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7 Habits of Highly Effective PersonalisationOrganisations
Dan Ross
Managing Director, Optimizely ANZ
linkedin.com/in/danross9
By the end of today’s session, you will have learned:
• 7 tactics you can employ to advance your personalisation practice
• The importance of experimentation in your program’s maturity
• Different technology and processes available to support your journey
Optimization Is A Journey
Skeptics still openly wonder if we can continue to deliver on this journalistic mission, given the seeming mismatch between the economics of news media and the scale of our operations. They suggest the days when a media company can fund a big, ambitious newsroom are over.
This is why we are setting the goal of doubling our digital revenues over the next five years, to reach more than $800 million in digital-only revenue by 2020.
Our Path ForwardOctober 7, 2015
Our overarching aspiration is to cultivate another generation of readers who can’timagine a day without The New York Times. Our first two million subscribers —including our more than one million newspaper subscribers — grew up with The New York Times spread out over their kitchen tables. The next million must be fought for and won over with The Times on their phones.
The sustainable path to long-term revenue growth requires that we always prioritize user experience and the needs of our customers over hitting quarterly revenue targets. These deep reader relationships are our most valuable asset.
Our Path ForwardOctober 7, 2015
+155%
opticon2017
See this island through an artist’s eyes
Explore 19th-century huts in rural Japan
Copenhagen: the new global hub for natural wines
Michigan: America’s new architecture hub?
Visit Slovenia’s glowing capital city, Ljubljana
Dine on modern camp food at an Oregon lodge
Campsite booked? Not anymore with online reservations
Buckling up for a bumpy ride: handling extreme weather
Every article is an experiment
Every offer is an experiment
Every product launchis an experiment
HABIT 1Create a
Vision
HABIT 2Experimentation Maturity
ARE WE READY TO STEP FORWARD?FORRESTER’S PERSPECTIVE
*Source: Forrester’s Q3 2015 Global Online Testing Platform Customer Online Survey
Dimensions
of continuous
optimization
Online testing is applied
mostly to the “explore”
and “buy” phases of the
customer life cycle
Online testing is
applied
mostly to websites
Online testing practices are
mostly executing only A/B
tests
A minority (i.e., 30% or fewer) of
customer interactions are included in
online testing*
Opportunity for improvement
ARE WE READY TO STEP FORWARD?FORRESTER’S PERSPECTIVE
*Source: Forrester’s Q3 2015 Global Online Testing Platform Customer Online Survey
Dimensions
of continuous
optimization
Online testing is applied
mostly to the “explore”
and “buy” phases of the
customer life cycle
Online testing is
applied
mostly to websites
Online testing practices are
mostly executing only A/B
tests
A minority (i.e., 30% or fewer) of
customer interactions are included in
online testing*
Opportunity for improvement
MATURE OPTIMISATION PROGRAMS • Do more complicated tests than A/B• Test through more than just a few pages• Are segmenting analytics
CULTURE OF EXPERIMENTATION
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MATURITY
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EXPERIMENTATION
HERO
MATURITY
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10000
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EXPERIMENTATION
PROGRAM
EXPERIMENTATION
HERO
MATURITY
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10
100
1000
10000
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EXPERIMENTATION
HERO
EXPERIMENTATION
PROGRAM
CULTURE OF
EXPERIMENTATION
MATURITY
0
10
100
1000
10000
Experimentation Maturity Model
LEADING
INDICATORS Experimentation Success
VELOCITYThe volume of experiments being run, the
reach of personalisation campaigns.
Throughput:
# of experiments per property per
month/week.
AGILITYThe degree that the experimentation
program acts on results.
Iteration:
The % of experiments put into production
and iterated upon.
EFFICIENCYThe efficiency that experiments get
through production cycle
Drag:
Average hours spent
redeveloping due to QA
QUALITYThe average likelihood that an
experiment will produce
business impact
Impact Rate:
% generating meaningful result
OPERATIONAL METRICS FOR EXPERIMENTATION
LEADING
INDICATORS Experimentation Success
VELOCITYThe volume of experiments being ran,
the reach of personalization
campaigns.
