1 provides brief definitions of our predictors and outcomes.

Figure 1
Predictors and Outcomes in Research Models

Theory and Hypotheses

Social Identity Theory

In practical terms, Social Identity Theory (SIT) helps explain why people work harder and stay longer when they feel they truly belong to a group or organization. Originating in social psychology, SIT argues that individuals define part of who they are through their membership in social groups, such as a company, a brand community, or a sales team (Fiske and Taylor, 1991; Tajfel, 1978; Tajfel and Turner, 1986; Ashforth and Mael, 2024). People tend to see their own group (the ingroup) more positively than other groups (outgroups) (Tajfel et al., 1971). When they believe their group has distinctive and attractive qualities, that pride in membership can boost self esteem and strengthen their desire to stay connected to the group (Fiske and Taylor, 1991). In sales and service settings, this logic underpins related ideas such as organizational identity (Karanika-Murray et al., 2015), moral identity (Itani and Chaker, 2022), perceived inclusion (Chen and Tang, 2018), salesperson brand identification, and salesperson company identification (Gammoh et al., 2014). All of these constructs capture how strongly people feel that “this organization or brand is part of who I am.”

For practitioners, the implication is straightforward: when representatives feel that “this company stands for what I stand for” and “this brand fits who I am,” they are more likely to stay, to advocate for the organization, and to deliver better performance.

Gammoh et al. (2014) and van Dick (2001) describe social identity as having cognitive, evaluative, emotional, and behavioral components. Cognitively, individuals see themselves as part of the group. Evaluatively and emotionally, they feel pride and positive regard for that membership. Behaviorally, they support the ingroup through actions that help and benefit it, such as going the extra mile for customers or defending the brand when it is criticized.

Gammoh et al. (2014) applied SIT to company employed salespeople and showed how these ideas translate into day-to-day performance. They found that when salespeople perceived strong value congruence (e.g., when their personal values matched the values of the brand and the company), they reported higher identification with both. This stronger sense of identity was linked to greater job satisfaction and organizational commitment. In turn, these attitudes were associated with more effective behavioral and outcome performance, including clearer communication and stronger sales results. For practitioners, the implication is straightforward: when representatives feel that “this company stands for what I stand for” and “this brand fits who I am,” they are more likely to stay, to advocate for the organization, and to deliver better performance.

Long-Term Orientation

Long-term orientation in selling captures whether a salesperson is focused on near-term transactions or on the future consequences of today’s behaviors for customer relationships and business outcomes. In sales settings, research suggests that a stronger consideration of future sales consequences, paired with customer-oriented selling, helps explain a salesperson’s long-term relationship orientation and related preferences such as favoring longer-term compensation approaches (Schultz & Good, 2000). Work on salespeople’s “relational time perspective” similarly argues that representatives with longer time horizons tend to set goals differently and rely more on cooperative, problem-solving approaches that support relationship development over time (Macintosh, 2006). More recent sales research also connects salesperson time orientation to outcomes such as sustained effort during a new product launch and customers’ willingness to pay more, highlighting that future-focused selling can matter for both salesperson behavior and customer responses (Agnihotri et al., 2019; Beuk et al., 2014).

Age as a moderator

For direct selling organizations, age is more than a demographic label; it can shape how representatives think about their role, how they relate to the company, and how they behave toward others. DSOs routinely attract people at very different stages of life, from young adults looking for a side income to older adults seeking flexible work in semi-retirement. Although research on how Social Identity Theory operates by age is still limited, several studies suggest meaningful age differences in the behavior component. Matsumoto et al. (2016) find that older individuals tend to engage in more prosocial behaviors, while Lockwood et al. (2021) report that adults aged 55 to 84 are more willing to help others than adults aged 18 to 36. Cutler et al. (2021) show that older adults are especially likely to direct prosocial behaviors toward people they see as members of their own group. In sales contexts, Day (1993) also finds that older salespeople set higher annual sales goals and achieve them just as well as younger colleagues. For DSOs, recognizing these age-related patterns can support more precise recruitment messages and motivational strategies that align with representatives’ life stage and long-term orientation, rather than using a single approach for all age groups.

