Data-Driven Decisions: Optimizing Revenue Performance at Atliq Hotels

Problem Statement:

"AtliQ Grands owns multiple five-star hotels across India. They have been in the hospitality industry for the past 20 years. Due to strategic moves from other competitors and ineffective management decision-making, AtliQ Grands is losing its market share and revenue in the luxury/business hotels category. As a strategic move, the managing director of AtliQ Grands wanted to incorporate "Business and Data Intelligence" to regain their market share and revenue. However, they do not have an in-house data analytics team to provide these insights."

To support the revenue management team of a hotel chain, I analysed three months of historical data. This analysis resulted in the development of a Power BI dashboard and actionable insights to optimise revenue generation.

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Dashboard Overview:

• This Power BI dashboard provides a comprehensive overview of Atliq Hotels' key performance indicators (KPIs), allowing for data-driven revenue management and operations decision-making. The dashboard is visually appealing, with clear and concise visualisations.

Key Metrics and Insights:

• Revenue: The total revenue is displayed as 1.69 billion, indicating a slight increase of 0.20%. This suggests that the hotel is generating income and experiencing some growth.

• The bar chart shows that a significant portion of the expected revenue has been realised, represented by the green "Checked Out" bar. However, substantial revenue has been lost due to cancellations and no-shows.

• The "Cancelled" and "No Show" segments highlight the revenue leakage due to these factors. This represents a significant loss of potential income for the hotel.

• Occupancy %: The occupancy rate is 57.79%, with a minor increase of 0.01%. This metric indicates the percentage of available rooms that are occupied. A higher occupancy rate generally translates to higher revenue.

• The heatmap shows occupancy rates across different weeks (W19 to W31) and days of the week (Sun to Sat), making identifying periods of high and low occupancy easy.

• Weekends (Friday to Sunday) consistently show higher occupancy rates than weekdays.

• Some specific weeks, such as W21, W23, W26, W30 and W31, show lower occupancy across most days.

• Total Bookings: The dashboard shows 132.9K bookings, a slight increase of 0.01%. This indicates a positive trend in demand for the hotel's services.

• The doughnut chart shows that 50.74% of occupancy occurs on weekdays (Monday to Thursday) and 49.26% on weekends (Friday to Sunday). This indicates that weekday occupancy is slightly higher than weekend occupancy.

• Realisation %: The realisation percentage, which measures the actual revenue achieved compared to the expected revenue, is 70.14%. This indicates that 70.14% of the expected revenue was realised. A slight decrease of -0.0% is observed in this metric.

• A significant portion of revenue is lost due to cancellations (24.84%) and no-shows (5.02%).

Actionable Insights:

• Implement flexible cancellation policies, such as partial refunds based on the cancellation timeline (e.g., 50% refund for cancellations made three days in advance, 10% refunds within 24 hours, and no refund for cancellations within 12 hours).

• Offer incentives for early bookings or pre-payments to discourage last-minute cancellations.

• Improve communication with guests to manage expectations and reduce the likelihood of cancellations.

• Apply dynamic pricing strategies by increasing rates during high-occupancy periods (weekends, peak weeks) and lowering them during low-occupancy periods.

• Introduce targeted promotions and special deals for weekdays or low-occupancy periods to attract more bookings.

• Optimise staff scheduling based on occupancy patterns, ensuring adequate staffing during peak periods and cost efficiency during low-occupancy times.

• Analyse seasonality, competitor pricing, and events to adjust revenue management strategies accordingly.

• Develop weekend-specific promotions or activity packages to attract more guests and balance weekday weekend occupancy rates.

• Adjust pricing strategies to capitalise on higher weekend demand by implementing higher weekend rates.

• Implement stricter no-show policies with appropriate penalties to reduce last-minute dropouts.

• Send pre-arrival reminders to guests to confirm reservations and decrease no-show rates.

• Introduce pre-authorization holds on guest credit cards to minimise financial losses from no-shows.

• Offer rescheduling options or apply cancellation fees to recover revenue from cancelled bookings.

Revenue Trend by Week:

Key Insights

• It presents the weekly revenue trend for two room classes: Business and Luxury.

• There are noticeable peaks and troughs in revenue for both classes. For instance, there's a dip in revenue for both classes on W21, W23, W26, W30, and W31. This is because of decreasing in the realisation we discussed above.

• After filtering between rooms (Elite, Premium, Presidential, and Standard) and cities (Mumbai, Bangalore, Hyderabad, and Delhi), Luxury hotels profit more than business hotels.

• Revenue by City: This bar chart shows the revenue generated by each city: Mumbai, Bangalore, Hyderabad, and Delhi.

• Mumbai generates the highest revenue, followed by Bangalore, Hyderabad, and Delhi.

• Revenue by Room Class: This bar chart shows the revenue generated by each room class: Elite, Premium, Presidential, and Standard.

• The Elite room class generates the highest revenue, followed by Premium, Presidential, and Standard.

Actionable Insights:

• Analyse historical data to identify seasonal trends and adjust pricing and marketing strategies accordingly.

