Avis et commentaires pour d'étudiants pour Analytique des clients par Université de Pennsylvanie

The course talks about various techniques and methodologies. But does not get into details. For example, churn analysis. They talk about churn analysis but does not get into the details on how to perform a churn analysis.

Good as an introductory course for customer analytics.

If you are completely new to analytics in general, this might be a good course to start with. However, if you have some prior exposure, you might not learn anything substantial. This was supposed to be a five week course, but I was able to complete it in less than seven days.

How this course can be improved:

I wish that the course included more quantitative elements. I wish that the course included some assignments for the students to actually develop and run some models.

Theoretical

Good survey of the topic but only focused on B2C. If that were my focus, I'd give it a higher rating. I was hoping for insights on B2B as that is my field, even if only a short video to address differences/similarities/peculiarities.

I would like to hear more about models especialy predictive one but all the things about models are bullshit. They generaly talk about demand curve which is a lesson of a economics more than customer analytics. They should choose more appropriate example. Moreover, I expect to see at least the type of models and where it is used generally. It was a waste of time. That's why I am not gonna take other modules. Adios !

Amazing course for even beginners in the field of customer analytics. Highly recommend to do this course for enhancing the analytical skills. Examples and case studies explains the concept very well.

I really enjoyed the class. Analytics are such an important part of today's understanding of the customer but have other uses even beyond that. Professor Fader has really great insight on the subect.

Hi,

I have enrolled into the Curriculum with aspirations of becoming expert in the field of Business Analytics. So, frankly, I took a calculated decision/question/probability regarding how much it would be beneficial and how much I will be knowledgeable by the time I complete the certification program.

Now that I just completed my my very 1st course, Customer Analytics, I got answer to my question that my money, my time and efforts I have put is ABSOLUTELY worth and more than that I become knowledgeable in the domain.

Especially the respected professors Mr. Iyengar, Mr. Peter Fader, Mr. Eric and Mr. Ron have explained each concept in a very vert understandable way. The way or the navigation, introduction to the subject, the problem, explanation, breakdown of the solution and conclusion and their applications with business cases and scenarios are too good.

Now, I feel very confident and CAN confidently say that my basics, foundation in Customer Analytics are very strong and I can apply them to any tool, any scenario, any domain and for any problem in a any situation.

Thanks to my Professors.

Regards

Perfect Course for those who want to inquire insight and knowledge of how tons of data that we generate in our day to day life is being utilized by big organizations in optimizing their productivity.

Wow! This was so insightful into how our social media and daily habits are used. I don't like feeling like a sheep, so this awareness is appreciated. I'm still trying to design how I could apply this to my Trade.

This course includes a comprehensive overview of the all the basic models that are used to analyze data concerning customer behavior. The real-life examples made it easier to relate to those theories.

The course is very good. I have learned a lot from the course. A lot of interesting topics and help me to under the marketing. I will for sure continue learning more courses from this specialization.

The material was good, but quizzes are riddled with issues.

Although several statistical terms may be confusing underway, a simple Google search can supplement you with understanding them. Other than that, minimal typographical/technological errors may be experienced in the course. All of the professors did an amazing job in mapping out possible points of interest, and did well in explaining concepts clearly. Descriptive Analytics was the standout lesson here that really encompassed various fields and mentioned useful apps to use in real life projects. Meanwhile on the learning side, data analysis is, of course, tricky for those without prior knowledge so although this is deemed as a Beginner-level course, do be ready to Google up some things along the way. Overall, the course is a great path to equipping yourself with managerial skills in predictive analytics through the use of data-driven studies.

Insightful and a great introduction to a very complex topic. Probably would recommend having a grasp of statistical analysis before starting (not required) as it would help understand some of the concepts better.

An excellent course to understand the value of data, how companies are collecting and harnessing their data an efficiently applying AI/ML concepts to provide better services and products to their customers.

Though I have worked on Customer Analytics with my previous work experiences and also on Surveys etc at George Brown College Canada, this module was more than insightful. Lots of learning to learn eh!

Brilliant Course !! Loved going through all the materials and the lectures !!

Very Detailed course on Customer Analytics. And it presented quite a few case studies too, to understand the concepts better. But I felt that a hands on experience of applying Customer Analytics to a problem in a step wise fashion would have cemented the knowledge gained by this course.

Covered some important foundation topics quickly to properly synch in with examples etc. and the last week of content was outdated from 2015 because so much has changed in last 6 years.

Entry level discussion on Customer Analytics. Some of the material is a bit dated.

Basic course on customer analytics, was expecting a little more advanced content looking at the course content and structure.

My sincere thanks to all the instructors who gave an informative rundown on customer analytics. Myself Pamidi Snehitha - 24 years old, i have done my graduation in National Institute of Technology, India in Electrical and Electronics Engineering. I have got a great interest in how business strategies work and optimization of profit. My father is a retired business man to whom i have looked up since my childhood on how he manages a small business , how he maintains good relationship with customers, how he takes feedback from customers constructively. Since then i have had a deep passion on marketing and business development. With that zeal i have enrolled to this Course through financial aid and i am more than delightful after completing this course as my outlook towards Customer analytics is more broadened than ever.  Thanks to Coursera . I thoroughly enjoyed the applications part of this course where many companies have diverse goals and analytical strategies to acquire customer interests where they are both benefiting customers as well as themselves.

A very interesting module emphasizing the importance of meaningful data, how to use it for predictive and prescriptive customer analytics. The insights into customer behaviour and how marketing has evolved to the present form of the all-encompassing digital marketing and how to use it for customer analytics has been very revealing. A combination of online and offline marketing is the way to go in future. I can look back in retrospect and just wonder at the number of customers with Indian banks who would not pass muster on any predictive model. While analytics was still at a nascent stage in Indian banks when I retired, I still feel that even where predictive models are used, there is hardly any follow-up action. I saw the hallowed halls of Wharton for the first time and really hope to see them in person at least once.

Highly recommended. Delivered by leading faculty for the Wharton school, we are first shown what exactly customer analytics is. The approach from the Wharton School is to look at each individual customer (or rather group customers according to their habits and demographics). We are shown the three types of analytics: descriptive, predictive and prescriptive. The predictive week delves into regression analyses, but at a high level. Very interesting is the Buy Til You Die probability model, which seeks to model how past customer activities will translate into future lifetime value. We also look at profit maximisation (MR=MC), and conclude with some real world examples of applying these techniques for profit in the real world.

Overall, an excellent primer.

Content: 10/10

Presenters: 10/10

Presentation: 10/10