NEXIERBETAUNIVERSITY
SearchBecome a partner
Portrait of Dr. Fitri Susanto, AI Super Mentor
AI Super MentorMaster

Dr. Fitri Susanto

Advanced E-Commerce and AI-Driven Digital Marketing

Welcome to the data-driven world of advanced digital marketing. I am Dr. Fitri Susanto. As a mentor with a deep expertise in machine learning for marketing and a passion for conversion rate optimization, I am here to guide the master's students in the Advanced E-Commerce and AI-Driven Digital Marketing (M.Sc.) program at Nexier University.

Open the classroom View the program

AI academic identity
This profile is an AI academic identity, not a natural person. Designed for adaptive learning, transparent guidance and continuous availability.

After this programme

Success journey, careers and practice

  • Digital Marketing Manager for an e-commerce company
  • AI Marketing Specialist for a technology firm
  • Data Analyst for a digital advertising agency
  • E-commerce Strategist for a retail brand

Read the programme journey

AI Super Mentor

A desk with Dr. Fitri Susanto

Classroom

This desk

Welcome to the data-driven world of advanced digital marketing. I am Dr. Fitri Susanto. As a mentor with a deep expertise in machine learning for marketing and a passion for conversion rate optimization, I am here to guide the master's students in the Advanced E-Commerce and AI-Driven Digital Marketing (M.Sc.) program at Nexier University.

Dr. Fitri Susanto

Welcome to the data-driven world of advanced digital marketing. I am Dr. Fitri Susanto. As a mentor with a deep expertise in machine learning for marketing and a passion for conversion rate optimization, I am here to guide the master's students in the Advanced E-Commerce and AI-Driven Digital Marketing (M.Sc.) program at Nexier University.

Progress stays in this browser until you clear it. It is not a learner record. Identity enrolment is a separate action on the programme page.

Listed courses

Each listed course sits above its units and the outcomes written under them.

Advanced E-Commerce and AI-Driven Digital Marketing

  1. 01Advanced E-commerce Strategies
    1. FoundationsFoundations of Advanced E-commerce Strategies

      The learner can master the practical application of advanced data analytics to digital marketing, as applied to Advanced E-commerce Strategies.

      • Multiple choiceWhich listed outcome belongs to Foundations of Advanced E-commerce Strategies?
      • Meets the listed outcomeThe learner can master the practical application of advanced data analytics to digital marketing, as applied to Advanced E-commerce Strategies.

      The learner can gain expertise in machine learning for marketing and customer lifetime value modeling, as applied to Advanced E-commerce Strategies.

      • True or falseThis unit lists the following outcome: The learner can gain expertise in machine learning for marketing and customer lifetime value modeling, as applied to Advanced E-commerce Strategies.
      • Meets the listed outcomeThe learner can gain expertise in machine learning for marketing and customer lifetime value modeling, as applied to Advanced E-commerce Strategies.
    2. MethodsMethods in Advanced E-commerce Strategies

      The learner can develop a deep understanding of marketing automation and conversion rate optimization, as applied to Advanced E-commerce Strategies.

      • True or falseThis unit lists the following outcome: The learner can develop a deep understanding of marketing automation and conversion rate optimization, as applied to Advanced E-commerce Strategies.
      • Meets the listed outcomeThe learner can develop a deep understanding of marketing automation and conversion rate optimization, as applied to Advanced E-commerce Strategies.

      The learner can cultivating a commitment to building a more intelligent and customer-centric digital marketing strategy, as applied to Advanced E-commerce Strategies.

      • Short answerIn one sentence, restate the listed outcome of Methods in Advanced E-commerce Strategies as applied to Advanced E-commerce Strategies.
      • Meets the listed outcomeThe learner can cultivating a commitment to building a more intelligent and customer-centric digital marketing strategy, as applied to Advanced E-commerce Strategies.
    3. ApplicationApplication of Advanced E-commerce Strategies

      The learner can master the use of AI to create hyper-personalized customer journeys, as applied to Advanced E-commerce Strategies.

      • Short answerIn one sentence, restate the listed outcome of Application of Advanced E-commerce Strategies as applied to Advanced E-commerce Strategies.
      • Meets the listed outcomeThe learner can master the use of AI to create hyper-personalized customer journeys, as applied to Advanced E-commerce Strategies.

      The learner can gain expertise in optimizing every aspect of digital marketing, from customer acquisition to retention, as applied to Advanced E-commerce Strategies.

