Make it Personal: Accelerating Your Personalization Game Plan

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2
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Anuj Lal
Sr. Manager, Global Strategy
Consulting - Optimizely
Ryan McGredy
Sr. Manager, Solutions Engineering -
Optimizely
Make it Personal: Accelerating
Your Personalization Game
Plan
Opticon 2019

Personalization is hard.
Cliché I know..

5
Tales from the field
1. Audience creation & definition is difficult
○ “Which audiences should we personalize for?”
2. Campaign ideation is intimidating
○ “We have entire teams working on this, where
should we even begin?”
3. Soliciting input from other parts of the organization can
be unwieldy
○ “How do we get more people involved w/o triggering
governance problems?”
3 “worries” w/ Personalization Strategy

6
1. Audience creation & definition is difficult
○ “Which audiences should we personalize for?”
2. Campaign ideation is intimidating
○ “We have entire teams working on this, where
should we even begin?”
3. Soliciting input from other parts of the organization can
be unwieldy
○ “How do we get more people involved w/o triggering
governance problems?”
3 “worries” w/ Personalization Strategy
Why not let machine
learning help you develop
valuable audiences?
Why not think of
recommendations as your
first personalization
campaign?
Why not use re-usable
shared code to help scale
& permissions to help
govern

Personalization is hard.
Cliché I know..
..but it can be a lot easier
to get started than you
think.

We want you to leave this
session knowing two
things:
1. The tenants of a strong
personalization strategy
2. Tangible & easy ways to accelerate
that strategy (real examples)

9
Agenda 1. Why? Revenue drivers
2. Who? Audience discovery
3. Pro-Tip: Adaptive Audiences
4. What? Campaign ideation
5. Pro-Tip: Recommendations
6. Where? Opportunity analysis
7. Pro-Tip: Extensions

10

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“In the online world, businesses have the opportunity to
develop very deep relationships with customers...so that
you can use that individualized knowledge of the
customer to accelerate their discovery process.”
Jeff Bezos
“Accurately predicting the movies Netflix members
will love is a key component of our service”
Neil Hunt, Chief Product Officer
The value of personalization is in the trillions of dollars
Personalization at scale
has the potential to create $1.7 trillion to
$3 trillion in new value.
Based on projections by McKinsey Global Institute

12
“In the online world, businesses have the opportunity to
develop very deep relationships with customers...so that
you can use that individualized knowledge of the
customer to accelerate their discovery process.”
Jeff Bezos
“Accurately predicting the movies Netflix members
will love is a key component of our service”
Neil Hunt, Chief Product Officer
The value of personalization is in the trillions of dollars
Personalization at scale
has the potential to create $1.7 trillion to
$3 trillion in new value.
Based on projections by McKinsey Global Institute

“
”- Anuj Lal
Let’s make this
real.

“
”Optimizely Strategy Consulting
A personalization campaign is an
experiment that is trying to move a
KPI or a set of KPIs for a GROUP of
audiences.

15
Goal Orientation (B2B/Lead-Generation Example)
Identify the KPIs that you aim to influence through personalization and define the metrics that will measure them
Increase Customer Lifetime Value (LTV)
Increase Customer Acquisition for Product Type (Close Rate %)
Increase Quote/Demo Form Submission Rate (Submission %)
Define Goals That Will Validate Hypothesis About User Behavior

16
Goal Tree – Revenue (B2B)
RetentionAcquisition

17
Goal Tree - Retail and E-Commerce
Revenue:
$2.0m
Revenue Per
Visitor:
$1.00
Average
Order Value:
$50.00
Average
quantity:
2.00
Average Price
per Unit:
$25.00
Conversion
Rate:
2.00%
Add to Cart
Cart
Checkout
Rate
Visitors:
2.0m
User
acquisition:
1.5m
User
retention
33%
UpsellsCross-sell ... generally

18
Revenue:
$2.0m
Revenue Per
Visitor:
$1.00
Average
Order Value:
$50.00
Average
quantity:
2.00
Average Price
per Unit:
$25.00
Conversion
Rate:
2.00%
Add to Cart
Cart
Checkout
Rate
Visitors:
2.0m
User
acquisition:
1.5m
User
retention
33%

19
Revenue:
$2.0m
Revenue Per
Visitor:
$1.00
Average
Order Value:
$50.00
Average
quantity:
2.00
Average Price
per Unit:
$25.00
Conversion
Rate:
2.00%
Add to Cart
Cart
Checkout
Rate
Visitors:
2.0m
User
acquisition:
1.5m
User
retention
33%

