Optimizing Sales Headcount for Revenue Growth in the US Rentals Market Using Machine Learning (2023)
DOI:
https://doi.org/10.56830/IJSIE12202301Keywords:
Sales headcount optimization, machine learning, SaaS, Random Forest, U.S. rentals market, sales performance, customer segmentation, sales territory management, employee attrition, seasonal demand, revenue growthAbstract
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.
References
Bureau, U. C. (2023). Seasonal Rental Trends in the United States. . Retrieved from https://www.census.gov/topics/housing.html.
Gill, A. (2018). Developing a real-time electronic funds transfer system for credit unions. . International Journal of Advanced Research in Engineering and Technology
(IJARET), 9(1), 162-
184.https://iaeme.com/Home/issue/IJARET?Volume=9&Issue=1.
Gladun, D. (2018). Subscription business model in mobile software industry. Case: Company X.
Glassdoor. (2022). Burnout and Attrition Rates in Sales Teams. Retrieved from https://www.glassdoor.com/research/employee-burnout/.
Guenzi, P., & Nijssen, E. J. (2023). The relationship between digital solution selling and value-based selling: a motivation-opportunity-ability (MOA) perspective. European Journal of Marketing, 57(3), 745-770. DOI: https://doi.org/10.1108/EJM-11-2021-0907
Gupta, P., & Tham, T. M. (2018). Fintech: the new DNA of financial services. Walter de Gruyter GmbH & Co KG. DOI: https://doi.org/10.1515/9781547400904
HubSpot. (2023). Average Sales Conversion Rates in Real Estate. Retrieved from https://blog.hubspot.com/sales/conversion-rate.
Jayasuriya, L. (2023). Managing Customer Success: An Evolutionary Process Model for Role Development in SaaS Entrepreneurial Ventures. Junior Management Science (JUMS), 8(3), 591-616.
Kauranen, T. (2023). E-signing companies on the European market and their qualities:.
study of SaaS-companies working in the e-signing market.
Li, B., & Kumar, S. (2022). Managing software‐as‐a‐service: Pricing and operations. Production and operations management, 31(6), 2588-2608. DOI: https://doi.org/10.1111/poms.13729
Maia, P. A. (2023). Customer Success Management in fintechs: an analysis of its main challenges and opportunities for improvements.
Marr, B. (2019). Artificial intelligence in practice: how 50 successful companies used AI and machine learning to solve problems. John Wiley & Sons.
National Multifamily Housing Council. (2023). Multifamily Rental Market Performance Report. Retrieved from https://www.nmhc.org/research-insight/.
Nethercote, M. (2023). Platform landlords: Renters, personal data and new digital footholds of urban control. Digital Geography and Society, 5, 100060. DOI: https://doi.org/10.1016/j.diggeo.2023.100060
PraveenaSri, P., Prasuna, V. N., Murugesan, R., & Usha, S. P. (2023). Business Challenges of Forecasting Sales in Bakery Industry: Applications of Machine Learning Algorithms. In Proceedings of the International Conference on Emerging Trends in Business & Management (ICETBM 2023). DOI: https://doi.org/10.2991/978-94-6463-162-3_30
RealPage. (2022). Regional Differences in the U.S. Rental Market. Retrieved from https://www.realpage.com/analytics/.
Rrucaj, A. (2023). Creating and sustaining competitive advantage in the software as a service (SaaS) Industry: best practices for strategic management.
Saadeldin, R. (2019). of Thesis: The fundamental analysis of the software industry in the USA. change, 2019, 29.
Schmitt, M. (2023). Automated machine learning: AI-driven decision making in business analytics. Intelligent Systems with Applications, 18, 200188. DOI: https://doi.org/10.1016/j.iswa.2023.200188
Shi, R., & Yeh, A. G. (2023). Do similar social groups have similar mobility in a city? Social areas and mobility in Shenzhen. China. Cities, 138, 104350. DOI: https://doi.org/10.1016/j.cities.2023.104350
Statista. (2023). Annual Housing Turnover and Rental Seasonality in the U.S. Retrieved from https://www.statista.com/statistics/226244/us-housing-turnover-rate/.
Umeh, C., Cornelius, N., & Wallace, J. (2023). Exploring equality, diversity, and inclusion in multiethnic settings: A context‐sensitive approach. Human Resource Management Journal, 33(1), 148-169. DOI: https://doi.org/10.1111/1748-8583.12441
Zhang, S., Pauwels, K., & Peng, C. (2019). The impact of adding online-to-offline service platform channels on firms’ offline and total sales and profits. Journal of Interactive Marketing, 47(1), 115-128. DOI: https://doi.org/10.1016/j.intmar.2019.03.001
Zillow Rentals. (2023). Housing Market Insights. Retrieved from https://www.zillow.com/research/.
