Newsflash! According to a trusted source, the millennials will hold the majority portion of the guests in a hotel room by 2025. Also, Forbes says that by 2020 the millennials will be taking and booking 320 million international trips per year, making them the largest travel market on the planet. But before catering to them we have to ask a question to ourselves; what do they want? How can a hotel manager exceed their expectation without intrusion? It has been seen that the millennials’ behavior are quite different from those of previous generations; most of them aren’t attracted by the same service and features. They want to connect with the world all the time and a hotel can render them with multiple opportunities to attain what they deserve. Millennials are also tech-savvy and they don’t shy to utilize technical skills to overcome most of their problems in a hotel. Have you ever felt that your customer complaint about services and you have no data to support why such complaint and which part of your service needs to improve? Does your customer has enough hyper-local information about your hotel? Are your customer aware of all amenities at your hotel which might have enlighten the customer's experience? But building a channel to comprehend the customer and suggest information is not that easy and require a big stack of technologies. Hotels Guests are most comfortable using their phones and having a channel to interact with the hotel related to service, facilities or even trip planning is what millennial look for without getting into complex User Interfaces (UI).
They connect easily with a chatbot for hyper-personalized recommendations and also, they utilize voice-based solution to connect with the hotel. These days, guests are embracing new apps on a daily basis, and hotel companies are trying their best to get into the fray by developing their own guest facing applications to drive loyalty via Natural Language Processing (NLP) and machine learning (let's discuss this on the later section of the blog). All of this comes during the most fascinating time for the hotel industry as it grapples with the disruption from alternative lodging companies such as Airbnb and HomeAway. To say that all of these dynamics combined to make it difficult for a hotelier to connect and build relationships with their guests would be a major understatement. In this blog, we will see the scope of the technicality in hotels and also we will try to understand how a hotelier can use it to its maximum to attain better customer experience and guest engagement. So, let’s start the ride with the basics -- what is ML?
What is machine learning?
“Computers are able to see, hear and learn. Welcome to the future.” Dave Waters. So, is that true? And the answer is yes! According to the simplest definition, machine learning is a process or an application of artificial intelligence (AI) that provides systems the ability to learn by itself with little or no direct human control and the system improves from experience only without the influence of programmed tactics or didactic approach. In a broader sense, machine learning mainly focuses on the evolution and nurturing of computer programs that can access data and use it to learn for themselves for automating several tasks. In the world of ML, the inception of learning begins with the observations or data, such as direct experience, examples, or instruction in order to look for new or existing patterns in data and make better decisions in the future based on the basic information that we provide. The chief aim is to allow a computer to learn automatically without human intervention or assistance and adjust the actions accordingly.
Few machine learning methods
Supervised machine learning algorithms can apply what has been learned in the past to new data using tagged examples to anticipate future events. Beginning with the analysis of the known training dataset, the training algorithm produces an inferred function to make predictions about the production values. The machine can provide targets for just about any new suggestions after sufficient training. The training algorithm can also compare its result with the right, intended output and discover errors to be able to change the model appropriately.
In contrast, unsupervised machine learning algorithms are employed when the information is used to teach is neither categorized nor tagged. Unsupervised learning studies how systems can infer a function to spell it out a hidden framework from unlabeled data. The machine doesn't find out the right products, but it explores the info and can get inferences from datasets to spell it out hidden constructions from unlabeled data.
Semi-supervised machine learning algorithms fall somewhere among supervised and unsupervised learning, given that they use both tagged and unlabeled data for training - typically a tiny amount of tagged data and a huge amount of unlabeled data. The systems that utilize this method have the ability to substantially improve learning reliability. Usually, semi-supervised learning is chosen when the attained tagged data requires skilled and relevant resources to be able to teach it / study from it. Often, acquiring unlabeled data generally doesn't require additional resources.
Reinforcement machine learning algorithms is a learning method that interacts using its environment by producing activities and discovers mistakes or rewards. Learning from your errors search and delayed praise will be the most relevant characteristics of support learning. This technique allows machines and software providers to automatically determine the perfect behavior within a particular context to be able to increase its performance. Simple reward feedback is necessary for the agent to learn which action is most beneficial; this is recognized as the reinforcement sign.
Tools available for machine learning
IBM Watson: Using Watson Machine Learning, you can build sophisticated analytical models, trained with your own data, that you can deploy for use in applications. Watson is a question-answering computer system capable of answering questions posed in natural language, developed in IBM's DeepQA project by a research team led by principal investigator David Ferrucci. Watson was named after IBM's first CEO, industrialist Thomas J. Watson.
DialogFLow: Dialogflow formerly Api.ai, Speaktoit is a Google-owned developer of human-computer interaction technologies based on natural language conversations. The company is best known for creating the Assistant (by Speaktoit), a virtual buddy for Android, iOS, and Windows Phone smartphones that perform tasks and answers users' question in a natural language. The SDK's contain voice recognition, natural language understanding, and text-to-speech. api.ai offers a web interface to build and test conversation scenarios. The platform is based on the natural language processing engine built by Speaktoit for its Assistant application.
