etailcore live nyc 2015 - using data to target customers

Jonathan IsernhagenMay 14, 2015

Using data to target new and existing customers

2014 Budget Review

Wyndham divisions and family of brands

World’s largest hotel company, based on number of hotels

World’s largest lodging loyalty program, based on participating hotels

Approximately 7,500 hotels and 646,900 rooms

More than 121 million room-nights sold in 2013

More than 9% of U.S. hotel room supply

World’s largest vacation ownership developer and marketer

Approximately 185 vacation ownership resorts with approx. 23,000 units throughout North America, the Caribbean and South Pacific

More than 900,000 owners of vacation ownership interests

World’s largest vacation exchange network World’s largest professionally managed

vacation rentals business Approximately 107,000 properties in nearly

100 countries More than 3.7 million exchange members Send approximately 4 million consumers on

vacation through vacation rentals

[email protected] @jon_isernhagen

2014 Budget Review

Wyndham Wyzard


2014 Budget Review

≠ Tormund Giantsbane


2014 Budget Review

Session agenda

Using data to target prospects and retarget customers: 1) Analyzing data to re-target new customers

1) search data2) customer data

2) How CRM databases can be used to improve site retargeting


2014 Budget Review

Discussion agenda

1) Marketing 1012) Targeting tasks3) Extracting insights from data

a) Data assemblyb) Data mining

4) Targeting and personalization examplesa) Emailb) Display retargetingc) Site


2014 Budget [email protected] @jon_isernhagen

Marketing 101

DU


Charles Kettering on beginning well


“A market well-segmented is a market half-targeted.”

2014 Budget Review

Discussion agenda





2014 Budget Review

Customer Experience Maturity Model

Breaks retailers’ CEX evolution down into 7 stages:1)Initiate;2)Radiate;3)Align;4)Optimize;5)Nurture;6)Engage, and;7)Lifetime Customer Cement.


2014 Budget Review

Customer Experience Maturity Model stages

Source: “Connect: How to Use Data and Experience Marketing to Create Lifetime Consumers”

Stage Description

Initiate Establish an initial web presence. Push brochure content online. Spam everyone.

Radiate Reach customers through appropriate channels. Visitor/conversion focus. Start making content consumer-relevant. Use personas.

Align Measure impact of marketing efforts (attribution). Articulate how marketing supports strategic goals. Rate campaigns. Communicate across departments.

Optimize Personalize website experience using all signals and data available and as much analytical horsepower as possible. A/B test and iterate.

Nurture Develop single customer profile. Listen for intent signals in all communications via all channels. Improve relationship through automated trigger-based dialog.

Engage Establish unified customer database to bridge between online and offline. Generate advocacy among your customers.

Cement Unify all departments to create great customer experiences fostering lifetime customer relationships. Optimize customer experience with real-time predictive analytics.


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Customer Experience Maturity Model actions



2014 Budget Review

Category Data types

Digital fingerprint When visitors arrive on website: marketing campaign, keywords, referring domain, location, device type, IP address.

On-site behavior Observed while on site: landing page, site areas, product/service areas, internal search keywords, content type.

Situation Weather, season/holiday, trending topics, time of day.

History Transactions, email response, website behavior, call center contact

Demographics Gender, age, status, job role, acquired from forms, data vendors and/or social data miners.

Psychographics Interests, activities, values, lifestyle collected from surveyors, social networks, and onsite behavior. E.g. spontaneous vs. methodical.

Connections Social activity, connections, network properties (e.g. influencer or connector)

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Model inputs



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Discussion Agenda





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

e.g.Select Name_first, Name_lastFrom tblCustomersWhere State = “AK”


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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

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: • .


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Extracting web data from Google/Adobe Analytics

Google Analytics

BigQuery

Google AnalyticsPremium

Your database

Live Stream

Adobe AnalyticsPremium

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 stage2) House value/type/residency length3) Income/net worth/affluence/financial stress4) Consumer-saver type/Coupon user5) Web consumer type/ISP domain6) Category bucket/Portrait7) Politics/Religion/Environmental concern/Veteran status8) Auto Make/Type/Fuel9) Hobbies/Interests/Fashion segment/Pets10) Medical interests


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Social data appends, DIY

Hands-on data mining text, using (free) Python• Introduces social sites• Describes the sites’

uniqueness and unique data

• Explains how to pull and analyze


2014 Budget Review

Social data collection DIY: Twitter

Teaches you how to:• Discover trending topics• Identify retweeters of a status• Identify all followers of a Twitter user• Analyze a user’s friends and followers• Perform tweet frequency analyses• Find the most popular tweets• Search for individual tweets• Harvest a user’s tweets• Crawl a Friendship Graph• Analyze a user’s favorite tweets.


2014 Budget Review

Social data collection DIY: Facebook

Teaches you how to:• Analyze social graph connections.• Analyze Facebook pages• Analyze things your company’s friends like• Analyze mutual friendships• Visualize directed graphs of mutual

relationships.


