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I want to thank the University of Pennsylvania and the team of professors for sharing their knowledge in relation to the topic of client analysis. I leave my observations at the level of Strengths and Weaknesses from my perception:

STRENGTHS:

1. The course has a certain level of updating in the topics according to the context of some typical companies that are in the international reference, it seems to me that it is necessary to put in the platform others very valuable in this topic

2. The course is more for a basic level - and allows to know the generality of the analytical model in its three pillars: descriptive, predictive and prospective

3. It is a first source to generate a self-training program or to enroll in a more complete program

AREAS OF OPPORTUNITY:

1. the slides have a lot of opportunity for improvement: it is not aligned with the prestige of the University, nor with the marketing theme, the slides leave a lot to be desired, they do not integrate the main models, nor the key concepts, slides of the themes are missing. One as a student has to complete the graphic models and give a structure to the visual presentation

2. There is no parallel between the visual aids and the expertise of the teachers. The teachers are very good, but the visual material is very poor

3. some tools, most of which are only talked about, but the level of support with tools that one can lower is very low. The student has to carry out a double process, follow the course and get tools on his own

4. Some material that is technical, of analysis of numbers, is talked about and no exercise of accompaniment is made

5. The threads of the forums both in the platform and in those that open in whatsapp, are not being managed by a third party as a facilitator. so it is left free to that if someone asks, that both await a response, ie the authority as a university does not follow up to meet spaces of feeding

6. This same exercise of course assessment should be done for each class, that is for each week with an individual level of approach, in other words, there is no congruence between what is given in the subject: to give value to the client /CLV) towards the future is to consider individually that value starting from the individual data, to make predictions and make decisions for the "clients" students who follow are mostly benefited in these courses.

6. I have the doubt if these same comments will really be considered in 2 ways: one: that the University returns to whoever generated them as attention and take them into account for improvement.

Conclusion: I believe that I will continue in the program of the training package that I acquired, but I do not stay yet with being a PROMOTING CLIENT, I hope that the University does something about it, and for my part not only as a student I am willing to support their improvement program.

Thank you

My basic interest of taking this course was to learn more about analytics and I did. I am new into this field and I found it useful to take as I am Sales Manager and it is a trending topic in the marketplace. Having said this, I do think some improvements can be made:

* such as explaining how to compute the R2 in regression analysis (basically two weeks of course are related to it and as it is not explained we missed a basic core knowledge in order to completely comprehend),

*professor Ron needs to explain better his lessons (example #1 he is talking about calculations, however does not show how he did it so we spend lot of time trying to figure out what he is talking about; example #2 minute 6:23 to 7:08 related to WTP, he talks about many calculations and he do not show in the slides how he did those calculations nor draw his conclussions and he moves forward without explaining so)

*Profesor Eric knows a lot and he has a lot of valuable information to share. However, he talks too fast and provides his lessons without clear structure. It is almost like he did not have a clear path while providing the classes so he is mixing examples randomly, going back and forward and at the end you somehow feel a bit confused about this teaching method.

Professor Raghu and Peter Fader are passionated and their teaching methodology is flawless, though quite different among them.

Professors need to answer all questions from students. I constantly review the discussion forum and there are unanswered questions.

Examples provided on week five are outdated. If you are charging in 2020 for it, you should update examples. Otherwise, provide a discount on it as it is outdated and most of five week is about examples.

As a wrap up, it is my first online course ever and I will certainly take more courses from Coursera. Hope you take in consideration my suggestions :)

This course gives you a basic understanding of what Customer Analytics is all about. In that regard it delievers just what you are promised in the beginning. What you need to know is that some of the course content dates back a few years. So don't expect up to date materials. At the same time of course one has to say that basics don't run old, so for a basic course that's ok.

It helps however if you already have a basic understanding of statistics. There are a couple of slides and models that will fall easier into place with you if you are at least a bit used to statistics. Not saying that it's impossible to pass this course without it (not at all), but if you really want to understand what is explained, the videos simply won't be enough. Again: That's ok for a basic course, because it has to start somewhere, and you can't teach on a certain expertise level if you have to start from scratch. Just be prepard that if you take this course you will come across a couple of results of data calculations where you might ask yourself where these results are coming from. The ansers to this questions are in some parts up to you to find.

All in all the videos are very intersting and (apart from the calculation and statistics stuff) easy to follow. In some part it is even entertaining - and that will probably will ignite further interest in Customer Analytics. At least that was the case with me.

The course focuses upon on Customer Analytics which draws and develops into three forms of Analytics applicable (explaining from Marketing (Customer- Centric) perspective) and the application section explains broadly about the overall real- life examples.

It is has a mix of theoretical and practical approaches( but somehow it could have been worked more upon, in terms of the formulaic & analytical technicalities as well), thereby making it a perfect and a staple course for interested individuals.

I would recommend this course to individuals as it is strongly cerebral, requires a certain amount hard-work to understand any newer concept (especially if you are from a relatively different domain), looks into granularity and helps develop a perspective.

P.S: Surprisingly, the most difficult section fetched me the perfect score! So broaden the horizon and put on your thinking caps.

