Welcome and thank you all for joining us at this
Audio Intelligence Optimizing Music Radio webinar which is going to be a 45 minutes session where
we'll be unpacking music programming and data. With the ever increasing amount of advancements
that are being made in the radio landscape there are avenues that radio stations can explore
that will aid them in gathering insights and then utilizing those findings to benefit the
programming status of their respective radio stations in order to, amo
ngst many other
things, improve the listener experience, increase engagement and also potentially unlock
new revenue streams. And so during this particular webinar we're going to be learning about the
latest trends, we're also going to be looking at technological advancements and best practices
in the field of music programming optimization with data from our esteemed panelists who
I will be introducing to you shortly. And we're very happy to see actually that this topic
has peaked a lot o
f interest globally we've got quite a wealth of different people from different
parts of the world, including those in Germany, France, the Netherlands, Belgium, Denmark,
Finland, Hungary, Italy, Austria, Portugal, Spain, Romania, the UK, Cyprus, as well as
further afield we've got representation in this webinar from people in South Africa, in
Russia, the United States, Indonesia, Qatar, as well as Trinidad and Tobago. So thank
you so much for availing yourselves. We do look forward to real
ly taking a look at this
very interesting topic of Audio Intelligence with music programming as the focal point.
My name is Andy Leve and I am your moderator for this session. Feel free to connect with us if
you have a comment or question, do pop it in that chat box. If we do have time we will absolutely
address questions as we go along or we will have a look at the questions and see what we can pick
up on at the end of the webinar. Without further ado let me go around the room by introduci
ng
our panelists. First up we have Antoine Baduel, who is the CEO and owner of Radio FG in Paris,
and then we have from Hyperworld, the Head of Research, Alexandre Houget, followed up by Lionel
Guiffant, who is the General Manager of RCS Europe and we also are joined by Robert Johansson who is
the Head of Programming and Music at Best Radio Programming, Gentlemen thank you so much for
availing yourselves and welcome to the webinar. Antoine, we will begin with you, who you've been
in this i
ndustry for three decades and more, and you've also got a passion for really discovering
a lot of talent, the likes of David Guetta, Martin Solveig, Bob Sinclar, Daft Punk to name but
a few, in your three decades plus of experience in the radio world you've seen a lot of evolution in
the space you know moving from analog to digital, and now we're in this artificial intelligence
era, can you take us through how you at Radio FG, as a niche specialist station, use these data
insights to achiev
e your intended results. Yes hello Andy and thank you for this question.
With Radio FG we've always been focusing on datas I mean this station was established in
1991 and I think we started the test and the panels in 95 while the phenomenon of
electronic music was emerging and because this phenomenon was increasing it was the
pure beginning, we really needed at this time to be able to make the difference between
an underground track and a high potential track. Of course we had discovered ma
ny famous French
artists with an international wide career such as Daft Punk, David Guetta or Bob Sinclar but of
course, if we identify those artists, it's because we made datas, because those datas gave us the
conviction that all those tracks had a much bigger potential than other tracks with the same quality
but for other reasons, maybe because they were released too early, too late, or maybe they were
too underground, they didn't fit with the market. So far at the time when we played the
track we
always had a very close relationship to datas. And of course the situation has changed a lot
because till the Y2K or 2010. The only way to have datas around music was the radio research whereas
since the DSP, the Digital Streaming Platforms, have been launched, the music industry now is
focusing on data, so we have all the datas and thanks [to] the partnership we have with Hyperworld
we changed a lot the way we asked for all the criteria and the way we
do the interpretation change
a lot as well Yes since the beginning we've been working with
datas and I think those datas contributed a lot to the marvelous and international careers
that Radio FG co-builds with the artists. So absolutely important points then you did
mention Hyperworld of course Alexander is going to take us through at the exact procedures that they
go through when they are conducting this research. But again looking at Radio FG with you Antoine how
does this research help you better understand the li
fespan of a track and also you know get to
know um more in detail listener habits ? it's so the the lifespan of a track really depends
on its exposure this is the first point. As Radio FG still is a niche because of course dance
music is a wide word but regarding the format of Radio FG we decided to keep our difference
and our specificity in playing two-thirds of the programmation of the titles as exclusive tracks,
that means they are not played anywhere else, so for focusing on these track
s, the lifespan
of a track is much different from another one produced by the same artists but playlisted
everywhere because there's a burn factor The datas are very useful for us because it helps
us to decide, first of all, for an exclusive track, that the people don't get bored or have
the feeling that, even if the track is not burned, that the station is always the same.
