Part 2: How to ENVISION an AI-ENABLED SERVICE?

The previous article triggered your imagination on how AI gives your business a competitive advantage. We also gave you a questionnaire to identify your AI potential. We showed how recognizing the importance of service data is a crucial step in AI enablement

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 This article will show how you can envision an AI-based system. We will help you conceptualize an AI-based recommendation system from scratch using the example of a hotel service business. With this, you should be able to reason an AI-based recommendation system for any service

A semi-luxury chain of hotels operates in a competitive segment of the hospitality industry. The hotels in this segment tend to offer guests a blend of comfort, quality, and value between luxury and budget options. The hotels cater to travelers seeking an upscale experience without the premium price tag of luxury accommodations. The business model focuses on delivering exceptional customer service, high-quality amenities, and personalized experiences that meet the unique needs and preferences of each guest.

How does a Semi-Luxury Chain of Hotels Differentiate in a Competitive Market?

• • Personalized Guest Experiences: They tailor services and amenities, from room selection to dining options.
  • Efficient Operations: Operational efficiency is crucial in managing costs while maintaining service standards. This includes everything from room occupancy management to service delivery.
  • Dynamic Pricing: Their vary prices based on demand fluctuations, special events, and booking lead times to maximize revenue.
  • Marketing and Loyalty Programs: They have targeted marketing campaigns and loyalty programs to attract new guests and retain existing ones, offering rewards and incentives for repeat bookings.
  • Quality Control and Feedback Loops: Typically they will maintain high-quality standards across all touchpoints and using guest feedback to improve service offerings continuously.

Business Use of Personalized Recommendations 

Before we explore how hotels can utilize an AI system, let us see how recommendations help a semi-luxury hotel business provide a better customer experience and improved revenue.

• • Improved Guest Satisfaction: By anticipating guest needs and making relevant suggestions, hotels can significantly enhance the overall guest experience. This could range from recommending the perfect dining experience to suggesting local attractions the guest might enjoy.
  • Increased Revenue Opportunities: Personalized recommendations can include upselling and cross-selling opportunities, such as room upgrades, extended stays, or special service packages. By making targeted offers that guests are likely to value, hotels can increase their average revenue per guest.
  • Operational Efficiency: You can use recommendations to optimize room allocations and service scheduling based on guest preferences and predicted needs, improving operational efficiency. For instance, knowing in advance that many guests prefer early check-ins can help with staffing and room readiness planning.
  • Marketing and Loyalty Programs: Recommendations can extend to marketing efforts, where the system suggests personalized promotions and loyalty rewards. This targeted approach can improve the effectiveness of marketing campaigns and loyalty program enrollment.
  • Feedback and Continuous Improvement: By integrating guest feedback into the recommendation system, hotels can continuously refine their offerings. If feedback indicates a preference for certain types of rooms or amenities, the hotel can adjust its inventory and marketing strategies accordingly.

As we have seen in the previous article, to build an AI recommendation system you must look into different types of data available to the business. Let us look at different types of data stored by semi-luxury chains of hotels,

1. Guest Information Data
• Personal Details: Names, contact information, and demographic details.
• Preferences and Special Requests: Room preferences, dietary restrictions, and other personalized requests.
• Stay History: Previous visits, room types booked, duration of stays, and special occasions celebrated.
2. Operational Data
• Room Occupancy and Revenue Data: Information on room occupancy rates, average daily rates, and revenue per available room.
• Service Utilization: Usage data of hotel amenities like the spa, restaurant, gym, or business center.
• Maintenance Records: Details of room and facility maintenance issues and their resolutions.
3. Feedback and Reviews Data
• Guest Feedback: Comments and ratings collected through in-house surveys, suggestion boxes, and digital platforms.
• Online Reviews: Public reviews and ratings on travel and hospitality websites.
4. Financial Data
• Transaction Data: Records of all financial transactions, including room bookings, service charges, and ancillary revenue.
• Expense Records: Operational expenses, including staffing, utilities, maintenance, and inventory costs.
5. Marketing and Communication Data
• Engagement Data: Interaction data from marketing campaigns, including email open rates, click-through rates, and social media engagement.
• Communication Logs: Records of communications with guests, including email, phone calls, and text messages.

