After a morning of exciting technology advancements and visionary discussion with industry leaders, get back into an operational mentality in the personalization workshop. Learn what makes organizations practicing personalization successful, and return to your team with clear action items to upgrade your organization into a personalization powerhouse.
7. 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
8. 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 OPTIMIZATION PROGRAMS
• Do more complicated tests than A/B
• Test through more than just a few pages
• Are segmenting analytics
9. ARE WE READY TO STEP FORWARD?
OPTIMIZELY’S MATURITY MODEL
INTERESTED INVESTED INTEGRATED INGRAINED
VALUE
Culture
Process
Strategy
Development
10. ARE WE READY TO STEP FORWARD?
OPTIMIZELY’S MATURITY MODEL
INTERESTED INVESTED INTEGRATED INGRAINED
VALUE
Culture
Process
Strategy
Development
YESMAYBENO
11. INTERESTED INVESTED INTEGRATED INGRAINED
VALUE
Culture
Process
Strategy
Development
• Inconsistent access
to resources
MATURE OPTIMIZATION PROGRAMS
• Are comfortable pushing boundaries
• Have processes and teams in place
• Speak language of testing
ARE WE READY TO STEP FORWARD?
OPTIMIZELY’S MATURITY MODEL
12. 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
13. 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
21. 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
1
22. 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
23. 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
27. Recency & Frequency
Cross-sells & Up-sells
Value Propositions
START BY REVISITING YOUR BUSINESS STRATEGY
Propensity Models
Customer Journey Model
Price Sensitivity
28. LAYER ON MORE AUDIENCES
LEFT- & RIGHT-BRAIN
PERSONAS ANALYTICS
29. 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
30. PRIORITIZE, PRIORITIZE, PRIORITIZE
PURSUE VARIETY OF AUDIENCES, MAXIMIZE 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
37. PHASED INTEGRATION OF PERSONALIZATION
CRAWL, WALK, RUN
0-12 weeks
BuildPhase 1
months 12-24
BuildPhase 3BuildPhase 2
months 3-12
38. Platform Implementation
Simple Audiences
Starter Campaigns,
Limited Integration of
Testing + Personalization
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 Behaviors
TACTICS: Modules (lightboxes), image swaps, little testing
0-12 weeks
Buil
d
Phase 1
PHASED INTEGRATION OF PERSONALIZATION
CRAWL, WALK, RUN
39. Integration with 1st & 3rd
Party Data
More Campaigns
Integration of testing &
Personalization workflows
More advanced use cases
Phase 3 Planning
Buil
d
Phase 2
months 3-12
PHASED INTEGRATION OF PERSONALIZATION
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 behaviors
TACTICS: Experiments drive campaign execution and iteration
40. Full system integration
Ongoing improvement
New audience strategy
Use cases continually iterated
Web personalization data feeds
email and ad deployment
Buil
d
Phase 3
months 12-24
PHASED INTEGRATION OF PERSONALIZATION
CRAWL, WALK, RUN
REACH: 75-100%
PAGES: Most pages, multiple elements per page
# CAMPAIGNS: 25+ ongoing personalization campaigns iterated on
AUDIENCES: Old audiences iterated, new granular audiences
TACTICS: Fully expressive strategy
42. Create a Vision
Experimentation Maturity
Assemble Your Dream Team
Enrich Your Perspective
Create Your Audience Strategy
Unify
Crawl Before You Walk