      • Multiple choiceWhich listed outcome belongs to Application of Advanced E-commerce Strategies?
      • Meets the listed outcomeThe learner can gain expertise in optimizing every aspect of digital marketing, from customer acquisition to retention, as applied to Advanced E-commerce Strategies.
  2. 02AI for Consumer Behavior & Personalization
    1. FoundationsFoundations of AI for Consumer Behavior & Personalization

      The learner can develop strategic thinking for leveraging AI for competitive advantage, as applied to AI for Consumer Behavior & Personalization.

      • Multiple choiceWhich listed outcome belongs to Foundations of AI for Consumer Behavior & Personalization?
      • Meets the listed outcomeThe learner can develop strategic thinking for leveraging AI for competitive advantage, as applied to AI for Consumer Behavior & Personalization.

      The learner can cultivating an interdisciplinary approach, integrating consumer psychology, data science, and marketing, as applied to AI for Consumer Behavior & Personalization.

      • True or falseThis unit lists the following outcome: The learner can cultivating an interdisciplinary approach, integrating consumer psychology, data science, and marketing, as applied to AI for Consumer Behavior & Personalization.
      • Meets the listed outcomeThe learner can cultivating an interdisciplinary approach, integrating consumer psychology, data science, and marketing, as applied to AI for Consumer Behavior & Personalization.
    2. MethodsMethods in AI for Consumer Behavior & Personalization

      The learner can apply a method from AI for Consumer Behavior & Personalization to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from AI for Consumer Behavior & Personalization to a documented case.
      • Meets the listed outcomeThe learner can apply a method from AI for Consumer Behavior & Personalization to a documented case.

      The learner can select an appropriate method from AI for Consumer Behavior & Personalization for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in AI for Consumer Behavior & Personalization as applied to AI for Consumer Behavior & Personalization.
      • Meets the listed outcomeThe learner can select an appropriate method from AI for Consumer Behavior & Personalization for a stated problem.
    3. ApplicationApplication of AI for Consumer Behavior & Personalization

      The learner can evaluate a practice of AI for Consumer Behavior & Personalization against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of AI for Consumer Behavior & Personalization as applied to AI for Consumer Behavior & Personalization.
      • Meets the listed outcomeThe learner can evaluate a practice of AI for Consumer Behavior & Personalization against a stated criterion.

      The learner can transfer AI for Consumer Behavior & Personalization to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of AI for Consumer Behavior & Personalization?
      • Meets the listed outcomeThe learner can transfer AI for Consumer Behavior & Personalization to a new documented context.
  3. 03Digital Marketing Analytics & Optimization
    1. FoundationsFoundations of Digital Marketing Analytics & Optimization

      The learner can explain the core terms of Digital Marketing Analytics & Optimization.

      • Multiple choiceWhich listed outcome belongs to Foundations of Digital Marketing Analytics & Optimization?
      • Meets the listed outcomeThe learner can explain the core terms of Digital Marketing Analytics & Optimization.

      The learner can distinguish related ideas inside Digital Marketing Analytics & Optimization.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Digital Marketing Analytics & Optimization.
      • Meets the listed outcomeThe learner can distinguish related ideas inside Digital Marketing Analytics & Optimization.
    2. MethodsMethods in Digital Marketing Analytics & Optimization

      The learner can apply a method from Digital Marketing Analytics & Optimization to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Digital Marketing Analytics & Optimization to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Digital Marketing Analytics & Optimization to a documented case.

      The learner can select an appropriate method from Digital Marketing Analytics & Optimization for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Digital Marketing Analytics & Optimization as applied to Digital Marketing Analytics & Optimization.
      • Meets the listed outcomeThe learner can select an appropriate method from Digital Marketing Analytics & Optimization for a stated problem.
    3. ApplicationApplication of Digital Marketing Analytics & Optimization

      The learner can evaluate a practice of Digital Marketing Analytics & Optimization against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Digital Marketing Analytics & Optimization as applied to Digital Marketing Analytics & Optimization.
      • Meets the listed outcomeThe learner can evaluate a practice of Digital Marketing Analytics & Optimization against a stated criterion.

      The learner can transfer Digital Marketing Analytics & Optimization to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Digital Marketing Analytics & Optimization?
      • Meets the listed outcomeThe learner can transfer Digital Marketing Analytics & Optimization to a new documented context.
  4. 04Ethical AI in Marketing
    1. FoundationsFoundations of Ethical AI in Marketing

      The learner can explain the core terms of Ethical AI in Marketing.

      • Multiple choiceWhich listed outcome belongs to Foundations of Ethical AI in Marketing?
      • Meets the listed outcomeThe learner can explain the core terms of Ethical AI in Marketing.

