An introduction to Artificial Intelligence (AI) for business leaders. What is AI, HOW IT WORKS and how does it impact your business?

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

Written by AI & ML Simplified Editorial

Introduction

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.

We will use the case of a hotel service business to exemplify this.

Case of A Semi-Luxury chain of hotels

A semi-luxury chain of hotels operates in a competitive segment of the hospitality industry, offering 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: Tailoring services and amenities, from room selection to dining options.
    • Efficient Operations: Operational efficiency is crucial, managing costs while maintaining high service standards. This includes everything from room occupancy management to service delivery.
    • Dynamic Pricing: Implementing pricing strategies that reflect demand fluctuations, special events, and booking lead times to maximize revenue.
    • Marketing and Loyalty Programs: Engaging in 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: Maintaining high-quality standards across all touchpoints and using guest feedback to improve service offerings continuously.

How a Recommendation System Can Be a Useful Tool

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

    • Enhanced Personalization: A recommendation system can analyze guest data, including past stays, preferences, and feedback, to offer personalized room choices, amenities, and services. For example, if a certain demographic of guests prefers a spa package, the system could recommend a special spa offer to a guest fitting that demographic profile.
    • 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 also 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.

Different Categories of Data Maintained by a Hotel

To build an AI recommendation system you must look into different types of data available to the business. A semi-luxury chain of hotels will typically store different types of data,

 

  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.

AI SYSTEMS THAT CAN BE BUILT USING THIS DATA

Let’s see what kind of AI systems you can conceptualize using the data held by a hotel.

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

Each of these AI systems, when implemented thoughtfully and ethically, can significantly enhance the efficiency of operations, the quality of guest services, and the overall profitability of a semi-luxury hotel chain.

A Personalized Guest Experience Recommendation System is an AI System of Great Value

A personalized guest experience recommendation system, powered by AI, stands out as particularly impactful because it can help the hotel differentiate its services.

An AI-based recommendation system can significantly elevate guest satisfaction, loyalty, and ultimately, the hotel’s brand value.

What Is This Recommendation System Supposed To Do

  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 can predict future guest needs and preferences, enabling proactive service and experience customization.

How does It Help Enhance Guest Experience?

  1. Tailored Experiences: Guests receive recommendations that resonate with their specific likes and needs, making their stay more enjoyable and memorable.
  2. Increased Convenience: Automated, personalized recommendations reduce the decision-making burden on guests, providing them with convenient choices.
  3. Anticipatory Service: By predicting future preferences, the AI system allows the hotel staff to prepare in advance, leading to smoother, more efficient service.

How can the AI Recommendations be Used in the GUEST Life Cycle Stages?

  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.
  1. 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).
  1. 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.
  1. 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.
  1. 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.

Building the AI-based recommendation system

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. 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.

The AI system will broadly consist of these components,

    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.

 

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. Marketing Integration: The AI system will send recommendations to marketing tools to send 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.

Conclusion - What DOES IT Take to Build an AI-based Recommendation System

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: Consider partnering 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.

Want to make a difference with AI in Your Business? Answer these 5 questions.

  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. 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?
  4. 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?
  5. 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?

 

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