The team of the Marketing Center Münster announces that Professor Raoul Kübler will leave the Marketing Center Münster at the end of the coming summer semester to join ESSEC Business School in Paris as an Associate Professor of Marketing.
While the MCM is sad to lose a highly esteemed colleague and friend, we wish Raoul Kübler all the best for the next step in his career path. While it always comes as a disappointment to lose a valued colleague to another school, we acknowledge that ESSEC, as a world-renowned business school, is high-level competition.
“This was a difficult decision to take,” explains Prof. Kübler. “I enjoyed the last four years in Münster a lot. The productive and friendly working atmosphere at the Marketing Center Münster was astonishing, and MCM will certainly keep a special place in my heart and career path. Still, one of my life dreams was to work in France for a prestigious French Grande Ecole. Although I highly enjoyed working with the very talented and passionate MCM students, I felt I have to follow my dreams and thus decided to go to Paris.”
Professor Kübler and the JPM team will continue offering classes and seminars during the summer semester of 2022. This means that “Integrated Marketing Communications,” “Data Science,” as well the seminar “Standing Together While Being Apart? An Analysis of Covid-19 Marketing Strategies” will be fully offered and open to MCM’s master students. However, the team will no more be able to supervise master theses starting later than Q2.
In addition, Raoul Kübler plans to continue teaching in Münster’s unique Marketing MBA, continuing his strong connection with WWU and the Marketing Center.
This year we are again offering the Data Science course together with our practice partner Dr. Wolff Group! The course offers our students the opportunity to train the analytical and methodological skills they need to master the challenges of today’s data rich environment based on real-world business cases.
Within the first phase of the course, students will have the opportunity to learn the basics of R and R Studio with the help of an exclusively curated online education program. Once they have acquired the necessary base skills, they will move forward to their own business intelligence project.
To all interested students of MCM: Please send a current CV, a short letter of motivation and a transcript of records to Stefanie Dewender (firstname.lastname@example.org) by March 30 at the latest. The course is limited to a maximum of 50 participants. If more than 50 people want to attend the course, the course leaders will make a selection based in the documents you submitted.We are looking forward to your applications!
Due to the nationwide vaccination week that started on Monday, September 13, 2021, Prof. Dr. Raoul Kübler talked with Radio Kiepenkerl about possible further creative vaccination offers to increase the vaccination rate in Germany. In this context, the marketing expert suggests “Vaccination Cafés” or a “Night of Vaccination” to encourage population groups that have not yet been reached by previous vaccination campaigns towards vaccination. Furthermore the MCM scholar suggests classic "Word of Mouth" campaigns in which already vaccinated people inform others about their experience. "With people sharing information amongst each other, we may be able to reduce hesitancy and common misbeliefs", argues Raoul Kübler. Meanwhile, he warns authorities of using a negative framing or threatening or shaming people. "We know from classic advertising research that relying on anxiety, mostly backfires and provokes negative associations. We just saw this with the latest vaccination campaign that relied on influencers and that many people perceived to be too pushy and too negative." Instead, Professor Kübler suggested to think about gamification elements like having a vaccination tournament between cities or villages and to give prices like a village fest or free mulled wine during the upcoming Christmas markets to the city or village with the most vaccinated citizens. You can listen to the complete interview (in German) in the audio excerpts below. We thank Radio Kiepenkerl for the invitation.
In today’s digital world, marketers have access to millions of data points which are helpful to understand and predict consumer and customer behavior. With the help of social media data, clickstream and web journey data, as well as consumption data, marketers can better segment and target consumers, enhance cross-selling through recommendations, predict customer churn, optimize consumer communication and marketing budget allocation. Nevertheless, the many opportunities made possible by the data availability do not come for free as they require marketers to understand complex analytical methods that all rely on artificial intelligence and machine learning.
Although companies have been using AI for several years already, available methods are not yet fully exploited by marketers. A recent survey by McKinsey showed that only 14% of companies are using machine learning for customer segmentation and 17% for customer-service analytics. These numbers are in great opposition to the potential revenue increase to be gained from AI in marketing. To tackle this gap, the JPM therefore offered a research seminar in the summer term 2021 to investigate the opportunities and challenges of machine learning in marketing. Here, students got the opportunity to work together in groups on marketing problems which had to be solved by applying unsupervised and supervised machine learning algorithms.
