It has been a tough few weeks for users of Mac computers.
First, Apple had to revise its official stance on its computers’ ability to
prevent all viruses when 600,000
Macs were infected with a various called “Flashback” or “Flashflake”. Then, The Wall Street Journal reported
that Orbitz was showing Mac users “different, and sometimes costlier, travel
options than Windows visitors see.” Orbitz decided to use this segmented
pricing strategy because it found that customers who use Mac computers spend up
to 30% more per night on hotels because they were more likely to book four and
five start hotels. This equated to $20-$30 more per night in bookings. Providing
Mac users with higher priced hotels as the default option likely meant more
revenues for Orbitz.
Orbitz
discovered that Mac users generated more revenue after reviewing purchasing
behaviors of their customers. Only by having and mining the data did Orbitz
identify a new opportunity to create a different customer buying experience for
a particular demographic – in this case Mac users.
Sports
organizations can follow Orbitz example to try to maximize the revenue coming
from more lucrative demographics – particularly when it comes to in-game
attendance. Pricing has traditionally been a tricky problem for sports
organizations to solve. In the past, many sports organizations have
predominantly priced season and individual game tickets at the beginning of a
season or academic year. Yet, demand for tickets is rarely static for a variety
of reasons (i.e. team performance, rise of star players, weather, etc.).
Therefore organizations have often not been able to capture the value that
comes with the fluctuation in demand for tickets to games, events, or contests.
The rise in
popularity and security of the secondary ticket market created by companies
like Stubhub has shown how ticket holders and organizations can monetize
changes in demand. In addition, numerous sports organization, most notably the
San Francisco Giants, have implemented dynamic ticket pricing technologies from
companies like Qcue that monitor ticket demand and change pricing for
individual game tickets.
However,
the secondary ticket market and dynamic pricing are more macro targeting
strategies focused on increasing gameday revenue from a larger audience. from
numerous different types of consumers for a game. What Orbitz has done is implemented a
microtargeting strategy focused on one specific demographic – in this case Mac
users. B6A has written about a microtargeting
in a previous post, but Orbitz shows exactly how sports organizations can use
this practice to generate revenue. More importantly, it shows the affects of
being able to collect data from online users who are making purchases on the
company’s website. Sports teams and leagues often receive a significant amount
of traffic to their sites both during the season and during the offseason. It
is critical for organizations to capture this information using some form of
analytics platform (most sites use some form of Google Analytics) to better
understand and predict consumer behavior in similar way that Orbitz had done
with Mac users.
While it
should be credited for using data mining to increase revenue from a specific
demographic, Orbitz communication strategy has not matched the success of its
new pricing tactics. A Reuters
article proclaimed “Orbitz Sends Mac Users to More Expensive Hotels” and
claimed “You have to pay a premium if you're a Mac owner.” CNBC asked is this “smart
marketing or can it be perceived as misleading?” while Apple focused blog 9to5
Mac started its
post about the topic by saying “Smug Alert: Orbitz shows Mac users higher
priced hotels by default”. Upon hearing the news, many Orbitz customers left critical
Facebook comments including “You hide the cheaper hotels so basically you are
misleading the Mac customers. HOW DARE YOU!! I will never use Orbitz again!!!"
CEO Barney
Harford led Orbtiz efforts to try and complete damage control after the release
of The Wall Street Journal article by
focusing on how the new policy would enhance the overall user experience. For
example, he stressed that Orbitz was improving its recommendation engine to
provide its users with the best and cheapest options. For Mac users, four and
five start hotels often better fit their lodging preferences. Yet, the
company’s efforts at crisis management were mostly reactive and even though it
knew article about this controversial pricing tactic would be published (its
Chief Technology Officer was quote by The
Wall Street Journal). By this point, the damage had been done and Orbitz
has done significant damage to its brand that likely surpasses its potential
increase in revenue.
Sports organizations
should take note of what can happen when an effective revenue generating tactic
is not accompanied by an effective communication strategy. The lesson learn
from Orbitz should not be throw the baby out with the bath water – i.e. do not
complete data mining because their could be negative reaction to the results.
Organizations should do the best possible job of anticipating criticism to
determine the best proactive and reactive responses to potential criticism. By
completing a thorough audience analysis of all key stakeholders (i.e. fans,
media, sponsors, and employees) before implementing a decision based off data
mining, sports managers can more likely mitigate blowback and fully take
advantage of new revenue generating opportunities.
No comments:
Post a Comment