Let us now see what kind of services you can conceptualize using the data held by the hotel.

1. Personalized Marketing and Booking
• You can utilize guest information and engagement data to tailor marketing communications and booking suggestions.
• You can make predictions about ideal travel times, promotional offers, and room types based on previous behaviors and preferences of guests.
2. Dynamic Pricing
• You can leverage room occupancy and revenue data to adjust room prices in real-time, maximizing revenue based on demand.
• You can also offer personalized pricing and special offers to repeat guests or during off-peak times.
3. Guest Experience Enhancement
• Using guest preferences, stay history, and feedback data you can personalize room settings (temperature, lighting) and services (meal preferences, activities).
• You can recommend concierge services to guests and then handle requests through in-room devices or mobile apps.
4. Operational Efficiency
• By analyzing maintenance records and operational data you can predict and schedule maintenance, reducing downtime and improving guest experiences.
• You can optimize staffing levels and resource allocation based on predictive analysis of occupancy data and service usage patterns.
5. Feedback and Reputation Management
• You can monitor online reviews and guest feedback, using sentiment analysis to identify areas for improvement and celebrate strengths.
• You can analyze feedback in real-time and generate automated responses or alert management to critical feedback needing attention.
6. Safety and Security
• You can utilize surveillance and access control data to enhance guest safety and property security.
• Uses pattern recognition to identify unusual activities or potential safety hazards.

As you can see the data can be used to create many types of automation that result in enhanced experience, better services, and efficiency for the hotel. Let us pick the automation of a service that impacts the revenue the most.

A personalized guest experience recommendation system can help a semi-luxury chain of hotels to differentiate its services. It can significantly elevate guest satisfaction, loyalty, and ultimately, the hotel’s brand value.

When combined with marketing it can result in more bookings. When combined with check-in it can result in more sales. When combined with check-out it can result in great feedback and repeat business. As more and more guests realize that they get better experience and services in the hotel they prefer to use the services again and again.

So, a system that can provide personalized service recommendations to the guests can have a big impact on revenue.

The personalized recommendation is where AI shines. This is how the AI will work.

1. Data Analysis and Pattern Recognition: The AI system will analyze vast amounts of guest data, including – guest profiles, past behavior, preferences, feedback, and more – to identify patterns and preferences.
2. Personalized Recommendations: Based on this analysis, the system will generate personalized recommendations for each guest. These could range from room choices, amenities, and services to personalized marketing offers.
3. Predictive Modeling: The AI will then predict future guest needs and preferences, enabling proactive service and experience customization.

1. Marketing & Booking

• • Pre-arrival: AI analyzes past booking patterns and preferences to send personalized marketing communications and offers, encouraging bookings.
  • Booking Process: During booking, guests receive personalized room and service suggestions, enhancing the likelihood of a satisfying stay and upsells.

2. Check-in

• • Personalized Welcome: AI can inform front desk staff of guest preferences, ensuring a tailored welcome experience.
  • Efficient Room Allocation: AI recommends room allocations based on guest preferences (e.g., high floor, away from elevators).

3. Stay

• • Customized In-room and Hotel Services: Recommendations for room settings, dining options, spa services, and activities based on individual preferences.
  • Real-time Suggestions: AI can provide real-time suggestions based on current behavior, like recommending a quiet lounge area if the guest is looking for a place to relax.

4. Check-out

• • Personalized Departure: Tailored check-out experiences and offers for future stays based on guest feedback and stay history.
  • Feedback Collection: AI-driven systems to collect and analyze feedback, making it easier for guests to provide meaningful insights.

5. Planning

• • Future Visit Planning: Post-stay, the AI can send personalized communications with offers and suggestions for future stays.
  • Continuous Learning: The AI system continually learns from each interaction, refining its recommendations for each subsequent stay.

 Data

As we have pointed out to build the AI-based recommendation system you must identify data sources. The typical data sources for the semi-luxury hotel will include,

1. 1. Hotel’s Data: Reservation systems, customer relationship management (CRM) software, and feedback systems.
   2. Third-Party Data: Social media platforms, online review sites, and market research data.
   3. Public Data Sources: Weather forecasts, event calendars for local happenings, and tourism trends.

