Analyzing Data-Driven Decisions: Marathon Petroleum Case Study
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Case Study
AI Summary
This case study examines Marathon Petroleum's challenges in customer service and proposes data-driven solutions. The company, a major player in the petroleum industry, faces issues related to customer dissatisfaction. The study defines the desired outcome of improved customer care and reduced unattended customers. It identifies obstacles such as the cost of employing new staff or hiring trainers. The proposed solution involves a low-cost strategy of in-house training conducted at each refinery station, supplemented by short notices to reinforce the importance of customer service. The implementation is suggested in a randomized manner to mitigate potential risks. The study emphasizes the significance of customer care in boosting revenues and returning Marathon Petroleum to its peak performance. References to supporting literature are also included.

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RUNNING HEAD: DATA-DRIVEN DECISION MAKING
Data-Driven Decision Making
Name
Institution
RUNNING HEAD: DATA-DRIVEN DECISION MAKING
Data-Driven Decision Making
Name
Institution
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DATA-DRIVEN DECISION MAKING
Data-Driven Decision Making
Marathon petroleum is a United States based petroleum company. Its former parent
company was Marathon oil company before it split up to stand on its own in 2011 (Alley, 2017).
It acquired Andeavor company in October 1st 2018 and this made it the controlling company in
the petroleum industry. It boasts over three million casks per twenty-four hours of refining
abilities and has sixteen refineries all over the United States of America. It has been positioned at
number fourteen in the fortune five hundred list of the biggest corporations in the United States
by total revenue. Marathon petroleum has seen its glory days but has also not lacked challenges
along the way. In this case study I focus on some of its challenges and how to overcome them.
Defining the outcome to be achieved
Though Marathon petroleum has proved to be a giant over the years, it has focused more
on doing more sales, getting more profits and maximize on revenue (Smith, 2019). But they have
forgotten about the customer care service and thus they are losing potential customers for simply
not serving them well. This number of unsatisfied, unattended customers should reduce and if
possible, should be unheard of and this defines our expected outcome. We expect that the
number of unattended customers reduces ad there be good customer care service. The number of
unsatisfied customers to rise or fall will be determined by a poll that will be done online for
customers to answer some few questions. This would enable us to measure if our intended
outcome has been achieved.
Identifying the obstacles
If the company chooses to employ staff that have studied customer care service and even
have experience on the same, there will be a huge cost incurred on the company which may
DATA-DRIVEN DECISION MAKING
Data-Driven Decision Making
Marathon petroleum is a United States based petroleum company. Its former parent
company was Marathon oil company before it split up to stand on its own in 2011 (Alley, 2017).
It acquired Andeavor company in October 1st 2018 and this made it the controlling company in
the petroleum industry. It boasts over three million casks per twenty-four hours of refining
abilities and has sixteen refineries all over the United States of America. It has been positioned at
number fourteen in the fortune five hundred list of the biggest corporations in the United States
by total revenue. Marathon petroleum has seen its glory days but has also not lacked challenges
along the way. In this case study I focus on some of its challenges and how to overcome them.
Defining the outcome to be achieved
Though Marathon petroleum has proved to be a giant over the years, it has focused more
on doing more sales, getting more profits and maximize on revenue (Smith, 2019). But they have
forgotten about the customer care service and thus they are losing potential customers for simply
not serving them well. This number of unsatisfied, unattended customers should reduce and if
possible, should be unheard of and this defines our expected outcome. We expect that the
number of unattended customers reduces ad there be good customer care service. The number of
unsatisfied customers to rise or fall will be determined by a poll that will be done online for
customers to answer some few questions. This would enable us to measure if our intended
outcome has been achieved.
Identifying the obstacles
If the company chooses to employ staff that have studied customer care service and even
have experience on the same, there will be a huge cost incurred on the company which may

