1. Improving sales
and customer
experience
using chatbots
USE CASE IN THE HOSPITALITY INDUSTRY

2. CRM SOLUTIONS
ABOUT US
AI services as “helpers” in everyday work
• Email classification
• Named Entity Recognition
• Chatbots
Domain-specific solutions (hospitality industry)
• Hospitality CRM
• Contact Center
• Loyalty
• Marketing Automation

3. Knowledge
Knowledge of hospitality, several
industrial sectors and trends.
Long-term work in CRM area, using
Microsoft Technology.
Latest technologies and processes.

4. SERVICES
Business Solutions developed
on Dynamics CRM platform
Custom Development
Business consulting

5. AWARDS
World prizes and awards for
the achievements

6. Use Case – chatbot for
the hospitality industry
Domain-specific question-answering system
Multichannel (Web, WhatsApp, Facebook, …)
Integration with call center (“agent escalation”)
24/7 customer
service
Data sources:
• Hotels’ FAQ’s
• Existing communication via emails
• Client MUST have access to the knowledge base
(a "back office of question-and-answer pairs")

7. MICROSOFT BOT FRAMEWORK
Building a chatbot with Microsoft Bot Framework
Advantages
• easy to use
• pre-built templates and workflows
• integration with other Microsoft services
• good documentation and support
• multichannel communication
Disadvantages
• limited customization options
• not suitable for complex use cases
• requires some technical knowledge

8. Bot service – Azure
MICROSOFT BOT
FRAMEWORK
Building a chatbot with Microsoft bot framework
Cognitive services
Registering multiple channels
Custom integration with our call center software (“agent” intents)

9. KNOWLEDGE BASE
The platform provides a structured way to store and retrieve
information, easier to build conversational bots
Built with Microsoft’s Cognitive services – Why?
Building a custom large language model requires significant
expertise in natural language processing and machine learning,
which may not be available to all organizations

10. KNOWLEDGE BASE
• Based on BERT transformer architecture
• Fine-tuned using smaller dataset of question-answer pairs
• Best answer based on question provided
similarity between question and answer
relevance of entities mentioned in the question
overall coherence of answer
Microsoft Cognitive Services – Question-Answering system

11. Hospitality bot - v1
Multichannel (Web, WhatsApp, Facebook, …)
Question-answering system
Not perfect...

12. Hospitality bot - v1
Implementation and results
Reduction in calls received
(YoY)
(Seasonality)
Chatbot Engagement

13. Use Case 2 – recommending
hotels and creating reservations
Intent Detection
Users wanting to “make a reservation”, “cancel a reservation”,
“modify a reservation”
Service will pass to Question-Answering model all the
other input messages
User preferences
• Predefined list of (mandatory) attributes location,
most-common facilities
• any other important feature the client deems necessary…
Matching availability
Integration with booking systems
- Passing request for the "recommended" objects
- Matching the "best option", along with budget and luxury option
A recommender model that matches the user’s
preference to available objects and facilities

14. Use Case 2 – recommending
hotels and creating reservations
(Phases)
Phase 1
• Using transfer-learning (any basic
language model) and string
matching (e.g., cosine similarity)
• Comparing the user inputs with
available attributes
Phase 2
• Using blind annotations from
agents – data of manual
bookings
Phase 3 – after rollout
Using collaborative filtering and
content-based models together
• Conversion rates
• Feedback from loyalty
members
• Improving existing content

15. USE CASE 2 – RECOMMENDING HOTELS AND CREATING
RESERVATIONS (CHALLENGES)
Using both intents and QnA answers to "navigate" users to a booking
scenario
Complexity:
• Scarce data ("hotel attributes" as seen on popular B2C booking apps)
• Client-specific information
• Domain-specific information ( Boarding, upselling options, room types ... )
• Choosing the right option from availability response

16. Use Case 2 – use chatbot to "get
customers to book hotels"
Implementation
Exemplary attributes:
• Name:Marko Polo
• Location: Korčula
• Rating: 4 stars
• Facilities:private parking, free Wi-Fi, beachfront, family rooms, airport shuttle, non-
smoking rooms, bar, very good breakfast, 2 swimming pools, terrace
Using customer's wishes to match best options

17. Use Case 2 – use chatbot to "get
customers to book hotels"
Step 1
Number of conversions (bookings per conversation): ??
Step 2
Test dataset annotated by agents manually
Step 1&2 ? (no feedback or booking
history)

18. Use Case 2 – use chatbot to "get
customers to book hotels"
Adding and/or changing the content (descriptions, facilities, hotel
attributes)
Do the additional classes and/or content affect recommendations
and/or conversions
• Yes
• How?

19. REFERENCES IN HOSPITALITY

20. Thank you!
You can find us at:
sales@inskysolutions.com
www.inskysolutions.com