musicovery.com

09/10/2015

Personalizing broadcast radio

03/03/2016

Presented by Vincent Castaignet, Musicovery CEO

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Speaker

Personalizing broadcast radio Realizing the full value potential of

behavioural data of your listeners by

providing personalized experiences

Vincent Castaignet

Musicovery, CEO

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• How to create smart radios ? • How to built personalized YouTube channels ? • How to substitute a song that a listener skips for one that he will like ? • How to generate notifications of personalized and geolocalized concerts ? • How to identify for an emerging artist most receptive listeners ? • How to send personalized notifications of new releases/hot news ? • How to built prize competiton/loyalty programmes, with ultra-targeted gifts

(concerts, collector editions, goodies) ?

Personalizing broadcast radio

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Appetite for

discovery Appetite for

popular songs

Appetite for

new releases Needs for

repetition

Appetite for

genre diversity

Preferences

for songs,

artists, genres

Saturated

songs Current interest for

artists, genres

Mood

diversity

Smart radios adapted to each user profile

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Karl Böhm Claudio Abbado Eugen Jochum Riccardo Muti

• Recommendation of an artist: Karajan

• Selection of a video from the artist

• Proposition of alternative videos/tags/playlist

• Channel adapts according to interactions

Personalized YouTube channels

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Song substitution

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Personalized and geolocalized concerts

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likely to like

very likely to

like

fans

Finding most receptive listeners for an

emerging artist

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Notification of new releases

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Prize competition, loyalty programmes

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How does it work ?

User dynamic music profile

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I don’t

like I know

My

tribe

Music I

don’t know

I like

Influencers

I trust

My

friends

mood,

activities

my habits :

when, where

Expanding listeners music universe

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Not familiar

with this

song/artist

I can say if it

fits my music

universe

I like it

Optimal number of

plays

I like but I’m

saturated now

The right repetition rate

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listener

filters by genres, years, popularity

artist song

mood

genre

search

interacts with propositions

generates behavioural data

Behavioural data collection opportunities

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Analytics tool :

• Measuring quality objectively :

playlists duration, skip rates, …

• Running A/Z tests for various

scenarios to generate playlists

• Selection of the best scenario to

each listener once minimum

confidence is reached

Optimizing recommendation generation

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Behavioural

data

Exploration of

the music

universe

User dynamic

music profile

Personalisation: Musicovery robust model

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Personalized

services

Behavioural

data

collection

The virtuous circle of personalization

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Introduction to Musicovery API

• generates all kinds of playlists: Mood, activities, artist, genre, song, theme…

• generates all types of recommendations: Similar songs and artists, for a specific listener

• various parameters to finetune

playlists/recommendations

API documentation

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Recommendations for a specific listener

• Parameters to call the API : &userid=xxx

• Response : a list of recommended songs

artists

genres

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A very fast personalization process

focused on big

beat genre

mainstream

Listener 2 favorite artists:

Daft Punk, David Guetta

Listener 1 favorite artists:

Daft Punk, Propellerheads

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Recommendations user 1

favorite artist: Daft Punk

searched artist: The Chemical Brothers

favorite song: Propellerheads – Take California

banned song: Christina Aguilera – Ain’t no other man

satured song: Daft Punk – One more time

Get recommendations for this

listener

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Recommendations user 2

favorite artist: Daft Punk

searched artist: David Guetta

favorite song: Swedish House Mafia – One (your name)

banned song: Groove Armada – Dirty Listening

Get recommendations for this

listener

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Compatible with industry content identifiers

ISRC

API Musicovery

ISRC

INPUT : Deezer id for Rihanna

Rihanna id OUPUT : Instagram ids for similar artists to

Rihanna

+

Get the results from the API

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Iterative process

Test Musicovery

API

Design a project

Develop a prototype

Develop a pilote

Roll out

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Case study :

Broadcast : leader in the US, 238 M audience radios/TVs

Smart radio IHeartRadio • Launched 2008

• True challenger to Pandora in the US

• Designed to complement their 750 local FM stations

• provides similar artist data

• Similarity type optimized : genre/era/social

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Thanks for your attention

Test the API on musicovery.com: access is open

and free !

vincent.castaignet@musicovery.com - +33 6 13 17 35 61

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Source of data

Data generated

automatically (from

audio and web)

Data produced and

structured by experts

SmartPlayTM

Users behaviour

and preferences

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Technology

Acoustic

descriptors

Semantic

descriptors

Personalized recommendations

Social

activity

Charts &

trends

orchestration,

rhythmic…

situations,

mood, genres,

themes

Individual

personalization

profiles, tribes,

friends… by country, real

time

preferences,

behavior (time,

location), history

Algorithm

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A global and a local tool

• SmartPlay is now available in : US, Canada, UK,

France, Italy, Spain, Germany, Portugal, Norway.

=> Additional territories on request.

• SmartPlay is built to adapt to local specifics :

o It includes local artists for each country

o Genres, themes, mood are catered to local

language(s) and the local catalogue

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Music catalogue

• Comprehensive and exhaustive for

Europe, North America and South

America

o all genres and all times

o 290,000 artists

o 1,5M songs

o Charts from 1960: US, Canada,

UK, France, Spain, Italy, Portugal,

Germany and Norway

• Continually growing

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Musicovery engine efficiency

= total listening duration : +35%

Numerous and

diverse ways to launch

playlists

Recommendations more consistent

with listener profile and listening

history

More playlists are launched playlists are played longer

Personalized playlists

playlists are more

relevant and consistent

+

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Professional SLAs

• SmartPlay runs on professional infrastructure :

o Front-end on Co-host on Bearstech

o Algorithm, webservices and API on Bearstech

o Back-end on Amazon Cloud Services

• SLAs:

o Guaranteed 99.9 % uptime by infrastructure suppliers

o Bandwidth: peak bandwidth at least 70% higher than past top

bandwidth

o Computing capacity: peak capacity at 70% past top

o Network provider: Availability 99% on a rolling 30 days

• Support:

o Customers (B2B partners and clients): 9-5 Monday to Friday

o Consumer (B2C users): 24 hour email support, answer in next

working day

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Playlist by genre

Get the results from the API

Tuning parameters to call the API :

&tag=techno

&tracksnumber=30

&listenercountry=fr

&popularitymin=25

&popularitymax=100

&yearmin=1992

&yearmax=2005

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Artists similar to

Astrud Gilberto

Get the results from the API

&artistmbid=bc710bcf-8815-42cf-bad2-3f1d12246aeb

&focusgenre=false

&focusera=true

&obscureartists=false

&popularitymin=25

&popularitymax=100

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Songs similar to

Rock the casbah

Get the results from the API

&id=66380

&similaritytype=100

&limitgenre=false

&listenercountry=fr

&popularitymin=50

&popularitymax=100