Speaker 1: I'm off my game today. No, you're not. People are
Speaker 1: going to have to start making better content. I think
Speaker 1: we're gonna be talking about this for a long time.
Speaker 1: When you program for everyone, you program for no one.
Speaker 1: I think it's that we're purpose driven platform, like we're
Speaker 1: trying to get to substance. How was that? Are you
Speaker 1: happy with that? This is marketing therapy right now? It
Speaker 1: really is? What's up? I'm Laura Coarntia and I'm Alexa Kristen.
Speaker 1: Welcome back to at Landia. So today onto Ganska from Notch.
Speaker 1: She's the co founder and CEO. So she really kicks
Speaker 1: us off into a conversation that we've been having. I think,
Speaker 1: in a lot of ways more implied. And Laura and
Speaker 1: I have been talking a lot about dipping our toe
Speaker 1: into shark infested waters, maybe just jumping into shark infested waters.
Speaker 1: Around the data and analytics topic and what it really
Speaker 1: means for marketers, what it means for media, what it
Speaker 1: means around insights forward and data back. Because we're so
Speaker 1: focused on benchmarks that don't mean anything today, we've created
Speaker 1: kind of these false indicators. Yeah, I think it's interesting,
Speaker 1: you know, as data becomes democratized or commoditized, and everybody
Speaker 1: has the first party set, the third party set, how
Speaker 1: they're translating that into how they're translating that into dashboards
Speaker 1: and insights. And you know, what's the right talent that
Speaker 1: you should have at the table? Are they analysts? Are
Speaker 1: they scientists? Are they engineers? You know, it's interesting. We've
Speaker 1: had everybody from brands to publishers, um to agency executives
Speaker 1: on this show talking about what data means to their business.
Speaker 1: And I think what's interesting and what you were just
Speaker 1: alluding to is that we're an industry that continually looks
Speaker 1: for the standardization why why? And I think what we're
Speaker 1: going to hear from Onda is that there isn't actually
Speaker 1: a one size fits all solution and that the KPI
Speaker 1: s are indicators from the brands that she's working with,
Speaker 1: are very indicative of the fact that maybe there shouldn't
Speaker 1: be And so what works for your business? How do
Speaker 1: you move the bottom line? How do you understand your consumer?
Speaker 1: And should you be looking for the same consumer persona
Speaker 1: profile that your competitor is. She might suggest probably not,
Speaker 1: and she may suggest that your competitor may create this content.
Speaker 1: Actually you should be over here on the complete other side,
Speaker 1: creating another piece of content or whole content program. Yeah.
Speaker 1: And I think as an industry, we we talk a
Speaker 1: lot obviously as data as an output. And I think
Speaker 1: what Ada is going to talk a bit about today
Speaker 1: is where where it becomes an input, right and how
Speaker 1: understanding as she'll call it system one versus system two,
Speaker 1: which is a very articulate way that she'll talk about
Speaker 1: UM emotion versus the analytical side. UM is a really
Speaker 1: interesting filter for I think about how we try to
Speaker 1: jam everything together, but really the importance of allowing them
Speaker 1: to live independently to come to direction or a decision
Speaker 1: in your marketing. So put on your Scoopa suit. We'll
Speaker 1: be back with Onto Gonska from Notch and we're back
Speaker 1: in the studio with the founder and CEO of Notch ANDASA.
Speaker 1: Welcome and thank you so much. Thank you for pronouncing
Speaker 1: my name right. So you're from Transylvania. Yes, I am
Speaker 1: a very lovely place, a lot more, a lot friendlier
Speaker 1: than you would think. Brn and Rays grew up there,
Speaker 1: became very good at math early on, kind of like
Speaker 1: a celebration of all Eastern European stereotypes. I was really
Speaker 1: really good at math and a very good ballroom dancer.
Speaker 1: So from Eastern Europe, right, you're supposed to be a
Speaker 1: ballerina and a computer scientist. So I'm just saying I
Speaker 1: kind of fit into the stereotype. How do you have
Speaker 1: time to do anything else? Well, now I don't dance anymore.
Speaker 1: I just do the nerd stuff. So on to tell
Speaker 1: us about Notch, it's about four years old. Yes, how
Speaker 1: did you get started? What was the farthest through the
Speaker 1: trees that you saw? And how is that company evolved? Yeah,
Speaker 1: so we got started by not being at all focused
Speaker 1: on marketing. I initially was really obsessed with figuring out
Speaker 1: how people felt in real time about something. I was
Speaker 1: trying to figure out what are the kind of big
Speaker 1: missing data sets about humans online? And I realized that
Speaker 1: there wasn't a very good way to collect feedback. UM
Speaker 1: surveys were really crappy consumer products. No one wanted to
Speaker 1: engage with them, and as such, there was this big
Speaker 1: missing data set of how people actually felt about stuff.