To ensure that everyone is on the same page, students were provided with access to DataCamp courses in advance, covering basic coding in R instructions as well as several machine learning topics. Subsequently, a joint introduction to the subject matter assured that students were equipped with all necessary tools to complete their machine learning projects. Students then familiarized themselves with datasets containing information on customer behavior and product features. The discussed topics covered three unsupervised, three supervised and one semi-supervised machine learning problem from which the groups could choose.
Working in groups of three, students who were interested in unsupervised topics investigated how to segment customers as well as clustering products to make appropriate recommendations and targeting. For customer segmentation, survey data from the airline industry was used to identify different clusters of airline customers with the help of the k-means algorithm to give airlines recommendations which clusters deserve more or less attention and how to optimally address the different clusters. Another group was looking at the products itself to make product recommendations and used market basket analysis tools which are often applied on retailing websites like Amazon. Relying on a whiskey data set, students had the opportunity to develop their very own recommender system for a set of whiskeys with different bodies and characteristics. Diving deeper into the topic of recommender systems, the third group examined a large shopping data set from one of Europe’s largest online retailers with information on very different product groups. Using cluster analysis, the students were able to detect several patterns in buying behavior and group products which are commonly bought together. Watch the videos below to learn more about the projects applying unsupervised machine learning algorithms.
Since losing and re-acquiring customers is quite expensive, it is not only important for marketers to cluster their customers, but also to predict which consumers are likely to quit the company before they actually do so. With the help of support vector machines, the first group focusing on supervised machine learning algorithms therefore used previous churn data to identify customers who might leave the company. The students had access to real data from a large German telecommunication company to train their own model and prediction skills. Working on a related topic, another group investigated the chances and obstacles of predictive analytics. Using a large number of advanced machine learning algorithms such as random forest, adaptive boosting and extreme gradient boosting, they looked at credit card data to predict which customers deserve more ore less attention. Further, addressing another important topic in marketing analytics, the sixth group applied marketing mix modeling to understand how specific channels and ads are convincing customers to behave in the intended way and to eliminate the non-working ones. With access to click-stream data from an online retailer that uses social media ads, search engine advertising and banner advertising, they relied on different regression models to filter out the non-working channels. To see how the group made inferences about the optimal marketing budget allocation and to learn more about the other projects described above, see the videos below:
The last group focused on semi-supervised machine learning and scraped more than 36,000 Amazon reviews across ten product categories. These reviews provide valuable insights for marketers as they can help them to gain a better understanding of potential product or service improvements. With the help of topic models, the group members then made sense of the obtained review data and derived specific recommendations for companies. A summary of the collection process and analysis is described in the video below:
At the end of the very successful seminar, the groups got the opportunity to present their results and potential obstacles to the other students and engaged in lively discussions about the insightful managerial implications. All in all, the seminar provided students interested in machine learning and marketing analytics a suitable and detailed introduction to the large and quickly evolving field of AI and provided them with useful tools for dealing with huge data sets while working on real-life marketing problems.
On August 20th, 2021, the participants of MCM’s Data Science class taught by Prof. Dr. Raoul Kübler were invited to spend a day at the Dr. Wolff Group Headquarter in Bielefeld. Here, the groups presented their analyses and solutions, which they came up with during their Data Science course, to the staff of Dr. Wolff.
The class has been conducted in close cooperation with the company and 17 students were invited to present their ideas and managerial implications to leading employees at Dr. Wolff.
After making sure that everyone was tested COVID-19-negative, the day started with a nice breakfast in the sun. A short welcome reception by the company’s CFO Dr. Christian Mestwerdt was then followed by the groups’ presentations and discussions.
The students’ task was to come up with solutions regarding different marketing problems after having analyzed specific data sets, which were given to them or had to be collected in advance.
The results included a competitor eCommerce analysis of the brand Alcina, the analysis of Dr. Wolff’s Social Media accounts in order to use User Generated Content as a brand insight tool and a SEO and SEA analysis of the brand Alpecin.
After a light lunch, the students gave insights about the User Experience and Customer Journey on the Alpecin website, as well as managerial implications and improvement suggestions for the Vagisan website.
The students developed various and important insights for Dr. Wolff Group. On the one hand, students coded and programmed tools to measure customer satisfaction and brand equity with the help of user generated content, which allow Dr. Wolff to benchmark marketing performance with competing brands in the market. Furthermore, various projects delivered suggestions for optimization potentials with regard to SEA spending and website and customer journey optimization possibilities. The Dr. Wolff employees present were pleased to receive valuable input and engaged in lively discussions. Overall CFO Dr. Mestwerdt was not only very satisfied with the insights, but also pointed out the high competence and dedication of the MCM students: “We are impressed to see how well this incoming generation of marketing experts masters complex data problems and is able to address marketing challenges with adequate and often non-trivial machine learning and AI-based tools. We will certainly use some of the insights from today’s presentations to further and advance our own online activities.”