So it's a criteria of originality and DNA. And on the other hand of course the lifespan
of a track depends on its expo
sure everywhere especially on the other stations and the DSPs.
And regarding our listener habits we have, with that format of course, very faithful listeners.
Of course, as faithful listeners they expect a lot from the station. That means a regular renewal
of the playlist, that means to risk a lot, to take risk every time, when you're playing a track, that
means again to do tests, and panels, and research, and datas, so that you optimize your chances
to combine audience, risks and originali
ty. Wonderful and I think that very much
encompasses your formatting strategy. You are quite exclusive but you've got also
your finger on the pulse of what your audience wants by continuously conducting research.
Yes absolutely Andy, our formatting strategy consists in a hand having very precise and
serious results and datas, but on the other hand, sometimes, and Hyperworld know how
we work, sometimes not follow the datas. Because the datas are tools but they are not
our mind, I mean it's
like the AI, it's a tool, it's an assistance, it's support, but it's
never your mind and your conviction. And your conviction that sometimes you can have high
speed tracks but you can also have diesel tracks. They start slowly, but when they take
off, they are very strong and that's the purpose because you need also to have
an artistic consideration if you play hot tracks of your
format, that high format tracks, they're in your central format, it's much
easier to have them tested quickly
rather than other tracks who will sound more underground
So our formatting strategy consists in respecting those two-thirds of exclusive tracks and
two-thirds of new releases. That's the concept. But on the other hand, it consists in
really forcing our convictions letting time to several tracks even if the tests are very
difficult. Because it's, I mean, you have successes but you also have tracks that
are not in the blockbusters but we call it in French "succès d'estime".
"Succès d'estime" m
eans it's not a success but the professionals,
audience, the fans, the followers, fully respect the track, and it's the first step
for a follow-up that could be very successful. I love that because that still speaks to
the human element right still being necessary in combination with the data.
So that's quite an important consideration especially when it comes to music and
and really engaging and retaining fans. Antoine just another thing what are some
of the obstacles that you've been face
d with and how did the findings from the data help you
overcome those particular obstacles particularly because you are in such a niche market
I would say it's precisely the purpose of being a niche I mean when you're a niche especially in
France because France electronic music is very particular because in a hand you have worldwide
talents with a worldwide reputation and David Guetta, who still has for instance a weekly radio
show on radio actually since 25 years who kept amazing relationsh
ip with us and all the artists
who started on that station, have the same close and emotional relationship with us so first of
all first of stay cool when an artist becomes very mainstream what's part of him keeps inside of your
format in your station and what do... What is I mean outside your formats because it's sometimes
too mainstream so it's the obstacle first up first thing is when it sounds too commercial the datas
are here to give you the conviction that no your fan base loves the t
rack even if it sounds
a little bit too commercial because when you're with an underground DNA you always need to keep
your image but also following the tracks and the artists you've been always supporting that's
the first point yeah the odd point is always make a balance between your own convictions even
if sometimes you're right but when you write too early you're wrong and this is another point it
means we are on a French market and there are few comparisons for instance with the British
market.
I mean on the British market when you're very innovative you have like a bonus whereas in France
if you're too early it's a malus. I mean it's more difficult and we always need to be always pushing
the new genres and aesthetics of music forward but also respecting the market we're working on,
respecting the more conservative listeners. And the datas are an amazing tool, an opportunity
for us to have us play the right tracks, at the right time, at the right schedule, and
clocks in
the day, so it's a perfect tool for us. Great right tracks, right time and of course in
radio they often say the right kind of people. And those people not just being your audience
but the hosts that speak to the audience. So my next question to you Antoine then relates
to the importance, or what role, does having flagship drive shows, so the morning shows,
as the afternoon show, with strong hosts play on FG particularly when it comes to wanting to
retain your listeners and to further engag
e them. It's a good question of course as a moderator of
one of the big shows of Radio FG I will never tell you that the shows are unusual or not worth it.
No the shows are really useful for several reasons because when you are listening to a radio station
you're not listening to a DSP of course with no interaction, no moderation, no presenters. On
Radio FG the purpose consists in having two live shows with the moderators that are presenters since
many years. And of course with the flagship
show we have with big big names hosted on the station
with the big names, the big stars, I mean we had Madonna, we had Kylie Minogue, we had Rihanna, but
also all the DJs can come to the station yesterday we had Ofenbach, tomorrow we have Sven Vath you
know from the techno DJ. I mean it's amazing, because it's an amazing way to confirm with
interviews and the presence of the artists, our convictions and our decisions in the playlist.
