One of the key things is let's not the research company pretend to be operating in isolation
. Let's work with our customers to be integrating their data in our data together , because that tells the complete story .
Speaker 2Welcome to Futureview . That's the very engaging and quite brilliant Tom Vice . He's the CTO and chief data scientist at MarketCust . For those of you who don't know , MarketCust has been one of the traditional powerhouses in entertainment research and has expanded significantly in recent years , introducing a broader remit of services combining data science , marketing , effectiveness solutions and primary research across sectors . In full disclosure , I remain a small shareholder in the company , but I haven't been involved operationally for a number of years , so it was great to catch up with Tom , find out what's going on and get his perspective across a whole range of areas , building from his almost 30 years of experience working with multiple data streams , launching mobile phone networks to measuring , TV building , ACR and attribution solutions and revamping brand tracking . There's always a ton to learn from Tom .
Speaker 2So on to the interview . Tom , thanks so much for joining me today . Real pleasure to have you on the podcast , Thank you . Lots and lots to talk about , but first of all
I want to start with a bit of an icebreaker and see if I can delve into your dark and possibly dubious past . No , I'm sure it isn't dubious , but what I wanted to know is just one thing that most people wouldn't know about you , that they wouldn't be able to find out just in the public domain .
Speaker 1Well , I did release a punk rock album last year that is an actually publicly associated with me under the Monica Dr Rope Maker . It was a lockdown project . It's very angry music but if you feel you need to challenge that anger , then the Dr Rope Maker is available on all good streaming services . I was very pleased . Actually , I got my first royalty check from Spotify $41.83 , which I thought not bad for years streaming .
Speaker 2Tom , I'm so impressed . I would dare to say it's about $40 more than I expected you might have made Without being rude , so I think you're doing very well . I also heard a rumor I don't know whether you can talk about this on a podcast that you may once have been in trouble for breaking into the police computer , is that ?
Speaker 1true ? No , I never got into trouble at all . It's certainly true that when I was younger I mean this was back in the 1980s yeah , I mean everyone had to get on the police computer . Yeah , well , it was just a dial-up number and you were dialing and you could get in . You couldn't get far enough to really see very much without a little bit more work . I mean , everyone with a modem . They knew what they were doing was always and it was just one of the fun things that you did . But of course back in the 80s I'll authorize that accessible computer . What's the criminal offense ?
Speaker 2I hadn't thought about that , of course it wouldn't be . I suppose the legislation wouldn't have even conceived of that at that point . Anyway , that is the start , clearly
, of a glorious career and interest in all things tech and data . And so before we get onto marketcast and how the business is evolving , I wanted to maybe take a little bit of a trip down memory lane and some of the highlights of your career . I mean , you've done a lot , but I was wondering if you could just sort of pull out a few highlights from your perspective , tom , in terms of kind of major career milestones , what you did and some of the things you've learned along the way .
Speaker 1So I mean I started off as I'm starting playing around with computers when I was about 10 , I think my first bit of software was probably published when I was about 12 or something like that and then I became a proper professional software developer . I was doing software with electron microscopics basically , and I worked with a company called Oxford Instruments who were based in High Wycombe and it was fabulous . So the team was just moving to Windows when I first joined them , having previously done everything themselves in scratch . So I got to work with a bunch of really smart technologists who were just learning how to deal with off-the-shelf software , I'd say for the first 70 years of my career .
Speaker 1Probably the highlight for me was when I graduated from being a software developer to being a project manager and I used to basically run all the projects out in Japan . So I mean that was great for someone in kind of their mid-20s going out to Japan learning how to eat sushi , how to eat sake , et cetera , but also learning a little bit more about what international business was like . And the thing that I got the bug for was actually it's really interesting working with people from different cultures , and that probably characterized most of what I was doing , I guess . Then the dot-com era happened and , as everyone did , you had to get involved and do a dot-com and we raised a load of money . But it all blew up very , very quickly .
Speaker 2Why was that , Tom ? What was the business ?
