5 Learnings I Gained from University of Leeds Consumer Analytics and Marketing Strategy Master data science courses
After ending my second semester in Consumer Analytics and Marketing Strategy Master (CAMS), I’ve started my dissertation. Finally realized time went by quickly than I thought, I wanted to set down everything I’ve been through in CAMS and so in Leeds.
At UoL, the initial two semesters was filled with mandatory courses. CAMS is part of the business school, as taught by leading academics from Leeds University Business School and the School of Geography, the course is mainly consisted of a range of analytical techniques including applied Geographic Information Systems (GIS) and retail model, consumer and predictive analytics and data visualisation.
While all of the content taught in CAMS courses is not familier for me, I still embrace the challenges and gain access to a wealth of expertise and resources in data sciences. (which almost kill me ! )
Thus, as a new novice in data science, I have generate some learnings and findings in CAMS data science courses :
- Experience how data can be analyzed and interpreted the result with simulation case studies for further market work
- Identify market problems by leveraging geography data and retail data
- Articulate critical thinking in assignment writing
- Leverage R studio and address market problem with marketing analytics
- Absorb diverse perspectives from students with different backgrounds
1. Experience how data can be analyzed and interpreted the result with simulation case studies for further market work
CAMS courses offer a wealth of analytical techniques courses like QGIS, SPSS and R studio. With a 120-minute lecture per week and a 120-minute practical training per week, the course help us to get the principle and various approach of every data analysis method then practice in the same week to gain the knowledge thoroughly.
For each course, students are expected to apply the analytics method and complete two academic reports by leveraging the results. For me it is not easy for I have no background of data analysis, but I gradually find the interesting thing of data.
As most of the reports, students are required to analyze data with a simulation based on real case data which closely tied to today’s business world. For example, after learning how to use R studio, we are assigned to analyze packages of data from a chocolate factory and demonstrate all the outcomes to address the problem of new product marketing strategy. By using Clustering Analysis and Conjoint Analysis as approach, we can identify the existing customer preference in chocolate market and the preference of different product attributes to foster the new product combination of attributes.
These reports empower me to make more in-depth knowledge in data-driven decision in marketing which I had hardly use in past four years in advertising industry.
2. Identify market problems by leveraging geography data and retail data
From Geographic Data Visualisation & Analysis and Applied GIS and Retail Modelling, we gain knowledge in analysing geographic data using GIS software. As my experience in the past often focus on market strategy for branding content and image, retail marketing seems so strange to me.
However, geography data turns out to be significantly useful for addressing a company’s selling problem and help lead to profitable success. While using geography data like each retailer’s performance segmented by geography difference, we can discover fundamental problem of retail marketing and address it.
It’s an overwhelming learning for me that finding geography data like culture, demographics and lifestyle background can generic vital impact on consumption and purchase behaviour.
3. Articulate critical thinking in assignment writing
Different from Asia, critical thinking is an essential aspect of education in the United Kingdom (UK) and Europe. Universities in UK like Leeds place a strong emphasis on fostering critical thinking skills at the postgraduate level.
Students are asked to outlines the problem of existing analytical techniques and explain the gaps between theory aspect and practical experiences. In addition, in some courses, students are assigned in group to do pre-class study and discuss in seminar. We have to summarize our findings with the cases and criticize about the strategic decisions made by these companies in the past or present.
Hence, during these process, students can generic critical thinking and cultivate independent thinking. Furthermore, the assignment encourage us to form evidence-based arguments that helps to demonstrate our own work based on reading materials and present different opinion.
4. Leverage R studio and address market problem with marketing analytics
Learning coding seems to be just pie in the sky for me before. In addition, I barely think coding with coding. However, through CAMS training, I’ve learned how to use R studio to analyze transaction data and summerize the customer behaviour and customer preference.
In the contemporary era, marketing analytics is a significant task in marketing field which helps to accurately predict the company’s market problem and identify the strategy to address the challenges. This empowers the company to optimize its marketing strategy and conduct solutions. Comparing with my experience at work, leveraging R studio can help me identify more specific problems of companies and provide valuable insights on marketing or product line optimization.
R studio application in marketing theme:
- Managing customer heterogeneity
- Managing customer dynamics
- Managing sustainable competitive advantage
- Managing resource trade-offs
We apply the data outcomes like customers segmentation cluster plot, brand attributes, choice-based Conjoint Analysis attribute data, customer willingness to pay and market results correlation to suggest the simulation case with future instructions.
5. Absorb diverse perspectives from students with different backgrounds
One biggest difference between Asia and Europe is that during the lecture, students are encourage and eager to ask question as well as provide self perception. Take <Digital and Interactive Marketing> course for an instance, we were told to construct a landing page and optimize it every week by changing the web page itself and the ad campaign.
Upon enrolling, each student is randomly assigned to a team, and the school strives to maintain diversity within each team. In our group meeting, students from different background share each opinions and also share the culture differentiation so that we can precisely determine our target audience and market.
For each stage of customer journey, the simulation of this course aims to adjust the keywords based on different stages’ objectives. Therefore, the group members have to define their market strategy in each stages. I share some of the experiences from works and find that many things that I take for granted in agency are new perspectives for classmates who works in other industries before. That’s the take away of group discussion I’ve gained in CAMS — — the topics that we are familiar with are fresh content to others and vice versa.
Though with years in agency, I still often find myself insufficient in many fields and feel reinforce in data science. But having practical working experiences in business world also make me feel more realistic when applying these data science into practical cases which I can connect with my past cases.
Learning data science will not turn me into an expert in analytics field overnight, but after being exposed to these data analytics metrics and practical simulation, a framework and basic understanding are naturally be internalized in mind and help me see the marketing in a total wider vision.
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