Method

A quantitative survey was sent via email to independent representatives of two established member companies of the Direct Selling Association. Both companies were similar in product characteristics but differed in overall revenues. All participant responses were anonymous and went directly to the research team. Companies did not have access to any raw, individual-level data.

Measures

The dependent variables in the analysis are each respondent’s gross income from direct selling in the previous year, the likelihood of recommending becoming an independent sales representative to a friend or family member, and the likelihood of continuing to represent the company over the next year. The independent predictor variables included: salesperson/company identification, salesperson/company values congruence, salesperson/brand identification, brand distinctiveness, brand attractiveness, organizational commitment, and long-term orientation. All SIT predictors were measured using established scales from Gammoh et al. (2014). Long-term orientation was measured using the Bearden et al. (2006) scale, which operationalizes long-term orientation at the individual level as two related factors: planning and tradition.

Sample profile

A total of 592 representatives started the survey; 295 respondents had complete data across all predictor and outcome measures. The ages of all current independent sales representatives ranged from 18 to 74, with a mean of 45.81 (SD = 11.24) years, with no significant differences in age between the companies.

Most respondents were between 40 and 50 years old (39.5%) and female (88.3%).

Almost 70% are married, with approximately equal percentages of single (8.2%) and divorced/separated (9.5%) respondents. Most representatives had no children under 18 at home (40.8%), with equal percentages having one (17.4%) or two children (20.8%). Most respondents were not of Hispanic or Latino origin (84.8%) and had attained an associate’s degree or some college (29.9%) or were college graduates (30.6%).

Results

We first estimated separate stepwise regression models for each of the three outcomes.

We used all respondents in these initial analyses and did not separate by age group.

For sales performance (as measured by the respondent’s reported gross income from direct selling), organizational commitment was the only predictor that showed a statistically significant relationship. Representatives who reported higher organizational commitment also tended to report higher gross income from direct selling. None of the other predictors added meaningful explanatory power once commitment was taken into account.

For the advocacy outcome (as measured by the likelihood of recommending the DSO), organizational commitment and salesperson/company values congruence were the only significant predictors. Representatives who felt more committed to the organization and who perceived a stronger match between their personal values and the DSO’s values reported a higher likelihood of recommending the opportunity to others.

For the retention outcome (as measured by the likelihood of continuing as a representative over the next year), salesperson/company values congruence and brand distinctiveness emerged as significant predictors. Representatives who saw a stronger match between their own values and the values of the DSO, and who viewed the brand as meaningfully different from alternatives, were more likely to say that they intended to continue representing the company.

Table 1
Significant Predictors of Direct Selling Outcomes for all Representatives

Age as a Moderator

We then examined whether the patterns just described varied by age group.

Respondents were grouped into three categories: under 40, 40 to 50, and 51 and older, and the regression models were re-estimated within each group. For performance, no variables remained significant once the sample was split by age, so age related differences in income are not interpreted further.

For the likelihood of recommending the DSO, the predictors differed by age group.

Among representatives under 40, those who saw the brand as attractive and who felt strongly committed to the organization were more likely to say they would recommend the opportunity. For representatives aged 40 to 50, recommendation was highest when three elements coincided: they felt that their personal values matched the DSO’s values, they viewed the brand as attractive, and they reported strong organizational commitment. Among representatives 51 and older, a different pattern emerged. In this group, advocacy was higher when they perceived a strong values be match with the DSO, saw the brand as clearly distinctive from others in the market, and the sales rep had a stronger long-term, tradition-oriented outlook. At the same time, those over 50 who perceived the brand as highly attractive were actually less likely to recommend it.

