We’ve lived to see the day when a CMS is not enough – using one is just the basis, an absolute prerequisite for an organization’s presence in a digital world. Each company that aspires to grow must take a step further and leverage other digital marketing products. When faced with this challenge, many organizations consider the Adobe Experience Cloud a prime choice.

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Get the most out of
your Adobe Experience
Cloud stack
Jakub Kasznia & Dawid Kłos
08-16-2018

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Reasons to consider something more than CMS

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Reasons to consider something more than CMS
Audiences
Social media E-mail
Analytics OmnichannelData Personalisation
Mobile

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Business cases

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Sharing audiencesSharing audiences
5
Business cases
Sharing audiences
Personalised experiences
Look-alike modelling
Event driven e-mail campaigns
Content driven e-mail campaigns
Experience Customisation

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Look-alike Modelling

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Look-alike Modelling
User journey
User navigates
through
website...
...signs up for
newsletter...
…instantly
receives
personalized
email
Two or more
audiences
are set up... Likely
Very Likely
...sees sidebox
newsletter
form...
...sees popup
newsletter
form...
...his past
activity is
analysed

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Look-alike Modelling
Data flow
Look-alike
algorithms
build new
audiences
Experiences
on website
are different
for each
audience
Email is sent to
custom
microservice
after
newsletter
subscription
Email is
passed to
Adobe
Campaign
Personalized
newsletter
subscription
confirmation email
is sent
Data about
past
activity is
derived

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Look-alike Modelling
Data flow
Look-alike
algorithms
build new
audiences
Experiences
on website
are different
for each
audience
Email is sent to
custom
microservice
after
newsletter
subscription
Email is
passed to
Adobe
Campaign
Personalized
newsletter
subscription
confirmation email
is sent
Data about
past
activity is
derived

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Look-alike Modelling
Setting Up Model
- Look-alike modelling needs baseline trait/segment
- Source of baseline data can be Audience Manger or Adobe Analytics (historical
data)

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Look-alike Modelling
Population Graph
- Population graph represents correlation between reach and accuracy of generated traits

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Look-alike Modelling
Algorithmic trait creation
- Algorythmic traits can be based on different accuracy level

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Look-alike Modelling
Trait Reach & Accuracy
- Trait and reach accuracy is fully adjustable
- Generation process may take up to 48 hours

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Look-alike Modelling
Data flow
Look-alike
algorithms
build new
audiences
Experiences
on website
are different
for each
audience
Email is sent to
custom
microservice
after
newsletter
subscription
Email is
passed to
Adobe
Campaign
Personalized
newsletter
subscription
confirmation email
is sent
Data about
past
activity is
derived

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Look-alike Modelling
Experience configuration
- Audiences from look-alike modelling are visible in Adobe Target
- Each audience receives relevant experience

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Look-alike Modelling
Data flow
Email is sent to
custom
microservice
after
newsletter
subscription
Email is
passed to
Adobe
Campaign
Personalized
newsletter
subscription
confirmation email
is sent
Look-alike
algorithms
build new
audiences
Experiences
on website
are different
for each
audience
Data about
past
activity is
derived

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Look-alike Modelling
Data flow
Email is sent to
custom
microservice
after
newsletter
subscription
Email is
passed to
Adobe
Campaign
Personalized
newsletter
subscription
confirmation email
is sent
Look-alike
algorithms
build new
audiences
Experiences
on website
are different
for each
audience
Data about
past
activity is
derived

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Opportunities:
- ability to increase number of subscriptions and decrease rejection rate
- ability to use any data for modelling new audiences
- ability to avoid duplicating individuals in resulting and base audience
Limitations:
- various delays involved in processing, process may take up to a week
- higher accuracy audiences are subsets of lower accuracy audiences
- exact algorithm is not known
Look-alike Modelling
Opportunities and limitations

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Personalized Experiences

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Personalized Experiences
User journey
User looks for
specific products
on website...
...user sees products
based on scoring
(business needs)
and his interests
Marketer wants to
emphasise
specified
products...
...applies
product
multiplier...

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Personalized Experiences
Data flow
Optional
segments from
AAM
Choosing
experience
for segment
Counting page views,
calculating scoring ,
determining product
to promote
Assigning visitor
to relevant
segment
Experience
is visible on
the website

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Personalized Experiences
Data flow
Optional
segments from
AAM
Choosing
experience
for segment
Counting page views,
calculating scoring ,
determining product
to promote
Assigning visitor
to relevant
segment
Experience
is visible on
the website

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Personalized Experiences
Creating Profile Script
- Profile script is an utility to execute piece of JS code per user
- Results can be used during audience creation process

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- Profile script is responsible for counting page views
- Counting page views is crucial for further processing in scoring
Personalized Experiences
Example Profile Script

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- Profile script is responsible for scoring calculation for each product
- Profile script can store variables separately for every user
Personalized Experiences
Example Profile Script

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Personalized Experiences
Data flow
Optional
segments from
AAM
Choosing
experience
for segment
Counting page views,
calculating scoring ,
determining product
to promote
Assigning visitor
to relevant
segment
Experience
is visible on
the website

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Personalized Experiences
Setting Up Audiences
- Audience is based on preferred product
- Additional rules can be applied for an audience

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Personalized Experiences
Setting Up Activities
- Activities are matched with relevant audiences

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Opportunities:
- ability to define experiences based on quite complex conditions
- ability to hide logic in Profile Scripts and configure experiences in Target’s UI
- ability to mix audiences from Adobe Target and Adobe Audience Manager
Limitations:
- requires programming skills to implement (at least on start, at defining targeting logic)
Personalized Experiences
Opportunities and limitations

