OPEN LECTURE: Machine Learning in MarTech
- Megi Gogua
- Apr 6
- 4 min read
Introduction
Hello everyone!
I’m really happy to welcome you to my Online Open MarTech lecture.
My name is Megi, I have taught and studied MarTech for many years already, and with this series of Online Open Lectures, I would like to share with you what I know so far.
This series of lectures will contain all the topics related to MarTech, from broad approaches to certain examples and cases.
These lectures are for those who consider MarTech to be the topic, field, or direction of their career development. Also, these videos are for those who already have marketing experience but want to see a broader horizon and more examples of the application of technologies in marketing. Those who generally wish to learn something new can also benefit from these recordings of Online Open Lectures.
I really hope that this initiative of mine will be useful for you and helpful because I would love to share with you everything I know and invite you to this wonderful journey of finding something new, something interesting. If you ask me, this field of MarTech is incredibly fascinating and useful, and I’m happy to be able to guide you through it.
MarTech operates on Data - today, we will discuss this fundamental aspect.
Introduction of me as a lecturer
Before we get to the lecture, I would like to introduce myself with this slide.
Full information about my Education and Experience is available here:
What is the definition of Machine Learning?
Let’s start with the definitions.
Machine Learning is a branch of artificial intelligence (AI) and computer science that focuses on the using data and algorithms to enable AI to imitate the way that humans learn, gradually improving its accuracy.[1]
Machine Learning Process Stages [1]
A Decision Process
An Error Function
A Model Optimization Process
Machine Learning Methods
Supervised machine learning
Unsupervised Machine Learning
Semi-Supervised Machine Learning
+ Reinforcement Machine Learning
As a reminder, here is a definition of the term MarTech:
MarTech applies to major initiatives, efforts, and tools that harness technology to achieve marketing goals and objectives. [2]
The following table defines Machine Learning Algorithms and their Benefits and Drawbacks
Benefits | Drawbacks |
Decision trees are easier to validate and audit than a neural network | Decision trees can be more unstable than other decision predictors |
Machine learning can identify patterns and trends in massive volumes of data that humans might not spot at all | ML requires large training datasets that are accurate and unbiased: the case of GIGO (garbage in / garbage out) |
ML analysis requires little human intervention: feed in the dataset of interest and let the machine learning system assemble and refine its own algorithms | Gathering sufficient data and having a system robust enough to run it might also be a drain on resources |
Algorithms continually improve with more data input over time | Machine learning can also be prone to error, depending on the input. With too small a sample, the system could produce a perfectly logical algorithm that is completely wrong or misleading |
Customers and users can enjoy a more personalized experience as the model learns more with every experience with that person | To avoid wasting budget or displeasing customers, organizations should act on the answers only when there is high confidence in the output. |
Machine Learning: Cases of Use
1. Speech recognition
(automatic speech recognition (ASR), computer speech recognition, or speech-to-text) - a capability which uses natural language processing (NLP) to translate human speech into a written format.
2. Customer service
Online chatbots are replacing human agents along the customer journey; chatbots answer frequently asked questions (FAQs) about topics such as shipping, or provide personalized advice, cross-selling products or suggesting sizes for users.
3. Computer vision
enables computers to derive meaningful information from digital images, videos, and other visual inputs; computer vision has applications in photo tagging on social media
4. Recommendation engines
Using past consumption behaviour data, AI algorithms can help to discover data trends that can be used to develop more effective cross-selling strategies.
Recommendation engines are used by online retailers to make relevant product recommendations to customers during the checkout process.
5. Marketing Analytics [2]
Customer-centric focus: Building stronger relationships
Optimize customer acquisition and retention
Dynamic and relevant marketing outputs — meeting evolving customer expectations
We will discuss the applications in one of the future posts!
I hope it was useful! Do not hesitate to share this article with anyone you consider to be interested in this topic.
If you wish to learn more, you can follow the links:
Explore the MarTech field
Learn about MarTech: Attend my Open Online MarTech Lectures
Talk about MarTech: Participate in the “MarTalks” – my open speaking/discussion club
Practice for your MarTech communications: Complete my Free English Exercises for Marketers
Excel in your MarTech career
Learn about your MarTech career possibilities:
Explore the specificities of the developing MarTech field by going through my guide – The MarTech Guide
Ask me directly – register for a consultation with me to learn about the MarTech career trajectories, required skills and examples of the tasks
Improve your English for marketing purposes
Register for the Individual consultation or lessons based on your request (ex. presentation rehearsal, preparation for negotiations, MarTech terms and vocabulary enrichment)
Register to develop your individual learning programme for a chosen career path in MarTech
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Sources:
[1] Definition of data from the Cambridge Advanced Learner's Dictionary & Thesaurus
[2] Brinker Scott, "Hacking Marketing: Agile Practices to Make Marketing Smarter, Faster, and More Innovative", Wiley; 1st edition (March 4, 2016)
[3] Amazon Web Services (AWS), What is Structured Data? https://aws.amazon.com/what-is/structured-data/
[4] IBM. Structured versus unstructured data: What's the difference? https://www.ibm.com/think/topics/structured-vs-unstructured-data
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