International Journal of Sustainability and Innovation in Engineering (IJSIE)
December 2023
DOI https://www.doi.org/10.56830/IJSIE12202301
Authors
Krupa Goel
Abstract
In the context of the American rental market, it is essential to underline that managing sales
teams is one of the critical factors affecting the revenue increase for property management
software providers. In this article, the author discusses issues of sales head management in
SaaS firms, how to increase sales headcount and some barriers the firms themselves
experience due to variations in demand, differences in the regions, and client segmentation.
Existing sales labour models rely on historical data and must be more adequate to capture the
fluctuating landlord market. ML’s best solution is based on the given data to help firms predict
the variability in demand, distribute workload efficiently, and minimize churn rates.
Organizations in the SaaS segment can now classify their sales labour models with the
Random Forest algorithm to anticipate the need for hiring and cutting costs for truly
sustainable growth. This article also shows how they use machine learning to solve problems
such as seasonal fluctuation, lead quality, managing sales territories, and employee turnover.
This paper confirms how the implementation of machine learning models supports more
tangible and accurate workforce planning, cuts costs, and optimizes the sales team’s
performance in a highly saturated market.
Keywords:
Sales headcount optimization, machine learning, SaaS, Random Forest, U.S. rentals market,
sales performance, customer segmentation, sales territory management, employee attrition,
seasonal demand, revenue growth.
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