Wit.ai: Wit is a natural language interface for applications capable of turning sentences into structured data. This means that you can create bots that can interact with humans on messaging platforms. With Wit.ai developers can build applications you can talk or text to. Bots keep on learning as they are spoken to. They get smarter with every interaction and with this Wit.ai helps a system to adapt and learn better so that it can comprehend simple and natural language.
What are NLP and NLU?
Natural Language Processing: NLP is the application of computational techniques to the analysis and synthesis of natural language and speech. Natural language processing (NLP) is the ability of a computer program to understand human language as it is spoken. NLP is a component of artificial intelligence (AI). Natural Language Processing refers to all systems that work together to handle end-to-end interactions between machines and humans in the preferred language of the human. In other words, NLP lets people and machines talk to each other “naturally.”
Natural Process Understanding: Natural Language Understanding (NLU) encompasses one of the more narrow but especially complex challenges of AI: how to best handle unstructured inputs that are governed by poorly defined and flexible rules and convert them into a structured form that a machine can understand and act upon. While humans are able to effortlessly handle mispronunciations, swapped words, contractions, colloquialisms, and other quirks, machines are less adept at handling unpredictable inputs. Natural language understanding is a branch of artificial intelligence (AI) that uses computer software to understand input made in the form of sentences in text or speech format.
Role of machine learning in hotels
Revenue Management principles remain the same regardless of underlying software. Nevertheless, a tangible quality shift started in the hospitality industry as machine learning and data science-based techniques were introduced in revenue management software. Machine learning entails building and training statistical models using data inputs to classify input items or forecast output continuous values. Generally, there are two commonly used types of machine learning implementation: supervised and unsupervised learning. The former requires a historic data-set with labeled output values to base predictions on the new data. The latter uses unlabeled data to find a connection between different attributes. Personalized recommendations powered by artificial intelligence and machine learning technologies can improve the experience of business travelers at every stage of their travel journey. These features are especially advantageous in the hotel space, which is plagued by non-compliant bookings. It is extremely important to establish a clear communication channel between your customers and your service which can be tracked and seamless for customers to use it. As a hotel owner or manager you might not want to get deep into technology, but having an understanding of the futuristic technologies will make you king of the ring.
What is data mining?
In data mining, association rules are created by analyzing data for frequent if/then patterns, then using the support and confidence criteria to locate the most important relationships within the data. Support is how frequently the items appear in the database, while confidence is the number of times if/then statements are accurate. Other information mining parameters incorporate Sequence or Path Analysis, Classification, Clustering, and Forecasting. Grouping or Path Analysis parameters search for designs where one occasion prompts another later occasion. A Sequence is an arranged rundown of sets of things, and it is a typical kind of information structure found in numerous databases. A Classification parameter searches for new examples and might bring about an adjustment in the way the information is sorted out. Grouping calculations anticipate factors in light of different factors inside the database.
Data mining techniques and tools
Data mining techniques are used in many research areas, including mathematics, cybernetics, genetics, and marketing. While data mining techniques are a means to drive efficiencies and predict customer behavior, if used correctly, a business can set itself apart from its competition through the use of predictive analysis. Web mining, a type of data mining used in customer relationship management, integrates information gathered by traditional data mining methods and techniques over the web. Web mining aims to understand customer behavior and to evaluate how effective a particular website is.
Other data mining techniques include network approaches based on multitask learning for classifying patterns, ensuring parallel and scalable execution of data mining algorithms, the mining of large databases, the handling of relational and complex data types, and machine learning. Machine learning is a type of data mining tool that designs specific algorithms from which to learn and predict.
Push notification for smarter audience building
Ignore all that advice about minding your manners. Businesses that avoid getting pushy do so at their own peril. Your best customers are walking around with your branded application on their hips or in their purses and you need to engage with them. Without surprise, push notifications have emerged as a key channel of conversation between brands and their mobile customers because they can be sent even when the consumer is not engaged with the branded app.
However, even in its infancy, overuse, and misuse of push can already be seen. There is a fine line between what is effective engagement and what ? for lack of a better term is too pushy? A poorly executed push notification campaign that provides little value to end-users can prompt users to opt-out of notifications or, worse, uninstall your app. What a lost opportunity. How should a progressive brand engage their mobile customers to build a healthy, long-term relationship? When, where, with what and how often should messages be sent? What follows are a few high-level pointers that mobile marketing managers should consider when deciding how to leverage this unprecedented engagement opportunity. They include content selection, targeting, cadence, and performance review.
What is Trilyo doing?
Trilyo uses proprietary algorithms to build AI-enabled chatbots for hotels to attain better brand relevance, attain better guest-base, render custom-tailored recommendations based on their preference and behavior. Trilyo assists hotels and the entire hospitality industry to achieve better ROI, hotel bookings via Artificial Intelligence, and also, Trilyo redefine guest experience by boosting the existing guest customer engagement style and approach with machine learning techniques i.e., AI-enabled chatbots.