Following the presentations, the participants had a coffee break and were invited to get to know more about the family company during a factory tour. During the tour, they learned interesting facts about the company’s history and could take a look behind the scenes, while walking through the logistics halls, confectioning department and offices.
The perfect end to a great day was a delicious barbecue on the rooftop terrace with a view over Bielefeld. The students took the chance to network and shared their thoughts during interesting conversations with marketing employees.
Once again, the MCM thanks Dr. Wolff Group for this most excellently organized day and the great cooperation.
The high enthusiasm on both sides already lead to an agreement for a subsequent cooperation. Dr. Wolff as well as Professor Kübler are delighted to announce that they will offer another Data Science class in the summer semester of 2022.
Feel free to check out our Instagram channel “marketingcentermuenster” for more impressions of the day.
On July 12, 2021, Grabarz & Partner, represented by Bastian Goldschmidt (Head of Strategy) and Dennis Ullner (Senior Strategist), visited the lecture "Integrated Marketing Communications" for the third year in a row. Following the invitation of Prof. Dr. Raoul Kübler, the advertising experts once again presented the trend topics of the current year to the marketing students from Münster.
Following the motto "Back to people - moving more with empathy", every year G&P compiles an overview of topics that touch and concern society to an extraordinary degree. These topics, which are discussed and developed within the G&P team, play a central role in the planning of new advertising campaigns within the agency, as they help the advertising experts to develop communication potentials and thus to develop particularly empathically designed campaigns. This way, G&P succeeds in capturing the spirit of the times and taking responsibility not only for brands, but also for society.
For illustration purposes, Bastian Goldschmidt and Dennis Ullner went into more detail on five of the nine trend themes for 2021, such as "Mindful sexuality", "Loneliness pandemic" or "Conscious intoxication". In doing so, they underpinned the trend areas in a very entertaining and lively way with current clips from film, television or social media as well as results from recently published studies.
The (virtual) visit of G&P was eagerly awaited by students and MCM staff alike. On the one hand, the direct exchange with the advertising experts offers the opportunity to establish contacts in practice and to look behind the scenes of an agency that is responsible for well-known advertising campaigns. On the other hand, the lecture each time holds new impulses and impressions that the students can use for the development of their own integrated marketing campaign, which is the ultimate goal of the course. Thus, the visit of Grabarz & Partner has meanwhile become an essential part – and highlight - of the course! Therefore, the team of the Junior Professorship for Marketing & Marketing Analytics around Prof. Dr. Kübler would like to thank Grabarz & Partner, especially Bastian Goldschmidt and Dennis Ullner, very much.
Grabarz & Partner is one of the most successful German advertising agencies and was recently awarded as one of the "Cannes Lions Independent Agencies of the Decade". Their clients include companies such as Porsche, Volkswagen, Burger King, Fielmann, IKEA, and Indeed.
Social media has changed the way we interact and communicate. It provides us with great opportunities to meet with friends and colleagues all over the world. It delivers interesting information on a daily base and makes us continuously discover new things. It keeps us up to date and helps us to navigate through a rocky and often overwhelmingly complex world. By nurturing us with the necessary knowledge and giving us the needed bonds with our peers, social media has become a vital part of our daily life.
And still, as our own digital fingerprint within the social media realm may tell complete strangers more about us than we are willing to share with the public, it bares the potential to horribly betray us. In 2013, Kosinski et al. published a widely noticed study in the Proceedings of the National Academy of Science that demonstrated how individual likes of Facebook fan pages can be used to predict personal traits such as our individual age, gender, political and sexual orientation, eating and drinking habits or our very own heritage and racial profile. While the authors intended to warn the public about the possible side effects of the happy social media universe, dark forces made profit from these insights and started to collect information of what people liked on Facebook. Alexander Kogan’s app “This Is Your Digital Life” used a loophole in Facebook’s API and crawled information about following behavior from more than 80 million Facebook user profiles - in many cases even without the specific consent of the involved profile owners, as the app did not only access the information of the specific app user, but also of all his/her friends. Kogan then shared this data with Cambridge Analytica which claims to have used the data for various political campaigns within the context of the Brexit referendum, the 2016 Republican primaries and the subsequent 2016 US Presidential elections. While few hard facts are known about what Cambridge Analytica could achieve with the data, the company’s CEO Alexander Nix explained in various keynotes that Cambridge Analytica similarly used the data to predict personal traits and to use this information subsequently to target users with specifically designed political advertisements.