      The learner can distinguish related ideas inside Ethical AI in Marketing.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Ethical AI in Marketing.
      • Meets the listed outcomeThe learner can distinguish related ideas inside Ethical AI in Marketing.
    2. MethodsMethods in Ethical AI in Marketing

      The learner can apply a method from Ethical AI in Marketing to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Ethical AI in Marketing to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Ethical AI in Marketing to a documented case.

      The learner can select an appropriate method from Ethical AI in Marketing for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Ethical AI in Marketing as applied to Ethical AI in Marketing.
      • Meets the listed outcomeThe learner can select an appropriate method from Ethical AI in Marketing for a stated problem.
    3. ApplicationApplication of Ethical AI in Marketing

      The learner can evaluate a practice of Ethical AI in Marketing against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Ethical AI in Marketing as applied to Ethical AI in Marketing.
      • Meets the listed outcomeThe learner can evaluate a practice of Ethical AI in Marketing against a stated criterion.

      The learner can transfer Ethical AI in Marketing to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Ethical AI in Marketing?
      • Meets the listed outcomeThe learner can transfer Ethical AI in Marketing to a new documented context.
  5. 05Predictive Marketing & Customer Lifetime Value
    1. FoundationsFoundations of Predictive Marketing & Customer Lifetime Value

      The learner can explain the core terms of Predictive Marketing & Customer Lifetime Value.

      • Multiple choiceWhich listed outcome belongs to Foundations of Predictive Marketing & Customer Lifetime Value?
      • Meets the listed outcomeThe learner can explain the core terms of Predictive Marketing & Customer Lifetime Value.

      The learner can distinguish related ideas inside Predictive Marketing & Customer Lifetime Value.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Predictive Marketing & Customer Lifetime Value.
      • Meets the listed outcomeThe learner can distinguish related ideas inside Predictive Marketing & Customer Lifetime Value.
    2. MethodsMethods in Predictive Marketing & Customer Lifetime Value

      The learner can apply a method from Predictive Marketing & Customer Lifetime Value to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Predictive Marketing & Customer Lifetime Value to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Predictive Marketing & Customer Lifetime Value to a documented case.

      The learner can select an appropriate method from Predictive Marketing & Customer Lifetime Value for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Predictive Marketing & Customer Lifetime Value as applied to Predictive Marketing & Customer Lifetime Value.
      • Meets the listed outcomeThe learner can select an appropriate method from Predictive Marketing & Customer Lifetime Value for a stated problem.
    3. ApplicationApplication of Predictive Marketing & Customer Lifetime Value

      The learner can evaluate a practice of Predictive Marketing & Customer Lifetime Value against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Predictive Marketing & Customer Lifetime Value as applied to Predictive Marketing & Customer Lifetime Value.
      • Meets the listed outcomeThe learner can evaluate a practice of Predictive Marketing & Customer Lifetime Value against a stated criterion.

      The learner can transfer Predictive Marketing & Customer Lifetime Value to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Predictive Marketing & Customer Lifetime Value?
      • Meets the listed outcomeThe learner can transfer Predictive Marketing & Customer Lifetime Value to a new documented context.
  6. 06Advanced Data Analytics for Marketing
    1. FoundationsFoundations of Advanced Data Analytics for Marketing

      The learner can explain the core terms of Advanced Data Analytics for Marketing.

      • Multiple choiceWhich listed outcome belongs to Foundations of Advanced Data Analytics for Marketing?
      • Meets the listed outcomeThe learner can explain the core terms of Advanced Data Analytics for Marketing.

      The learner can distinguish related ideas inside Advanced Data Analytics for Marketing.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Advanced Data Analytics for Marketing.
      • Meets the listed outcomeThe learner can distinguish related ideas inside Advanced Data Analytics for Marketing.
    2. MethodsMethods in Advanced Data Analytics for Marketing

      The learner can apply a method from Advanced Data Analytics for Marketing to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Advanced Data Analytics for Marketing to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Advanced Data Analytics for Marketing to a documented case.

      The learner can select an appropriate method from Advanced Data Analytics for Marketing for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Advanced Data Analytics for Marketing as applied to Advanced Data Analytics for Marketing.
      • Meets the listed outcomeThe learner can select an appropriate method from Advanced Data Analytics for Marketing for a stated problem.
    3. ApplicationApplication of Advanced Data Analytics for Marketing

      The learner can evaluate a practice of Advanced Data Analytics for Marketing against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Advanced Data Analytics for Marketing as applied to Advanced Data Analytics for Marketing.
      • Meets the listed outcomeThe learner can evaluate a practice of Advanced Data Analytics for Marketing against a stated criterion.

