Hotels using commercial analytics grow revenue faster

What is commercial analytics?

• Revenue Management involves analyzing sales and revenue data to optimize room rates and inventory to maximize revenue and profitability. This includes demand forecasting, dynamic pricing, and inventory optimization techniques.
• Customer Analytics involves analyzing customer data to understand preferences, behavior, and patterns. This can include analyzing data from loyalty programs, online reviews, social media, and customer transaction data to identify opportunities for upselling and cross-selling.
• Marketing Analytics involves analyzing data related to marketing campaigns and distribution channels to optimize marketing strategies and improve conversion rates. This includes analyzing website traffic, social media engagement, email marketing campaigns, and tracking the performance of online travel agencies (OTAs) and other distribution channels.

Take a 360-degree view

1. Revenue management: Analyzing room rates, inventory, and demand to optimize pricing and revenue.
2. Marketing and distribution: Analyzing marketing campaigns, website traffic, and OTA performance to optimize distribution strategies and increase bookings.
3. Sales: Analyzing sales data, performance metrics, and customer behavior to identify opportunities for upselling and cross-selling.
4. Customer experience: Analyzing customer feedback, online reviews, and loyalty program data to optimize the customer experience and drive repeat business.
5. Competitive intelligence: Analyzing concepts, reviews, performance, and pricing of competitors to inform pricing and marketing strategies.
6. Operations: Analyzing operational data, such as housekeeping, maintenance, food & beverage, meetings, spa, etc., to identify revenue, efficiency, and cost savings opportunities.

Challenges

• Data quality and availability: Commercial analytics requires high-quality, accurate, timely data, but hotels may struggle to collect and integrate data from disparate systems and sources. Poor data quality or availability can lead to inaccurate or incomplete analysis, negatively impacting decision-making.
• Data silos and fragmentation: Hotels may have data and analytics silos, with different departments or systems collecting and analyzing data independently. This can lead to fragmented insights and difficulty integrating data for a comprehensive business view.
• Analytics talent and capabilities: To make the most of commercial analytics, hotels need skilled data analysts and data scientists who can extract insights from data and translate them into actionable recommendations. However, finding and retaining such talent can be challenging and expensive.
• Resistance to change: Implementing commercial analytics may require processes, culture, and organizational structure changes. Resistance to change from employees or stakeholders can impede the adoption and implementation of analytics initiatives.
• Integration with existing systems: Commercial analytics requires integration with existing technology systems, such as property management systems (PMS), customer relationship management systems (CRM), point of sale systems (POS), and revenue management systems (RMS). Integrating these systems can be complex and time-consuming, especially if they are from different vendors.
• Cost and ROI: Implementing commercial analytics can require significant technology, talent, and training investment. Hotels may need to justify these costs with a clear understanding of the expected return on investment (ROI) and the long-term benefits of analytics.

Implementation plan

1. Identify business objectives: The first step in implementing commercial analytics is identifying the hotel's goals. These objectives could include improving revenue management, increasing direct bookings, or enhancing the customer experience.
2. Assess current data capabilities: The hotel should assess its current data capabilities, including the data sources and systems in use, data quality, and the availability of skilled data analysts or data scientists. This assessment will inform the design of the analytics infrastructure.
3. Develop an analytics infrastructure: The hotel should develop an analytics infrastructure that includes data storage and processing capabilities and software tools for data visualization and analytics. In addition, the infrastructure should be designed to integrate data from multiple sources and systems.
4. Collect and integrate data: The hotel should collect and integrate data from all relevant sources, including property management systems, sales & catering systems, point of sale systems, customer relationship management systems, revenue management systems, and other internal and external systems.
5. Develop analytics models and processes: The hotel should develop analytics models and processes allowing data-driven decision-making. These models and methods could include demand forecasting, pricing optimization, customer segmentation, and campaign tracking.
6. Train staff: The hotel should train staff to use the analytics infrastructure and interpret data-driven insights. This will help ensure that employees can effectively use analytics to drive business performance.
7. Test and refine: The hotel should test the analytics infrastructure, models, and processes to ensure they deliver accurate and actionable insights. The hotel should also refine the infrastructure and models over time to ensure they continue to meet the needs of the business.
8. Establish data governance and security protocols: The hotel should establish data governance and security protocols to ensure that data is adequately protected and data usage complies with regulations.
9. Monitor and evaluate results: The hotel should continuously monitor and assess the effects of commercial analytics, using metrics such as revenue growth, average revenue per guest, and customer satisfaction. This will allow the hotel to refine its analytics approach and ensure it continues to drive business performance.

Demand Calendar = commercial analytics

1. Data quality and availability: Demand Calendar integrates data from various sources, including property management systems, revenue management systems, rate shopping, benchmarking, point of sales systems, meetings and events systems, customer relationship management systems, and many other systems in hotels. Demand Calendar verifies that data is accurate, complete, and up-to-date, making it easier to develop effective analytics models.
2. Data silos and fragmentation: Demand Calendar provides a unified view of data across all hotel business areas, which can break down silos between departments and provide a more comprehensive view of the business. In addition, Demand Calendar has, with its multi-property functionality, the ability to consolidate commercial analytics across a group of hotels. This can make developing integrated strategies easier and make more informed decisions across a hotel group.
3. Analytics talent and capabilities: Demand Calendar provides various analytics tools and algorithms to help hotels make data-driven decisions without requiring significant investment in analytics talent. The system is designed to be easy to use, with intuitive data visualizations and user-friendly interfaces.
4. Resistance to change: Demand Calendar is specifically designed for hotels to meet the needs of each commercial and operational role with functions for their specific needs to reach their objectives. This can help overcome resistance to change by ensuring the system makes the job easier for each role.
5. Integration with existing systems: Demand Calendar is designed to integrate with various existing systems, including PMS, RMS, POS, Sales & Catering, Benchmarking, CRM, etc. Demand Calendar makes it easier to implement the system without requiring significant changes to existing technology infrastructure.