Throughput:
# of experiments per property per
month/week.
AGILITYThe degree that the experimentation
program acts on results.
Iteration:
The % of experiments put into
production and iterated upon.
EFFICIENCYThe efficiency that experiments get
through production cycle
Drag:
Average hours spent
redeveloping due to QA
QUALITYThe average likelihood that an
experiment will produce
business impact
Impact Rate:
% generating meaningful result
OPERATIONAL METRICS FOR EXPERIMENTATION
MATURE EXPERIMENTATION PROGRAMS • Are high throughput• Develop efficiently (business as usual!)• Get consistent wins
VE
LO
CIT
Y
EXPERIMENTATION
HERO
EXPERIMENTATION
PROGRAM
CULTURE OF
EXPERIMENTATION
MATURITY
0
10
100
1000
10000
Habit 2 Takeaway:
Experimentation Maturity
HABIT 3Assemble
Your
Dream Team
DISCOVERY IMPLEMENTATION PLANNING PRODUCTION REPORTING
PERSONALISATION PLAYBOOKEND-END PROCESS + MILESTONES
CORE PERSONALISATION TEAMSKILLSETS & TEAM ROLE
Executive Sponsor Project Manager Technical Lead Developer Content
B U I L D I N G A P R O G R A M I S H A R D
I D E A T I O N & P R I O R I T I S A T I O N
C O L L A B O R A T I O N &
O V E R S I G H T
HYPOTHESIS
CREATIVE
DEVELOPMENT
SETUP &
QA
TESTING
ANALYSIS
SHARE
K N O W L E D G E & R E P O R T I N G
IDEATION
Democratise
ideation across
your organisation
COLLABORATION
Bring teams together
to collaborate on
experiments
KNOWLEDGE
SHARE
Document and
share learnings &
detailed analysis
PROGRAM
REPORTING
Track success with
program level
reporting
Integrated hub for capturing ideas and
enabling collaboration across your
organisation
HYPOTHESIS CREATIVE DEVELOPMENT SETUP & QA TESTING ANALYSIS SHARE
3X Testing Velocityimprove collaboration and
Habit 3 Takeaway:
Assemble Your Dream Team
HABIT 4Enrich Your
Perspective
YOUR
Team
Status Quo:Tech: current capabilities and limitations
People and Process
Audience StrategyLook Internally
Your Systems
Your Analytics
Your Personas
Your Competitors
Your StrategyFuture States:Potential capabilities
Audience Proposal
Use Cases
YOUR TEAM’S TASKGATHER INTELLIGENCE: LOOK INWARD
1
YOUR
Team
Validation and Alternate Perspectives:Tech: Potential capabilities
People and Process: Alternate Approaches
Audience Strategy
Consult
External Experts
Vendors
Consultants
Agencies
Analyst ReportsFuture States:Potential capabilities
Audience Proposal
Use Cases
2
YOUR TEAM’S TASKGATHER INTELLIGENCE: LOOK OUTWARD
YOUR
Team
Status Quo:Tech: current capabilities and limitations
People and Process
Audience Strategy
Validation and Alternate Perspectives:Tech: Potential capabilities
People and Process: Alternate Approaches
Audience StrategyConsult
External Experts
Vendors
Consultants
Agencies
Analyst Reports
Look Internally
Your Systems
Your Analytics
Your Personas
Your Competitors
Your Strategy
Future States:Potential capabilities
Audience Proposal
Use Cases
YOUR
Brief
3
YOUR TEAM’S TASKGATHER INTELLIGENCE: CONSOLIDATE
YOUR
Team
Status Quo
Validation and Alternate Perspectives
Consult
External
Experts
Look
Internally
Future States
YOUR
Brief
3
YOUR TEAM’S TASKGATHER INTELLIGENCE: CONSOLIDATE
Habit 4 Takeaway:
