The implementation of Big Data and AI on Digital Marketing
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The document discusses leveraging big data and artificial intelligence in digital marketing. It describes using AI to gain a deeper understanding of customers, including their intent, motivations, and behaviors to predict future interactions. It also discusses using webhooks to provide real-time data to other applications. Finally, it provides an overview of machine learning and deep learning, how they are used in artificial intelligence, and compares machine learning and deep learning.
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The implementation of Big Data and AI on Digital Marketing
- 1. The implementation of Big Data and AI on Digital Marketing By: Eng. Mohamed Hanafy Director of ITI Digital Academy
- 2. Leveraging Big Data AI in your Digital Marketing Strategy •Deeper understanding of: • the customer’s intent • motivations • behaviors • predict future interactions
- 4. buzzfeed
- 6. webhook •A webhook (also called a web callback or HTTP push API) is a way for an app to provide other applications with real-time information. A webhook delivers data to other applications as it happens, meaning you get data immediately
- 9. FB App Permissions user_link user_gender user_friends user_age_range user_posts user_likes user_birthday publish_pages manage_pages leads_retrieval Groups API LiveVideoAPI Page Mentions user_videos user_photos user_location user_hometown user_events read_insights publish_video user_tagged_places read_page_mailboxes Page Public Content Access publish_to_groups pages_manage_instant_articles pages_manage_cta groups_access_member_info business_management
- 10. Twitter API
- 14. Social Ad or Post Data Lake Website CRM Data Layer UTM
- 15. Data Layer • dataLayer (GoogleTag Manager) • universal_variable (Qubit) • tm_params (Tagman) • utag_data (Tealium)
- 16. BigQuery
- 19. Data Studio
- 20. Google Data Studio has over 150 connectors
- 21. Artificial Intelligence (AI) Artificial intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience.
- 23. History • 1943: McCullough and Pitts’ invented “artificial neurons” • 1950: Inventions of AlanTuring’s “Computing machinery and Intelligence” • 1951:AI was using in Games • 1956: Dartmouth conference; And the birth of AI • 1965: Robinson’s complete algorithm for logical reasoning • 1969 – 1979: Early development of knowledge based system took place • 1980- 1988: Expert system industry booms • 1988- 1993: Expert system industry busts “AI winter” • 1993- Present: AI is now using rapidly in different technologies; And is achieving its goal
- 24. Machine learning Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves
- 25. What is Machine Learning? Generally, to implement Artificial Intelligence, we use Machine Learning. We have several algorithms that are used for Machine Learning. For example: • Find-S • Decision trees • Random forests • Artificial Neural Networks
- 26. Generally, there are 3 types of learning algorithms: 1. Supervised Machine Learning Algorithms make predictions. Further, this algorithm searches for patterns within the value labels that were assigned to data points. 2. Unsupervised Machine Learning Algorithms: No labels are associated with data points. Also, these ML algorithms organize the data into a group of clusters. Moreover, it needs to describe its structure and make complex data look simple and organized for analysis. 3. Reinforcement Machine Learning Algorithms: We use these algorithms to choose an action. Also, we can see that it is based on each data point. After some time, the algorithm changes its strategy to learn better.
- 27. What is Deep Learning? Machine Learning focuses only on solving real-world problems. It also takes a few ideas from Artificial Intelligence. Machine Learning goes through the Neural Networks that are designed to mimic human decision- making capabilities. ML tools and techniques are the two key narrow subsets that only focuses more on Deep Learning. We need to apply it to solve any problem that requires thought — human or artificial. Any Deep Neural Network will consist of three types of layers: • The Input Layer • The Hidden Layer • The Output Layer We can say Deep Learning is the newest term in the field of Machine Learning. It’s a way to implement Machine Learning.
- 28. Deep Learning vs. Machine Learning We use a machine algorithm to parse data, learn from that data, and make informed decisions based on what it has learned. Basically, Deep Learning is used in layers to create an Artificial “Neural Network” that can learn and make intelligent decisions on its own. We can say Deep Learning is a sub-field of Machine Learning. Comparison of Machine Learning and Deep Learning Data Dependencies Performance is the main key difference between both algorithms. Although, when the data is small, Deep Learning algorithms don’t perform well. This is the only reason DL algorithms need a large amount of data to understand it perfectly. Deep Learning and Machine Learning But, we can see the use of algorithms with their handcrafted rules prevail in this scenario. The above image summarizes this fact.
- 33. Chatbot • A chatbot (also known as a smartbot, conversational bot, chatterbot, interactive agent, conversational interface, Conversational AI, or artificial conversational entity) The term "ChatterBot" was originally coined by Michael Mauldin (creator of the first Verbot, Julia) in 1994 to describe these conversational programs.
