Personalize the Sales Offer through 150 hours of the best digital teaching"

In a business context marked by competitiveness, institutions seek to analyze conversations and trends to know consumers in detail. For this reason, Machine Learning systems have gained special prominence. This is due to their application for monitoring brands, tracking mentions of firms on social networks to assess the public's perception of their products or services. Specialists can develop comprehensive strategies to maintain a positive image of organizations and address any problems that arise in a timely manner.

Given this reality, TECH develops a pioneering program that will delve into the analysis of communicative data for strategic decision-making. The syllabus will provide students with the latest systems for identifying and evaluating user emotions. In addition, the syllabus will emphasize the location of Big Data to analyze communications. In line with this, the didactic contents will address the automatic resolution of problems through instruments such as Chatbots. The program will also encourage graduates to make improvements in the digital experiences of users, in order to retain them. In addition, training will focus on automated monitoring of competitors and predictive competitive surveillance.

Relearning’s pedagogical methodology, together with multimedia resources, allow students to access the teaching material 24 hours a day, 7 days a week, and flexibly. This program is delivered 100% online, which means students can learn at their own pace and according to their schedule. In addition, the outstanding teachers are experts in Artificial Intelligence in Communication and have extensive work and research experience. This Postgraduate diploma is a unique opportunity for marketing professionals to improve their skills. With an innovative format and updated content, TECH offers rigorous and high quality teaching.

You will be able to predict trends in the market to develop innovative products that differentiate you from the competition"

This Postgraduate diploma in Communication and Business Relationship Management using Artificial Intelligence contains the most complete and up-to-date program on the market. Its most notable features are:

• The development of case studies presented by experts in Artificial Intelligence in Marketing and Communication
• The graphic, schematic and eminently practical content of the system provides complete and practical information on those disciplines that are essential for professional practice
• Practical exercises where self-assessment can be used to improve learning
• Its special emphasis on innovative methodologies
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments
• Content that is accessible from any fixed or portable device with an Internet connection

Create interactive dashborads to view data and metrics dynamically in one place"

The program’s teaching staff includes professionals from the sector who contribute their work experience to this training program, as well as renowned specialists from leading societies and prestigious universities.

The multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide immersive education programmed to learn in real situations.

This program is designed around Problem-Based Learning, whereby the professional must try to solve the different professional practice situations that arise during the academic year For this purpose, the students will be assisted by an innovative interactive video system created by renowned and experienced experts. 

You’ll conduct the most advanced Predictive Analytics to verify the performance of your ads"

Thanks to the revolutionary Relearning methodology, you will integrate all the knowledge in an optimal way to successfully achieve the results you are looking for.

This Postgraduate diploma will focus on Automation and Optimization of Marketing Processes with AI. Consisting of 3 modules, the syllabus will address both data integration and platforms using Machine Learning strategies. In this way, graduates can improve their advertising campaigns, personalizing them according to audiences. In addition, the materials will provide tools for the analysis of large volumes of data. The program will also emphasize the application of AI during sales, using techniques to generate Leads. Graduates will be able to make predictions about customers' needs to develop products that respond to them.

A complete and cutting-edge program that will allow you to advance progressively and completely, from the comfort of your home"

Module 1. Automation and Optimization of Marketing Processes with AI

1.1. Marketing Automation with AI

1.1.1. AI-based Audience Segmentation
1.1.2. Workflow Automation
1.1.3. Continuous Optimization of Online Campaigns

1.2. Integration of Data and Platforms in Automated Marketing Strategies

1.2.1. Analysis and Unification of Multichannel Data
1.2.2. Interconnection between Different Marketing Platforms
1.2.3. Real-time Data Updating

1.3. Optimization of Advertising Campaigns with AI

1.3.1. Predictive Analysis of Ad Performance
1.3.2. Automatic Personalization of the Advertisement according to the Target Audience
1.3.3. Automatic Budget Adjustment according to Results

1.4. Audience Personalization with AI

1.4.1. Content Segmentation and Personalization
1.4.2. Personalized Content Recommendations
1.4.3. Automatic Identification of Homogeneous Audiences or Groups

1.5. Automation of Responses to Customers through AI

1.5.1. Chatbots and Machine Learning
1.5.2. Automatic Generation of Responses
1.5.3. Automatic Problem Solving

1.6. AI in Email Marketing for Automation and Personalization

1.6.1. Automation of Email Sequences
1.6.2. Dynamic Personalization of Content according to Preferences
1.6.3. Intelligent Segmentation of Mailing Lists

1.7. Sentiment Analysis with AI in Social Media and Customer Feedback

1.7.1. Automatic Sentiment Monitoring in Comments
1.7.2. Personalized Responses to Emotions
1.7.3. Predictive Reputation Analytics

1.8. Price and Promotion Optimization with AI

1.8.1. Automatic Price Adjustment based on Predictive Analytics
1.8.2. Automatic Generation of Offers Adapted to User Behavior
1.8.3. Real-time Competitive and Price Analysis

