Travel Distribution Summit 2014, NYC Jonathan Isernhagen · Cracking the Code of Personalization...

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

jonathan.isernhagen@wyn.com @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

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

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

2014 Budget Review

Strategic Focus: Customer, Product or Cost

There are 3 value propositions:

• Operational Excellence

• Product Leadership

• Customer Intimacy

Choose any one (1).

2014 Budget Review

Strategic Focus: Operational Excellence

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

2014 Budget Review

Strategic Focus: Customer Intimacy

“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

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

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.

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

2014 Budget Review

Pulling profile data together: back office transactions

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

• Simple, English-like language

• Enables you to play with the data and understand its possibilities

Select Name_first, Name_last

From tblCustomers

Where State = “AK”

2014 Budget Review

Pulling profile data together: web site behavior

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

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

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

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

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

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

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.

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

Data Science on the cheap: Coursera and R

2014 Budget Review

Discussion Agenda

1) Definitions

2) Decisions

3) Data collection

a) Direct

b) Indirect

4) Personalization

a) Email

b) On website

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

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 jonathan.isernhagen@wyn.com @jon_isernhagen

Personalized email best practice: Dropbox

• Behaviorally triggered

• Provides education on how best to use their product.

• Increases “stickiness”

2014 Budget Review

Personalized email best practice: Twitter

• Association mining

• Favorite restaurants and people of other washsquaretavern followers turn out to be good recommendations.

2014 Budget Review

Discussion Agenda

1) Definitions

2) Decisions

3) Data collection

a) Direct

b) Indirect

4) Personalization

a) Email

b) On website

2014 Budget Review

N-tier website architecture

Source: “The Power of One”

2014 Budget Review

Personalization using SiteSpect A/B testing tool

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

Travel Distribution Summit 2014, NYC Jonathan Isernhagen · Cracking the Code of Personalization...

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