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Cracking the Code of Personalization Travel Distribution Summit 2014, NYC
Jonathan Isernhagen September 11, 2014
2014 Budget Review
Session Agenda
1) Gain insights into how you can collect data more intelligently to effectively re-market and boost conversion
2) Maximize the benefits of the mobile paradigm: Now that it is possible to know your customers’ every move, learn how to capitalize on this information
3) Geo and hyper-locality: Understand how travel brands can best reap the benefits from knowing exactly where the customer is
4) Hear insightful case studies on the most effective ways to personalize your offers, deals, loyalty discounts and more
[email protected] @jon_isernhagen
2014 Budget Review
Discussion Agenda
1) Definitions
2) Decisions
3) Data collection
a) From internal systems
b) From external vendors
4) Analysis
5) Personalization
a) Email
b) On website
[email protected] @jon_isernhagen
2014 Budget Review
Definitions
• Customization: changing the characteristics of a product to meet individual customer needs (e.g. Dell PCs)
• Optimization: rigorously A/B testing all aspects of your marketing presence to find the highest value combination
• Segmentation: a marketing strategy that involves:
– dividing a broad target market into subsets of consumers who have common needs and priorities, and then;
– designing and implementing strategies to target them.
• Personalization: presenting potential consumers the most relevant products, offers, content and services
[email protected] @jon_isernhagen
2014 Budget Review
Discussion Agenda
1) Definitions
2) Decisions
3) Data collection
a) From internal systems
b) From external vendors
4) Analysis
5) Personalization
a) Email
b) On website
[email protected] @jon_isernhagen
2014 Budget Review
Strategic Focus: Customer, Product or Cost
There are 3 value propositions:
• Operational Excellence
• Product Leadership
• Customer Intimacy
Choose any one (1).
[email protected] @jon_isernhagen
2014 Budget Review
Strategic Focus: Operational Excellence
[email protected] @jon_isernhagen
Wal-Mart’s innovations:
• Cross dock
• Satellite ordering
• Aggregating consumer demand to squeeze suppliers
2014 Budget Review
(Jobs pull quote about showing customers what they’ve never seen before)
Strategic Focus: Product Leadership
[email protected] @jon_isernhagen
2014 Budget Review
Strategic Focus: Customer Intimacy
[email protected] @jon_isernhagen
“Be everywhere, do everything, and never fail
to astonish the customer.”
2014 Budget Review
Discussion Agenda
1) Definitions
2) Decisions
3) Data collection
a) Direct
b) Indirect
4) Analysis
5) Personalization
a) Email
b) On website
[email protected] @jon_isernhagen
2014 Budget Review
Personalization Purposes
1) Adapting navigation
2) Helping consumers find information
3) Personalizing the presentation of information
4) Recommending products or experiences
5) Providing help and tutoring/education
6) Identifying relevant communities
7) Supporting collaboration
[email protected] @jon_isernhagen
Source: “The Power of One”
2014 Budget Review
Personalization….a request of travel sites
1) Remember my previous search inputs
a) Keywords (if applicable)
b) Where and when I’ve traveled (from and to)
c) What kinds of accommodations I’ve booked
2) Condition your offerings/content on past behavior
3) Make suggestions which go beyond my basic request.
[email protected] @jon_isernhagen
Sources: http://www.evergage.com/blog/3-personalization-recommendations-travel-websites/ http://www.monetate.com/2013/04/3-ways-travel-needs-to-personalize-now/
2014 Budget Review
Approaches to gathering data
Approach Direct Indirect
Description Posing questions to candidates/customers
Inferring customer wants/needs from behavior
Works when They give complete and honest answers.
The remaining 98% of the time.
Gathered via Surveys/forms Server logs, accounting systems, vendor purchases
How you’ll use it
Programming Machine learning*
Logical programming
Decision-theory inference
Requires
Some reason for customers to answer.
Massive amounts of data
Knowledge of user beliefs
Knowledge of probabilities
[email protected] @jon_isernhagen
2014 Budget Review
Pulling profile data together: back office transactions
[email protected] @jon_isernhagen
Customer/Visitor Records • Customer #1, Mike Johnson, ... • Customer #2, Amy Morris,… • Customer #3, Frieda Zimmerman… • . • .