1.9. Integration of AI into Existing Marketing Tools

1.9.1. Integration of AI Capabilities with Existing Marketing Platforms
1.9.2. Optimization of Existing Functionalities
1.9.3. Integration with CRM Systems

1.10. Trends and Future of Automation with AI in Marketing

1.10.1. AI to Improve User Experience
1.10.2. Predictive Approach to Marketing Decisions
1.10.3. Conversational Advertising

Module 2. Communication and Marketing Data Analysis for Decision Making

2.1. Specific Technologies and Tools for Communication and Marketing Data Analysis

2.1.1. Tools for Analyzing Conversations and Trends in Social Media
2.1.2. Systems to Identify and Evaluate Emotions in Communications
2.1.3. Use of Big Data to Analyze Communications

2.2. Applications of AI in the Analysis of Large Volumes of Marketing Data

2.2.1. Automatic Processing of Massive Data
2.2.2. Identification of Behavioral Patterns
2.2.3. Optimization of Algorithms for Data Analysis

2.3. Tools for Data Visualization and Reporting of Campaigns and Communications with AI

2.3.1. Creation of Interactive Dashboards
2.3.2. Automatic Report Generation
2.3.3. Predictive Visualization of Campaign Results

2.4. Application of AI in Market Research

2.4.1. Automatic Processing of Survey Data
2.4.2. Automatic Identification of Audience Segments
2.4.3. Prediction of Market Trends

2.5. Predictive Analytics in Marketing for Decision Making

2.5.1. Predictive Models of Consumer Behavior
2.5.2. Campaign Performance Forecasting
2.5.3. Automatic Adjustment of Strategic Optimization

2.6. Market Segmentation with AI

2.6.1. Automated Analysis of Demographic Data
2.6.2. Identification of Interest Groups
2.6.3. Dynamic Personalization of Offers

2.7. Marketing Strategy Optimization with AI

2.7.1. Use of AI to Measure Channel Effectiveness
2.7.2. Strategic Automatic Adjustment to Maximize Results
2.7.3. Simulation of Strategic Scenarios

2.8. AI in Marketing ROI Measurement

2.8.1. Conversion Attribution Models
2.8.2. ROI Analysis using AI
2.8.3. Customer Lifetime Value Estimation

2.9. Success Stories in Data Analytics with AI

2.9.1. Demonstration through Case Studies where AI has Improved Results
2.9.2. Cost and Resource Optimization
2.9.3. Competitive Advantages and Innovation

2.10. Challenges and Ethical Considerations in AI Data Analysis

2.10.1. Biases in Data and Results
2.10.2. Ethical Considerations in Handling and Analyzing Sensitive Data
2.10.3. Challenges and Solutions for Making AI Models Transparent

Module 3. Sales and Leads Generation with Artificial Intelligence

3.1. Application of AI in the Sales Process

3.1.1. Automation of Sales Tasks
3.1.2. Predictive Sales Cycle Analysis
3.1.3. Optimization of Pricing Strategies

3.2. Techniques and Tools for Lead Generation with AI

3.2.1. Automated Lead Identification
3.2.2. User Behavior Analysis
3.2.3. Personalization of Content for Recruitment

3.3. Leads Scoring with AI

3.3.1. Automated Evaluation of Leads Qualification
3.3.2. Lead Analysis Based on Interactions
3.3.3. Leads Scoring Model Optimization

3.4. AI in Customer Relationship Management

3.4.1. Automated Tracking to Improve Customer Relationships
3.4.2. Personalized Recommendations for Customers
3.4.3. Automation of Personalized Communications

3.5. Implementation and Success Cases of Virtual Assistants in Sales

3.5.1. Virtual Assistants for Sales Support
3.5.2. Improving Customer Experience
3.5.3. Optimizing Conversions and Closing Sales

3.6. Predicting Customer Needs with AI

3.6.1. Analysis of Buying Behavior
3.6.2. Dynamic Offer Segmentation
3.6.3. Personalized Recommendation Systems

3.7. Personalization of the Sales Offer with AI

3.7.1. Dynamic Adaptation of Sales Proposals
3.7.2. Behavior-based Exclusive Offers
3.7.3. Creation of Customized Packs

3.8. Competitive Analysis with AI

3.8.1. Automated Competitor Monitoring
3.8.2. Automated Comparative Price Analysis
3.8.3. Predictive Competitive Surveillance

3.9. Integration of AI in Sales Tools

3.9.1. Compatibility with CRM Systems
3.9.2. Empowerment of Sales Tools
3.9.3. Predictive Analytics in Sales Platforms

3.10. Innovations and Predictions in the Sales Environment

3.10.1. Augmented Reality in Shopping Experience
3.10.2. Advanced Automation in Sales
3.10.3. Emotional Intelligence in Sales Interactions

Make the most of this opportunity to surround yourself with expert professionals and learn from their work methodology"