20
Reasons for Goal Tree:
Comprehensive:
● Visually analyzes the entire
site’s opportunity to weigh
opportunity cost
Alignment:
● Allows teams to explicitly
align testing goals to business
goals; keeps ideas tightly
focused
Productive Brainstorming:
● Strategic foundation for
creative brainstorming
*Orange = KPIs that can be
influenced by digital optimization
via Optimizely
KPIs that can be influenced
using upsell or cross-sell
strategies
Revenue:
$2.0m
Revenue Per
Visitor:
$1.00
Average
Order Value:
$50.00
Average
quantity:
2.00
Average Price
per Unit:
$25.00
Conversion
Rate:
2.00%
Add to Cart
Cart
Checkout
Rate
Visitors:
2.0m
User
acquisition:
1.5m
User
retention
33%

21
Key Learnings 1. A personalization campaign
is an experiment where you
are trying to move a KPI or a
set of KPIs for a GROUP of
audiences
2. A goal tree is an effective way
to evaluate the purpose(s) of
your personalization strategy
– the “why?”

22

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A framework we like to use at
Optimizely to categorize different
types of audiences is: Demographic,
Contextual, & Behavioral

24
Personalized Audiences
Framework for categorization
Demographic:
Who are they? (Can be
gleaned using first party or
third party data)
• Store Preference
• Vertical (I-I)
• Gender (I-I)
• Member/Non-Member
• Demographic (I-I)
• CRM Data (I-I)
Contextual:
What can we infer from the
context of the browser?
• Marketing Campaign
• Referral Source
• Device
• New/Returning
• Time of Day
• Browser
Behavioral:
What is their intent?
• Viewed category of
content
• Entered certain
city/zipcode
• Past Browse Behavior
• Clicked ‘Plans’ twice but
didn’t convert
• Browsed at a particular
time of day

25
Actionable & Big: Do Both
Pursue a variety of both to maximize reach/quality
Obviously Actionable
Large
Need for Creativity
Granular
Visitor Cohort; New, Returning, Active,
Loyal
Browsed Twice; Product Category
Large Geos; Coastal Urban, State,
Key Cities
Past Purchasers
Second Priority

26
Current challenge: If we wanted to
pick an audience to start with, we’d
have to guess at exactly what the
right granularity is:

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Simply define your
audience using keywords

How it Works!
Interest Score
Our estimate of a
visitor’s interest in the
keyword-defined
persona.
Predicted
Intent
The new Audience
Condition that
evaluates a visitor’s
Interest Score.
Key Ideas
Compute Score
Optimizely compares the visitor’s profile to the
keywords to generate an Interest Score.
Audience Builder
Customers supply keywords in plain English that
define the persona.
Browsing History
Visitors browse product, article, or landing pages and
we construct a profile based on the text content.

Browsing History: Tags
Tags
Page Tags can capture
any text elements,
search terms, invisible
meta tags, or data
layers. One time setup!
Visitor Profile
Most recent 10 page
views are used to
construct profile of
this visitor.
Key Ideas

34
Key Learnings 1. Traditionally, organizations
suffer from “analysis
paralysis” trying to define
audiences.
2. Using machine learning to
define your audiences for
you is a quick way to start
experimenting and iterating.

35
Crate & Barrel
Success Story
● Running a number of major campaigns
using Adaptive Audiences today
○ Furniture vs housewares
○ Parents of Kids vs Infants
● In all instances, bounce rates dropped.
● Conversion rates (purchase rate)
improved substantially. Across all three, it
was more than a 20+% improvement.
● Revenue per visitor was a double digit
improvement as well.

Using Optimizely’s new personalization offering,
we have been able to increase conversion
rates on the homepage by 20%+ while reducing
bounce rates by up to 30%.
Within one month of using Adaptive Audiences →
Crate & Barrel had 16 different audiences they
are using.
CHRISTINE GARVEY
Senior Manager, Personalization & Optimization