Transaction data: • Customer #1: 3/18/12, Ramada Yonkers, $119.00 • Customer #1: 11/22/13, Best Western Inn Ramsey, $551.18 • Customer #1: 2/14/14, Days Inn Nanuet, $93.81 • Customer #2: • .
Transaction summarized data: • Customer #1: 209 days ago, 3 stays, $763.99 total spend • Customer #2: • .
2014 Budget Review
SQL: Visual QuickStart Guide = easy SQL onramp
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• Simple, English-like language
• Enables you to play with the data and understand its possibilities
e.g.
Select Name_first, Name_last
From tblCustomers
Where State = “AK”
2014 Budget Review
Pulling profile data together: web site behavior
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Customer/Visitor Records • Customer #1, Mike Johnson, ... • Customer #2, Amy Morris,… • Customer #3, Frieda Zimmerman… • . • .
Transaction data: • Customer #1: 3/18/12, Ramada Yonkers, $119.00 • Customer #1: 11/22/13, Best Western Inn Ramsey, $551.18 • Customer #1: 2/14/14, Days Inn Nanuet, $93.81 • Customer #2: • .
Transaction summarized data: • Customer #1: 209 days ago, 3 stays, $763.99 total spend • Customer #2: • .
Site visit data: • Customer #1: 2/1/14 13:40:00 Days Inn Home Page • Customer #1: 2/1/14 13:40:10 Days Inn Results Page • Customer #1: 2/1/14 13:40:25 Days Inn Property Detail Page • .
Site data: • Customer #1: 225 days ago, 12 page viewed, 5 minutes on site • Customer #2: • .
2014 Budget Review
Extracting web data from Google/Adobe Analytics
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Google Analytics
BigQuery
Google Analytics Premium
Your database
Live Stream
Adobe Analytics Premium
Your database
Data feeds
Adobe Analytics
Your database
2014 Budget Review
Pulling profile data together: email data
[email protected] @jon_isernhagen
Customer/Visitor Records • Customer #1, Mike Johnson, ... • Customer #2, Amy Morris,… • Customer #3, Frieda Zimmerman… • . • .
Transaction data: • Customer #1: 3/18/12, Ramada Yonkers, $119.00 • Customer #1: 11/22/13, Best Western Inn Ramsey, $551.18 • Customer #1: 2/14/14, Days Inn Nanuet, $93.81 • Customer #2: • .
Transaction summarized data: • Customer #1: 209 days ago, 3 stays, $763.99 total spend • Customer #2: • .
Site visit data: • Customer #1: 2/1/14 13:40:00 Days Inn Home Page • Customer #1: 2/1/14 13:40:10 Days Inn Results Page • Customer #1: 2/1/14 13:40:25 Days Inn Property Detail Page • .
Site data: • Customer #1: 225 days ago, 12 page viewed, 5 minutes on site • Customer #2: • .
Email records (Sends, bounces, opens, clicks, bookings)
2014 Budget Review
Pulling profile data together: vendor data
[email protected] @jon_isernhagen
Customer/Visitor Records • Customer #1, Mike Johnson, ... • Customer #2, Amy Morris,… • Customer #3, Frieda Zimmerman… • . • .
Transaction data: • Customer #1: 3/18/12, Ramada Yonkers, $119.00 • Customer #1: 11/22/13, Best Western Inn Ramsey, $551.18 • Customer #1: 2/14/14, Days Inn Nanuet, $93.81 • Customer #2: • .
Transaction summarized data: • Customer #1: 209 days ago, 3 stays, $763.99 total spend • Customer #2: • .
Site visit data: • Customer #1: 2/1/14 13:40:00 Days Inn Home Page • Customer #1: 2/1/14 13:40:10 Days Inn Results Page • Customer #1: 2/1/14 13:40:25 Days Inn Property Detail Page • .
Site data: • Customer #1: 225 days ago, 12 page viewed, 5 minutes on site • Customer #2: • . Vendor-provided
demographics/psychographics • Customer #1, retired construction
foreman, $485K net worth, 3 children, 13 grandchildren, 2 Pomeranians….