Speaker 1: And so I set out initially to build a consumer
Speaker 1: product that people would want to engage with, something that
Speaker 1: was elegant, simple, frictionless, UM an action that you could
Speaker 1: do within your attention span, which is about six seconds
Speaker 1: um and something that you'd really enjoy engaging with, because
Speaker 1: you know, human beings are rage atmental creatures. We have opinions.
Speaker 1: It's just a matter of having the right product to
Speaker 1: get them to to share that opinion. So we built
Speaker 1: something it really worked. Um you know, surveys get around
Speaker 1: a zero point zero one sent response rate, and we
Speaker 1: were getting on average, and the exponential increase in response
Speaker 1: rate meant that we could get a lot of data
Speaker 1: at scale about how people feel, which then we started
Speaker 1: thinking who cares about this data? And we had all
Speaker 1: sorts of assumptions around it. At the time, we were
Speaker 1: in Silicon Valley still, which is where we got started,
Speaker 1: and I started traveling to New York meeting with a
Speaker 1: bunch of different people. And one of the first marketers
Speaker 1: I've ever met was someone that Alexa knows really well,
Speaker 1: Beth Comstock. And thank god I met with her because
Speaker 1: you know, as we all know, she's this incredible, you know,
Speaker 1: legend marketer in our space who sees innovation and manages
Speaker 1: to kind of pinpoint and had lifted up for everyone
Speaker 1: else to see. And that's exactly what she did for us.
Speaker 1: So with time, UM, we kind of went from just
Speaker 1: tracking how people felt, too, then using our technology to
Speaker 1: track everything else about a person, behavior, demographic data, literally everything.
Speaker 1: So we kind of blended what someone says about themselves
Speaker 1: and what their behavior says about them um into kind
Speaker 1: of a more holistic version of themselves. UM. And then
Speaker 1: we started working with you know, all the big financial brands,
Speaker 1: all the big auto brands, all the big telco brands,
Speaker 1: and we kind of are on our way to becoming
Speaker 1: a standard for how content, in particular r o I
Speaker 1: is measured. So we when you started, you weren't working
Speaker 1: with brands. What were you measuring in terms of people's
Speaker 1: feelings and perceptions of things? Great question. So we were
Speaker 1: working with editorial teams. We were measuring, Yeah, we were
Speaker 1: measuring the response of people two topics that editorial content
Speaker 1: was about. Um. But as you can imagine, we realized
Speaker 1: that one, you know, by large, creators don't love being
Speaker 1: told what data data says. Right, and this is a
Speaker 1: generalization that might not be true today, but it definitely
Speaker 1: was about three years ago. UM. And too, there wasn't
Speaker 1: a lot of money and selling creative sentiment type of
Speaker 1: data to creators, and so we started taking a step
Speaker 1: back and thinking what's the best go to market here?
Speaker 1: And we realized that marketers were becoming content creators a k.
Speaker 1: Content marketing was on the rise still is, and that
Speaker 1: they needed a better way to measure it because all
Speaker 1: the standards for display advertising like mode and integral ad science,
Speaker 1: we're not really doing the job. So I don't think
Speaker 1: it's a surprise actually that you have an artistic creative
Speaker 1: side as a former ballroom deal because one of the
Speaker 1: things that I have always been impressed by your product
Speaker 1: is the aesthetic of this online survey, if you will,
Speaker 1: and the sort of creativity that went into that engagement
Speaker 1: rate output. Right, So can you talk about how you
Speaker 1: blended art and science to create an online survey that
Speaker 1: could engage consumers in a way that isn't just a
Speaker 1: backs out totally. So we kind of left off from
Speaker 1: the idea that UM surveys were not very engaging consumer
Speaker 1: products and started asking ourselves why. We realized that there's
Speaker 1: two issues. The first was the context. Usually surveys are
Speaker 1: targeted to you in a disruptive way. They kind of
Speaker 1: pop up or they're sent to you via email two
Speaker 1: days later. By then, you know, in today's world there
Speaker 1: was so a d D that you have no idea
Speaker 1: what happened two days ago. So it's really kind of
Speaker 1: a disruptive context in both cases. Right, So we realize
Speaker 1: that it has to be at the end of an experience,
Speaker 1: and ideally and immersive experience, not a transactional and like
Speaker 1: a display at And therefore we realized that content was
Speaker 1: not only a winning mechanism for brands to tell their
Speaker 1: story and engage with audiences, but also a really good
Speaker 1: way to get someone to share their feedback. The second
Speaker 1: piece was the methodology. As it turns out, you know,
Speaker 1: if you if you believe that the brain has kind
Speaker 1: of like system one system too, As it turns out,
Speaker 1: you know, System one is where we have kind of
Speaker 1: store feelings or analyzed feelings. When you smile, I know
Speaker 1: exactly how you're feeling because you know it's in system one.