3
DATA-DRIVEN DECISION MAKING
translate into affecting negatively the revenues of the company. Hiring trainers from outside may
also be a solution but trainers from outside would also cost the company large sums of money
which means the salaries of the employees may be slashed to cater for the training. Lastly, even
if the staff gets the training and they do not own the customer service care in their minds then the
company would have lost all the money they would have spent. Thus, a better way has to be
found that will not cost the company.
Sorting the obstacles based on scope
The obstacles are company-wide scope-based. These obstacles affect the whole company
and thus may cost a lot more than expected. Employing new staff and hiring trainers from
outside would all be a huge cost on the company and thus a more friendly strategy should be
used. A low-cost strategy targeting one branch at a time should be designed and implemented.
Redesign the incentives architecture
Sometimes the only thing that brings about the customer not being served well is because
an employee may not have been taught about it, others are simply not sensitive about it while
others do not see the need. It has been shown that with the right customer care service a company
can greatly increase its revenues (Cockburn, 2019). New ways should be devised so that the staff
may attend to the customers as required.
A number of things can be done which include: sending some of the staff back to school
or employing a new group of staff that knows how to handle customers and have even gone to
school for the same. But these two approaches will greatly cost the entire company. One redesign
architecture that can be used is conducting a simple training of customer care service within the
company and encouraging the customers to focus on the customer care service in order to
DATA-DRIVEN DECISION MAKING
translate into affecting negatively the revenues of the company. Hiring trainers from outside may
also be a solution but trainers from outside would also cost the company large sums of money
which means the salaries of the employees may be slashed to cater for the training. Lastly, even
if the staff gets the training and they do not own the customer service care in their minds then the
company would have lost all the money they would have spent. Thus, a better way has to be
found that will not cost the company.
Sorting the obstacles based on scope
The obstacles are company-wide scope-based. These obstacles affect the whole company
and thus may cost a lot more than expected. Employing new staff and hiring trainers from
outside would all be a huge cost on the company and thus a more friendly strategy should be
used. A low-cost strategy targeting one branch at a time should be designed and implemented.
Redesign the incentives architecture
Sometimes the only thing that brings about the customer not being served well is because
an employee may not have been taught about it, others are simply not sensitive about it while
others do not see the need. It has been shown that with the right customer care service a company
can greatly increase its revenues (Cockburn, 2019). New ways should be devised so that the staff
may attend to the customers as required.
A number of things can be done which include: sending some of the staff back to school
or employing a new group of staff that knows how to handle customers and have even gone to
school for the same. But these two approaches will greatly cost the entire company. One redesign
architecture that can be used is conducting a simple training of customer care service within the
company and encouraging the customers to focus on the customer care service in order to
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DATA-DRIVEN DECISION MAKING
achieve more sales. These trainings should be done as per refinery stations, since there are
sixteen in total then sixteen trainings will be done. This is for easy training and low-cost
transactions. There will be a lot of money spent and time wasted if the training is done once for
the whole company, the company may end up losing a lot. Thus, different trainings should be
held at different times and suiting the employees of every refinery station.
To remind the staff of the importance of customer care service then short notices should
be placed around the company especially in places that the staff are found most of the times. This
can be done continuously until their minds registers it and thus becomes a part of them.
This strategy is of low cost since no trainer from outside will be required. An executive
member of every refinery board of directors will handle training for the refinery branch they
belong to. This will ensure that no money is spent in the training while the objective gets
accomplished. Short notices can also be used such as
“serve the customer well and there will be more sales which adds up to good salaries”
which do not cost the company a lot of money. Therefore, the strategies will be in work while
the company spends less money on the same.
These redesign solutions can be implemented in a randomized manner. A random control
trial is an experiment where a person is picked at random and then he is allocated a different
group from a group that will also be picked randomly for an alternative treatment (O'Cathain et
al., 2013). A group of people may be chosen and made to act both as the staff and later on as the
customers. This will help reduce any unforeseen risks that may emerge later in the future when
the whole process is put in place.
DATA-DRIVEN DECISION MAKING
achieve more sales. These trainings should be done as per refinery stations, since there are
sixteen in total then sixteen trainings will be done. This is for easy training and low-cost
transactions. There will be a lot of money spent and time wasted if the training is done once for
the whole company, the company may end up losing a lot. Thus, different trainings should be
held at different times and suiting the employees of every refinery station.
To remind the staff of the importance of customer care service then short notices should
be placed around the company especially in places that the staff are found most of the times. This
can be done continuously until their minds registers it and thus becomes a part of them.
This strategy is of low cost since no trainer from outside will be required. An executive
member of every refinery board of directors will handle training for the refinery branch they
belong to. This will ensure that no money is spent in the training while the objective gets
accomplished. Short notices can also be used such as
“serve the customer well and there will be more sales which adds up to good salaries”
which do not cost the company a lot of money. Therefore, the strategies will be in work while
the company spends less money on the same.
These redesign solutions can be implemented in a randomized manner. A random control
trial is an experiment where a person is picked at random and then he is allocated a different
group from a group that will also be picked randomly for an alternative treatment (O'Cathain et
al., 2013). A group of people may be chosen and made to act both as the staff and later on as the
customers. This will help reduce any unforeseen risks that may emerge later in the future when
the whole process is put in place.
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DATA-DRIVEN DECISION MAKING
Conclusion
A customer care service is important for any particular company. Good customer care
increases the revenues being sold by any company and in this case would increase for Marathon
Petroleum. Increasing revenues means bringing Marathon petroleum back to its glory days.
DATA-DRIVEN DECISION MAKING
Conclusion
A customer care service is important for any particular company. Good customer care
increases the revenues being sold by any company and in this case would increase for Marathon
Petroleum. Increasing revenues means bringing Marathon petroleum back to its glory days.

6
DATA-DRIVEN DECISION MAKING
References
Cockburn, S. (2019). Improve Customer Service, Increase Sales (Infographic). Retrieved from
https://growingsocialbiz.com/improve-customer-service-increase-sales-infographic/
Alley, R. (2017). Who Owns Marathon Gas Stations? Retrieved from https://bizfluent.com/info-
8098338-owns-marathon-gas-stations.html
Smith, U. (2019). Marathon Petroleum Corporation [MPC] – Why is it on the decline. A Focus
on Fundamental Analysis? | The Dwinnex. Retrieved from
https://www.dwinnex.com/2019/05/20/marathon-petroleum-corporation-mpc-why-is-it-
on-the-decline-a-focus-on-fundamental-analysis/
O'Cathain, A., Thomas, K. J., Drabble, S. J., Rudolph, A., & Hewison, J. (2013). What can
qualitative research do for randomised controlled trials? A systematic mapping
review. BMJ open, 3(6), e002889.
DATA-DRIVEN DECISION MAKING
References
Cockburn, S. (2019). Improve Customer Service, Increase Sales (Infographic). Retrieved from
https://growingsocialbiz.com/improve-customer-service-increase-sales-infographic/
Alley, R. (2017). Who Owns Marathon Gas Stations? Retrieved from https://bizfluent.com/info-
8098338-owns-marathon-gas-stations.html
Smith, U. (2019). Marathon Petroleum Corporation [MPC] – Why is it on the decline. A Focus
on Fundamental Analysis? | The Dwinnex. Retrieved from
https://www.dwinnex.com/2019/05/20/marathon-petroleum-corporation-mpc-why-is-it-
on-the-decline-a-focus-on-fundamental-analysis/
O'Cathain, A., Thomas, K. J., Drabble, S. J., Rudolph, A., & Hewison, J. (2013). What can
qualitative research do for randomised controlled trials? A systematic mapping
review. BMJ open, 3(6), e002889.
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