Enrich Your Perspective
HABIT 5Create Your
Audience
Strategy
Recency & Frequency
Cross-sells & Up-sells
Value Propositions
START BY EXAMINING YOUR BUSINESS
STRATEGY
Propensity Models
Customer Journey Model
Price Sensitivity
LAYER ON MORE AUDIENCES LEFT- & RIGHT-BRAIN
PERSONAS
Brain by
the Noun Project
ANALYTICS
WHAT TECHNICAL SIGNALS CAN WE LEVERAGE?CONNECT CONCEPT TO TACTIC
Viewed 2 Products, Didn’t Buy
Keyword contains ‘discount’
Most frequently viewed
category
DMP + Uploaded Lists
Abandoned Checkout
Data Warehouse (Customer
ID
Geo-Targeting)
Came from Ad Campign = Gift
Technical Signal Consideration-Stage
Wants a discount
Preference for a specific
product type
High-Propensity
Needs a push
VIP Member
Urban Location
Shopping for a Gift
Audience Characteristic
PRIORITISE, PRIORITISE, PRIORITISEPURSUE VARIETY OF AUDIENCES, MAXIMISE REACH/QUALITY
Obvious Need
Large
Need for Creativity
Granular
Visitor Cohort; New,
Returning, Active, Loyal
Large Geos; Coastal
Urban, State, Key Cities
Browsed Twice;
Product Category
Past Purchasers
Second Priority
Habit 5 Takeaway:
Create Your Audience Strategy
HABIT 6Unify
View of the
Customer
CONNECT YOUR DATAHOUSEKEEPING BEFORE TECHNOLOGY
Everyone has to work together for personalisation to work for you
Habit 6 Takeaway:
Unify
HABIT 7Crawl Before
You Walk
PHASED INTEGRATION OF PERSONALISATIONCRAWL, WALK, RUN
0-12 weeks
BuildPhase
1
months 12-24
BuildPhase
3BuildPhase
2
months 3-12
Platform Implementation
Simple Audiences
Starter Campaigns,
Limited Integration of
Testing + Personalisation
Phase 2 Planning
REACH: 0-15%
PAGES: 1-3; only most critical ROI points
# CAMPAIGNS: 2-5
AUDIENCES: Natively available, simple, large, simple conditions;
Metro, Single Behaviours
TACTICS: Modules (lightboxes), image swaps, little testing
0-12 weeks
Buil
dPhase
1
PHASED INTEGRATION OF PERSONALISATIONCRAWL, WALK, RUN
Integration with 1st & 3rd
Party Data
More Campaigns
Integration of testing &
Personalisation workflows
More advanced use cases
Phase 3 Planning
Buil
dPhase
2
months 3-12
PHASED INTEGRATION OF PERSONALISATIONCRAWL, WALK, RUN
REACH: 30-60%
PAGES: Multiple campaign/audiences on top ROI pages
# CAMPAIGNS: 10-20 ongoing campaigns
AUDIENCES: Target intersecting audiences, 3rd & 1st party data
used, more and complex behaviours
TACTICS: Experiments drive campaign execution and iteration
Full system integration
Ongoing improvement
New audience strategy
Use cases continually iterated
Web personalisation data feeds
email and ad deployment
Buil
dPhase
3
months 12-24
PHASED INTEGRATION OF PERSONALISATIONCRAWL, WALK, RUN
REACH: 75-100%
PAGES: Most pages, multiple elements per page
# CAMPAIGNS: 25+ ongoing personalisation campaigns iterated on
AUDIENCES: Old audiences iterated, new granular audiences
TACTICS: Fully expressive strategy
Habit 7 Takeaway:
Crawl Before You Walk
Experimentation Maturity
Create a Vision
Assemble Your Dream Team
Enrich Your Perspective
Create Your Audience Strategy
Unify
Crawl Before You Walk
Experimentation Maturity Model
Dan Ross
Managing Director, Optimizely ANZ
linkedin.com/in/danross9
T H A N K Y O U
