Optimising Bookings and Revenue for Onkaparinga Golf Course
- Yanitha Preedee
- May 8
- 4 min read
🎯Objective of Showcase
This mock project focuses on improving operational efficiency at a council-managed golf course. It demonstrates my ability to analyse commercial asset performance using data and process insights, and to translate these findings into actionable business strategies.
Note: To ensure realistic analysis and insights, the dataset was self-generated using variables modelled on publicly available booking patterns from Noarlunga Golf Hub, assumed to be a potential partner of the City of Onkaparinga.
📝 Executive Summary
The data-driven analysis of the Onkaparinga Golf Course reveals three critical opportunities to optimise bookings and boost revenue. Firstly, with 60-minute sessions making up the majority of bookings, introducing a multi-visit loyalty pass for short-play users can enhance customer retention and increase repeat visits. Secondly, off-peak time blocks remain underutilised, particularly between 9am–3pm. To address this, targeted promotions such as ‘Midday Escape’ packages can drive weekday engagement. Lastly, the booking process can be enhanced by offering tailored slots and package suggestions based on user preferences, alongside post-payment incentives such as coupons or member perks to drive loyalty and encourage non-member registration.
1️⃣ Project Title & Objective
Title: Optimising Bookings and Revenue for Onkaparinga Golf Course
Objective: To identify usage patterns and develop a data-driven pricing strategy aimed at increasing revenue during off-peak periods, while improving booking process efficiency and customer segmentation.
2️⃣ Problem Statement
While the City of Onkaparinga’s golf course experiences strong demand during peak hours and weekends, weekday and off-peak slots remain underutilised. This project analyses booking trends, customer types, and package preferences to uncover revenue opportunities and improve the user experience through system mapping.
3️⃣ Approach
3.1 Dataset:
Tool: Python
Created a mock dataset simulating booking data, including customer type, time slot, package type, and transaction ID.
For more details, see my article:
How to Create a Realistic Mock Dataset in Python for Project Showcases Dataset:
3.2 Analysis and Interactive Dashboard:
Used Excel to analyse and visualise booking data through two interactive dashboards:
These two dashboards clearly show volume vs value, and support 60-min preference, off-peak underuse, and product and customer insights.
Sales Dashboard: Focuses on revenue trends with time-based filters, enabling targeted analysis of peak vs off-peak performance. It helps identify key factors contributing to revenue — including customer type, package selection, booking channel, time slot, and day of the week.
Product Analysis Dashboard: Displays player distribution across time slots, package types, and bays. Includes slicers for customer type and booking stage to support segmentation and product usage insights.
3.3 Business Process Modelling :
Created a Business Process Model and Notation (BPMN) diagram using Draw.io to map the current booking workflow and identify potential optimisation points across the user journey.
4️⃣ Key Insights & Recommendations
Quick-play Preference: Majority of customers choose 60-min sessions. Promote multi-visit passes or loyalty discounts for short sessions.
Underutilised Off-Peak Hours: Time blocks like 9am–12pm and 12–3pm offer room for promotional pricing or bundled packages.
Customer Segment Opportunity: Target senior and tourist segments with weekday specials and local partnerships.
Booking Efficiency: Streamline payment confirmation and integrate log-in requests to reduce drop-off in the funnel.
Most Popular Booking Channel: Online — indicating a strong digital engagement and a good opportunity to run digital promotions or upselling.
Most Common Customer Type: Tourist — suggesting marketing partnerships with local hotels and tourism platforms could increase bookings.
Average Revenue per Transaction: $90.85 — this gives a baseline for evaluating pricing strategy effectiveness.
Average Group Size: 1.97 — most bookings are for solo or pair play, ideal for short-duration or fast-play packages.
Busiest Day: Saturday — expected for leisure activity, but reinforces the need to boost weekday appeal.
4.1 Implementation of Business Strategy:
4.2 Optimising Business Process Modelling :
As highlighted in Section 4.1, insights R09 and R10 have been integrated into an improved booking process. These enhancements are illustrated in the updated BPMN diagram below, with new steps highlighted in green.
The revised process aims to reduce booking drop-offs by suggesting alternative available time slots (R09) and to boost post-payment engagement through perks or discounts (R10). These improvements help extend customer interaction within the system, increase upselling opportunities through bundled offers, and reduce cancellations caused by unavailable preferred time slots.
✅ 5. Conclusion & Reflection
This mock project demonstrates how data analysis and process mapping can uncover meaningful insights to optimise commercial assets such as council-operated facilities. By identifying underutilised time blocks, recognising booking behaviours, and streamlining user processes, I was able to develop actionable strategies aimed at increasing revenue and improving user experience.
Through this project, I showcased key business analyst skills — including stakeholder-focused thinking, data storytelling, segmentation, and process improvement. It reflects not only my technical capability with tools like Excel, Python, and BPMN, but also my ability to turn insights into value-driven recommendations.
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