Email records (Sends, bounces, opens, clicks, bookings)
2014 Budget Review
Demographic/Psychographic data appends
1) Age/Sex/Race/Marital status/# and age of kids/Life stage
2) House value/type/residency length
3) Income/net worth/affluence/financial stress
4) Consumer-saver type/Coupon user
5) Web consumer type/ISP domain
6) Category bucket/Portrait
7) Politics/Religion/Environmental concern/Veteran status
8) Auto Make/Type/Fuel
9) Hobbies/Interests/Fashion segment/Pets
10) Medical interests
[email protected] @jon_isernhagen
2014 Budget Review
Discussion Agenda
1) Definitions
2) Decisions
3) Data collection
a) Direct
b) Indirect
4) Analysis
5) Personalization
a) Email
b) On website
[email protected] @jon_isernhagen
2014 Budget Review
Definitions: Data Mining
“The computational process of discovering patterns in large data sets … the automatic or semi-automatic analysis of large quantities of data to extract previously unknown interesting patterns such as:
• groups of data records (cluster analysis), and;
• dependencies (association rule mining).
http://en.wikipedia.org/wiki/Data_mining
[email protected] @jon_isernhagen
2014 Budget Review
Data mining by Clustering: flower categorization
http://www.mathworks.com/help/stats/examples/cluster-analysis.html
Fisher’s iris data
2014 Budget Review
Practical uses for clustering
1) Predicting whether a site visitor belongs to a high-value segment based on data available during by the time the first search is executed.
2) Examining a new purchased list of potential consumers for characteristics which predict high lifetime value.
[email protected] @jon_isernhagen
2014 Budget Review
Data mining by Association Rules: politics v. beers
http://www.marketplace.org/topics/life/final-note/what-your-beer-says-about-your-politics
2014 Budget Review
Discussion Agenda
1) Definitions
2) Decisions
3) Data collection
a) Direct
b) Indirect
4) Personalization
a) Email
b) On website
[email protected] @jon_isernhagen
2014 Budget Review
Advantages to personalizing e-mail
1) Technically simple and cheap
1) No architectural changes needed
2) No A/B test tool required
2) Asynchronous: time to analyze results instead of responding real-time
3) Email address is ready-made primary key for combination with other data sources
[email protected] @jon_isernhagen
Source: TheEmailGuide.com
2014 Budget Review
Personalized email best practice: Slingshot
• Not highly subdivided
• Softened #Fname#
• Top-of-funnel offer (for re-engagement campaign)
• Sent only to people who hadn’t already downloaded this ap.
Source: http://blog.hubspot.com/blog/tabid/6307/bid/34146/7-Excellent-Examples-of-Email-Personalization-in-Action.aspx
2014 Budget Review [email protected] @jon_isernhagen
Personalized email best practice: Dropbox
• Behaviorally triggered
• Provides education on how best to use their product.
• Increases “stickiness”
Source: http://blog.hubspot.com/blog/tabid/6307/bid/34146/7-Excellent-Examples-of-Email-Personalization-in-Action.aspx
2014 Budget Review
Personalized email best practice: Twitter
• Association mining
• Favorite restaurants and people of other washsquaretavern followers turn out to be good recommendations.
Source: http://blog.hubspot.com/blog/tabid/6307/bid/34146/7-Excellent-Examples-of-Email-Personalization-in-Action.aspx
2014 Budget Review
Discussion Agenda
1) Definitions
2) Decisions
3) Data collection
a) Direct
b) Indirect
4) Personalization
a) Email
b) On website
[email protected] @jon_isernhagen
2014 Budget Review
N-tier website architecture
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Source: “The Power of One”
2014 Budget Review
Personalization using SiteSpect A/B testing tool
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Browser Web server / Application server
Algorithm engine
Personalization engine / A/B testing tool
Cookie
Cookie data
Page request w/cookie data
Personalized Page response
Request and Cookie data
Recommended Content
Recommendation request
Recommendation Response
2014 Budget Review
Site personalization: Guardian Royal Baby toggle
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2014 Budget Review
Site personalization: Netflix
2014 Budget Review
Recommended Reading: The Power of One
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This one
Not This one
2014 Budget Review
Summary take-aways
1) Know the main value you provide your customers
a) Is Customer Intimacy your main differentiator?
b) Prioritize personalization accordingly
2) Identify the low-hanging fruit
a) Learn your data
b) Do the simple stuff (e.g. email) at least
3) Adopt a customer-centric point of view
a) Subscribe to your own email distributions
b) Understand your customer’s goals
c) Manage your customer relationships as a scarce resource
[email protected] @jon_isernhagen