Real Time Personalisation and Recommendations Engine Strategy, Executive Briefing.

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Real Time Personalisation and
Recommendations Engine Strategy
Executive Briefing
Ralph Overbeck
“Ralph has been instrumental in helping develop an
industry leading personalisation programme for Sky”
“Ralph was the lead with Sky's
recommendations engine project.”

2. Copyright © 2011 Overbeck Analitica, All Rights Reserved.
2000 20101990
Content Search
Association Rules
Latent Factorization
Context Awareness
Adaptive Feedback
DegreeofMaturity
Collaborative Filtering
Content Based Filtering
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Evolution of Personalised
Recommendations Strategy

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Content Overload
Too Much Choice, Too Little Time…
With the convergence of the Internet, Mobile and TV, most
of your customers are probably only aware of a fraction of
the content you are able to provide.
Their perception of your brand and what you offer is
therefore governed by a very narrow experience.
Undoubtedly you have much wider content that would
interest and delight your customers.
In today's content rich world your consumers need help to
find relevant content, right at their fingertips…
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A Different View of Marketing
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“I have an offer …”
offer
”Let me find the best people
for this offer.”
“Let me find the best
offer for this person.”
“I have a person …”
offer
offer
offer
offer
Outbound
Days
Inbound
Milliseconds

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Understanding the journey from screen-to-screen device, as well as the context each screen device has for
the consumer, is key to creating context-aware personal messaging that helps your brands establish profitable
relationships with your customers —relationships based on relevance instead of repetition.
Meet Tom, he mainly uses his
blackberry for business & travelling
when meeting regional clients and his
iPhone to listen to music and stay in
touch with family & friends etc…
His wife is an international journalist
working from her laptop to research new
stories. Jenny relies on her tablet to read
the daily newspapers and never leaves
home without it etc…
Context-aware user
experience across
multiple devices
Context Aware User Experience

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10’s10,000’s1,000,000’s
Personalised Recommendations
PersonalizationSpace
BUSINESS RULES
Business Rules:
• Regulatory Constraints
• Promotional Boosts
CONSUMER
CHOICE
Consumer Preferences:
• Watches romantic comedies
• Dislikes science fiction
• Likes Hugh Grant
• Follows Tennis on mobile
Media Catalogue:
• Linear TV
• Video On Demand
• Offer/Packages
RECOMMENDATION
ENGINE
Ranked
Item
Love Actually
Wimbledon Live
Gavin & Stacey
:
Star Wars
Jurrasic Park
Preference
Score
0.95
0.90
0.85
:
0.45
0.35
Seamless user
experience across
multiple devicesUpgrade
Behavioral Feedback Loop
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Scalable Recommendations

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Business Rules enable operators to control recommendation output to push
selected content, up-sell and cross-sell revenue-generating content.
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Personalized experience across multiple devices * optimized for lean forward and lean back environments.
Device *
Balancer
Injecting Real Time Business Rules

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Translating characteristics of
your rich media content into
attributes your consumers
care about…
Content
Classifier
Learning historical behavior of
your consumers and their
current preferences and
context…
Consumer
Profiler
Personalised recommendations that are relevant to consumer choice.
• Based on the inherent characteristics of content (nature debate) as well as influenced by
people choices around you (nurture debate).
• Adaptive learning of user preferences from explicit (e.g. user ratings, like/dislike) as well as
implicit (e.g. actual viewing habits) behavioural feedback.
• Sensitive to context-awareness, taking into account different environment situations in order
to make the most sensible recommendations depending on time of the day, location of the
user and display device.
relevance
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Matching Content to Consumers

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NATURE
Taking the nature side, Pandora's
recommendations are based on the inherent
characteristics of the music.
Give Pandora an artist or song, and it will find
similar music in terms of melody, harmony etc…
Pandora likes to call these musical characteristics
"genes" and its database of songs, classified
against hundreds of such characteristics, the
"Music Genome Project.”
In technical literature Nature based
recommendations are referred to as Content
Based Filtering (CBF) methods.
NURTURE
On the nurture side, last.fm’s recommendations are
based on the people around you and allowing you
to be nurtured i.e. influenced by the choices they
make.
Last.fm knows little about the songs' inherent
characteristics like melody, harmony etc…
It just assumes that if you and a group of other
people enjoy many of the same artists, you will
probably enjoy other artists popular with that group
choices.
In technical literature Nurture based
recommendations are referred to as Collaborative
Filtering (CF) methods.
Comparing recommendation services is something like the nature versus nurture debate.
The trick is to combine Nature & Nurture based methods into a unified consumer experience!
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Nature vs. Nurture

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Personalisation Technology
The goals of personalisation technology are clear to
anyone who has received an online recommendation for a
movie, television program or product:
● Personalisation must deliver relevant, precise recommendations
based on each individual’s tastes and preferences.
● It must determine these preferences with minimal involvement
from consumers.
● And it must deliver recommendations in real time, enabling
consumers to act on them immediately.
Personalisation technology must embrace
recommendations that are based on inherent quality of
content (nature debate) as well as people choices around
you (nurture debate).
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Converting your prospects into customers
Through tailoring and promotion of personalised recommendations.
Steering your prospects to select the package which best meet their needs.
Engaging and retaining your existing customers
Guiding your customers to discover content that is outside their perception of
what is available.
Demonstrating depth & breadth that will entice your customers to upgrade or
take up additional offers.
Enhancing customer affiliation with your brand
Personalised experience creates brand differentiation and customer loyalty.
Converting sceptical consumers into satisfied advocates enables word of
mouth recommendations.
Predicting demand for content accurately
Reducing content distribution network cost by balancing video streaming traffic.
Enabeling content package optimization around consumer demand.
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What are the business benefits of
personalised recommendations?

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