Speaker 1So , funnily enough , the business model was to do streaming games over broadband and so it was exactly the Steam business model . But in the late 1990s and the year 2000 , the broadband was only just starting and the bandwidth wasn't there for streaming games and so we just tried to do too much too quickly and it didn't work out . But again , work with a whole bunch of great people there . Harris used it out of that into a role where it was basically retraining COBOL programmers how to do stuff in the internet , which I think there was a lot of people doing in my position and I ended up then working for a shop called Kevin Cunnington at T-Mobile and he was kind of the lead technologist for all things innovative and I was basically , you know , I was running kind of change management and stuff like that for him . If I'd learned if a doctor in instrument said , learn how to write code in the dot-com era I'd learned how to scale things quickly Like it's . When I was at T-Mobile I learned how to manage things within a larger organization .
Speaker 2What lessons would you see together in terms of scaling quickly ?
Speaker 1Well , I mean it was very interesting . I mean , I think the key thing is getting the right . So when you're scaling , one of the biggest problems is getting the right people through the door , training them up , and I've always kind of focused on the new things , so it's always been about getting new people in , or the game folks . At the dot-com . We didn't have anything to hold us back and we were just trying to be do too much too quickly . At T-Mobile there was an awful lot of organizational inertia and so that kind of naturally gave you a slightly more conservative approach and slowed you down , and there was more about OK , how do we work within that , but still do things faster ? I think the average cycle time was kind of 18 months and we started doing things in three , four months .
Speaker 1I remember it was Mobile World Congress when we launched 3G . It was this great demo where you could see how fast 3G was , but only worked if the phone was on the table , Because the only 3G base station we had working was underneath the table . If you took it too far away , the signal wasn't strong enough anymore . So it was things like that where we were yes , we were doing a lot of innovation , but we weren't going too quickly because we had these conservative people to hold us back a little . And I think one of the things I've kind of always learned is you need I've always been very much at let go quickly , do the new thing . I've always needed to find people I can partner with , who are a little bit okay , let's think a little bit more about this , let's not be too impulsive , etc .
Speaker 2So then , moving on , Tom , through your career . So then you then got heavily involved in the TV data world . So can you talk to me a little bit about that ?
Speaker 1Absolutely . So basically I left T-Mobile shortly after I got married and I kind of pushed by my wife into let's try a little bit of entrepreneurialism , yeah , and with that , you know , started off doing a TV recommendation
engine , which was great fun when recommendation engine was a thing , then did some stuff around , set up box data when that was a thing , and they all went really well . It's been two years with inside GFK , which I kind of I learned how to do market research . And then a few years later I started working with Zed and Michael who had a company called Cognitive Networks outside of San Francisco , and I spent two weeks over there with them looking at their smart TV data . And at this point there wasn't such thing as smart TV data but they had this ACR algorithm automatic content recognition that would detect what was on the glass .
Speaker 1Yeah , what does that mean ? Do you think we can make things that look like you know needless and ratings doing this ? So you know , I went in there . Look , I don't know I was looking at the right tool , blah , blah , blah . But at the end of the two weeks I actually was starting to see the shapes that did look correct . You know , we were seeing that kind of time of day , day of week type shapes . Yeah , we were seeing numbers that were , you know , looking similar to the kind of ratings that were published in the trade press etc .
Speaker 1And you know , for me I thought , okay , well , actually here's something really interesting that I think could be heavily disruptive . So I spent , you know , most of the next few years working with them . Yeah , michael would go in and say , hey , we've got 7 million TV . I think it's 20 million today , but at the time we've got 7 million TV . I knew we didn't quite have 7 million , but you know , we had , we had almost that number , and then like , let's go to the data , let's go to the phone , let's try and find how it works . So for me , this was , this was my kind of break into the US media market and you know , for me it was a fascinating market .
Speaker 2Yeah , and then Tom , just to back up a little bit for those who are less familiar , can you explain what ACR data is and then how you were able to use it ?
Speaker 1So what Michael and Zev had ? They had this technology that sat on the TV and it would detect anything . It had automatic content recognition , so basically it would detect any image that hit the glass , yeah . So what they'd set up is they'd set up a system where they would ingest video from the top few hundred TV networks in the US and each frame of those videos they'd create a fingerprint and the software that ran on the TV it would detect when it saw the same fingerprint . So the software would have to say , hey , I'm seeing this fingerprint , send out a stream of them , and then those fingerprints would be processed . It would say , okay , actually this TV is watching NBC , this TV is watching Fox , this TV is watching that . Yeah , and that looked like a signal , but it would look like Nielsen ratings .
