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It's no accident that JetBlue
continuously tops J.D. Power's airline satisfaction survey and is one
of only two North American airlines to receive four-stars from Skytrax's
World Airline Star Ranking. Such distinctions are largely due to
substantial analysis of the airline's customer and operational data.
sample set is so large that we're able to do significant analytics
around it and find out what's important to a larger set of our
customers," says Bonny Simi, director of customer experience and
analysis at JetBlue.
In doing so, JetBlue relies on Net Promoter Score
(NPS) as its gauge of customer satisfaction. The airline's belief is,
the more promoters it has, the fewer customer will defect. "It's about
measuring and monitoring what they like and doing a lot less of what
they don't like," Simi says. "It's about driving more promoters and
To keep a vigilant eye on its NPS, the airline analyzes
volumes of data monthly. It gathers 50,000 email survey responses,
results of text analysis, social media data, and Web mentions, and then
analyzes those findings to uncover customer pain points. Next, the
airline combines that data with operational and commercial data. On the
operational side, the airline determines elements like whether the
airline is keeping its customer promises and how specific teams are
delivering service. On the commercial side, it analyzes such data as
whether the TVs work and airplane turn times.
Next, Simi's team
drills down into three areas: C-level, departmental, and front line. At
the C-level, Simi's team analyzes high-level macro issues; at the
departmental level, they analyze elements like comparative airport data;
and for the front line, they may look at flight attendant data to
determine which employees require additional coaching. Once all the data
is analyzed, JetBlue provides the necessary training or makes the
appropriate changes to move the needle on its NPS.
JetBlue applies predictive analytics. Annually, Simi's team combines the
customer, operational, and demographic data with historical data, like
on-time performance, and sets monthly NPS targets. Then if the team sees
that, for example, it's likely that NPS won't hit a sufficient target
six months down the road, they mitigate that through treatment
strategies. In doing so, JetBlue has been successful in reversing the
decline of NPS in specific cities and regions.
The entire West
region, for example, was experiencing declining NPS, so the airline
changed signage in airports, fixed public address systems, and even
convinced the Transportation Security Administration to add more lines.
In January the West region jumped from the lowest-scoring NPS region to
the highest-scoring. "You don't get that by having a great recipe, you
get that by changing the recipe over time," Simi says. "You can wow
somebody once, twice, or maybe three times, but after three times,
they're not wowed anymore so we're constantly looking to get better."
What you find depends on where you look
is taking a holistic view, applying data from several areas to get a
more accurate view of potential churn. The airline is enhancing that
data by adding voice of the customer (VOC) insight. According to Chris
Cottle, executive vice president of marketing and products at Allegiance,
the ability to collect unsolicited feedback and combine those elements
with business data is critical. "If you can combine voice of the
customer data with operational, CRM, and financial data, then you've got
a really unique area that we refer to as VOC intelligence," Cottle
says. "At the heart of it is a value proposition of having greater
Dhiraj Rajaram, CEO of Mu Sigma,
agrees that companies need to invest in the right data, and that
traditional sources might not suffice. Businesses must understand
customers' needs, and conduct real-time monitoring in the contact center
and in social media. "Understanding the customer is the key," Rajaram
says. "From knowing who your customer is to figuring out what his or her
current sentiment is becomes they key in building good customer
Another data source for churn propensity, one that is often overlooked, is new-customer behavior. Connie Hill, president of Veracentra,
says that while understanding every customer at every touchpoint is
crucial for churn prevention, stopping customer defection happens back
when a company first onboards a customer. "You can conduct analytics to
understand if customers are at risk of defection, but typically what you
see are the [churn] drivers being a lack of engagement or purchasing,"
Hill says. "To prevent churn, start at the beginning of the cycle and
take steps appropriately to get value from customers."
engagement or purchasing, as well as finding patterns that lead to
those changes, is also valuable insight later in the customer lifecycle.
"Companies try to identify drivers of attrition by analyzing the
transactional behavior and demographic information of customers who have
defected in the past," Mu Sigma's Rajaram says. "Reduction in current
spending and reduction in frequency of store visits can all serve as
valuable clues as to what customers are likely to do in the future."
Dan Thorpe, senior director of advanced analytics in insights and innovation at Sam's Club and former senior vice president of statistics and modeling at Wachovia,
says that a newer metric that banks in particular are putting into
place is a "new/lost ratio," the ratio of new customers to lost
customers (the definition of lost customers varies by industry. In a
membership organization, one way to define a lost customer is one who
does not renew membership. In financial services, it may be a customer
who has dropped three of his seven services.). But according to Thorpe,
the ratio alone is not enough, as it does not immediately reflect churn.
"We also need to understand and take into account the tenure of a
customer," Thorpe says. "The struggle is to understand what is a
lost customer and how can you predict it if you're losing a customer."
can build a downgrade model, or a departure model, or a model that will
predict churn," he adds. "The trick is predicting it early enough where
you can take action."
The power of predictive analytics Thorpe
says that most industry metrics are backward looking. Banks, for
instance, can calculate a loss ratio to determine churn, but what
matters most is achieving foresight. "We want to predict where [churn]
is going in the future so we can take future action now. That's where
the financial services industry has to go…The big move will be to
forecast your churn statistics, understand what's influencing that, and
take action now for future value, not just immediate value."
analytics is essential in every industry, and can help companies assess
not only churn, but also changes in customer value. Veracentra's Hill
says that predictive analytics help move the needle to understand if the
customer is becoming less valuable over time. "It's like a shiny new
penny, she says. "Everyone wants to leverage their data, but if you can
use predictive analytics to [enhance] strategies, then you can leverage
that in a more powerful way."
Hill adds that effective churn
practices depend on the creation of a baseline—understanding current
churn and retention levels. Then companies must work to analyze customer
behaviors, design a customer contact strategy that helps to improve
retention, measure the results of that strategy, and create a roadmap
for continued success. "Basically what you want to do is try to keep it
simple," Hill says. "Once you understand what data can do for your
programs, create a roadmap for continuous improvement."
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