Speaker 1: I don't have to think about it. But if you
Speaker 1: ask me how much is seven times pretty seven? I
Speaker 1: go into system two, which is the nalytical brain now surveys.
Speaker 1: What they do is they ask you to convert something
Speaker 1: that lives in system one, which is a feeling into
Speaker 1: a percentage or a number or multiple choice, which automatically
Speaker 1: send you in system too. Therefore, there's a massive friction
Speaker 1: and people don't tend to express that feeling. So what
Speaker 1: we did is we basically said, let's create a really
Speaker 1: the intuitive design. Um, let's take a universal metaphor. And
Speaker 1: this is I think where we really kind of hit
Speaker 1: the nail on the head. Because universal metaphors or something
Speaker 1: you learn between zero and FOD regardless of whether you're
Speaker 1: from Transylvania or from California or Nebraska. There you go. Um,
Speaker 1: and we realize that temperature is one of them. So
Speaker 1: if you present someone with a temperature scale, they automatically
Speaker 1: know what to do with it without having to think
Speaker 1: about it. Visual like a visual temperature temperature I like,
Speaker 1: I don't like a slider. Yes, And we realize that
Speaker 1: it also gives us, as like data nurse, it gives
Speaker 1: us the ability to create granularity because you could have
Speaker 1: as many skills to the temperature as you want, so
Speaker 1: you could have really granular feeling. You could enable people
Speaker 1: to quickly answer a question without thinking about it, and
Speaker 1: you could enable them to have a really positive experience
Speaker 1: as opposed to making them feel like they've had to
Speaker 1: spend minutes figuring out how they actually feel. You just
Speaker 1: give them are really easy and um and rewarding way
Speaker 1: to share that feeling like they want to actually share
Speaker 1: their feedback they do. Yeah, I mean this is kind
Speaker 1: of the premise on which re build the companies that
Speaker 1: human beings and nice say judgmental, but you know, and
Speaker 1: like its scientifically speaking, we wouldn't be able to survive
Speaker 1: unless we made judgments all the time. So you know,
Speaker 1: I do believe that we're very opinionated and we do
Speaker 1: want to share that feeling. But the methods in which
Speaker 1: we are given that opportunity today or what Twitter and Facebook?
Speaker 1: We have to sit there and write a hundred forty characters.
Speaker 1: It takes me like an hour to do that every
Speaker 1: time I try to do it. So yeah, we gave
Speaker 1: people a much more frictionless, easy way to do it.
Speaker 1: And what kind of surveys or what kind of questions
Speaker 1: are you asking for brands? Is it you know, around
Speaker 1: brand sentiment like that high level? Is it around products? Well?
Speaker 1: What are the types of things that you're actually going
Speaker 1: out there for brands and asking consumers? So taking just
Speaker 1: a quick step back, I think the bigger question is
Speaker 1: how do brands think of content marketing success. I realized
Speaker 1: that when it comes to display um I mean not
Speaker 1: to discredit any of the products that have done so
Speaker 1: well in the market, but measuring displays a little bit
Speaker 1: more binary. The ad has either been seen or not
Speaker 1: or clicked on or not right. But when it comes
Speaker 1: to content, people just have very different definitions of success um,
Speaker 1: but they tend to gravitate towards five big categories sentiments
Speaker 1: one of them, or attitude rather, engagement, impressions, social, and conversion.