- 34. Today, most chatbots are accessed via • virtual assistants such as Google Assistant and Amazon Alexa, • via messaging apps such as Facebook Messenger orWeChat, • or via individual organizations' apps and websites
- 36. Chatbot development platforms • The process of building, testing and deploying chatbots can be done on : • Cloud-based chatbot (SasS) • Building Chatbot from scratch • Building chatbot on 3rd party platform
- 37. FB FB App Messenger Hostingwebhook Building Chatbot from scratch API
- 40. Dialog Flow Requirements • Google Account • Python, PHP, JS, or even HTML skills (For Web-Hooks) • Good English Understanding • Facebook App (For Advanced Bots) • Web Server (For Advanced Bots) • AI & ML Skills (For Advanced Bots)
- 42. Watson Studio: Build and train AI models, and prepare and analyze data, all in one integrated environment. Watson Knowledge Catalog: Intelligent data and analytic asset discovery, cataloging and governance to fuel AI apps. Watson Assistant Build and deploy chatbots and virtual assistants. Watson Discovery Uncover connections in data by combining automated ingestion with advanced AI functions. Watson IoT Platform Leverage a fully managed, cloud-hosted service for device registration, connectivity, control, rapid visualization and data storage. Watson Speech to Text (STT) Easily convert audio and voice into written text. Watson Text to Speech (TTS) Convert written text into natural-sounding audio in a variety of languages and voices. Watson Language Translator Dynamically translate news, patents or conversational documents. IBM Cloud - AI
- 43. Watson Natural Language Classifier Interpret and classify natural language with confidence. Watson Natural Language Understanding Analyze text to extract metadata from content such as concepts, entities and sentiment. Watson Visual Recognition Tag, classify and search visual content using machine learning. Watson Tone Analyzer Analyze emotions and tones in written content. Watson Personality Insights Predict personality characteristics, needs and values through written text. Data Refinery A self-service data preparation tool for data scientists, engineers and business analysts. Watson Machine Learning Create, train and deploy self-learning models using an automated, collaborative workflow. Deep Learning Design and deploy deep learning models using neural networks, easily scale to hundreds of training runs. IBM Cloud - AI
- 44. Building chatbot on 3rd party platform
- 46. Questions ???
Editor's Notes
- http://customerthink.com/how-to-apply-machine-learning-to-your-digital-marketing-strategy/ Every customer interaction leaves a digital footprint that can be leveraged to develop a deeper understanding of the customer’s intent, motivations, behaviors and predict future interactions. ML enables organizations to leverage large datasets to develop customer insights, incorporate external data sources such as competitive insights and weather data, analyze shopping histories, interpret and categorize behaviors and create actionable insights and customer specific personalization.
- What is the use of TensorFlow? It is an open source artificial intelligence library, using data flow graphs to build models. It allows developers to create large-scale neural networks with many layers. TensorFlow is mainly used for: Classification, Perception, Understanding, Discovering, Prediction and Creation With the evolving capabilities of IBM Watson and the proliferation of machine learning enabled platforms such as Azure Machine Learning, TensorFlow, and Amazon Machine Learning, etc., access to the power of Machine Learning will become available to more marketers and the integral role that ML plays in the effectiveness and efficiency of digital marketing will continue to increase. Every interaction is a potential machine learning data point and successful marketers and agencies will build the capabilities and hire the resources and partners to help them maximize this opportunity.
- Tableau and Qlik
- 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 الذكاء الاصطناعي (AI) ، قدرة الكمبيوتر الرقمي أو الروبوت المتحكم فيه على الكمبيوتر على أداء المهام المرتبطة عادةً بالكائنات الذكية. يتم تطبيق المصطلح بشكل متكرر على مشروع تطوير أنظمة تتمتع بعمليات فكرية مميزة للبشر ، مثل القدرة على التفكير أو اكتشاف المعنى أو التعميم أو التعلم من التجربة السابقة. الاب بيعلم ابنه ان ده كلب من خلال انه بيورىره الكلب ثلاث مرات مثلا
- https://dzone.com/articles/comparison-between-deep-learning-vs-machine-learni
- proposed what is now called the Turing test as a criterion of intelligence. This criterion depends on the ability of a computer program to impersonate a human in a real-time written conversation with a human judge, sufficiently well that the judge is unable to distinguish reliably—on the basis of the conversational content alone—between the program and a real human. The notoriety of Turing's proposed test stimulated great interest in Joseph Weizenbaum's program ELIZA, published in 1966, which seemed to be able to fool users into believing that they were conversing with a real human. However Weizenbaum himself did not claim that ELIZA was genuinely intelligent, and the introduction to his paper presented it more as a debunking exercise:
- In-Cloud AI Local AI on-premise
- https://en.m.wikipedia.org/wiki/Chatbot
- Chatbots can be classified into usage categories such as conversational commerce (e-commerce via chat), communication, customer support, design, developer tools, education, entertainment, finance, food, games, health, HR, marketing, news, personal, productivity, shopping, social, sports, travel and utilities.
- The process of building, testing and deploying chatbots can be done on cloud-based chatbot development platforms offered by cloud Platform as a Service (PaaS) providers such as Oracle Cloud Platform Yekaliva and IBM Watson. These cloud platforms provide Natural Language Processing, Artificial Intelligence and Mobile Backend as a Service for chatbot development.
- https://www.youtube.com/watch?v=x2P5z0AXjmI
- Dialogflow is a Google-owned developer of human–computer interaction technologies based on natural language conversations. The company is best known for creating the Assistant, a virtual buddy for Android, iOS, and Windows Phone smartphones that performs tasks and answers users' question in a natural language.