37

A/B Testing
Finding the AVERAGE best experience OVERALL for
everyone

Personalization
Exploring the BEST experience for each audience

Category Affinity
Create relevant offers and product placements
across key points of your experience based on
affinity shown for product or content types from
prior browsing behavior
Customer Experience as a
Competitive Advantage
Tailor your experiences to target your customers
and test how each experience resonates
Symmetric Messaging &
Session Cohorting
Ensure that customers receive messaging that
is consistent and applicable based on search
terms, creating a cohesive customer journey
Localizations
Localize web experiences based on
customer location
Behaviorally Triggered
Campaigns
Based on previous behavior, target specific customers
with personalized experiences in real time
ABM
Create relationships with your most important
customers across your experience through 1-to-1
messaging and value propositions based on which
company or industry they are in
Loyalty Tiers
Push your loyal customers to the most important
groups by offering unique benefits based on current
tier of loyalty as well as ensure consistent messaging
for a seamless experience
Abandoned Cart &
Purchase History
Personalize based on individuals purchase
history and cart abandonment
Key Personalization Use-Cases

45
How do we think about our
audiences & exactly what we should
do for each one of them?

46
Reminder: Audience Framework
Framework for categorization
Demographic:
Who are they? (Can be
gleaned using first party or
third party data)
• Store Preference
• Vertical (I-I)
• Gender (I-I)
• CRM Data (I-I)
Contextual:
What can we infer from the
context of the browser?
• Referral Source
• Device
• New/Returning
• Time of Day
• Browser
Behavioral:
• Viewed category of
content
• Entered certain
city/zipcode
• Clicked ‘Plans’ twice but
didn’t convert
• Browsed at a particular
time of day

47
Consider where users are in their journey with you
Demographic
Who are they?
(First party or third party data)
• Geo, Vertical, Gender, Member Non-Member,
Demographic, CRM Data, Customer Tier, or
Loyalty
Behavioral
● Viewed category of content, Entered certain
city/zipcode, Past Browse Behavior, Viewed
lead form, Scrolled 50% down the page, but
didn’t engage
Contextual
What can we infer from the context of
the browser?
• Marketing Campaign, Referral Source, Device,
New/Returning, Time of Day, Browser
NEW
VISITORS
RETURNING
VISITORS
KNOWN
VISITORS
Awareness Consideration Service LoyaltyPurchase

48
Who are they? What is important to them?
What are they looking for?
What can you do?
New Visitor
● Looking for info/education
● Someone who can sympathize or
relate to her individual problem or
need
● Acknowledge her “newness” to
brand,
● Aim to get her contact information
● Educate her on offering &
return visit
Return Visitors
● Ability to continue her shopping
session from where she last left
● Incorporate her past site behavior
position offering
● Acquire her contact information
A Repeat/Loyal Customer
Engaged)
● Recognition and benefits as a
customer
● Use rewards or awards to
him/her for being a good customer
● Surface your ‘premier’ loyalty
programs
● Encourage him/her to share or
promote the brand
Connecting your users to a “mission”
Connecting the ‘Who?’ to the ‘What?’

49
Most difficult part in practice:
Building content to map to each of
these personas or audiences

50
Most difficult part in practice:
Building content to map to each of
these personas or audiences
Why not think about adding a
recommendations algorithm for your
first personalization campaign?

51
Recommendations Use-Cases for All Verticals
Retail, B2B/Lead-Generation, Media
1. Retail/eCommerce Website
○ In the checkout funnel, show accessories that complement the items a
visitor is purchasing
○ On product detail pages, show alternative items that are related to the
product a visitor is browsing
○ Highlight crowd favorites on the homepage
2. B2B/Lead-Generation Website
○ Show visitors whitepapers, infographics, blog posts, and other content
based on their browsing behaviors
○ Suggest knowledge base articles or community posts to reduce support
call volume
3. Media Website
○ Show visitors articles, sectionals, videos, and other content based on
their browsing behaviors

52

INTERNAL USE
Browsing History: Tags
Tags
Page Tags can capture
any text elements,
search terms, invisible
meta tags, or data
layers. One time setup!
Visitor Profile
Most recent 10 page
views are used to
construct profile of
this visitor.
Key Ideas

54
Demo 1. Adaptive Audiences
2. Optimizely
Recommendations

Recommendations V1
CHALLENGES
● Could not be implemented by the
customer w/o assist from services +
ongoing maintenance from services.
● Was not built into the Optimizely Web
interface.
● Therefore, often wasn’t incorporated into
our Personalization pitch

57
You don’t have to create new copy
or content, it already exists on your
site today

58
Key Learnings
1. A personalization campaign is all
about connecting users to a
MISSION.
2. Recommendations are a great way
to 1) show value early, 2) back into
implementation, & 3) force up-front
discovery – so you can quickly learn
from your first personalization
campaign

