ACCT3200A California State Power of Big Data and Analytics Case Study Discussion 1.How much capital should an organization invest in marketinganalyticaltools?
2.Are big data and marketing analyticaltools worththe investmentin any organization?Explain why or why not?
3.Do you agree with this case study? Explain why or why not?
1.Each assignment is worth 10points.
2.Each papermust be formatted to APA guidelines(please review the APA 6thedition manualfor compliance).
3.Times New Roman 12 Font (Use of ANYother font will result in a reduction of points).
4.The writing assignment must consist of a minimum of fourpages, which includes a cover page and a references page.
5.A minimum of 5 scholarlyreferencesin addition to the case study. Do notuse Blogs or Wikipedia, etc.(Use of ANYblog will result in a reduction of points).
6.Assignment questions are noted below.
7.All assignments are due before class as noted on the syllabus. Late submissions will notbe accepted. ALAT
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A
In the travel industry, pricing has always been a tough job. Overprice an airline seat, hotel
room, cruise cabin, or rental car today, and you won’t get a second chance to move
that unsold unit tomorrow. Set the price too low, and you destroy value by selling out
limited inventory too soon. No wonder, then, that the industry boasts some of the most
sophisticated pricing capabilities anywhere. But in the era of Big Data, those capabilities
are looking increasingly outdated and inadequate. To meet the demands of large data
sets and respond rapidly to fluctuations, targeted automation is a must have.
D.
MAN
BERBERT
Blame the Internet. An explosion of new sales channels, with price-comparison sites
such as Expedia and Kayak, has ratcheted up competition and ever more frequent price
changes. Digital technology and social media have also greatly raised the scope for one-
to-one marketing -and made it possible to track the behaviors of millions of individual
customers.
As a result, pricing managers are faced with an overwhelming amount of information
stored in a variety of places. For example, one travel company we know has three
terabytes of pricing data in eight systems -including inventory data, current booking
levels, historic demand patterns, and competitor information across thousands of
categories.
How can companies make sense of all this data -and use it to drive value? The answer
lies in a new generation of pricing and revenue management practices that can yield
meaningful results quickly. These have helped travel companies improve revenue per unit
by 3-8 percent, and market share by 1-2 percentage points. But it is not just a matter of a
software tool or a single new analysis. It requires a sharp prioritization, iterative building of
tools, and hands-on engagement of many functions across the organization.
Name your price: The power
of Big Data and analytics
A new generation of pricing and revenue management
practices can lead to meaningful results quickly.
What, then, are the practical steps that pricing managers can take to master Big Data?
Companies must recruit a new generation of pricing talent with more of a “trader” profile
than an “analyst” one.
1. Pinpoint the most promising opportunities
February 2013 Jay Jubas, Dieter Kiewell, Stefano Redaelli, and Aziz Shaikh
In the travel industry, those opportunities include determining exactly what each
customer is willing to pay through customer segmentation, targeted promotions and
micro-marketing, sell-up, and cross-selling), and maximizing the use of available
inventory (through, competitive pricing, overbooking, substitutions and upgrades,
and so on). Effective pricing and revenue management organizations must have the
talent and know-how to identify such opportunities consistently and systematically.
2. Move quickly to automate key analyses
To meet the demands of large data sets and respond rapidly to fluctuations, targeted
automation is a must have. To maximize use of inventory, for example, companies
functions beyond pricing. For example, inventory and sales managers must also leam
to calibrate demand and supply through pricing to maximize revenue generation.
can develop automated utilization forecasts based on past booking patterns, current
advance reservations, competitor information, and so on. Just as importantly, the
output needs to be in easy-to-use, flexible formats, such as Excel-based tables or
web interfaces. One tool we worked with automated the combination of revenue
forecasts and inventory utilization data that was previously stored in separate
systems; this allowed managers to track progress in real time and make pricing
decisions that were much faster and better informed.
Pricing data is just going to get bigger and move faster in the travel industry-and in many
others, for that matter. Companies that can keep up will find the growth and margin to
stay ahead of the competition. O
To analyze and simplify large volumes of sales data across locations, you’ll generally
need customized IT solutions and applications. The best performers can get
advanced systems in place in weeks then test and adjust, rather than waiting for
months or even years to implement new applications.
3. Align the organization around pricing performance
Dieter Kiewell
Companies often spend most of their energies and resources on building advanced
analytics tools. But in our experience, they need to spend as much or more time
making necessary changes to organizations and processes. At one travel company,
pricing team interactions with supporting business units were originally ad-hoc and
unstructured. So they developed a systematic process that allocated responsibilities
for pricing and revenue management amongst the relevant departments, including
pricing, marketing, inventory management, and distribution-and described when
and how they should work together to create alignment on pricing decisions. As
one output of this process, inventory managers developed a new appreciation
for revenue metrics, and understood that these should take precedence over the
utilization metrics that had previously been their focus. It’s also important to develop
clear incentives that reward managers for pricing performance; and to recruit a new
generation of pricing talent with more of a “trader” profile than an “analyst” one, ie.
results-driven, comfortable with risk and experimentation, and able to make quick
decisions.
Jay Jubas
Director
Jay, leader of McKinsey’s North
American Wireless group and
Pricing service line, serves
consumer electronics, wireless
and media companies.
Stefano Redaelli
Aziz Shaikh
Principal
Senior Digital Expert
Stefano is a leader in McKinsey’s Aziz is an expert in Big Data,
Pricing Practice and is based in data intelligence and advanced
the Stamford office.
analytics. He is based in the
New York office.
Director
Dieter is a director in McKinsey’s
London office, where he leads
our Marketing & Sales Practice
and co-leads our work on
pricing globally.
4. Train to sustain
Gains from improved pricing performance are hard to sustain unless companies
commit to extensive and intensive training. Training should focus on the most critical
elements of pricing that drive revenues, best practices for pricing and inventory
management, and how to use new tools. Rather than using traditional classroom
instruction, training needs to emphasize simulations involving real data and decisions
that affect the company’s pricing in the market. This approach allows participants to
review actual results and the impact of the decisions they’ve made. In addition, this
type of training prepares participants to take calculated risks, rather than relying on
standard pricing principles, and inculcates an innovative mindset that characterizes
the best pricing managers. To be successful, such training must involve the relevant
Case Study Assignment Rubric (3)
Criteria
Ratings
Pts
Formatted to APA guidelines
Title Page: In-text Citations; References; Times 12 New Roman Font.
4.0 pts
Structure
Consists of a minimum of four pages, which includes a cover page and a reference page. The student uses complete sentences, proper grammar, spelling, and correct punctuation.
2.0 pts
Content
4.0 pts
A minimum of 5 scholarly references in addition to the case study. Do not use Blogs or Wikipedia, etc. All assignment questions are answered.
Total Points: 10.0
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