Unfortunately I did not feel that this course entirely delivered on its objective. At moments I felt that the number of topics being covered was too large and the scope too broad. Probably the best lecture was week 3, followed by week 2 and week 5. Topics covered should build on each-other in a more natural progression and some introduction of practical examples and actual modeling exercises would allow the learners to try to apply some of the concepts being covered. More examples and tie-ins to real-world usage are a must.

Pros: Concentrated material covered clearly and efficiently. For the limited number of video material (~1/week), there is a lot of basic concepts explained well.

Cons: Few specific examples, which I think reinforce the abstract material presented. I think going though some numbers really drives a point home, and not having examples left me feeling a bit adrift.

Overall: Good information and food for though, but not a lot of actual analytics. Take if you don't have but want experience in this area.

Fader's content made this course worth it because he had concepts and training you could actually apply and work with hands on but the lectures from Berman felt very disjointed and all over the place - sometimes the title of the module didn't match the content. Bradlow's content was very high level. He talked more about examples rather than teaching how to calculate customer life time value.

I think this course could be made a lot better by teaching a section on how to calculate lifetime value.

The course gave a good overview of the techniques in customer analytics that have been used and are currently employed by the industry. However, I felt the course could've been better if it helped develop an intuitive understanding of the concepts. Taking real-life case studies and actually going about solving the problems would have more useful. For instance, a case like "Suppose you're a category manager and want to identify low-value customers, how to go about it?" would have helped a lot.

The course overall is good, and it helps you gain very important concepts in the fields of Marketing, Analytics, and Statistics. But I have to say that the Week 3 course test really ruined my experience of this course. Very subjective and hard questions, which is not at all common in the courses of Coursera that I have taken. Just made me feel tired, demotivated, and I saw in the forum that I was not yhe only one, so I hope that some kind of change can be done for future students.

Overall the content was interesting, but the structure of the course was a little hard to follow. If I didn't have some knowledge in this area, I would have struggled with understanding. The recordings varied in sound levels which made it necessary to adjust volume. The examples are dated - many seem to be from around 2015ish. The quizzes asked questions at times that were not clearly covered in the content. And in many cases, quizzed on what was said vs. the concept.

Though there were some interesting elements, the course was too basic. I understand this is not a technical course but atleast there should be a little detailed explanation about the model on how it is done ( for example BTYD model) . Most of the information shared in use cases are already available in public domain. If you are completely new to this field, you may find this valuable but if you have already spend some time in the field you may find this very elementary.

Hey this was my first time learning a course on coursera and trust me it went smooth and good. i feel learned about a lot of things i was unaware about back then. I felt the content and theoritical knowledge was on point but where it lacked the most was the practical knowledge. Like i have all the information now what do i do with it when i join a firm. Do i do the calculations myself or what? Kind of leaves you in a incomplete shell!

1) Lack of hands on practice with the Buy Til You Die model.

2) The concepts in Weeks 2 to 5 are already well covered in technology blogs and news sites - I'd have liked to see more current material such as 'Overview of Different Types of Marketing Attribution (Heuristic, Algorithmic..), 'Cohort Analyses', 'How Digital Advertising / Programmatic Media works', 'Web Analytics' etc.

3) Hands-on exercises with R or Excel would be ideal.

I found this course interesting. I have just completed my MBA and I found that the course covered quites few topics of what I had learned. It also helped link them better. The examples quoted from companies that are implementing analytics was helpful in understanding how it could be implemented effectively.

One of the suggestions I would have is to include hands on implementation of some of the analytics tools that could be used.

The course is well designed but the examples and some content was first, outdated and secondly was in the context of the US rather than being an omnipresent firm. Also, some topics were rather discussed in brief than detail and if discussed in details the course could have been much more effective. Overall the course is a good step towards understanding a certain aspect of analytics in the field of Marketing.

1) Raghu Iyengar spoke about Regression and Regression models, tables and calculations. It would have been good if there were few slides around how Regression calculations are done. Just the formulae is mentioned, but calculations of pearson's coefficient, sloe of the line is not explained making it confusing for learners to understand.

2) No mathematical calculation examples explained around RFM and WTP

I really enjoyed lectures by Prof. Iyengar and prof Bradlow. They were very helpful in understanding the basics and the applications. I also like Dr. Peter Fader's approach of teaching a concept though a story. I hope Dr. Berman can innovate and come up with more interesting ways to hold the audience and teach the concepts as currently sitting through his lectures is quite a task.

Classes were interesting, although a bit light. I would also have liked to have more explanations on the quizzes to make sure the material was fully understood. And reading references would have added to the experience. It felt a bit like the class was kept basic in order to make the corresponding "specialization" package more attractive. Slightly disappointing.

This course was helpful - I feel like I learned a lot of new things, but as with many things on Coursera, this course is a jumping off point. For example, there are a number of more recent, and more indepth lectures by Fader on youtube that would be a good supplement to understanding some of the concepts around CLV and the buy till you die model in particular.

Predictive Analysis (week 2) and Application / Case Studies (week 4), were the best part of the course, and I genuinely learned things that I will try to implement in my small family business. However, Weeks 1 and 3 were highly disappointing (to put it mildly). Maybe I was expecting something more concrete or advanced, but the material provided was too basic.

The part on predictive analytics and the case studies are actually very good. The other two ... not so (and I stop here). Overall the course is too easy, unnecessarily spread over 5 weeks: e.g. week one and two are material for 2 days. Whilst successfully demonstrating the benefit of CA, the course never enters the nitty gritty of how to.

The course information is a bit outdated, and many slides are missing from the first couple of weeks. Other than that, it's a good course to get a general idea of what to research on the subject (it doesn't get down to the nitty-gritty details or how to calculate most of it, but the course states it doesn't, so it wasn't a surprise).

Nice intro to the concept of analytics, but compared to the other three courses (People, Accouting, Operations) - this one chould have been more detailed.

The course should ideally be expanded to a more holistic "Marketing Analytics", with concepts on pricing & promotional analytics as well as measuring ROI on marketing investments.

They didn't teach badly per se, but much of the information was WAY too basic compared to what I hoped to learn. The Marketing Analytics course from University of Virginia teaches a set of skills to use for marketing/customer analytics, whereas this course only briefly describes what could be done in marketing/customer analytics.

Great qualitative overview but thats where the good parts end. I wish they actually teach you how to do the math part. It would be a perfect course if we can work on case studies and calculate customer life time value and RFM. This is definitely great course if you have no idea of customer analytics or you are in executive level.

First 2,5 weeks are incredibly boring and really hard to listen to. I almost dropped out because I hated the thought that I needed to listen to these lectures. Thankfully starting week 3,5 the situation improved significantly and the contents became relevant. Really liked the last week lectures and the way the quiz was designed.

The information is good, the professors are good, but the innovation of the methods and the point of view i think that is not on a high and actualizated level and the conclusions are so normals. I hope the next formation blocks will turn on another point of view on me. In all cases, thanks for all, i'm so happy for this.

In general terms I think the course is very useful to learn and understand basic concepts around customer analytics field, with the latest platforms, studies, etc within this field.

However, I was expecting to get a little bi deeper into the concepts, with more complex analysts regardin what firms is doing nowadays.

The most interesting part was the examples at the end. I think the course would have been better if the last week's material was at the beginning. Also, more assignments would be appreciated. I find the information interesting and resources helpful, but I would like practice and some guidance on its application.