59
Recommendation Use-Cases for All Verticals
Retail, B2B/Lead-Generation, Media
1. Retail/eCommerce Website
○ In the checkout funnel, show accessories that complement the items a
visitor is purchasing
○ On product detail pages, show alternative items that are related to the
product a visitor is browsing
○ Highlight crowd favorites on the homepage
2. B2B/Lead-Generation Website
○ Show visitors whitepapers, infographics, blog posts, and other content
based on their browsing behaviors
○ Suggest knowledge base articles or community posts to reduce support
call volume
3. Media Website
○ Show visitors articles, sectionals, videos, and other content based on
their browsing behaviors

60
Optimizely Recommendations
Co-browse
Co-buy
Popular
Recently viewed
Collaborative
Filtering
Items that other visitors who viewed this item also viewed
(ideal for PDP)
Items that other visitors who purchased this item also bought
(ideal for PDP, Cart)
Items most frequently viewed or bought across the entire
catalog (ideal for Homepage)
Items a visitor had previously browsed (“pick up where you
left off”)
Unique recommendations for each visitor, based on their
unique behavior on your site in the past

61
Recommendations Examples by Page – Retail example
Page Sample Algorithms that we may use:
Homepage Popular products
Ratings-based
Affinity
Landing Page Popular products
Similar products
Similar offers
Category Page Popular products
Similar products
Zero results & 404 Pages
Product Page Similar products
Bought / viewed this…
Frequently bought together
Cart Page Accessories
Frequently bought together

62
Top Retailer: Recommendation Strategies & Custom Algorithms
Example
Recommendation Type Reasoning
Recommended for you Most common but effective (add name to personalize)
Frequently bought together Increase AOV
Recently Viewed Introduce shoppers to new items
Browsing History Access Relocate items viewed earlier
Related items you’ve viewed Help with ideas
Customer who bought this also bought this Social proof and peer-generated recommendations
Newer Version Alert viewers of products that have been updates
Related to previous purchase Personalize on what they might want / simplify re-
purchase
Best Selling Items Indirect social proof and way of adding confidence
Product Bundles Frequently bought together bundles to increase AOV
Rating Highest rating items as social proof

63

64
Ideating Solutions
Ideate often at the start of a problem!

65
Lack of Focus on Initial Campaigns
Pitfalls to Avoid
Focus on Top ROI Pages to
Start, Progress Incrementally
Homepage
Landing
Page Product
Page
Checkout
1st iteration
2nd iteration
3rd iteration
Launch

66
Lack of Focus on Initial Campaigns
Pitfalls to Avoid
Focus on Top ROI Pages to Start,
Progress Incrementally
Homepage
Landing
Page Product
Page
Checkout
1st iteration
2nd iteration
3rd iteration
Launch
...Not Everything at Once
Homepage Landing
Page
Product
Page
Checkout
Launch
● Successful practitioners start with a tight
focus on addressing top audiences and
pages.
● Pages should be high traffic + high value.
● Audiences relatively high reach.

67
Data-Driven Approach:
WHO on the site?
● Customers: 1,000 Visitors x 50% Exit Rate = 500 Visitors
● Return Visitors: 5,000 Visitors x 25% Exit Rate = 1,350 Visitors
● New Visitors: 10,000 Visitors x 60% Exit Rate = 6,000 Visitors (audience opportunity)
WHERE on the site?
● App Homepage: 30,000 Visitors x 50% Exit Rate = 15,000 Visitors
● Desktop Homepage: 15,000 Visitors x 25% Exit Rate = 3,750 Visitors
● Mobile Homepage: 60,000 Visitors x 60% Exit Rate = 36,000 Visitors (impactful opportunity)
Where Can You Have The Largest Impact??
Easiest way to figure out who/where to start is to look at your exit rate on each part of your flow and
take it one step further and segment by “Mobile/Desktop”; “New/Returning”; “SEM/Direct”

68
Previously, how customers of ours
maintained governance of their
personalization program is through
“shared code.”

Two main challenges heard over & over:
1. “We know where we want to personalize and for whom (ie.
Homepage for ad-campaign audiences), but we don’t want to
have to re-create (copy/paste/edit) the code every campaign.”
2. “It’s hard to collaborate & expand/grow personalization across
the organization without having the same key stakeholders
involved in every decision."