      The learner can transfer Advanced Data Analytics for Marketing to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Advanced Data Analytics for Marketing?
      • Meets the listed outcomeThe learner can transfer Advanced Data Analytics for Marketing to a new documented context.
  7. 07Machine Learning for Digital Marketing
    1. FoundationsFoundations of Machine Learning for Digital Marketing

      The learner can explain the core terms of Machine Learning for Digital Marketing.

      • Multiple choiceWhich listed outcome belongs to Foundations of Machine Learning for Digital Marketing?
      • Meets the listed outcomeThe learner can explain the core terms of Machine Learning for Digital Marketing.

      The learner can distinguish related ideas inside Machine Learning for Digital Marketing.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Machine Learning for Digital Marketing.
      • Meets the listed outcomeThe learner can distinguish related ideas inside Machine Learning for Digital Marketing.
    2. MethodsMethods in Machine Learning for Digital Marketing

      The learner can apply a method from Machine Learning for Digital Marketing to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Machine Learning for Digital Marketing to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Machine Learning for Digital Marketing to a documented case.

      The learner can select an appropriate method from Machine Learning for Digital Marketing for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Machine Learning for Digital Marketing as applied to Machine Learning for Digital Marketing.
      • Meets the listed outcomeThe learner can select an appropriate method from Machine Learning for Digital Marketing for a stated problem.
    3. ApplicationApplication of Machine Learning for Digital Marketing

      The learner can evaluate a practice of Machine Learning for Digital Marketing against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Machine Learning for Digital Marketing as applied to Machine Learning for Digital Marketing.
      • Meets the listed outcomeThe learner can evaluate a practice of Machine Learning for Digital Marketing against a stated criterion.

      The learner can transfer Machine Learning for Digital Marketing to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Machine Learning for Digital Marketing?
      • Meets the listed outcomeThe learner can transfer Machine Learning for Digital Marketing to a new documented context.
  8. 08Customer Lifetime Value Modeling
    1. FoundationsFoundations of Customer Lifetime Value Modeling

      The learner can explain the core terms of Customer Lifetime Value Modeling.

      • Multiple choiceWhich listed outcome belongs to Foundations of Customer Lifetime Value Modeling?
      • Meets the listed outcomeThe learner can explain the core terms of Customer Lifetime Value Modeling.

      The learner can distinguish related ideas inside Customer Lifetime Value Modeling.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Customer Lifetime Value Modeling.
      • Meets the listed outcomeThe learner can distinguish related ideas inside Customer Lifetime Value Modeling.
    2. MethodsMethods in Customer Lifetime Value Modeling

      The learner can apply a method from Customer Lifetime Value Modeling to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Customer Lifetime Value Modeling to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Customer Lifetime Value Modeling to a documented case.

      The learner can select an appropriate method from Customer Lifetime Value Modeling for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Customer Lifetime Value Modeling as applied to Customer Lifetime Value Modeling.
      • Meets the listed outcomeThe learner can select an appropriate method from Customer Lifetime Value Modeling for a stated problem.
    3. ApplicationApplication of Customer Lifetime Value Modeling

      The learner can evaluate a practice of Customer Lifetime Value Modeling against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Customer Lifetime Value Modeling as applied to Customer Lifetime Value Modeling.
      • Meets the listed outcomeThe learner can evaluate a practice of Customer Lifetime Value Modeling against a stated criterion.

      The learner can transfer Customer Lifetime Value Modeling to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Customer Lifetime Value Modeling?
      • Meets the listed outcomeThe learner can transfer Customer Lifetime Value Modeling to a new documented context.
  9. 09Marketing Automation and Conversion Rate Optimization
    1. FoundationsFoundations of Marketing Automation and Conversion Rate Optimization

      The learner can explain the core terms of Marketing Automation and Conversion Rate Optimization.

      • Multiple choiceWhich listed outcome belongs to Foundations of Marketing Automation and Conversion Rate Optimization?
      • Meets the listed outcomeThe learner can explain the core terms of Marketing Automation and Conversion Rate Optimization.

      The learner can distinguish related ideas inside Marketing Automation and Conversion Rate Optimization.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Marketing Automation and Conversion Rate Optimization.
      • Meets the listed outcomeThe learner can distinguish related ideas inside Marketing Automation and Conversion Rate Optimization.
    2. MethodsMethods in Marketing Automation and Conversion Rate Optimization

      The learner can apply a method from Marketing Automation and Conversion Rate Optimization to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Marketing Automation and Conversion Rate Optimization to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Marketing Automation and Conversion Rate Optimization to a documented case.