In the aftermath of the 2016 US presidential elections and its mostly unexpected outcome, Cambridge Analytica’s activities have been put into the spotlight of public attention. While the company has been seized for malpractice, the heat on its stakeholders and Facebook increased. Five years after the initial scandal, public awareness about the possibility to predict personal traits with the help of a social media user’s footprint has cumulated in heavy media coverage and multiple widely acclaimed documentaries such as e.g. “The Great Hack” or “The Social Dilemma”.
Despite the large public attention to the possible mis-use of social media data, we see social media engagement still to increase. While Facebook usage declines, younger target groups switched their attention to other platforms such as e.g. Instagram or TikTok. Many users believe that the changes in structure and communication style make these platforms less vulnerable to information betrayal. And even though communication styles switched from text-based information more to images and videos, both popular platforms require users to follow accounts to receive content and information.
Still, what many users seem to ignore or not realize, is that information about who is following an account is still publicly observable. This implies that one may again collect user followership information and pair this information with personal traits to build a prediction algorithm that forecasts a user’s personal traits based on the accounts a user is following on a platform. In other words: What Kosinski et al. showed in 2013 may still be very feasible in today’s new social media world.
Therefore, we decided to use one of our very own research seminars at the MCM to understand how much personal information of a user can be predicted with the help of his or her social media usage. To do so, we first replicated the study by Kosinki et al. (2013) in the context of Instagram. While Kosinki et al. could rely on large sample of 40,000 participants, we needed to constrain ourselves to a much shorter sample. So, we conducted a survey with approx. 2,000 Instagram users in which we asked participants to indicate which popular accounts they followed on Instagram. Users could choose between 200 accounts. Furthermore, we asked participants to answer a survey that measured, amongst other factors, personal traits (like e.g. the 5-factor OCEAN model), sexual orientation, gender, age, drug usage, political preferences, race and location within Germany. Following Kosinki et al. (2013) we then predicted the traits with the help of information on which accounts participants followed on Instagram. Relying on holdout sample validations, we can show - even though our sample is much smaller than the one of the initial study – that we similarly well predict major personal traits. Our replication thus already shows that once you have enough personal information and pair it with social media data, predictions become easy. In other words, with enough survey data, we could also deliver reliable predictions for social media users who did not participate in our study.
The video below gives a great summary of the survey work and the prediction accuracy obtained by our students.
One may now claim that social media data only becomes dangerous once one has a large enough training data set with enough personal information. Or in other words: If you don’t have enough survey data, you can’t predict something. This made us question if you really need survey data to obtain enough personal information to feed the follower prediction model. So we started looking around for alternative personal information sources which we may use to train our algorithm. Surprisingly, we found that many Instagram users happily share such information with the public. Not only that specific hashtags or types of posts may allow you to predict someone’s preferences, many users also often provide more sensitive and concrete information within their profiles. Consequently, we crawled Instagram user bios and looked for sensitive information. We were awed to find that many users happily share information on where they live, their birth year or age, gender, their main interests, sexual orientation and sometimes even their drug habits right in their Instagram bio. Crawling more than 200,000 user profiles we similarly built a large training set and combined the bio information with information about which public or well know accounts these people followed on Instagram. Again, we replicated the Kosinski et al. (2013) approach and developed a predictive model. The holdout sample validation indicated that the predictive power of these models was comparable to the findings of our replication study, showing that training the algorithm with publicly available information instead of survey data delivers similar results.
Just to understand what we could predict and how we adapted our approach to the new data sources within Instagram, check the two following videos.
All participants were similarly shocked to see that even though public awareness of social media’s potential of information betrayal is high, people seem to not understand how easily critical information can be acquired and used to deliver valid and reliable predictions of someone’s private traits.
A key issue here is that one does not even need any more survey-based information for training. Instead, training data may be directly obtained from privacy insensitive users which may then finally be used to predict personal traits of people who in fact do not share their personal traits, but become predictable through what they like and follow on social media.
Kampagnen Effizienz in Social Media - Ein Überblick
🤝 Thanks a lot again for tuning in and showing interest into how to manage social media campaign efficency with the help of an empirical approach. We agree that it is almost impossible to grasp all the exciting insights we talked about in the little time we had. Therefore we thought it might make sense to provide you with some links to the discussed studies so that you can read things again.
Please find below the three main studies we referred to👇 . All of them should be publicly available