etailcore live nyc 2015 - using data to target customers
TRANSCRIPT
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
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
[email protected] @jon_isernhagen
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
[email protected] @jon_isernhagen
2014 Budget [email protected] @jon_isernhagen
Charles Kettering on beginning well
2014 Budget [email protected] @jon_isernhagen
“A market well-segmented is a market half-targeted.”
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
[email protected] @jon_isernhagen
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.
[email protected] @jon_isernhagen
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.
[email protected] @jon_isernhagen
2014 Budget Review
Customer Experience Maturity Model actions
Source: “Connect: How to Use Data and Experience Marketing to Create Lifetime Consumers”
[email protected] @jon_isernhagen
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
Source: “Connect: How to Use Data and Experience Marketing to Create Lifetime Consumers”
[email protected] @jon_isernhagen
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
[email protected] @jon_isernhagen
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”
[email protected] @jon_isernhagen
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: • .
[email protected] @jon_isernhagen
2014 Budget Review
Pulling profile data together: web site behavior
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 protected] @jon_isernhagen
2014 Budget Review
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
[email protected] @jon_isernhagen
2014 Budget Review
Pulling profile data together: email data
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)
[email protected] @jon_isernhagen
2014 Budget Review
Pulling profile data together: vendor data
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)
[email protected] @jon_isernhagen
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
[email protected] @jon_isernhagen
2014 Budget Review
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
[email protected] @jon_isernhagen
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.
[email protected] @jon_isernhagen
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.
[email protected] @jon_isernhagen
2014 Budget Review
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
[email protected] @jon_isernhagen
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
[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
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
Source: “Connect: How to Use Data and Experience Marketing to Create Lifetime Consumers”
[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) Marketing 1012) Targeting tasks3) Extracting insights from data
a) Data assemblyb) Data mining
4) Targeting and personalization examplesa) Emailb) Display retargetingc) Site
[email protected] @jon_isernhagen
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
[email protected] @jon_isernhagen
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
[email protected] @jon_isernhagen
2014 Budget Review
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
[email protected] @jon_isernhagen
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
[email protected] @jon_isernhagen
2014 Budget Review
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
[email protected] @jon_isernhagen
2014 Budget Review
The “re-” is important, or else it’s just “targeting”
“It was already blue before.”
[email protected] @jon_isernhagen
2014 Budget Review
Definitions: Site Retargeting is…
Someone arrives at your site (often from search)…
…then leaves without buying(or buying enough).
“The Retargeting Playbook,” Berke
[email protected] @jon_isernhagen
2014 Budget Review
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.
“The Retargeting Playbook,” Berke
[email protected] @jon_isernhagen
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
“The Retargeting Playbook,” Berke
[email protected] @jon_isernhagen
2014 Budget Review
Segmentation: Basic
“The Retargeting Playbook,” Berke
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
[email protected] @jon_isernhagen
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/
[email protected] @jon_isernhagen
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
“The Retargeting Playbook,” Berke
[email protected] @jon_isernhagen
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
[email protected] @jon_isernhagen
2014 Budget Review
Site personalization: Guardian Royal Baby toggle
[email protected] @jon_isernhagen
2014 Budget Review
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
[email protected] @jon_isernhagen
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…
[email protected] @jon_isernhagen