I mean there's no playlist if there's no editorial envir
onments. And it's very important for the
listeners, for the station but also for the relationship we establish with the artists,
to have that talk show, that conversation, that makes the thing easier and sometimes I
need to tell you that some tracks that have very difficult results in the datas when the artist
comes to the station and presents his tracks, does a live mix, and when I interview
him and I see the conviction, sometimes I retest the track just because, I mean there's
something,
and sometimes after an interview the test can have changed a little bit or have
changed significantly because the interview and the presence of the artist has the thing go
quicker and that's pretty important. Wonderful, wonderful I love that yes the presence of good
radio host, as well as content and great music, and then of course the data aspect is all working
seemingly quite well for you at Radio FG. Antoine thank you so much for all that most insightful
conversation that we've had ther
e. Of course you're not going anywhere so if any of you that
are with us in this webinar have any questions for Antoine, as I said in the beginning feel free
to pop through your questions in that chat box. Now we're moving to the research side of
things because we've just had a lovely practical, best practice conversation with Antoine.
And now we're going to get into this whole business of data research and we've got Alexandre Houget
who is the Head of Research at Hyperworld Hyperworld is a
media marketing research institute
that is specializing in audio studies for radio operators such as Radio FG and the motto at
Hyperworld is "smart research for your audio". Now Alexandre welcome to you.
One of your main responsibilities is to define and implement study protocols that are you
now adapted to the specific needs of the clients. Can you take us through the process of the
recruitment, in the data collection process? Yes of course, but first I would like to react
to something Ant
oine said. He told us that musical research he is using is only a tool and
this is exactly what we think at Hyperworld. It may sound strange but when I first meet with
a client I always tell him if you are looking for answers I won't give you answers you will find
them in my datas but at the end of the process the answer is never "yes" or "no". You have to
place a title, you have to change its category, we don't provide with that kind of information,
this is the expertise you believe but al
so the way you feel music that will make the difference.
The way you read the datas will help but of course this is still talent and human which is
the main added value in the process. Ok, so at Hyperworld, we are in line with what Antoine
said, and it used to be the case previously with one client who told me I stopped doing
musical research because I feel like my music programmers lost their mojo if I can say so
because they were always hiding behind datas and the radio lost its soul, its
personality.
As an introduction, I just wanted to say that this is the exact same way we see musical research
but to answer your questions the way it works is that I have to say that the whole process is
online from data collection to delivery. We recruit respondents into major international access
panels. We have agreements with that panels and we ask them first to interrogate some people
who get some specific profiles and that radio station wants to integrate so we qualify them
through an
online questionnaire and if they fit the profiles we are looking for then we ask them
to listen every songs, not the whole songs but maybe 15 seconds of each songs, and then to evaluate it.
Do they like it? a lot? have they heard it too much? Or maybe they never liked it. So we want to
have information, some feedbacks from them. And at the end of the process, we gather all the datas
together and we deliver datas on online platforms with Excel exportation and also possible
integration to R
CS Selector or MusicMasters Interestingly enough you know in this age of
instant access I think a rebuttal would be well you know I have a radio station and I've
got an audience on social media, for example, why don't I just connect with them and do my
own surveying but then I guess the answer to that question would be the quality aspect
right that would be the differentiator between what you offer versus someone
saying let me just do it myself. You can't do it yourself of course but you kn
ow
there are now more than 15 years that we are working on the subject
so I think we have kind of expertise on it and I like to say that we are the guardians of quality
when it comes to musical research Since you always have to apply some golden rules
like neutrality, consistency, security. Of course you can ask directly people
what they think about music but you have to make sure that
they involve into the study, that they can listen to your material,
that they take time to sincerely answer you
r questions
- that sometimes they lie to you so you have to be able to detect it -
and you have also to pay attention that this is not always the same people
who give their opinion because otherwise you will make decisions based on only few people.