Speaker 2And was that ultimately how you ended up using it ? I mean , I know there are lots of competitors to Nielsen out there and that's a big space that we may well get on to . So was it around actual TV ratings for programs or was it more about advertising ?
Speaker 1So there were lots of use cases . I mean so far all of the competitors of Nielsen live in the InScape data now as the video it really underpins all of the emerging currencies but a lot of the early use cases . So I mean I was going to vote in the ratings discussions because it hadn't made it look good . But I mean retargeting was a thing for a while . Yeah , advanced audiences has always been a thing . How do we target beyond age and gender ? But I mean a lot of it . Nielsen everyone's thrown at me .
Speaker 1Everyone trades on Nielsen because it's currency , but they wish they didn't have to pay hundreds of millions of dollars a year for it . So if there's an alternative data set that they think they may be able to use , they're always going to be interested in trying it . I think for me the kind of poster child would probably tune in . A lot of people did very well out of tune in , because you know tune in . What you do is basically you see , okay , someone being exposed to a promo for this TV show . Did they then go and watch that TV show ? I'm thinking you've got the same device . You can see where they are in most of the ad . You can see where they are converted . It's very easy to build attribution solutions around that kind of environment .
Speaker 2Yeah , I could very much see that , because you're not dealing on , you know repo , for instance from a survey , and then try to knit different data sets together . It's all through the same device .
Speaker 1It's all one thing . You don't need any density graph , you don't need anything like that .
Speaker 2It's funny what you said about Nielsen . Someone gave me the phrase the other day that you date the other providers but you end up going home with Nielsen , yup , yup , anyway , which may or may not have been a comment on their dating behavior , but anyway I just thought it was a funny little phrase around that type of world . So , moving on to where we are now and the connected TV landscape , I mean so people have been banging on about this forever , tom , about this kind of this , you know , supposed sort of panacea or wonderful world in which you're going to get the detail of digital targeting with the quality of TV inventory . Are we at that point now or where are we in terms of connected TV progression ?
Speaker 1So , yes , we are Absolutely , because you know , I mean , if you look at CTV ads , bender US , it's about 30% of TV advertising expense . It's a 70-30 split , yeah , and so pretty quickly that's going to go over and I think it's partly driven by advertising but really it's driven by consumer behaviors . Yeah , so consumers like streaming TV . It's cheaper than cable . Yeah , being able to watch what you want to watch when you want to watch it is . You know Netflix has kind of paved the way in that experience . But you know there's a lot of ad supported experiences as well , including there's an Netflix ad supported tier . Everyone needs to see seven ad supports in our nothing . It's more driven by consumers desire to watch on the streaming environment than it is by , you know , advertisers wanting to do that .
Speaker 1But you know ultimately what brands have to do is follow the audience and there's a whole bunch of audiences that you cannot reach on linear TV and cable anymore . You know , if you look at younger people , it was funny . So you know NFL has always been a stable cable . You know you go and you watch it on your Fox or NBC , and then Amazon did a deal for Thursday night video . What was fascinating is NFL has always skewed . Certainly in recent years it's always skewed older . As soon as you have 30 night football streaming on Amazon , you'll discover it's full of young people watching it . Yeah , and it wasn't that young people weren't interested in watching football , it's that young people didn't want to watch it on a linear TV network .
Speaker 2That's really , really interesting . So for the NFL , clearly that's a strategic decision . Which type of thing I imagine the NBA and whoever else's rights are coming it will also be considering in that if you want to keep the store fresh for a younger audience , you've got to think about new distribution platforms .
Speaker 1That's actually right and I think the reason advertisers , advertisers are following into CTV because that's where that younger audience is going . You know , if you're wanting to reach them , you have to buy CTV .
Speaker 2And , Tom , just the point you've made just now around it's really been more of the advertisers following the audience rather than the advertisers driving it . Why is that being why ? Advertisers not keen , and it seems to me , conceptually anyway , that connected TV or streaming TVs is a great proposition for them .
Speaker 1But why would you want to do anything different ? I mean , from an advertiser's perspective , if your current strategy is working , you want to continue doing that and , yes , you're always going to have some budget that you can experiment with , that you can try on new things . When you've got to make wholesale adjustments to your media plan , to shift hundreds of millions of dollars from one medium to another , there's always risk involved in that and it goes back to that thing I was saying earlier about me . We've been one of the guys to change everything . You actually need to be working also together with the person who's going to slow things down a little bit . I think when you've got billions and billions of dollars being traded , there's a lot of inertia behind that , I see , and you don't want to make changes and let it go after you and I guess , to be fair to everybody , there's probably inertia .