Speaker 1: So what we do now is we capture all those
Speaker 1: five categories and we try to work with the brand
Speaker 1: to understand what their custom version of successes and then
Speaker 1: create custom scores that help them kind of force rank
Speaker 1: every single content investment to see what's performing the best
Speaker 1: in real time. But when it comes to the kind
Speaker 1: of upper funnel goals, because I've kind of described upper
Speaker 1: funnel to lower front of right UM, I would say
Speaker 1: it also kind of depends on what the brand is
Speaker 1: really after. So we can ask any question whether it's
Speaker 1: intend to purchase or brand loyalty or perception of brand innovation,
Speaker 1: or brand love brand awareness. I mean, it really kind
Speaker 1: of depends. And that's the beauty of this universal metaphor
Speaker 1: that it is that it kind of enables you to
Speaker 1: to ask any type of question, as opposed to emojis
Speaker 1: or the thumbs up thumbs down, which only really gives
Speaker 1: you a limited range of responses. If brands aren't using notch,
Speaker 1: what are they using? So uh quickly kind of leads
Speaker 1: me to the other thing that really stand for, which
Speaker 1: is transparency. Um, we realize when we came into the
Speaker 1: market that when brands work with agencies, agencies then kind
Speaker 1: of pay let's say, ten different distribution channels, and then
Speaker 1: those distribution channels are actually the ones that pay data
Speaker 1: companies to measure their own performance. So say at the
Speaker 1: level of the distribution channel, you find a combination of
Speaker 1: a few different companies mode you know, Google Analytics sometimes Um,
Speaker 1: Simple Reach, Nudge. There's a bunch of different analytics companies
Speaker 1: that sell to distribution channels. But the problem is this
Speaker 1: is click attribution still though in a lot of ways, right,
Speaker 1: I mean then but like it's front end, yeah, yeah,
Speaker 1: I mean, there's not a lot of depth or variety
Speaker 1: in the type of data that is collected. But the
Speaker 1: problem is two fold. If you're allowing as a brand,
Speaker 1: I mean, you used to be the head of meter, right,
Speaker 1: So um, if you're allowing your distribution channels to grade
Speaker 1: their own homework, they're not going to give themselves a B.
Speaker 1: So you know, you end up as a head of media.
Speaker 1: Not only do you get good news by the time
Speaker 1: it gets to it's like, oh my god, you've done great.
Speaker 1: Keeps any one with us. But the other portion of
Speaker 1: the other problem is that you're allowing the distribution channels
Speaker 1: to spend all this money that you gave them on
Speaker 1: duplicate measurement. So what we came to brands and said was,
Speaker 1: we're not going to monetize from any distribution channel because
Speaker 1: we want to be you know, Switzerland. We want you
Speaker 1: to be our master, and we're going to represent your
Speaker 1: interests when we go and measure on your behalf across
Speaker 1: all these different channels. So we kind of centralized that data, um,
Speaker 1: the data collection essentially, not just the data reporting. And
Speaker 1: as such, I don't think, you know, there's a lot
Speaker 1: of competition in that space. I haven't heard of any
Speaker 1: startup that said I will actively try not to make
Speaker 1: money from two thirds of the market. You know, it's
Speaker 1: kind of like a pretty crazy bold thing to do
Speaker 1: on the how as an industry do we need to
Speaker 1: shake things up when when it comes to data? There
Speaker 1: have been standards of metrics that have been in place
Speaker 1: since I've been in the industry that don't seem to
Speaker 1: keep pace with the change of technology, with the types
Speaker 1: of advertising or non advertising that brands are going to
Speaker 1: market with. You know, laugh a lot about this idea
Speaker 1: of having to duct tape together measurement reports because there
Speaker 1: isn't a one size fits all solution and if there's
Speaker 1: nothing that allows me to see front to back. But yeah,
Speaker 1: we kind of chase to the lowest common denomina and
Speaker 1: say this was a success because X number of people
Speaker 1: engaged with something. Um, what's your long term dream vision
Speaker 1: for how we piece those things together? So I think
Speaker 1: the biggest shift that needs to happen is that cmos
Speaker 1: and marketing teams need to understand the power that they
Speaker 1: have in demanding that they are able to collect first
Speaker 1: party data across every single spend that they do. I
Speaker 1: don't care if it's offline, online, I don't care if
Speaker 1: it's podcast or digital marketing display advertising, it doesn't matter.
Speaker 1: They should understand that they are able to request that
Speaker 1: they get independent data reporting. Now, what this will do
Speaker 1: is to fold. First, it's going to declutter all the
Speaker 1: messs happening right now with all these different distribution channels
Speaker 1: measuring their own performance. It's also going to hopefully remove
Speaker 1: the need for these massive data aggregators like you have,
Speaker 1: including data Ama right that got acquired by Salesforce. They
Speaker 1: are an amazing platform and they do incredible work with
Speaker 1: AI and putting together all these different data sets. But
Speaker 1: why do we allow people to report their own data sets?
Speaker 1: Like why do we allow people to bring an Apple
Speaker 1: and a Draff and ask them data Rama to put
Speaker 1: the two together and make sense of them. But imagine
Speaker 1: if you had ten million dollars and you put a
Speaker 1: million across ten different channels, and you said, guess what,
Speaker 1: I'm going to put my own pixel on all of this,
Speaker 1: and I'm going to get the data in the format
Speaker 1: that I want, on the timeline that I want, and
Speaker 1: I'm going to make sense of it because now I
Speaker 1: can control the kind of collection of it. Right, So
Speaker 1: when you control the collection and the formatting of it,
Speaker 1: making sense of it is really easy. Like as a
Speaker 1: data scientist, I can tell you of the job of
Speaker 1: a data scientist is cleaning data sets and making them
Speaker 1: talk to each other. But should they hold on? But
Speaker 1: should it be? That's a problem in the industry right now.