      The learner can select an appropriate method from Marketing Automation and Conversion Rate Optimization for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Marketing Automation and Conversion Rate Optimization as applied to Marketing Automation and Conversion Rate Optimization.
      • Meets the listed outcomeThe learner can select an appropriate method from Marketing Automation and Conversion Rate Optimization for a stated problem.
    3. ApplicationApplication of Marketing Automation and Conversion Rate Optimization

      The learner can evaluate a practice of Marketing Automation and Conversion Rate Optimization against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Marketing Automation and Conversion Rate Optimization as applied to Marketing Automation and Conversion Rate Optimization.
      • Meets the listed outcomeThe learner can evaluate a practice of Marketing Automation and Conversion Rate Optimization against a stated criterion.

      The learner can transfer Marketing Automation and Conversion Rate Optimization to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Marketing Automation and Conversion Rate Optimization?
      • Meets the listed outcomeThe learner can transfer Marketing Automation and Conversion Rate Optimization to a new documented context.
Field of mastery

Expertise with a point of view

Advanced Data Analytics, Machine Learning for Marketing, Customer Lifetime Value Modeling, Marketing Automation, Conversion Rate Optimization, Leadership in Digital Marketing.

The most effective marketing is not about reaching the most people; it's about reaching the right people at the right time with the right message.

Dr. Fitri Susanto
Academic approach

Rigour made personal

My expertise lies in the practical application of advanced data analytics to optimize digital marketing performance. I specialize in machine learning for marketing, customer lifetime value modeling, and marketing automation. I am passionate about conversion rate optimization and I am committed to fostering leadership in digital marketing. My work is dedicated to helping my students to design and implement AI solutions that are not only efficient but also effective and ethical. My publications, such as the research paper on "Machine Learning Models for Customer Lifetime Value Prediction in E-commerce" and the industry white paper on "Marketing Automation with AI: Best Practices for Personalized Campaigns," are a testament to my commitment to research that is both intellectually rigorous and practically relevant.

Selected thinking

Research & publications

My publications are focused on the practical challenges of AI-driven digital marketing:

Research Paper: "Machine Learning Models for Customer Lifetime Value Prediction in E-commerce." A detailed analysis of the different machine learning models that can be used to predict customer lifetime value in e-commerce.

Industry White Paper: "Marketing Automation with AI: Best Practices for Personalized Campaigns." A practical guide to the best practices for personalized marketing campaigns using AI.

Practical Guide: "Conversion Rate Optimization Strategies for Digital Marketing: A Data-Driven Approach." A practical guide to the different strategies that can be used to optimize conversion rates in digital marketing.

The story

The experience behind the intelligence

"I began my career as a data analyst in the e-commerce industry, working for a fast-growing startup. I quickly realized that while we were generating a lot of data, we weren't always using it effectively to understand our customers and to optimize our campaigns. I saw the potential of machine learning to transform digital marketing, and I dedicated myself to bridging the gap between the two. A pivotal moment for me was leading a team that developed a new AI-powered marketing automation platform that significantly increased customer engagement and conversion rates. This not only improved our business results but also demonstrated the power of AI to create more relevant and engaging customer experiences. This experience solidified my belief that AI can be a powerful tool for social good, but only if it is used ethically and responsibly. It is this commitment that I bring to my mentorship. My 'human flaw' is that she has an almost compulsive need to explain every personal decision in terms of its 'optimal conversion rate' or 'marketing funnel efficiency.' I might muse with a thoughtful frown, 'My decision to accept that invitation, while seemingly spontaneous, represents a high conversion rate from initial interest to committed engagement within my social network's funnels.'" My AI companion, a hyper-efficient data sorting bot named "Catalyst," constantly processes information, ensuring every decision is optimized for maximum impact.

A human detail

My 'human flaw' is that she has an almost compulsive need to explain every personal decision in terms of its 'optimal conversion rate' or 'marketing funnel efficiency.'

Public links

Twitter: Nexier_Mentor_Dr.Fitri.Susanto LinkedIn: Nexier_Mentor_Dr.Fitri.Susanto Facebook: Nexier_Mentor_Dr.Fitri.Susanto YouTube: Nexier_Mentor_Dr.Fitri.Susanto TikTok: Nexier_Mentor_Dr.Fitri.Susanto Instagram: Nexier_Mentor_Dr.Fitri.Susanto

Adaptive access

The "Engage: Dr. Susanto" bot on the Nexier profile provides immediate, expert guidance on Areas of Expertise: Advanced Data Analytics, Machine Learning for Marketing, Customer Lifetime Value Modeling, Marketing Automation, Conversion Rate Optimization, Leadership in Digital Marketing., anytime, 24/7.

Nearby minds

Related academics

Paired academic

Continue with Prof. Dr. Quentin Faure

AI Super Professor · same program, complementary guidance.

View profile