So you have to control your datas through all the process making sure
that you are building something solid and strong because the main point is to
deliver valuable insights to your clients, because your client has to trust
100 percent the datas he
is reading because he will take decision based on it
so it has to be something very strong. And this is what we're intending
to do at Hyperworld. Valuable quality insights, I love that
and I think that is absolutely key especially in this AI era
that we're finding ourselves in. Now Alexandre without getting
too granular and too technical, can you just walk us through the indicators
that you provide to clients like Radio FG in order to classify the state of a song according to
uh your music tes
ts yeah like Antoine said the the main interest of musical research and specifically
when we talk about call outs call on lines is to follow the life cycle of one title week after week
in order to see how different indicators evolve. Is passion, for example passion is the percentage of
respondents who will tell you that they really like the song, for example, is passion growing
up ? week after week? is passion growing up along with the burn? is burnout stable? is rejection very high at the b
eginning and very low at the end? So this is the type of indicators we deliver to our clients and with this data they can adjust their musical programming. But of course we can deliver many other
indicators but the basic ones are passion, rejection and burnout which are the
easiest indicator to understand. Interesting so repeat those for us again so it's
passion, rejection, burnouts. Yeah burnout is the percentage of people would tell you that they
used to like the song but they heard it too
much. Interesting I'm sure that's something many of
us in this room have all felt and said before. Alexandre thank you very much for again
interesting insights about your process at Hyperworld. Of course Alexandre is not going
anywhere if you have any questions pertaining to what he said, perhaps you have more questions
around other things that are still though within the Audio Intelligence framework please
do pop us a message in that chat box. Now Robert Johansson from Better Radio
Progr
amming you're with us here today as well thank you very much for your time. You're
a music programming consultant you specialize in optimizing and creating stations programming
strategies and music programming as well and you've also been in this industry for a very
long time, over two decades, I'd like to just pick your brain if I may Robert around the
benefits of this data capturing technology because I do know that you uh you do use various
software um G-Selector being one of them and we
will of course be talking to Lionel Guiffant
from RCS Europe and he'll talk us through some of the technological advancements in that
space. But just from your end as someone who uses these kinds of products what are the
benefits of this data capturing technology? I think the important thing now is that you
get you can do it much quicker like of course 25 years ago when we had pen and paper we have 600
people coming to a room testing songs two days in a row. That's impossible to do now now
we have
to do it well online or in other more specific ways but when it comes to the data I think what
I do with my clients most of them are doing call outs and AMTS or percent or Library tests we
make sure that we include the ID for the song into the test so we have everything in the sort
and when we get back we can actually get it back into well in this case G selector the next day
and I think it's really good to see we have the history data so one of my clients they have all
the music
research since about three four years in their selector now you can see every single
song and when they do the selection for next time they would really say that well this song didn't
test for three years why should we test it again so that's quite interesting then so it's a
combination of speed, efficiency and I guess a legacy so something to look back on and uh and
best make uh sound results for your for your music programming strategy then correct exactly um
following on from that how do
es having metadata tags result in better optimization outcomes
for music programming? I think the metadata is crucial for programming you have to make sure
that we sound as we like of course there are some metadata like BPM, gender, artist the release here
that's quite static so you know that but but when it comes to the sound it's really different from
Station to Station and what is your strategy I think it's really important to define the codes
in a way that's relevant for your station so
if you are like a classic rock you will definitely
call this song is different than how hot they see and the the same for the few songs that goes into
that AC Arena that also play on CHR there might be some songs that are on the both stations but
they shouldn't have usually not have the same code all right and then what makes research good and
and what makes research bad we we obviously spoke sort of about this earlier on uh with Alexandre. I would say that the sample is crucial you really
need that the right people for
example why test the CHR music on people that love classic rock obviously most people say that that's
natural but if you do a too broad invitation of of people to Department you might not know
who are in the test right and and then when the data importing uh process happens what
exactly are sort of the milestone on that well if I have all the ideas on in the music
test and have all the the data it takes like two minutes to import into G-Selector so that's a
q
uick year and of course you get a relevant data in a fast way um don't have to do it manually like
a couple years ago many stations spent like three or four days just to make sure that all the songs
in the library test was properly and inserted into the system. So last week we did a sort of a music
task for a clients and they got everything sorted in in this case in Excel sheet and like the next
day the whole new music was up and running with the proper categories so once again that speed
t
hat efficiency that Legacy and of course I think the word that ties this all together is Quality.
Quality over.... exactly wonderful thank you very much Robert for your commentary then of course Robert
isn't going anywhere either so if if you have some questions around music programming and perhaps
want to expand further on what we just touched on please feel free to once again pop a message
into the chat box and I'll be sure to to get to to those questions if we do have time uh at
the end o
f the webinar now Lionel Guiffant who is the General Manager of RCS Europe is also with us
thank you Lionel for your time once again Lionel um you've also for decades upon decades uh at
RCS and RCS has a proud history of innovation currently holding 45 patents in the field
of broadcasting and you are the inventors of computerized music scheduling with the
legendary selector you continue to lead the way with multiple award-winning products
such as Zeta radio automation G-selector music sched
uling and a number of others.
