Salespersons performance predictor model: An exploratory factor analysis

Getting answer for a corporate that where its current stand in the industry is important for the strategy making, especially for the sales team. Few academic researches charted direction towards cross-functional sales factors, but getting answer whether we can quantify that sales performance and identify what is the numeric benchmark value, is difficult. For the companies to understand the need to focus on which cross-functional factors and when, is also difficult. The purpose of this research is to identify the cross-functional factors and their impact after Exploratory Factor Analysis (EFA), especially in B2B context, and constructing a predictive model to interpret and quantify the influences (sales performance score) specifically to the IT/ITes companies.

A quantative approach was employed with a field survey (10 months duration), 11.57% conversion ratio, with 35 questions addressing 33 indicators, and response was collected from 310 sales professionals randomly from 90+ IT companies. Respondents were all sales professionals between 1 year to more than 10 years of experience. Three items were removed as outliers using “Mahalanobis Distance Test” for Multivariate analysis (p< .001), dropped two variables by ‘Missing value Not at Random’ (MNAR) analysis and considering similar two variables asked as closed ended questions.

Researchers identified final 15 (out of 33) determinants of cross-functional sales performance indicators forming 4 (four) best factors with very high reliability (Cronbach α = 0.853), applying Exploratory Factor Analysis (EFA) with KMO (0.787), Bartlett’s X² (1544.093), p <.001, Principal Component Analysis (extraction method), and Varimax (Rotation Method) with Kaiser Normalization. After Confirmatory Factor Analysis (CFA), using Onyx platform, researchers established a statistical model to predict the sales performance for the IT companies.

Focusing on these identified factors companies can understand what is preventing its salesforce from giving their best performance. We contribute in creating a predictive model and computing a sales performance score, based on the final factor loading values. This would be unique and unprecedented to measure the current industry performance benchmark by quantifying its company specific at the moment performance standard value, for better strategic support towards the achievement of desired sales performance in business-to-business (B2B) sales environment.

Easily identifying, and focusing on these identified factors companies can improve its sales performance. Researchers contribute in creating a statistical model and computing a sales performance score, based on the final factor loading values, is be unique and unprecedented to measure the current industry performance by quantifying its standard, or benchmark value, for better strategic support towards the achievement of targets. This will help companies in advance to take the strategic measurement against a high fall of revenue measuring the sales performance with this predictive model. This is niche and exploratory research backed by confirmatory factor analysis.