Using the past data of the hotel, different machine learning models will be built that will be designed to carry out specialized intelligent tasks like sentiment analysis, pattern recognition, and personalized suggestions.

Components

The AI system will broadly consist of these components,

1. 1. Data Collection and Integration Module: Aggregates data from various sources.
   2. Data Processing and Analysis Engine: Cleanses and analyzes data and performs sentiment analysis and pattern recognition.
   3. Recommendation Engine: Uses Machine Learning algorithms to generate personalized suggestions.
   4. Feedback Loop: Collects feedback on recommendations to refine future suggestions.

Integrations

For the system to be useful it will be integrated into existing applications so that recommendations become available at the right time. Here are the different system integrations that can come into play,

1. 1. Marketing Integration: The AI system will send recommendations to marketing tools to publish personalized offers and communications.
   2. Booking System Integration: The AI system will send personalized room and service options to the booking engine during the reservation process.
   3. Operational Integration: During check-in, stay, and check-out, the system will integrate with operational systems to ensure seamless service delivery based on recommendations.
   4. Feedback System Integration: Post-stay integrates with feedback systems to gather data and inform future recommendations.

For business leaders looking to harness the transformative power of AI through a recommendation system, the journey involves a strategic blend of understanding customer data, leveraging technology, and aligning with business objectives. Here’s a roadmap to approach this development:

1. Start with Data: The foundation of an effective recommendation system lies in the quality and breadth of customer data available. Begin by assessing and consolidating data sources within your organization, such as purchase history, customer preferences, feedback, and interactions. This data should be clean, organized, and accessible.
2. Understand Your Customer Touchpoints: Identify key moments in the customer journey where personalized recommendations can add value. Whether it’s during the browsing phase on your website, at the point of sale, or post-purchase follow-up, each touchpoint is an opportunity to enhance the customer experience with tailored suggestions.
3. Evaluate Technological Infrastructure: Ensure your business has the necessary technological infrastructure to support an AI recommendation system. This might involve upgrading existing systems or investing in new technologies like CRM platforms, data analytics tools, and AI algorithms.
4. Define Clear Objectives: Set specific, measurable objectives for what you want the recommendation system to achieve. This could include goals like increasing sales, improving customer engagement, or enhancing personalization in marketing efforts. Clear objectives will guide the system’s design and implementation.
5. Address Ethical and Privacy Considerations: Develop your system with a strong commitment to ethical practices and data privacy. This involves not only adhering to regulatory requirements but also building trust with your customers by being transparent about how their data is used.
6. Collaborate with Experts: Partner with data scientists, AI experts, and technology consultants who can bring valuable insights and expertise to the development of your recommendation system. Their experience can help in navigating the complexities of AI implementation and ensuring the system is tailored to your specific business needs.
7. Implement and Test: Deploy the recommendation system in phases, allowing for testing and feedback at each stage. Monitor its performance closely against your set objectives and be prepared to make iterative improvements.
8. Continuously Learn and Adapt: A recommendation system is not a set-and-forget tool. It requires ongoing analysis and adjustments based on evolving customer behaviors and market trends. Encourage a culture of continuous learning and adaptation within your organization.

1. Understanding Customer Data: What types of data are you currently collecting about your customers (e.g., purchase history, preferences, feedback, demographic information)? How can this data be utilized to understand your customers’ needs and preferences more deeply?
2. Identifying Customer Touchpoints: What are the key touchpoints or interactions your customers have with your business (e.g., website visits, purchases, customer service interactions)? How could these touchpoints be enhanced with personalized recommendations to improve the customer experience?
3. Setting Business Objectives: What specific business objectives do you aim to achieve with a recommendation system? Are you looking to increase sales, enhance customer satisfaction, improve retention, or expand into new markets?
4. Considering Ethical and Privacy Concerns: How will you ensure the ethical use of customer data in your recommendation system, and how will you address privacy concerns? What policies or practices will you need to implement to maintain customer trust and comply with data protection regulations?
5. Assessing Current Capabilities: What is the current state of your technology infrastructure? Do you have the necessary tools and systems (like CRM, and data analytics platforms) to support an AI-based recommendation system?

In the next article we will look at what goes inside an AI-based recommendation system.