Table 2
Positive and Negative Patterns of Predictors for Advocacy Outcome by Age Groups

For the likelihood of continuing with the DSO, a similar pattern of age specific drivers emerged, as shown in Table 3. Among representatives under 40, salesperson or DSO value congruence and brand attractiveness were significant positive predictors of continuation intentions. In the 40 to 50 group, brand attractiveness and the long-term orientation dimension capturing individual planning were significant and positive. Retention again looked different for the oldest group of representatives (51+). For older representatives, a strong values match and a clear sense that the brand is unique went together with stronger continuation intentions.

However, in this same age group, those who said that the brand felt very much like “me” and those who saw the brand as very attractive were less likely to say they planned to continue.

Table 3
Positive and Negative Patterns of Predictors for Retention Outcome by Age Groups

Discussion

For performance, organizational commitment was the only consistent driver in the full sample. When the sample was split into age groups, the income patterns were less stable and did not yield clear age-specific drivers. For independent sales representatives in this dataset, sales performance is tied most closely to whether representatives felt a durable bond with the organization.

For the advocacy outcome, across the full sample, recommending the DSO was higher if representatives were committed to the organization and their sense that the DSO’s values matched their own. When we examined advocacy by age groups, organizational commitment mattered for younger and midlife representatives, and values congruence mattered strongly for older representatives (notably, though, brand attractiveness moved in the opposite direction for this group).

For the retention outcome, for all representatives, the strongest pattern for continuing with the DSO was a values match with the DSO plus a belief that the brand is meaningfully distinctive. When separated by age, values congruence remained important for the youngest and oldest groups, while brand attractiveness mattered for the younger and midlife groups. The older representatives in this sample appeared less likely to continue when the brand felt highly attractive or when the representative reported that the brand felt very much like “me.”

Practical Implications and Conclusions

Our results can be applied to existing independent sales representatives and extend to recruitment and onboarding new representatives. The DSO should begin with a shared core that strengthens organizational commitment and clarifies the organization’s values in observable, day-to-day terms; these two levers showed up repeatedly across this study’s three outcomes, and they are also the easiest to standardize across the salesforce.

Figure 2
Age-based Recommendations for Company Training and Onboarding

In Figure 2, we illustrate how the DSO can layer age-guided emphasis without changing the company’s fundamentals. Under 40 messaging and training could lean more heavily on brand attractiveness while quickly converting interest into commitment through early wins for the sales representative, belonging cues, and visible support through their upline. For ages 40–50, training might intentionally combine values fit, attractiveness, and commitment, then reinforce consistency through planning routines that keep the opportunity feasible alongside work and family obligations. For 51 and older, the emphasis could shift toward values congruence and brand distinctiveness, using concrete proof points and a trust-forward message; in this group, leaders should be careful about overreliance on hype or purely aspirational branding because it can work against advocacy and retention.

Figure 3
Proposed DSO Toolkit Architecture

In Figure 3, we propose an overall toolkit architecture that DSO managers and leaders can use in coaching, communications, and recognition. The first toolkit could be a values fit toolkit that includes brief screening prompts during recruiting, a “values in action” onboarding module that shows how values appear in product claims and team norms, and mentor check-ins that surface misalignment early. The second toolkit could be a distinctiveness toolkit that gives representatives a one-sentence “why us” statement, compliant comparisons, and customer stories that make the brand’s difference easy to explain and hard to copy. The third toolkit could be a planning toolkit that provides weekly activity plans, 90-day goals, and pipeline tracking, especially for the 40–50 segment where planning aligned with continuation.

Dr. Suzanne Altobello is the William H. Belk Distinguished Chair of Business Administration and Professor of Marketing at University of North Carolina Pembroke.

Dr. Caroline Glackin is the Thomas Family Distinguished Professor of Entrepreneurship at the University of North Carolina Pembroke.

Dr. William Collier is an Associate Professor of Cognitive Psychology at the University of North Carolina Pembroke.

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