Strategy Analytics
Case discussion + lecture notes
Case week 1 – CAPITAL ONE (Capgemini)
Doing business the digital way: How Capital One fundamentally disrupted the financial services
industry
What is the business challenge/problem/goal?
To build a fully digital banking company, not only focusing on digital operations. The company also
wants to focus on digital platforms and technologies that positive affect customer experience.
What data are used to address this challenge?
Predictive analytics such as sales, customer retention rate. Statistical models based on publicly
available credit and demographic data (descriptive analytics). The company has implemented an
information-based strategy -> insourced IT, big data/ analytics and digital talent
What methods/techniques does the case use to address this challenge?
Acquisitions of (smaller) companies, that already posses the needed digital talent and quantitative
skills to assess complex insights. Increased levels of collaboration between the IT development team
and business team.
What are the findings and results?
The company is a leader in the banking industry regarding digitalization. The company has not yet
met their goal and should focus on customer needs. The company is setting industry wide
benchmarks and challenging the players in the industry
How do you evaluate this case? What could they have done better?
Focused on in-house development of employee knowledge regarding the digitalization process.
Lecture week 1
From a strategic management perspective, data science can be viewed as a strategic capability
Data science is a resource, similar to R&D (when looking at leverage) which is thus an investment
that does not provide immediate profits or results to the company. It takes time to obtain benefits
from this resource. You have to conduct a lot of experimentation and exploration to come to
discoveries
Case week 2 – video game industry
Video game industry
- Business challenge = market success using predictive modelling
o Market success in the video game industry using predictive modelling – handle
uncertainty using a feedforward control strategy
- Data that is used = response variables: life-to-date sales (LTD). Classification model used a
discretized approach
Case discussion + lecture notes
Case week 1 – CAPITAL ONE (Capgemini)
Doing business the digital way: How Capital One fundamentally disrupted the financial services
industry
What is the business challenge/problem/goal?
To build a fully digital banking company, not only focusing on digital operations. The company also
wants to focus on digital platforms and technologies that positive affect customer experience.
What data are used to address this challenge?
Predictive analytics such as sales, customer retention rate. Statistical models based on publicly
available credit and demographic data (descriptive analytics). The company has implemented an
information-based strategy -> insourced IT, big data/ analytics and digital talent
What methods/techniques does the case use to address this challenge?
Acquisitions of (smaller) companies, that already posses the needed digital talent and quantitative
skills to assess complex insights. Increased levels of collaboration between the IT development team
and business team.
What are the findings and results?
The company is a leader in the banking industry regarding digitalization. The company has not yet
met their goal and should focus on customer needs. The company is setting industry wide
benchmarks and challenging the players in the industry
How do you evaluate this case? What could they have done better?
Focused on in-house development of employee knowledge regarding the digitalization process.
Lecture week 1
From a strategic management perspective, data science can be viewed as a strategic capability
Data science is a resource, similar to R&D (when looking at leverage) which is thus an investment
that does not provide immediate profits or results to the company. It takes time to obtain benefits
from this resource. You have to conduct a lot of experimentation and exploration to come to
discoveries
Case week 2 – video game industry
Video game industry
- Business challenge = market success using predictive modelling
o Market success in the video game industry using predictive modelling – handle
uncertainty using a feedforward control strategy
- Data that is used = response variables: life-to-date sales (LTD). Classification model used a
discretized approach