I would first be like to just pick your brain if I may around how how radio programming
software has evolved to leverage uh in a simple and powerful way all possible metadata and
the multiplication of programs and channels um making it possible to kind of manage all of
this what what take us through this process lots of questions one time well I will say that the
Robert already answered to some of them because they explained the good part of G Selector which is
importing the
data from the front external and you can now importing data from so many things you know as
research but also of course on the stream hope that how much people you had at the beginning
of the song how much people you have at the end of the song of course it's not very qualified but
it gives you some idea about what happened on your station. So G-Selector now it's become an old
software because it's more than 20 years old is on the market and we bring uh several new ideas and
t
he the main the main idea is that... you get you can choose the way you want to sell it to schedule
so if you want to schedule I said the old way was after artist, song and very very rigid things and
negative way we don't want that we don't want that today we turn that in the other way so you can
really say to the software this is what I want I would like and the algorithm try to answer to your
request you know and this is totally different um one other point you ask also is how we do to
ha
ve several stations the the original idea of our server one Library but this time we're coding so
we put metadata on the song for example you have a technical part titles title, name is it a national
or International music so this is technical because it will normally never change but all the
other spots like the sound card which is talking which is a very very important point it's totally
different from a station to another and sometimes from a human to another human you know it's if you
a
sk the question how do you feel the some color of these songs you never have the same answer but
um so so we have one base with technical artistic information and after you have the pure uh
scheduling way and all the things you want to add to it to to your content so it's very it's it's
giving us lots of flexibility for that and now Radio FG is one of the best example because today
they produce when maybe around 40, 40 stations from one base so uh you know it's uh of course
the quality of t
he the work you do how we say coding the coding the song is really crucial
of course it's the beginning of everything wonderful I think it's it's quite Innovative
that you can take as you said in the radio FG's example uh through one Library you can then
have substations uh that then create a different playlists and then you've got a whole other
you know captive market over there uh very very in Innovative indeed um Lionel now more
with you I I just want to ask you about I've spoken to to R
obert obviously about you know going
to research and bad research Etc but when it comes to data uh uh similar to the question asked
before what makes bad data versus good data level is not easy because we just received the
data from the external so we don't know if they are good or not but of course the you know the
quality of the data and how they are produced and it's very very very important to Alexandre
also explained lots of details around that so I would say that today I have some goo
d
data is really uh decision maker because well now scheduling in your song I would say is
something that more or less everybody know and we know the story how the song we lie uh
during two months three months six months but the decision is to know which song you put into
that so it's where data is really okay because it's give you the decision the final decision
and it's it's really why it's so important now And I guess as a follow-up to that uh you know
with live streaming becoming so po
pularized and a lot of people jumping on to listening
uh online listening platforms and kind of curating their own playlists Etc how does live
data assist programmers uh instead of having them rely on audience measurements
or surveys which aren't as frequent? it's another human decision because you can
directly inject the desired data and change the scheduling in life I would say or the day after
or the next hour but of course maybe it's the maybe too tight you know you have to take uh to
thinking a little bit and and to to analyze more the things in global and before before doing that
but of course now we have so many informations coming to the station you know and the last the
last very good is of course that the electronic result where we can have with several days
or weeks so very very good information regarding what we had in the past you know it's totally
different speaking of things that are totally different is there anything that's new and totally
different either i
n terms of new products that you are going to be releasing soon or new features
anything like that on the horizon from RCS? I would say that G Selector is is now very mature you know it's a product that's
uh not move a lot there's so many things inside it's we talked before about what AI what the
intelligence artificial can do for that and it's where we're working today of course take
all these data coming from all these places all the different research or informations coming
and see how w
e can work even you know I will say today the the idea is to know how worked with
the new things so we're all learning together All right thank you thank you so much Lionel
there I just now am quite time conscious it has just gone quarter to three CET so we have
been uh talking for about 45 minutes but as promised I did say that we would cast our eyes
to some questions from those of you who are in attendance here if you have any questions for our
respective panelists,please do let us know I
see there is one here on the screen that's saying a
lot of data are generated today on music streaming platforms some are available through API's is there
a way to integrate them into G-Selector, Lionel that one is for you...
for sure it's it's what we do every day
we're using the API to enter the information or etc etc except format or so so the station
can go the the RCS support somewhere and you will have the answer because
it's something we do every day now it's totally open to that.