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Event driven e-mail campaigns

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Event driven e-mail campaigns
User journey
User navigates
through
website...
...configures the
product...
...receives product
specification on e-
mail

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Event driven e-mail campaigns
Data flow
User data is
fetched from
database
Newsletter is
sent
User clicks on a
link to website
MCVID is
stored in
Campaign
Call to Analytics
is made on
product
configuration
finish
Adobe
Analytics
passes data to
Triggers
Data is
passed to
Campaign
Product
specification is
sent via e-mail
Already known
visitors are shared to
Audience Manager

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Event driven e-mail campaigns
Data flow
User data is
fetched from
database
Newsletter is
sent
User clicks on a
link to website
MCVID is
stored in
Campaign
Call to Analytics
is made on
product
configuration
finish
Adobe
Analytics
passes data to
Triggers
Data is
passed to
Campaign
Product
specification is
sent via e-mail
Already known
visitors are shared to
Audience Manager

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- Database as a source of information about customers
- Connection between Campaign and many types of databases is out-of-the-box
Event driven e-mail campaigns
Database connection

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Event driven e-mail campaigns
Data flow
User data is
fetched from
database
Newsletter is
sent
User clicks on a
link to website
MCVID is
stored in
Campaign
Call to Analytics
is made on
product
configuration
finish
Adobe
Analytics
passes data to
Triggers
Data is
passed to
Campaign
Product
specification is
sent via e-mail
Already known
visitors are shared to
Audience Manager

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- Newsletter as a way to collect user’s ID
- It is standard Email delivery
- Tracking server is enabled
- E-mail contains tracking link
Event driven e-mail campaigns
Newsletter

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Event driven e-mail campaigns
Data flow
User data is
fetched from
database
Newsletter is
sent
User clicks on a
link to website
MCVID is
stored in
Campaign
Call to Analytics
is made on
product
configuration
finish
Adobe
Analytics
passes data to
Triggers
Data is
passed to
Campaign
Product
specification is
sent via e-mail
Already known
visitors are shared to
Audience Manager

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- Adobe Campaign integration with Adobe Audience Manager is set up
- Query fetches customers with MCVID tracked in Adobe Campaign
- Optional, needed for user agreement to send an e-mail
Event driven e-mail campaigns
Audience sharing

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Event driven e-mail campaigns
Data flow
User data is
fetched from
database
Newsletter is
sent
User clicks on a
link to website
MCVID is
stored in
Campaign
Call to Analytics
is made on
product
configuration
finish
Adobe
Analytics
passes data to
Triggers
Data is
passed to
Campaign
Product
specification is
sent via e-mail
Already known
visitors are shared to
Audience Manager

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- Product configuration is reflected in call to Adobe Analytics
- In order to do that, product configuration is exposed in datalayer
- Call is made at the end of configuration
Event driven e-mail campaigns
Analytics

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Event driven e-mail campaigns
Data flow
User data is
fetched from
database
Newsletter is
sent
User clicks on a
link to website
MCVID is
stored in
Campaign
Call to Analytics
is made on
product
configuration
finish
Adobe
Analytics
passes data to
Triggers
Data is
passed to
Campaign
Product
specification is
sent via e-mail
Already known
visitors are shared to
Audience Manager

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- Marketing Cloud App and Pipelined configurations are prerequisite for triggers
- Triggers pass product configuration from Adobe Analytics to pipelined process
Event driven e-mail campaigns
Triggers

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Event driven e-mail campaigns
Data flow
User data is
fetched from
database
Newsletter is
sent
User clicks on a
link to website
MCVID is
stored in
Campaign
Call to Analytics
is made on
product
configuration
finish
Adobe
Analytics
passes data to
Triggers
Data is
passed to
Campaign
Product
specification is
sent via e-mail
Already known
visitors are shared to
Audience Manager

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Event driven e-mail campaigns
Pipelined
- Pipelined process is responsible for handling triggers
- It just stores product configuration in Campaign schema for further processing

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Event driven e-mail campaigns
Data flow
User data is
fetched from
database
Newsletter is
sent
User clicks on a
link to website
MCVID is
stored in
Campaign
Call to Analytics
is made on
product
configuration
finish
Adobe
Analytics
passes data to
Triggers
Data is
passed to
Campaign
Product
specification is
sent via e-mail
Already known
visitors are shared to
Audience Manager

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- Workflow handles custom schema entries and creates events
- Events are pushed to Message Center
- Message Center sends e-mail with product configuration to customer
Event driven e-mail campaigns
Workflow

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Opportunities:
- ability to utilise already collected data
- ability to link website visitor with other communication channels
- ability to reduce the barrier of obtaining product specification
Limitations:
- visitor could clear the cookies
- previously reconciled visitor on a device might receive an e-mail
Event driven e-mail campaigns
Opportunities and limitations

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ABOUT US
Mail
jakub.kasznia@cognifide.com
Jakub Kasznia
Software Engineer
Cognifide
Contact Us
www.cognifide.com
Phone
+48 500 256 202
/in/jakub-kasznia-241022138
/cognifide
@cognifide

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ABOUT US
/cognifide
Mail
dawid.klos@cognifide.com @cognifide
/in/dawidklos/
Dawid Kłos
Software Engineer
Cognifide
Contact Us
www.cognifide.com
Phone
+48 881 508 042

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THANK YOU!