Speaker 2that's just inertia because it's the way we've always done it . And then you've got ideas like sort of switching costs . You've got CMO of big CPG company X who's going okay , if I'm going to put whatever it's , a couple of hundred million dollars against connected TV , it's going to cost me more . There's the opportunity cost of people doing set that up . Where's the return ? I'd imagine that that's also the argument .
Speaker 1I think if you look at what people like NBC have done with Peacock , they will sell NBC and Peacock together in a package . So I think that's lower friction for a brand than if they're going to have to decide am I going to need to buy on Netflix as well , because actually they're buying from the same person . They're just saying okay , well , you shift some of those linear dollars on streaming .
Speaker 2And then is there any kind of makeup provision between the two different outlets ? So if they're not getting what they thought they were going to get on linear , then they want to make up to them on connected or streaming TV , or vice versa .
Speaker 1I don't know how widespread that is , but I certainly know it has occurred .
Speaker 2Interesting ? And how are the not necessarily newer entrants , but somebody like the YouTubes of the world , who I think of as more small screen mobile first content providers that were really in this game and now they're heavily , heavily in this game .
Speaker 1Yeah , absolutely . It's funny . I've had several debates with people over the last year and YouTube is a TV . It is a big stream medium . You just have to go and ask anyone who's got a big YouTube channel . Share their stats with you and you'll see that probably over half of their viewing is on the big screen . And that'll be everyone , from big things you expect to be business to business down to people who are showing stuff that you would expect here on the big screen . So YouTube is bundled with every smart TV . It's on every streaming stick you put in . It's on every set of box . It's absolutely a big screen proposition and that's what people are using to watch it .
Speaker 2And so it's one thing just taking linear TV and putting it in the streaming environment , but that probably not going to work as well as something I would have thought anyway as something that's native and actually designed for the different environments . And then you've got many different types of environments in user experiences . So how's that all working ?
Speaker 1Yes , I mean there are definitely . There's definitely more experimentation for different ad formats on TV . We see quite often the QR code type one . You've also got pause ads on some platforms . People are experimenting with different things . I think they also A B test in the wild a little bit easier . I think there are the kind of problems that you've had . You know , frequency is quite often a problem on CTV . You think you know the advertising is sold by so many different people .
Speaker 1Yeah , so , you know , let's say I want to be targeted by Dashwater . You know they've worked out , I drink their drink and they want to target me . Yeah , they're going to put out the bids to target me and you know all of the different streaming service providers I use will say , oh , yeah , we'll take a bit of that . Yeah , all of the ad nets will say , yeah , we'll take a little bit of that . And before I know it I'll have 30 ads for the same thing . And it's very hard as an advertiser to de-duplicate because you know they don't have a unique ID for me . They just know I'm in the target profile and you know there's no individual ID . So frequency has been a problem on CTV . The other problem is that there are either seems to be a glass of inventory or not enough . So certainly the retirees , where you know it's impossible to get on what you want to get on . But there are other times when it again that makes frequency problem even worse . You know , because you know there's just too much inventory .
And so by inventory you mean inventory you can buy . Yeah , Exactly . What are some of the ways in which the research and data world is beginning to solve these issues ? I mean , this is something you've explained to me in the past , Tom , but I'm thinking about how you knit together the different data sets .
Speaker 1Draw to speaking of two different ways to do it . You can do it behaviorally or you can do it by asking people questions . Most people tend to do a combination of both . Yeah , so if you look at the behavioral one first , the great thing about behavioral is what it can measure if you complete picture four . But it can't measure everything . So , for example , we can put what we call a pixel tag into it a lot of advertising and we can get the IP address if we've been exposed to it . We can license , for example , vzo's in-scope data and we can see who's been exposed to the add-on of VZO TV and what their IP address is . We can match that together with our pixel tags and we can start to see okay , this is exactly the frequency these people have been exposed to . The problem is that doesn't cover every form of advertising . So , for example , exposures on who you wouldn't get to see , exposures on any of the meta-properties you wouldn't get to see , any kind of search appetite that you wouldn't get to see . So that will give you a comprehensive but incomplete view of who's seen your ad .
