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Optimization Is A Journey
7 Habits of Highly
Effective Personalisation
Organisations
Dan Ross
Managing Director, Optimizely ANZ
dan.ross@optimizely.com
HABIT 1
Create a
Vision
“The best vision is insight.”
– MALCOM FORBES
Habit 1 Takeaway:
Create a Vision
HABIT 2
Experimentation 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
ARE WE READY TO STEP FORWARD?
OPTIMIZELY’S MATURITY MODEL
INTERESTED INVESTED INTEGRATED INGRAINED
VALUE
Culture
Process
Strategy
Development
INTERESTED INVESTED INTEGRATED INGRAINED
VALUE
Culture
Process
Strategy
Development
• Inconsistent access
to resources
ARE WE READY TO STEP FORWARD?
OPTIMIZELY’S MATURITY MODEL
MATURE OPTIMISATION PROGRAMS
• Are comfortable pushing boundaries
• Have processes and teams in place
• Speak language of testing
LEADING
INDICATORS
Experimentation Success
VELOCITY
The volume of experiments being run, the
reach of personalisation campaigns.
Throughput:
# of experiments per property per
month/week.
AGILITY
The degree that the experimentation
program acts on results.
Iteration:
The % of experiments put into production
and iterated upon.
EFFICIENCY
The efficiency that experiments get
through production cycle
Drag:
Average hours spent
redeveloping due to QA
QUALITY
The average likelihood that an
experiment will produce
business impact
Impact Rate:
% generating meaningful result
OPERATIONAL METRICS FOR EXPERIMENTATION
LEADING
INDICATORS
Experimentation Success
VELOCITY
The volume of experiments being ran,
the reach of personalization
campaigns.
Throughput:
# of experiments per property per
month/week.
AGILITY
The degree that the experimentation
program acts on results.
Iteration:
The % of experiments put into
production and iterated upon.
EFFICIENCY
The efficiency that experiments get
through production cycle
Drag:
Average hours spent
redeveloping due to QA
QUALITY
The 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
Habit 2 Takeaway:
Experimentation
Maturity
HABIT 3
Assemble
Your
Dream Team
DISCOVERY IMPLEMENTATION PLANNING PRODUCTION REPORTING
PERSONALISATION PLAYBOOK
END-END PROCESS + MILESTONES
CORE PERSONALISATION TEAM
SKILLSETS & TEAM ROLE
Executive Sponsor Project Manager Technical Lead Developer Content
make
a slack channel
make
the time
Habit 3 Takeaway:
Assemble Your
Dream Team
HABIT 4
Enrich Your
Perspective
YOUR
Team
Status Quo:
Tech: current capabilities and limitations
People and Process
Audience Strategy
Look Internally
Your Systems
Your Analytics
Your Personas
Your Competitors
Your Strategy
Future States:
Potential capabilities
Audience Proposal
Use Cases
YOUR TEAM’S TASK
GATHER 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 Reports
Future States:
Potential capabilities
Audience Proposal
Use Cases
2
YOUR TEAM’S TASK
GATHER 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 Strategy
Consult
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 TASK
GATHER INTELLIGENCE: CONSOLIDATE
YOUR
Team
Status Quo
Validation and
Alternate Perspectives
Consult
External
Experts
Look
Internally
Future States
YOUR
Brief
3
YOUR TEAM’S TASK
GATHER INTELLIGENCE: CONSOLIDATE
Habit 4 Takeaway:
Enrich Your
Perspective
HABIT 5
Create 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 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, PRIORITISE
PURSUE 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 6
Unify
View of the
Customer
CONNECT YOUR DATA
HOUSEKEEPING BEFORE TECHNOLOGY
Everyone has to work together
for personalisation to work for you
Habit 6 Takeaway:
Unify
HABIT 7
Crawl Before
You Walk
PHASED INTEGRATION OF PERSONALISATION
CRAWL, WALK, RUN
0-12 weeks
Build
Phase
1
months 12-24
Build
Phase
3Build
Phase
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
d
Phase
1
PHASED INTEGRATION OF PERSONALISATION
CRAWL, WALK, RUN
Integration with 1st & 3rd
Party Data
More Campaigns
Integration of testing &
Personalisation workflows
More advanced use cases
Phase 3 Planning
Buil
d
Phase
2
months 3-12
PHASED INTEGRATION OF PERSONALISATION
CRAWL, 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
d
Phase
3
months 12-24
PHASED INTEGRATION OF PERSONALISATION
CRAWL, 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

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7 Habits of Highly Effective Personalisation Teams | Dan Ross from Optimizely

  • 2. 7 Habits of Highly Effective Personalisation Organisations Dan Ross Managing Director, Optimizely ANZ dan.ross@optimizely.com
  • 3.
  • 5.
