This document analyzes loyalty program data from Etihad Airways customers between 2006-2012 to identify opportunities. Key findings include:
- A new data mining model can predict whether a new customer will become a high-value "Silver" member with 82% accuracy, much higher than 50% for extrapolation.
- The model was evaluated using different time periods and optimization levels. With 3 months of optimized data, it identified 82% of future Silver members with only 3% false positives.
- Even with just 2 weeks of non-optimized data, the model identified 22% of future Silver members, though it had a higher 11% false positive rate.
- Being able to accurately predict high-
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Omni channel experiences your customers want! Darrel Kammeyer
Now that customers are in the driver’s seat, the changes are happening faster and more frequently. Your customers have been through an unprecedented digital & mobile expansion, but there’s much more to come. In this hyper-active environment, what does it take for you to create relevant and insightful customer interactions across multiple channels?
In this fast-paced and entertaining session, Darrel will provide well-known brand examples in high tech, retail, financial services, travel, and hospitality which will demonstrate:
• why customers are adopting more technologies even more rapidly than you think
• how marketers need to predict, envision and invest in the new customer relationships that these channels can build
• what’s the best way for you to determine your biggest opportunities to engage customers in their mobile and digital moments
• why the lack of strategy and digital skills hold back digital advancement
• and what are the necessary steps that you need to take to integrate cross-channel journeys and experiences for the biggest return
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Part of The Leading Edge executive signature series, this presentation, “The Role of Software Integration in Driving Successful Post-Sale Customer Engagement” was delivered by Dustin Hall, President of Firesnap Inc., at SoCal BMA’s 3rd Annual Regional Conference, “Process to Profits: Customer-Centric Strategies for Accelerating Growth” — held November 17, 2016, in Irvine, CA.
[British Airways] 8 Persuasive Principle Used by British Airways to Boost the...Convertize
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I was extremely fortunate to be able to share what little I know on Post-sale marketing to our guests at MYCyberSALE two day workshop for e-commerce businesses in Malaysia. I hope this helps!
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What are the customer touchpoints for an airline in the future? How will customers use web 2.0 and different channels to interact with the airline, before, during and after flying? This presentation may not give all the answers, but is designed to get you thinking. Thinking in terms of customer journeys rather than cold features and functionality. http://www.dancingmango.com/blog/2009/01/13/what-is-the-storywhat-is-the-story/ for me details
The whitepaper is an attempt to introduce the reader to the benefits and development of a functional ‘Customer Value Scoring’ framework. The framework would help airlines draw valuable conclusions – for instance, a customer with significant realized revenue but low future estimated revenue can be identified as one who is taking the airlines business elsewhere. Likewise, a customer who consistently books the same flight segment at equal intervals would appreciate being recognized and being enrolled in a rewards program which lets them enjoy privileged status, thereby assuring their loyalty.
The Customer Value Score has the potential to introduce exciting new possibilities about how Airlines and other Travel organizations evaluate and treat their customers.
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Airlines can use advanced analytics as a competitive lever by increasing their understanding of customer behavior patterns, identifying cost-optimized ways of serving customers and enhancing opportunities for revenue generation and loyalty among existing and potential customers.
As part of the OESON Data Science internship program OGTIP Oeson, I completed my first project. The goal of the project was to conduct a statistical analysis of the stock values of three well-known companies using Advanced Excel. I used descriptive statistics to analyze the data, created charts to visualize the trends and built regression models for each company.
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Airline CRM big data opportunities
1. Berengueres
Opportunities in Etihad
miles program
Review of findings UAEU study on loyalty program data
February 2013
EB2013.02.28
CONFIDENTIAL AND PROPRIETARY
Any use of this material without specific permission of Jose Berengueres is strictly prohibited
Jose Berengueres
2. Berengueres |
Abstract
Project Background – CRM data from Etihad Airways was
analyzed. The data is three tables that comprise anonimized
information about passengers who enrolled the Etihad miles
program between 2006 and 2012. The tables contain data such
as: (1) Age and Nationality, etc. (2) Loyalty program purchases
etc. (Not used in this model) (3) Flight activity, miles earned etc.
The following pages explain what can be predicted by using the
data.
2
3. Berengueres |
Predicting if a new customer will become silver (high value) or not can
help concentrate the (limited) CRM resources on high potential customers.
To do this effectively accuracy is needed.
Using extrapolation is 50 % accuracy in predictions. Using new data mining techniques 82% can be achieved.
Gold%
Silver%
Basic%
30k%
50k%
Accumulated%%
miles%
7me%
Tier%
?"
3
4. Berengueres |
The D/S model can predict who will be high value with very high accuracy.
I am flying
Etihad for
first time!
D"days" S"days"
I can tell you if
she will become
Silver with 82%
accuracy
Silver"-er"
before""now"
I can tell you if
she will become
Silver with 50%
accuracy
4
5. Berengueres |
Example of an application that enhances the value of miles program:
optimal real-time upgrade allocation
Passenger Name Tier Status
Probability they
will become Silver
in X months
Wolfgang Amadeus Motzart Basic 0.99
Ludvig Van Beetoven Basic 0.97
Giuseppe Verdi Basic 0.89
Jean-Michel Jarre Basic 0.77
Yasuharu Konishi Basic 0.77
Maki Nomiya Basic 0.45
Teresa Teng Basic 0.42
Carl Philip Emanuel Bach Basic 0.41
Enric Granados Basic 0.00
Leonard Bernstein Basic 0.00
Carl Loewe Basic 0.00
Johan Strauss Basic 0.00
Isaac Albeniz Basic 0.00
George Gerschwin Basic 0.00
John Lenon Basic 0.00
John Williams Basic 0.00
5
6. Berengueres |
Evaluation of predictive power of D/S model (1/3)
Prediction*Model*with*3*months*of*data*&*lookout*1*months
Discovery*rate 82% of#future#Silver#where#identified#after#observ
False*Positive 3% 97%#of#predictions#are#correct#
Count*of*prediction prediction
Sivler*attain 0 1 Grand*Total
0 54752 37 54789
1 293 1309 1602
Grand*Total 55045 1346 56391
notes:#start#period#jul#1st#2012
of future Silver identified after observing them 3 months
97% of predictions are correct
6