Speaker 1The other approach is you do some kind of recall-based survey initiative . So , for example , we've got this brand at MarketCast . We've got this brand effect survey where we've talked about 3.2 million people a year and we ask them questions about do they remember seeing these ads ? We've got other survey-based solutions as well , but it's similar that you're relying on people recalling the ad . Now , the problem with this is recall is highly influenced by how good the creative is , how many times you've seen it . It's also impacted by how well-known the brand is . You'll more like to remember seeing a Coke ad than you are a Dashwater ad , and so the advantage of survey approaches is you can cover everything , including search ads and Hulu , but you're only slightly incomplete in terms of the fact that you're relying on human memory . So what most companies don't do is you end up doing a combination of both . You do something which is behavioral-based to give you full information on what that covers , and you do something which is then survey-based to give you a full market view , albeit slightly more incomplete .
Speaker 2But how do you then go further in terms of potentially then tying those two data sets together , and then you're trying to also go down the funnel and see what the outcome is . So what are the some of the techniques that are used to do that ?
Speaker 1Absolutely so , knitting the two together , you can either do it using that IP graph . If you've got the IP respondents , which can be , you can do it online . So you get the IP , that's the respondent . If your respondents are the same people who are in your device graph , that's all great . You can knit it together that way . And , of course , the other way you can do it is just using a more standard fusion technique where you say , okay , here's a cell of white women from Missouri in my survey data set and white women from Missouri in my behavioral data set , and you can tie things together that way In terms of outcomes . So you know , historically , you know you can do it online , but you can .
Speaker 1There's been a lot of . You know that digital has grown a lot based on , you know , attribution . Yeah , you know , Google's famously introduced their last click attribution , which means that you know if you click on a Google ad just before you convert , google takes all the credit for the ad Brilliant bit of marketing for from from Google . They , you know , and that works , basically because you know Google can see that you came through , you saw the ad and they can see you clicked on it . Yeah , and you can put stuff on the ad so you can work out if you can , if you converted yeah . There are a bunch of other behavioral approaches you could do around . Multi-touch , yeah , and they all involve time things together based on some kind of device graph , so it could be an IP address
, it could be a mobile ad ID , yeah , and this is a whole industry that you know . Apple is aggressively , you know , trying to manage out the big distance .
Speaker 2Yeah , that was what that was . One of the obvious follow-on questions is the extent to which all this is Privacy compliant or privacy forward . I guess it is in everybody's opted in at some level .
Speaker 1So it seems that everyone's opted in . But I mean , I would say you know definitely the move toward medium mix modeling , which is the more survey based approach and it's also a slightly more econometric approach as opposed to the deterministic . Were they exposed , did they convert them ? I think there's a whole bunch of categories where I think you know Media mix modeling and econometric solutions are more appropriate than ones which are , you know , the pure attribution based . You know if you're , for example , you know CPG data , you know you can't license that to . You know that that used to be .
Speaker 1Why do you like it in the US ? It's not . Why do you like it in the US anymore on a on a one-to-one match basis ? If you look outside of the US , you know matching on IP addresses is more problematic . Some people will do it on a hashed IP address . A lot of people take a more conservative view on the privacy side and finish it . They're not going to do it . So we're kind of entering this world where there's a degree of deterministic Attribution but actually people are relying a lot more on statistical methods now , so as if you're going to take the deterministic approach , you've got to have some form of deterministic data .
Speaker 2Well , that's a map against . If you can't get that , you've got to go back to some of the old methods .
Speaker 1Absolutely , absolutely . And also I think you know you can't just do lower funnel advertising . See that you're kind of your lower funnel advertising is what drives your conversions . You know , and that's always been your kind of direct response . You know , pull now and I will give you the special offer on the car . But you know , if you look at what you know , coke spends most of their money on it brand advertising .
Speaker 2It's anybody , as far as you're aware , trying to tie the the brand effect advertising and maybe actually playing this Direct kids , your , it's your hands , given that I think that that's what one of Mark Carson you products is is described . But the brand effect advertising Down to lower funnel and showing , if I can , whatever you know , create this level of that brand recall or I get these Brand attributes up to a certain up to a certain level , this has a direct effect on the bottom line is anybody managing to solve that conundrum ?