  • 6. “The best vision is insight.” – MALCOM FORBES
  • 9. 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
  • 10. 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
  • 11. ARE WE READY TO STEP FORWARD? OPTIMIZELY’S MATURITY MODEL INTERESTED INVESTED INTEGRATED INGRAINED VALUE Culture Process Strategy Development
  • 12. INTERESTED INVESTED INTEGRATED INGRAINED VALUE Culture Process Strategy Development • Inconsistent access to resources ARE WE READY TO STEP FORWARD? OPTIMIZELY’S MATURITY MODEL MATURE OPTIMISATION PROGRAMS • Are comfortable pushing boundaries • Have processes and teams in place • Speak language of testing
  • 13. LEADING INDICATORS Experimentation Success VELOCITY The volume of experiments being run, the reach of personalisation campaigns. Throughput: # of experiments per property per month/week. AGILITY The degree that the experimentation program acts on results. Iteration: The % of experiments put into production and iterated upon. EFFICIENCY The efficiency that experiments get through production cycle Drag: Average hours spent redeveloping due to QA QUALITY The average likelihood that an experiment will produce business impact Impact Rate: % generating meaningful result OPERATIONAL METRICS FOR EXPERIMENTATION
  • 14. LEADING INDICATORS Experimentation Success VELOCITY The volume of experiments being ran, the reach of personalization campaigns. Throughput: # of experiments per property per month/week. AGILITY The degree that the experimentation program acts on results. Iteration: The % of experiments put into production and iterated upon. EFFICIENCY The efficiency that experiments get through production cycle Drag: Average hours spent redeveloping due to QA QUALITY The 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
  • 17. DISCOVERY IMPLEMENTATION PLANNING PRODUCTION REPORTING PERSONALISATION PLAYBOOK END-END PROCESS + MILESTONES
  • 18. CORE PERSONALISATION TEAM SKILLSETS & TEAM ROLE Executive Sponsor Project Manager Technical Lead Developer Content
  • 20. Habit 3 Takeaway: Assemble Your Dream Team
  • 22. YOUR Team Status Quo: Tech: current capabilities and limitations People and Process Audience Strategy Look Internally Your Systems Your Analytics Your Personas Your Competitors Your Strategy Future States: Potential capabilities Audience Proposal Use Cases YOUR TEAM’S TASK GATHER INTELLIGENCE: LOOK INWARD 1
  • 23. YOUR Team Validation and Alternate Perspectives: Tech: Potential capabilities People and Process: Alternate Approaches Audience Strategy Consult External Experts Vendors Consultants Agencies Analyst Reports Future States: Potential capabilities Audience Proposal Use Cases 2 YOUR TEAM’S TASK GATHER INTELLIGENCE: LOOK OUTWARD
  • 24. 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 Strategy Consult 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 TASK GATHER INTELLIGENCE: CONSOLIDATE
  • 25. YOUR Team Status Quo Validation and Alternate Perspectives Consult External Experts Look Internally Future States YOUR Brief 3 YOUR TEAM’S TASK GATHER INTELLIGENCE: CONSOLIDATE
  • 26. Habit 4 Takeaway: Enrich Your Perspective
  • 28. Recency & Frequency Cross-sells & Up-sells Value Propositions START BY EXAMINING YOUR BUSINESS STRATEGY Propensity Models Customer Journey Model Price Sensitivity
  • 29. LAYER ON MORE AUDIENCES LEFT- & RIGHT-BRAIN PERSONAS ANALYTICS
  • 30. 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
  • 31. PRIORITISE, PRIORITISE, PRIORITISE PURSUE 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
  • 32. Habit 5 Takeaway: Create Your Audience Strategy
  • 34. View of the Customer CONNECT YOUR DATA HOUSEKEEPING BEFORE TECHNOLOGY
  • 35. Everyone has to work together for personalisation to work for you
  • 38. PHASED INTEGRATION OF PERSONALISATION CRAWL, WALK, RUN 0-12 weeks Build Phase 1 months 12-24 Build Phase 3Build Phase 2 months 3-12
  • 39. 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 d Phase 1 PHASED INTEGRATION OF PERSONALISATION CRAWL, WALK, RUN
  • 40. Integration with 1st & 3rd Party Data More Campaigns Integration of testing & Personalisation workflows More advanced use cases Phase 3 Planning Buil d Phase 2 months 3-12 PHASED INTEGRATION OF PERSONALISATION CRAWL, 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
  • 41. Full system integration Ongoing improvement New audience strategy Use cases continually iterated Web personalisation data feeds email and ad deployment Buil d Phase 3 months 12-24 PHASED INTEGRATION OF PERSONALISATION CRAWL, 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
  • 42. Habit 7 Takeaway: Crawl Before You Walk
  • 43. Experimentation Maturity Create a Vision Assemble Your Dream Team Enrich Your Perspective Create Your Audience Strategy Unify Crawl Before You Walk
  翻译: