Greetings everyone! In this article, we, Tobias Delago and Niklas Gaertner, aim to provide a concise overview of our LondonLAB experience

, which has been a highlight of our time at LBS. We will begin by introducing the LondonLAB course and then delve into our client organization, the project we undertook, and the key insights we gained.  Keep reading until the end for our suggestions on how to select suitable projects and make the most of this fantastic opportunity.

Both of us studied in the Masters in Analytics & Management (MAM) programme at LBS and recently graduated in the summer of 2023. One key element that sets the MAM apart from comparable programmes at other business schools is the “LondonLAB” course taking place in the last 10 weeks of the third term. While other Masters programmes culminate in a traditional Masters Thesis, LondonLAB focuses on acquiring real-world exposure and building one’s network in London’s vibrant business ecosystem.

The course is organized as follows: Each year, LBS collaborates with a selected group of seven companies, either headquartered in London or with a local presence in the city. All of these companies sponsor a project based on a real-world challenge they are currently facing. Students then have the chance to rank the companies in order of preference and are subsequently allocated to one of their top-three choices by an algorithm. Within ten weeks, the project team comprised of six students must devise a solution to the client’s challenge and present said solution in a final presentation.

While both the MAM and Masters in Management (MiM) programmes at LBS incorporate LondonLAB into their curricula, MAM projects specifically require a significant analytical component that usually involves finding insights from a real-world dataset.

To make this abstract description more tangible, we will quickly walk you through our project lifecycle.

We both worked for a large luxury retailer trying to increase revenue by improving their understanding of the customer purchase process. They gave us a large dataset (>20 GB) that contained all customer transactions from the past three years. This dataset demonstrates our client’s trust in LBS and our project team as this information is highly proprietary. The fact that the dataset was all the information we had received from the client is a key differentiator of LondonLAB compared to any other university course: We had to organize everything else on our own. In classic university projects, the objectives are delimited by faculty and the processes to get there are clearly defined. In contrast, LondonLAB is like an actual consulting project: Each group must independently organize client meetings and workshops, develop their own approach to address the problem statement, and tackle all complications arising along the way.

In our case, we first had to overcome the challenge of rendering our vast dataset readily accessible for further use. Since 20 GB exceeds most RAM capacities, we had to come up with new approaches that enabled us to work on the dataset without loading it into memory. In our case, we used Apache Arrow, a state-of-the-art software used for big data analyses.

This is a perfect example of what we encountered throughout our LondonLAB project, i.e., entirely new challenges that arise in real business contexts: Datasets from our LBS courses never exceeded 3GB, so we never practiced using frameworks such as Apache Arrow before.

After solving this challenge, we started to dive deep into the dataset, conducting an extensive exploratory data analysis (EDA) in PowerBI, R, and Python. We then researched and applied a wide range of data mining techniques to identify relationships between products sold by our client. While we can’t disclose our exact results, we found exciting and surprising associations between product categories for various customer personas. Having also made a business case for the identified patterns, the client can utilize our results to inform and execute targeted advertising campaigns. More than ten client employees, including senior management, attended our consulting-style final presentation, underlining the value attributed to the collaboration with our team and LBS as a whole.

To conclude this blog post, we would like to share our three key takeaways from this experience.

Number one: Achieving high client satisfaction.

The main goal is to find interesting and useful insights; however, how one communicates these insights to the client turned out to be almost equally important.

This ranges from using the client’s corporate identity colours in your slides to details such as adopting the client’s wording. For example, our client always described their marketing approach as “targeted CRM advertising”. However, in one of our first client workshops, we called their approach “marketing”, which led to some confusion.

Number two: Integration of proposed solutions.

Whenever feasible, utilize the client’s choice of visualization tools, coding languages and even coding style. Companies appreciate solutions that seamlessly fit their existing workflows.

Number three: project selection and team collaboration

The most successful groups shared two common traits: a strong team leader, ideally with prior consulting experience, and the team’s unwavering motivation throughout the ten-week duration to deliver a benchmark final presentation to the client. For us, the motivational aspect was never an issue since all team members selected a company they were genuinely interested in, either because they didn’t know anything about the subject matter and were eager to learn something new or because the topic aligned well with their career aspirations (yes, some students were able to leverage the direct client contacts to secure job positions).

In summary, it was an invaluable experience managing such a complex project. Everything about this project was demanding: The dataset was massive, the project scope was wide and left a lot of room for interpretation, and we only had ten weeks to execute it. LondonLAB gave us the necessary capabilities to effectively tackle such challenges and deliver outstanding solutions, making it an ideal preparation for our professional endeavours after graduation

Written by Tobias Delago and Niklas Gaertner MAM 2023

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