Speaker 1Well , so there's , funnily enough , We've just actually I know you didn't set me up , we did we did just launch our brand tracking plus product recently . As you know , I mean I've always been more of a data guy and a software guy and I guess in my kind of role at market cars is to try and take Things that have historically been survey based and research based and make it so they use a Con , a combination of different data sets . You know , one of the classic problems you have with survey data is If you ask people about their intention to purchase , you know it doesn't necessarily correlate to do they purchase or did they purchase . You know if you ask people did . If you ask people , you know did they purchase that something , you know actually half the time they're wrong because they miss actually what brand they bought , etc . Etc .
Speaker 1Yeah , so you know you , that's kind of stuff where you really need to be bringing behavioral data sets I'd have a very bottom of the funnel and then combining them with , you know , mid and upper final stuff . And so you know , last three years I've been in my past I've been working on lots of things like that . Brand tracking plus is one of the ones we just happened to have launched Literally in the last month , so thank you very much for raising that .
Speaker 1I'm very proud of that work that's gone into that .
Speaker 2I should say full disclosure . I'm still a small shareholder market cost , but not operationally
involved . But I do want to understand a little bit more about this . How does brand tracking plus work and how does it tie together the consumer funnel ?
Speaker 1So basically what ? So one of the said there are two more components of it . So we , we built together a founder model which is in a basic kind of model of how brand activity works . Yeah , and we have different drivers for that . We can help explain the story for how the brand is working now .
Speaker 1But what funnels up to that is , yes , absolutely survey data , but it's also other data sets . So you know social monitoring . So we know we , we pull in all of the Twitter data . We can look at tiktok data , reddit data , etc . Etc . So we can see what are people saying about the brand . You know that's a great are in real indication of intent , people talking about things and they're asking questions great indication of intent . Yeah , google search data is another great indication of intent . We've got that real time . Yeah , and the other thing is then , you know , integrating in brands first party data . All of these , you know , all of these consumer brands are building up , you know , mountains , the first party data . I think you know one of the key things is , you know , let's not , as a research company , you know , pretend to be operating in isolation . Let's work with our customers to be integrating their data in our data together , because you know that tells the complete story .
Speaker 2That makes an enormous amount of sense and both points and playing back the brand tracker . So it sounds like there's a certain sort of model schematic around what market cost believes is important to track in terms of what guys found them , and You're tracking those components and I see that will vary slightly , yeah , for a given client , and then you're building in multiple data sets on top of that to actually see Whether those perceptions and those brand attributes are moving , but not just rely on survey .
Speaker 1That's exactly right . I would say I'd probably go a little bit stronger than saying these are the different components that we think make it up . This is what we've looked at from all of the years of data we got and all of the extensive research we've done . When you start to include non-survey data , these are demonstrably the things that drive the value of your brand and fandom .
Speaker 2So , Tom , are you able to give me any sense of the components that you believe a brand should be tracking the elements of fandom , or is that proprietary and part of the secret source ?
Speaker 1No , no , it's based on the research that we've done . Basically , we've found fundamentally that there are three different things . We talk about them as presence , distinction and brand elements . The first thing is how much is the brand in the market and how much is it breaking to people's consciousness ? How much do they recognize ?
Speaker 1Presence is very much survey driven , but we're also looking at what's the media spend , what's out there , but how much we're talking about it and seeing it , remembering it . That's presence . The second one is distinction . How much is the brand unique ? How much does it stand out ? Is it trustworthy ? Yes , the brand might have presence , but if people don't remember it and it doesn't stand out , then it's not going to be effective . Again , we can see that a lot in social media , our people talking about it . Then the final one is relevance . Yes , a brand might be out there . You might understand that it's distinctive , but you might not feel it's unique , the best brand . They trigger this feeling in everyone that it's relevant for them . We can see that not only through survey data , but really that's what drives consideration and intention . But we see a lot in search data . We tend to talk about presence , distinction and relevance . Those are the three components come to make up our fandom score , which is what really drives passion amongst consumers for your brand .
Speaker 2Yes , that's really interesting . I imagine in time you can evolve a model with each client as well in terms of the type of scoring they should have against each pillar for their category . Then if you've got their first party data , as you said , you can also start to validate that in market , as in you're a brand where this is particularly important for you this pillar If we can see movements against these data strands , it's more likely to affect the bottom line .