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Social data collection challenges: record matching

http://mashable.com/2011/02/25/data-mining-social-marketing/

Methods include: • Company participation: “On Facebook…businesses can

gain access to the profiles of anyone who clicks the “Like” button on the company’s business site…”

• Mining + Algorithms: If a company has one or two key pieces of information about its customers — e-mail address is often the most important — that company can accurately identify them on a social site and extract a substantial amount of data


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Discussion agenda





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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


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Data mining by Clustering: flower categorization

http://www.mathworks.com/help/stats/examples/cluster-analysis.html

Fisher’s iris data

2014 Budget Review

Category Data types

Digital fingerprint When visitors arrive on website: marketing campaign, keywords, referring domain, location, device type, IP address.

On-site behavior Observed while on site: landing page, site areas, product/service areas, internal search keywords, content type.

Situation Weather, trending topics, time of day.

History Transactions, email response, website behavior, call center contact

Demographics Gender, age, status, job role, acquired from forms, data vendors and/or social data miners.

Psychographics Interests, activities, values, lifestyle collected from surveyors, social networks, and onsite behavior. E.g. spontaneous vs. methodical.

Connections Social activity, connections, network properties (e.g. influencer or connector)

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Model inputs



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Data mining by Association Rules: politics v. beers

http://www.marketplace.org/topics/life/final-note/what-your-beer-says-about-your-politics

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Data Science on the cheap: Coursera and R


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Discussion agenda





2014 Budget Review

Advantages to segmenting/personalizing e-mail

1) Technically simple and cheap1) No A/B test tool required2) No architectural changes needed

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 app.

Source: http://blog.hubspot.com/blog/tabid/6307/bid/34146/7-Excellent-Examples-of-Email-Personalization-in-Action.aspx


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Personalized email best practice: Dropbox

• Behaviorally triggered• Provides education on

how best to use their product.

• Increases “stickiness”



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Personalized email best practice: Twitter

• Association mining• Favorite restaurants

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



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Resources: The Retargeting Playbook

Articulates complete retargeting strategy and set of tactics:• Setting up your campaign• Segmenting your customers• Optimizing your ads• Meeting specific objectives• Optimizing for Social and

Mobile• Adhering to privacy laws


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The “re-” is important, or else it’s just “targeting”

“It was already blue before.”


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Definitions: Site Retargeting is…

Someone arrives at your site (often from search)…

…then leaves without buying(or buying enough).

“The Retargeting Playbook,” Berke


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Definitions: various “Retargetings”

Term Actually describes

Search retargeting

Targeting display ads based on Google search terms

Email retargeting

Sending e-mail to people who visit your site,or

Using in-message retargeting pixel to dynamically adjust e-mail

Social retargeting

Targeting a consumer based on Facebook “like,”or

Targeting site visitors on Facebook Exchange.



2014 Budget Review

Campaign Setup

1) Post your privacy policy on all data-collection pages.2) Tag your website to start building lists

a) Each ad marketplace has a JavaScript tag for each page headerb) Verify that the tags are workingc) Accumulate at least 500 visitors before impressions start serving.

3) Create and upload adsa) There are ten total ad types used in retargetingb) Five major types (300x250, 160x600, 728x90, 100x72, 200x200)

4) Launch your campaign5) Segment



2014 Budget Review

Segmentation: Basic


Basic retargeting segmentation is driven by intent signals.1) Funnel-based segmentation

a) Number of visits to the siteb) Time on sitec) # of pages viewed (and funnel depth)d) Items added to cart

2) Possible segmentation schemea) All site visitorsb) Viewers of at least one product pagec) Shopping cart usersd) Purchasers


2014 Budget Review

Facebook targeting parameters

1) Location (Country, State, City, Zip)2) Age (13-65 or 65+)3) Gender and relationship status4) Precise interests (liked “The Biggest Loser”)5) Broad categories (e.g. small biz owners, Hispanics)6) Connections (target/exclude fans)7) Friends of connections8) Education level9) Likes and Shares

http://socialfresh.com/facebook-ad-options/


2014 Budget Review

Throttling

1) Use Conversion charts to determine:a) Frequency cap: max impressions a user can see/dayb) Audience duration: how long to keep targeting? (30 days?)

2) Cadence modification: bidding less on successive impressions

3) Segment prioritization: e.g. exclude purchasers4) Inventory management: drop out of non-performing

spaces



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 requestw/cookie data

PersonalizedPage response

Request andCookie data

RecommendedContent

Recommendationrequest

RecommendationResponse


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Site personalization: Guardian Royal Baby toggle


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Site personalization: Netflix


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Site personalization: Orbitz


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Summary take-aways

1) Do the hard segmentation work, targeting will take care of itselfa) Gather all available datab) Slice creatively

2) Understand where you are on the Customer Experience Maturity Model and next actions to level up.

3) Even if you use vendors and/or an agency to do all the technical heavy lifting, learn what’s happening behind the curtain.a) Ask the dumb questions until you can explain the processes.b) Have at least a vague idea of how hard it would be to DIY


2014 Budget Review

Test length for statistical significance

Sample size = 2 * Z^2 * Conversion * (1 - Conversion) (Conversion * Change)^2

• Change: ….the smaller the lift you want to detect• Confidence: …the greater the confidence you want to have• Conversion:…the closer the page’s conversion is to 50%• Contamination: …the purer you want the results to be.

If you let experiments re-use each others’ traffic, you can get more data faster.

You have to test longer…


etailcore live nyc 2015 - using data to target customers

Documents

customer data

isernhagen marketing

search data

data available

data assembly b data

budget review session

budget review wyndham

jonathan isernhagen