The course is great on theory part. As it reaches to week 4 and week 5 it really gets very interesting. The instructors have efficiently described the internal analytics companies do and the method of graphs and real data was really helpful to learn. I loved last week course a lot when my whole perception about analytics. I remember Sir Eric Bradlow's quote i.e Analytics is all about " One customer at a time and we need to go at granular levels to find right statistics." Other than that Sir Peter made a lot of impact on my mind by really dealing with probability model. His quiz was really challenging and helpful. I had to study a lot.

One thing however I found missing was hand on practical experience for students. A assignment where learn mathematical and technical approach by using real software's and maths to to predict some valuable result over a good given data set.

I really enjoyed having classes with all of the professors - they are all great, no exceptions at all! The course gives you a good notion of what customer analytics is and its applications. It is is full of cases to illustrate the concepts and this makes it a lot easier to learn.

On the other hand, you are only invited to "practice" when it is "test time"- more exercises with on the spot feedback would be great! Also, I would love to have learned how to do all the math and graphs the professors show us!

To sum up, from my point of view, it is a really good intro course, but I will have to study A LOT more so that I can attain the level of knowledge I need if one day I intend to work in this field. If Wharton decides to make a continuation of this course, please, let me know! I would be glad to take it!

I work in customer analytics, so I was skeptical about being able to learn much from this course. There's a lot of buzzwords in this space, and it can get mind-numbing to listen to. There's a little of that in this course, but I was pleasantly surprised. This course is worth it for it for the exposure to the BTYD model alone, which is simple and compelling. This is the shortest course in the series at 4 hours of video time.

I recommend this course to other data scientists and analysts. Even if you have a good grasp of the statistics and the models used for prediction, I think this is course helps to step back and get some perspective on the bigger picture of the overall usefulness to the organization.

The information and professors were very helpful and eye-opening. It will definitely help with my job and even my life because I will think differently now about marketing, stores and ads.

The only negatives I have is the quizzes need to me more related to the lecture information. I feel as if some of the questions in the quizzes come out of nowhere with little to no explanation and you just have to guess correctly. Please base the quiz questions on the material, not necessarily word for word questions but things that are at least related to the new terms and models and calculations which the lectures teach about. Otherwise the course was good. No complaints except for the quiz questions.

I enjoyed all the classes on Predictive, Prescriptive and Descriptive analytics. Professors Fader and Bradlow's classes stood out for me though. I watched the videos, sometimes, twice and read their papers. Currently, I am trying to apply Prof. Fader's clumpiness metric to the analysis I am currently doing, and so far, I am seeing some positive results. Prof. Bradlow's examples all felt so real, and gave a broad insight into how many of these companies utilize business anlaytics. I'm still reading their papers, as well.

Props to Prof. Iyengar and his donor example- Helping me out at work. Props to Prof. Berman, as well, with his presciptive analytics lessons.

It spells out clearly that this course will not delve into the technical side of analytics (it would have been a 5 star course for me had a few of those portions been included) but I must say I decided to stick with the course and I found the discussions of the professors very enlightening . I can be quietly confident as a manager in the future or even talk about the managerial and actionable side of analytics right now to some extent...looking forward to explore. What really matters is the action you take with the insight and not just the fancy stuff we technical guys do with the data...Both components are equally important!!!

Nice general overview of new marketing tools and focus, and related basic concepts. Some improvement can be made with regard to Week 3 material, as subtitles, more practical examples, and a succint explanation about technical method (attached paper can be a little arid for laymen in the field of statistics, I guess), and summarize some key issues to be taken into account in a more extended way at the end of this module. This one was my second course with professor Fader and I enjoyed his enthusiasm a lot, as always. Professor Bradlow review was superb, very clear and from a practical point of view. Thanks Wharton, once again!!

I really enjoyed this course and I'm glad I took it. My favorite part was, of course, Week 5 with its examples of practical application of this information, where I went like "Oh wow, that's true" every two minutes. On the other weeks, however, I sometimes felt a bit out of my element, given Customer Analytics has nothing to do with my primary major nor do I have any real experience doing it, so when the lecturers said things like "I won't focus on this, I'll just give you a quick example..." it was a bit frustrating since I felt like I was missing out on things that customer analytics professionals would understand.

Course is very useful, lots of key insights and knowledge shared by professors. The concepts are easy to understand. The models (the calculations behind) are a bit difficult to understand. I wish those were explained in one of the videos (instead of them being in reference/reading materials). Also, one or two practical examples (companies, businesses) showing end to end analytics (descriptive, predictive and prescriptive) as to the findings and actions from each would have made it even better. In any case, overall, highly recommended for those interested in understanding customer centricity analytics.

The questions have not be revised for correctness. Professor. Fader's quiz questions were hard to understand especially when negative connotations were used in the sentence 'not a beta....' OR ' not favor a probability....' . These questions threw me off so please rectify the quizzes here and in the future quizzes for my time & benefit as well as for the rest of the students. You do not have to make the questions easy but I do not want to spend time thinking of your underlying meaning but would rather put thought in solving for something other than English. Please have these quizzes re-read.

It is a great course and even though I have a decent amount of knowledge in marketing, I was still able to learn new concepts and new ways of applying the concepts in order to gain more customers.

The course could be even better if there were small practice quizzes after each video as it will help to reinforce the concepts in the minds of the students and also I personally believe that the course should be slightly rearranged and restructured in such a way that there should be at least one case study video related to the main subject matter for each of the first 4 weeks.