Speaker 1Yes , absolutely . What do you need to do if something's going wrong ? If you lack presence , then you probably need to be spending more . If you lack distinction , maybe there's something wrong with your creative . You need to be a little bit more edgy . Perhaps you don't communicate . Your messaging isn't quite right . Messaging is probably primarily about relevance . You know what your audience is . If you're not resonating with your audience , you can think about what you need to change in your messaging .
Speaker 2Again makes a lot of sense . It also feels like a version of brand tracking that's a little bit more dynamic and actionable over a lot of the old solutions .
The key thing to me is it's got to be actionable and it's got to be fast . The main frustration that I see in the research area quite often a lot of this data arrives when it's too late to do anything about it . It's like oh , I discovered at the end of the campaign that my consumers don't find it relevant . That's a bit disappointing , isn't it ? We'll try and do better next time . A lot of what drives me in a way , is how can we get survey results and big data results in people's hands faster ? They're not only actionable , but actually they come within a time frame where you can action them .
Speaker 2That's probably a very nice segue on the next subject we're going to talk about in terms of usage of AI and AI in terms of efficiency and speed . I think there's also a sense of talking to brands and different research agencies that for too long , the research industry has made perfect the enemy of good . 80% there in many cases is good enough because brands need to make decisions and they need to get on with it .
Speaker 1It's funny I've actually got a post coming out later this afternoon where I talked about exactly that point . Survey data is crucial because it tells you about what goes on in people's minds . If it comes there too late to be acting upon it , then we're forcing marketeers to use imperfect signals to optimize on . The real opportunity for us as a research industry is to get that data really , really quickly . If you think about something like a copy test , where you want to get 200 respondents , you can get 200 respondents within an hour . Why does it take three weeks in order to get my copy test results ? It's because there's time wasted up front when you're setting it up . There's time wasted when you're testing it . We're probably not targeting it sufficiently . When we're in field or we're going for a segment which is too granular , then we're wasting time doing things manually . Afterwards we might have made mistakes and have to refield . Then we're having to go through it manually . Do a bunch of data processing and reporting For me .
Speaker 1A research analyst should be thinking a lot about what are the questions they want to be asking people and how are their minds working , and they should be thinking a lot about what do these results mean and how can a brand make adjustments in order to improve their performance , whereas actually at the moment , what they're spending far too much time worrying about is how do I get this damn table working in Excel ? How do I get this survey programmed in this way ? And I think the promise of AI is we can take a lot of that out of the research process and we can focus on the value-adding areas . If we can do that and really do a survey within an hour rather than weeks , then I think , first of all , whoever gets their first , you have a math professor advantage . But also , I think research has become so much more valuable again .
Speaker 2Yes , as you say , it actually creates the space and the capacity for humans to add the value on top . It's fascinating .
Speaker 1It's absolutely right . I mean one of the things I've done a lot in data science area . We do a lot of predictive models and one of the metrics we use is is my model better than random ? Because if you want to know which category does someone go into , basically random is your baseline . If you've got a model that's 10% better than random , that might sound like it's absolutely useless , but if the only alternative the client's got is random , then 10% better than random is an awful lot . It can have massive impact on their business . When we're building machine learning and predictive models , again , we're not always trying to shoot . Let's get it right 90% of the time . What we've got to do is just get it right better than it is at the moment so that we can have more positive business impact .
Speaker 2Yeah , that makes a lot of sense . What was the phrase you used to use in the land of the blind ? The one-eyed man is king .
Speaker 1That's exactly right . The land of the blind , the one-eyed man , is king .
Speaker 2Fantastic ,
Tom . I'm conscious of time and that we should probably start to move on and let you can wrap up . Just a couple of final questions then I want to do a quick fire round , if that was all right . You've reached a lot around the importance of maintaining a human component of measurability rather than just relying on , say , A-B testing and cutting lots of crazy A-B testing it and to see what works . Why doesn't that work in your view ? Why do you need a human component to measurability ?
Speaker 1I'm so delighted that I can answer this question with someone who's got a UK audience . My favorite example of this one is the gorilla playing the drums ad Cadbury's Dairy Milk . Huge people in the UK would recognize that if they saw it . Huge hit for Cadbury's . It was just so random . That ad would never have tested well by an AI looking at it . It didn't look like anything that came before . It didn't look like anything that came after it . There are lots of ads like that . Lots of ideas .