• Focus on business hotels in Delhi instead of luxury hotels to align with demand and reduce expenses.

• Develop city-specific strategies to optimise revenue in each location, including targeted promotions, pricing adjustments, and inventory management.

• Evaluate the profitability of each room class and adjust pricing and inventory to maximise revenue from high-performing categories.

• Identify factors causing revenue fluctuations, implement mitigation strategies, and capitalise on opportunities.

Comparison between DSRN, DBRN, and DURN:

• DSRN (Daily Sellable Room Nights): Represents the total number of rooms available for sale daily.

• DBRN (Daily Booked Room Nights): Represents the number of rooms booked daily.

• DURN (Daily Utilized Room Nights): This represents the number of rooms occupied daily.

Key Insights:

• The DSRN (grey bars) remain relatively consistent across the weeks, indicating a stable number of rooms available for sale.

• Only half of the rooms are booked from the sellable rooms, and fewer are utilised compared to booked rooms.

• The DBRN (yellow bars) show some fluctuations across the weeks. The weeks W21, W23, W26, W30 and W31 show less than 1000 bookings.

• The DURN (green bars) are generally lower than the DBRN, indicating that some booked rooms were not utilised. This could be due to cancellations, no-shows, or early check-outs.

• The gap between DBRN and DURN suggests that there is potential to improve occupancy rates and revenue by reducing no-shows and cancellations.

Booking distribution across different platforms:

• The bar chart displays the realisation percentage for different booking channels. It shows fluctuations in realisation across various channels. The realisation from each booking platform is almost the same.

• From the Booking% chart, "MakeYourTrip" stands out with the highest share of 20.02% of bookings, indicating its significant contribution to the overall booking volume.

• The line chart illustrates the ADR for each booking channel. There are variations in ADR across different channels, suggesting potential pricing differences or variations in room types booked through each platform.

Comparison of different Atliq Hotel properties:

• ADR (Average Daily Rate): The average revenue generated per occupied room per night.

• ADR WoW (Week-over-Week) Change %: The percentage change in ADR compared to the previous week.

• RevPAR (Revenue Per Available Room): The total revenue generated per available room.

• RevPAR WoW (Week-over-Week) Change %: The percentage change in RevPAR compared to the previous week.

• Cancellation %: The percentage of bookings that were cancelled.

Key Insights:

• Property-Level Performance: The table provides a detailed breakdown of key metrics for each Atliq property. This allows for granular analysis and identification of areas for improvement at the property level.

• ADR Variation: There is some variation in ADR across properties, with Atliq Seasons having the highest ADR at 16.60K and having a good margin in RevPAR WoW (Week-over-Week) Change % 7.09% compared to others, but it only has a 2.3 average rating.

• Cancellation Rates: Cancellation rates are relatively high across all properties but almost the same across the properties, ranging from 24% to 25%.

Actionable Insights:

• Analyse DBRN and DSRN data to identify high and low occupancy periods and implement strategies like dynamic pricing, targeted promotions, and revenue management.

• Allow hourly booking during the off-season to reduce the gap between DSRN and DBRN.

• Diversify booking channels to reduce dependency on "MakeYourTrip" and mitigate risks.

• Negotiate better rates with OTAs, optimise pricing strategies, and improve guest communication to enhance realisation percentage.

• Analyse ADR variations across booking channels and optimise pricing strategies for specific channels and room types.

• Reduce reliance on direct online and direct offline bookings to minimise platform fees and commissions.

• Improve ADR in lower-rate properties through pricing adjustments, upselling, and service enhancements.

• Increase RevPAR in declining properties by optimising occupancy rates, pricing strategies, and minimising revenue leakage.

• Implement flexible cancellation policies, targeted communication, and loyalty programs to reduce cancellations across properties.

• Conduct a detailed performance analysis of each property and implement targeted interventions to improve overall performance.

• Enhance customer response and service at Atliq Seasons to improve its online rating while maintaining its strong ADR and RevPAR performance.

Conclusion & Call to Action

AtliQ Grands' performance analysis highlights key areas where improvements can significantly impact revenue. Currently, 24.84% of revenue is lost due to cancellations and 5.02% due to no-shows, leading to unrealized earnings. Implementing flexible cancellation policies, pre-arrival reminders, and pre-authorization holds can help recover a significant portion of this lost revenue. If the realisation percentage improves from 70.14% to 80%, AtliQ Grands could potentially gain an additional ₹166.6 million in revenue.

Similarly, the occupancy rate stands at 57.79%, and even a modest 5% increase in occupancy through targeted promotions, optimised pricing, and weekend-specific offers could generate an extra ₹84.5 million in revenue. By implementing these data-driven strategies, AtliQ Grands could see a potential revenue boost of ₹251.1 million over three months.

By taking these strategic actions, AtliQ Grands can regain its market share and improve operational efficiency and overall profitability. Now is the time to leverage Business and Data Intelligence to drive revenue growth and strengthen the company’s position in the luxury and business hotel industry.