70

71
Demo 1. Adaptive Audiences
2. Optimizely
Recommendations
3. Optimizely Extensions

72
Optimizely Extensions
1. Extensions let developers create reusable
features for non-technical team members
2. Marketers can add custom features to
experiments without ongoing developer
support
3. Developers can scale by enabling marketers
and other business users to do more on their
own
Enabling scale

73
Additionally, see how Banana
Republic (GAP Inc.) expanded
access to their personalization
program via extensions:

78
Key Learnings 1. Determining “where” you want to
personalize is just as important as deciding
“what” you want to personalize  use a
data-driven approach.
2. Scaling personalization across the entire
organization can seem daunting, but with
the right tools, it can turn that challenge
into a key differentiator for your business
by reinforcing a culture of experimentation

79
IBM Story

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IBM Story
 Rollout of Testing &
Personalization Center of
Excellence
○ 300+ experiments globally
 Focus on Personalization w/
Extensions
○ 10x 2017 volume
 Audience integrations

81

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Takeaways
1. Audience creation & definition is difficult, why not let
machine learning assist you in finding your most
valuable audiences?
2. Campaign ideation can be prohibitive, but a
recommendations engine is another form of
personalization, and can be a great way to start,
show value early and accelerate initial learnings.
Make sure your idea is rooted in the “why?” with a
measurable KPI.
3. Expanding access & controls for personalization can
be unwieldy, but utilizing the right tools (ie.
Optimizely extensions), you can actually use
personalization strategy as a way to build
excitement internally.

Thank You!
& any questions?
Anuj Lal: anuj@optimizely.com
Sr. Manager – Global Strategy Consulting
Ryan McGredy: ryan.mcgredy@optimizely.com
Sr. Manager – Sales Engineering

 Head to the Expo Hall for refreshments and to say to our
sponsors
 Up next...hear how IBM accelerated digital optimization to
achieve digital excellence

Behavioral Audiences
Demographic:
Who are they? (Can be gleaned
using first party or third party data)
• Geo
• Vertical (I-I)
• Gender (I-I)
• CRM Data (I-I)
Contextual:
What can we infer from the context
of the browser?
• Referral Source
• Device
• New/Returning
• Time of Day
• Browser
Behavioral:
• Viewed category of content
• Entered certain city/zipcode
• Clicked ‘Plans’ twice but didn’t
convert
• Browsed at a particular time of
day

Things to think about:
● What are your largest audiences? What is the ‘reach’ of these audiences? (% of that page’s visitors)
● What is the unique value of these audiences (or user personas)?
● How feasible is it to target these audiences?
Media Sample Audiences
Local Sports
Fan
Fan of specific sports teams, by viewing more than X
pieces of related content
Topical
Affinity
Visitors that have viewed a type of content more
frequently than others (i.e. Politics, Sports, Crossword,
Editorial, Cars, Travel etc.)
Hit Paywall
Previously
Visitors that have visited paywall page and not
subscribed
International Visitors from an international location
Article Meter Visitors at level X in a meter (i.e., read 5 of 7 articles)
Video
Watchers
Visitors that have watched videos more than some
threshold (i.e., more than the average by 30%, or
more than X times in last 30 days)
Attention
Span
Visitors that on average reach less than 25%, or more
than 75%, article completion
e-Commerce Sample Audiences
Price Based Visitors who have completed a purchase at least once in
the last X days where price >=< $X
Category
Affinity
Visitors that have viewed a product of category X at least
Y times in the last Z days
Abandoned
Cart
Visitors who triggered Add to Cart at least once but did not
Purchase
Window
Shoppers
Visitors that have viewed X number of product pages in
the last X days and have not added to cart
Repeat
Purchasers
Visitors that have completed a purchase more than X
times in the last X days
Coupon
Shoppers
Visitors that have completed a purchase while using a
coupon code
Product Filter Visitors that have refined a search, product, or category
landing page filter (e.g. size, color, type of product)
Behavioral Audiences - Examples

88

89
Reasons for Goal Tree:
Comprehensive:
• Visually analyzes the entire site’s opportunity,
and help avoid opportunity cost
Alignment:
• Allows teams to explicitly align testing goals
to business goals; keeps ideas tightly
focused
Productive Brainstorming:
• Strategic foundation for creative
brainstorming, helping to garner a volume of
ideas for prioritization
*Orange = Areas available for digital
optimization via Optimizely

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Make it Personal: Accelerating Your Personalization Game Plan