Okay Omar thank you for that question I hope that answer answers your question. Anyone else ? any more
questions ? please do feel free to pop your your question into the chat box we do have
a few minutes left to further engage with you What Lionel and Robert said was really interesting and I think
we also need to consider the data as something global. I mean you have of course the research
that we do with, for instance the call out as Alexandre said, I mean so when you talked about
the good
and the bad datas I mean they are the good and the bad questions. Sometimes the
questions you ask but you know the answer, so this is a bad question. And you also
have the other datas like, of course, among and beside the call outs, you got of
course the Shazams, the social networks, the top streaming and this is pretty
important I mean with Radio FG in the inside of the Radio FG Group, we run four
radio stations in Paris terrestrial, I mean so FG for house music, deep house and I would sa
y
quality music in general, FG Chic for lounge which is a very small niche Maxximum for alternative
genre in electronic music and of course for that music you you can't invest on data like call out
which would be never recut in your business model of course but on the other hand these are emerging
genres, and all the other datas also need to be considered, because at the end this is the balance
is the one who made by the Program Director and it's pretty important because if you only focus o
n
one kind of research, one kind of data, of course you will really consider focus on the target
group but you will - now the market is hybrid people are listening to radio stations inside
their cars FM, DAB, sometimes with their app, in the street, on special devices like you know
Sonos or whatever in their living room so I mean and of course they're not listening to radio
stations only, they're listening to your brand on YouTube they're listening to your playlist on
Spotify and it's real
ly important to consider that each device also is a way to a target group and a
kind of people who follow you so the if you do the right balance I mean the right balance it consists
in considering all the jobs but also all the datas Robert do you disagree or agree or have
anything to add to what Antoine has just said No I agree on when it comes to well the
difference when it has the music the new song is really hard because one problem I would
say when songs don't get the good score is that
they try to test the song way too early right
and of course I guess for Radio FG that must be much harder because it's niche station they
don't have the same reach as let's say if you have a new song with Miley Cyrus of course
that song it's exposed all over the world in all different platform it's much easier to to
quickly get a reactions whether it's good or bad Anyone else, Alexandre anything from your end I mean Robert told before that the key point
when you make some research if we ar
e only talking about research it will have a good sample
and that's where that's the key but also you need a strategy behind you need to have a station
positioning you you need to have many things this is not only about the research about data
this is also about how you are you what do you want to do with your station and what are
your belief that also a key point to me. Lionel any closing commentary from your end.
You know we are the machine we own the machine from everywhere so how is wo
rk how it is and after
the people decide so we are in the machine but yes all the last commentary are totally right yes it's
someone some year ago we're talking about metadata you know Big Data but somewhere today we have lots
of channels you can give us some data about what's how react the audience how reactive people which
kind of people as as a Antoine explained it's really very different today we must be capable to
use all this information and after that it's how to make the decision bu
t uh it's the work from the
people scheduling or making the station the things but uh but I don't know if uh AI will really
help us one day for that let's see I would change yes we'll see I think this is you know.. Uh, we've
only scratched the surface here we've done what we could you know in an hour's conversation
I think the AI conversation is something that uh can be had for a much longer period of time and
of course we are hoping to further extend and have more of these kind of webinars
where we we
delve into other aspects of audio intelligence so I would like to just uh close off by thanking
you all for attending and giving us your time uh to the gentlemen uh my panel thank you very much
for engaging and also availing yourselves to uh to have this uh much interesting uh conversation
around audio intelligence looking at data and music programming. Suffice to say it's a good
blend of data music and artistic intelligence that are the markers of the industrialization
at sca
le of customizable programming and the hyper distribution across multiple platforms so
it's a large space like I said to you and we will um look at perhaps having another or
several other webinars where we can further expand on some of these topics and also delve into other
avenues uh around the audio intelligence domain so uh if you've got any other questions and that
we haven't necessarily gone to now or suggestions around subject matter or topics around this
umbrella topic once again ple
ase feel free in these closing minutes of this webinar to pop in
whatever questions comments that you may have in the chat box from my end my name is Andy Leve
once again thank you very much for your time and yes we will be certainly doing this again so
do look out for all of us on social media where we will keep you informed about any other webinars
that we will be having. Gentlemen on the panel once again thank you very much for your time and your
expectations it's been a great pleasure.
thank you.
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