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It was a good hands on basic Introduction for Customer analytics. However The introduction of descriptive,predictive and prescriptive analytics was mixed with marketing so the amount of time that could be dedicated to marketing was dedicated to introducing analytics. This course was more general knowledge and E-Commerce rather than marketing. I wanted the course to be more specific: How marketing theories help in building predictive models and What a marketing analyst knows that other data analysts don't know. If you are an absolute beginner in analytics, give it a try. Otherwise it may be too simple for you.

The lectures cover material at a strategic-high level that is easy to understand and apply (very limited technical guidance). I do wish the lectures provided a bit more technical guidance, but I got value from the strategic delivery. The quizzes sometimes dove into a level of detail that the lectures really didn't prepare me for. They also covered content that wasn't really aligned with the themes of the lectures and didn't add much value to me to learn. I found this confusing and really did not find the quizzes to be a good representation of the material, or a very useful component of the courses.

The information was useful - some material was review and some was new to me. I found that several of the quiz questions were not answerable based on the content presented in the lectures, PPT slides and additional reading material. This was very frustrating. I listed to the lectures and took copious notes. I reviewed the PPT slides and transcripts of the lectures. I read the additional material and took notes on it. But I had to take the quizzes multiple times in order to pass because some of the questions were not directly or indirectly related to the material.

The lecturer for Predictive Analytics had barely any slides so being successful meant following him completely through the video which I do not think he was as good as explaining certain concepts as the first and third professors were. The last quiz also had barely to do with the Applications content in the final week of lectures - looking through the slides after failing on the first attempt was pointless. Overall, I learned a lot but the teaching style of the second professor and the final quiz were not really indicative of what was taught during the session.

The general overview was good and a refresher what I was taught in uni 15 years ago. The material looks a bit old-fashioned and some slides look like the content was 'thrown together'.

I am missing going into details, particularly in calculating optimum price. The use of Excel Solver was not even mentioned to calculate the exact price, rather than determining it through a graph which is there or there about.

Also was missing kind of how to actually calculate CLV with Excel (I have seen a formula in the slides once, but that doesn't tell me much).

You can see that the teachers now what they are talking about... but this seems to be a stripped and then again stripped version of a real class. Some courses at coursera are the entire university classes and you learn a lot from them. In those 5 weeks of this course there is not that much content, so you end up thinking... where can i get more information about each single topic mentioned. References to external resources are pretty weak so far. I've loved the classes, but it each "week" could be a course on its own, with much more content.

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.

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.