Speaker 1The best creative ideas in AI is never going to sink off , but also it's never going to work out whether it's going to work . It's never going to work out what's really bad either , where something is just jarring culturally . Sometimes it's humans receiving and it's like no . That makes me feel a bit squeamish . What AIs tend to do is they do regression to mean , which is basically it's all about , let's try and do the standard stuff . It's kind of the enemy of great . I think we should be thinking less about . Can we get an AI to test things ? I think for sure there are uses for AI in the creative process . If we're going to use AI in the creative process , we need to make absolutely certain that a human has a look at it afterwards . If you're investing even tens of millions of dollars in that advertising campaign , you don't want to put in fields unless you've had a good number of humans looking at it . You're not trying to sell products to machines yet . I think you need to have people in the
process .
Speaker 2Tom , as mentioned , conscious of times , I'm going to just wrap up with a few final questions . Quickfire question is a slightly cheeky one , if you don't mind me asking , but interested to know . What would your partner say are your best and your worst fallacies my best ?
Speaker 1quality would definitely be my full head of hair .
Speaker 2This is not a video podcast . I'll put up a photo of Tom when we promote it .
Speaker 1My worst quality would be Sorry , could you repeat the question ? I wasn't listening . What she would say is the worst quality is I'd constantly say sorry , I wasn't listening . Could you repeat the question ?
Speaker 2That sounds directly accurate . Slightly more serious . Sorry , it doesn't have to be . If you were a mid-sized research company , or even largest research company like Market Cast , and you were hiring a CTO , what type of interview questions would you be asking them ?
Speaker 1The first thing I would always make sure is it doesn't want to steal code and not keep it up to date . I think a lot of people who try to run technology teams without cutting code themselves , and I think the simple reality is that cutting code nowadays is totally different to cutting code four years ago . If you're hiring a CTO who hasn't written code for 20 years , you're going to find that he or she is managing development teams as if they were developing code 20 years ago . I would always ask that . I think also the research industry has a bunch of peculiarities . If you're not hiring someone with the domain knowledge , then I think you need to understand how quickly they're going to be able to get up to speed on them .
Speaker 1There's complexities about how we did Code . Data is not something that's very easy to structure . It's not well-structured data . You've got peculiarities around . There's a lot of complexity of the way we have deeply nested data sets and how you deal with things like that . Those would be the kind of areas Basically , I would say you still hands on technically and I need to get some credibility on how quickly they're going to get up to speed on the domain .
Speaker 2Thank you , tom . That's very good advice . Final couple of questions . If you could have one or two guests for dinner , who would they be and , even more importantly , what would be on the menu ? What would you eat and drink ?
Speaker 1Well , it would all If I could only have two guests for dinner , probably my wife and my mother , and we would probably eat caviar with homemade chive blinis . I guess we'd probably drink a bottle of Harrow and Hope Blanc de Noir .
Speaker 2It was a very politic answer , Tom . Final one what's your favourite almost impactful book or recent book or piece of media ? It could be a movie or something like that , or TV show or podcast or whatever .
Speaker 1I mean Star Wars obviously had a massive impact , but I think it did on over one of my age , but I guess the one I mean Small is Beautiful by ES Schumacher . I read that when I was a teenager . Although I'm probably not the strongest environmentalist on the planet , I think the whole idea of sustainability and how you stop things growing out of control has been something that I've thought a lot about throughout my career .
Speaker 2Tom , thank you so much . It's been a pleasure , as always , talking to you . I'll see you in a month or so . It's fabulous .
Speaker 1Thank you , Henry .
Speaker 2Always so much fun talking to Tom . I've borrowed and probably garbled many of his lines in the past , and they'll be fascinating to see how his belief that AI and creative development is potentially the enemy of great , to use his phrase . Whatever the case , it's certainly got to be healthy to have a dose of realism and cautious skepticism amid all the hype . Thanks once more to Insight Platforms for their support . I'm releasing the podcast every couple of weeks now . Next up , I'm planning to dive more into the world of Samsung with Rupesh Patel and then jumping onto a different tack to look at the appetite for investment or acquisition in the sector with Tony Wolford of Green Square . Thank you for listening and see you next time .