My Personal Review of Marketing Analytics Courses on Coursera
Welcome to another article, and today I will talking about a review of Marketing Analytics course on Coursera. What is the course contains, information about, and of course, why should you take this course. I will explained in a simple way and talked based on my perspective in data science, because many of you maybe thinking what is correlation between learning marketing and data science, it is two different thing, right?
I found this course on the Class Central. This website is very useful if you want to know a good course around many MOOC such as Udemy, Udacity, Coursera, and many more. You can refer to this link if you want to know what course is free to enroll.
Let’s get into the review.
The Course
This Marketing Analytics course is in Coursera, one of the biggest MOOC in the world, and provided by University of Virginia. I decided to go for this course because, they offered a free course (not audit or financial aid) due to pandemic, until July. So it is a good start if you want to know about marketing.
When I checked more depth about this course, actually it takes not much of your time, because in total, it would take 16 hours in total. So it is a short course and for people who is very busy but looking for a new knowledge, this course is good for you.
While it is just 16 hours, but Coursera recommends for 5 weeks study, with approximately 2–6 hours/ week. If you have many spare time I think you can complete it faster.
The Syllabus
What you will learn on this course, you will learn so many things, especially about marketing, which is:
- How to build and define a brand architecture and how to measure the impact of marketing efforts on brand value over time
- How to measure customer lifetime value and use that information to evaluate strategic marketing alternatives
- How to design basic experiments so that you can assess your marketing efforts and invest your marketing dollars most effectively
- How to set up regressions, interpret outputs, explore confounding effects and biases, and distinguish between economic and statistical significance
Did you see any interest materials? For people who learn about data science, the customer lifetime value and especially regression are eye-catching, because these two type is important thing, while actually you will learn about CLV in marketing sight, but it is a good start if you want to know about CLV more depth and regression, we know that it have an impact with the value and prediction.
This course also have a positive ratings with 4.6 of 5 (Data checked on 1st July 2020) and enrolled by almost 188,000 people. So it is very interesting course if you are people who always see for rating for every course you want to enrolled.
One thing that I like when I enrolled is the way Mr. Rajkumar Venkatesan is teach. I think he is success to bring myself about how marketing works, especially with me who loves with data, how he teach the theory of experimental design is very nice, and sometimes with some joke. It is a complete package for me personally to learn with.
The Materials
Actually when the total time to finished this course is less than 24 hours, I think this course would be a fun ride, because at some point you need to research by yourself about how to design marketing experiment and comparing between two brand in same market. Let me tell you how things going on.
Week 1 — You will learn about basics, how marketing process is, and of course they have tell a little bit about text analytics, while not in practical, because this is not a programming courses, but I got the point. Data science absolutely can places in this subject.
Week 2 — You will learn about how to measuring brand assets, as we know that brand assets are important thing to know how far the brand is compete. From there you will learn about brand architecture, I give a simple example, Apple is premium, Xiaomi is value-for-money. That is one thing about brand architecture. Most important thing is in this week you need to research about two competing brands, and tell about how was the brand architectured, brand assets, and many more. Interesting, right?
Week 3 — You will learn about customer lifetime value, how valuable are your customer, how to calculate that metrics, based on many things, and important thing is you can calculate how many product should you sell to gain break even point, which is a basic thing especially if you want to go deeper to marketing sight. For me it is a good thing learn new knowledge and CLV are needed for gain another insight.
Week 4 — You will learn about marketing experiment, marketing need to do that, not only data scientist with well-known A/B Testing, marketer need to do an experiment to know how was the sales from certain times if you want to do some different advertising. Example, with your sales of food product is pretty good with campaign “delicious” how was the sales if you changed to “healthy” food? On this week you need to design a marketing experiment, what is your control and test data, and then what experiment you do, and many more.
Week 5 — Related things for myself, learning regression for finding price elasticity, of course for evaluate marketing. Data science is applicable on marketing and learning this materials would make me thinking that, you don’t need to be a data scientist to learn data science, even people who in marketing are used that.
Why I learn this?
Because I want to know basic of business and I think it start with how marketing is work, knowing part of them, how to get a break even sales, do some experiment and know your CLV are important thing to get a better understanding of it. When I have this knowledge, I can expand this into many applications, for example find CLV for my e-commerce dataset, which customers is loyal to us, and then design an experiment and many more.
For this course, if you want to enroll, you can refer to this link.
Thank you so much for reading this article. Follow my Medium, my Curious with Data publication and Linkedin here and share it if you see it is very helpful. Feel free to ask me on Linkedin and see you soon in another article. Keep learning!
