What question did this study set out to answer?

This research aims to evaluate the profitability of memberships, virtual office services, and meeting room bookings.

May 7, 2026JOURNAL OF ADVANCE AND FUTURE RESEARCH0 citationsOpen Access

A Study On Evaluating the Profit Contribution of Memberships, Virtual Office, and Meeting Room Services

RRR RevathyAnna University, Chennai DCD. ChitraAnna University, Chennai SKS. Satheesh KumarAnna University, Chennai

Key Points

This research aims to evaluate the profitability of memberships, virtual office services, and meeting room bookings.
Analyzed monthly data from three years of financial records
Utilized time series methodologies including Exponential Smoothing and ARIMA models
Assessed revenue forecasts and expense ratios for services offered.
Membership services emerged as the largest revenue source
All expense ratios decreased, indicating effective cost management
Holt-Winters model showed the least error and captured seasonality effectively.

Abstract

This research looks at the profitability aspects of 3 major types of service lines (Memberships, Virtual Office Services and Meeting Room Bookings) of Avaikalam Coworking Spaces located in Puducherry, India. Monthly data from all three years' worth of services was collected from their internal financial records and analysed using 4 time series methodology types (Exponential Smoothing, Holt-Winters Triple Exponential Smoothing, Linear Regression Trend Analysis and Auto-Regressive Integrated Moving Average (ARIMA) models). Revenue was always trending upwards over the study period due to new member increases and relatively stable demand for virtual offices/services. Membership services were the leading source of service revenue, while total expense ratios decreased throughout the entire study, confirming that cost management was still being maintained as they grew their business. Of the models used to create revenue forecasts, Holt-Winters had the least amount of errors and accurately tracked seasonality, with the highest demand occurring midway through the fiscal year and the lowest happening right after the beginning of the fiscal year. For net profit forecasting models, analyses were completed to compare three ARIMA models of different configurations; ARIMA (0,0,1) was demonstrated to be the model that best fit data based on error metrics and residual diagnostics. Predictions for monthly net profits for the following year, based on average monthly profits over a 12-month time frame (in conjunction with previous years' growth rates), illustrate that they will continue to generate moderately positive and consistently show growth from historical trends over time.

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