Speaker 1: By the way, the problem, one of the biggest problems
Speaker 1: in the industry in my opinion, is that data scientists
Speaker 1: are actually people who need to be innovating and be
Speaker 1: more like data entrepreneurs and be actually getting more from
Speaker 1: insight and be evaluating insight versus cleaning data. Right now,
Speaker 1: we have built this system that is all about data prep. Yeah,
Speaker 1: and it's because people are freaked out and I don't
Speaker 1: even know is this the right question? Are we asking
Speaker 1: the right question around just gathering data? To me, it's
Speaker 1: are we gathering the right insight? Do we actually right?
Speaker 1: Do we can? We evaluate the right insight? But if
Speaker 1: we agree that their time is spent on cleaning data sets,
Speaker 1: then we need to figure out how to remove that
Speaker 1: time so they can spend time on insights and strategies.
Speaker 1: So my proposition is that there's gonna be really bold
Speaker 1: brands like Jimpy, Morgan Chase. We're going to go in
Speaker 1: and say notch will be my data collection practice. Right,
Speaker 1: my data collection tag picks whatever you want to call it,
Speaker 1: and we collect all that data. It's already clean. So literally,
Speaker 1: our data science team, all they do is find insights.
Speaker 1: All they do is get on the phone with Jamie
Speaker 1: Morgan and their agency and say, put more money behind here.
Speaker 1: Decrease the importance of that that audience is not responding well,
Speaker 1: because we don't waste any time trying to clean up
Speaker 1: stuff for can we trust this data? No, it's all
Speaker 1: first party, it's all transparent, and we can, you know,
Speaker 1: go ahead and truly analyze it. So my big outlook
Speaker 1: is CMOS basically going to Facebook and YouTube and Twitter
Speaker 1: and Amazon and everyone and saying you want my money, great,
Speaker 1: put my pixel on it. I need to collect data
Speaker 1: about my audience and how they're responding to my advertising.
Speaker 1: And they're doing that through not they're doing that well.
Speaker 1: So you have an agreement with any third party site
Speaker 1: out there. I assume right that if someone is interacting
Speaker 1: in a notch ui that you have rights to all
Speaker 1: of that data. So that's how it works. So the
Speaker 1: data belongs to the brand, it doesn't belong to us.
Speaker 1: We're literally just a conduit for data collection, and we
Speaker 1: reserve the right to create benchmarks and aggregate insights. You
Speaker 1: would do work with you know, publisher X. We would
Speaker 1: embed in that content piece, and any data that comes,
Speaker 1: like the audience that came to that audience, to that
Speaker 1: content pies, the type of audience, the way they engaged,
Speaker 1: the way they felt, all of that would be passed
Speaker 1: on immediately to you and the publisher and the agency.
Speaker 1: So complete transparency. But you would own the data, and
Speaker 1: we would aggregate data from publisher X, publisher, why, publisher whatever,
Speaker 1: and you all of a sudden you would be left
Speaker 1: with this first party data pool that you could then
Speaker 1: put into your CRM, put into your DVP, etcetera. And
Speaker 1: what you just said, no one's doing. But let's like no,
Speaker 1: but let's let's like like explain that you are saying
Speaker 1: that a brand could actually own data from a publisher,
Speaker 1: from an audience, from audiences they came to a specific
Speaker 1: publisher versus a publisher giving you the data they want
Speaker 1: you to have. Yeah, exactly. Hello, And and also the
Speaker 1: reason why publishers are okay with this, just for a second,
Speaker 1: there's some publishers out there that are definitely pushing back
Speaker 1: and have against transparency on this idea, but publishers are
Speaker 1: actually waking up to realize the more they embrace the
Speaker 1: transparency rhetoric and approach, the better it is for them,
Speaker 1: because it's coming anyways, right, and if they truly trust
Speaker 1: their engagement, it makes sense for them to do this.
Speaker 1: The question they ask us is can you please make
Speaker 1: sure that this data that we're going to give to
Speaker 1: j Imraan Chase is not going to be put into
Speaker 1: a audience extension platform, Like they don't want their data
Speaker 1: or their audience to be monetized. And yes, we do that.
Speaker 1: We promise them that because the data that we capture,
Speaker 1: we don't have a data exchange, We don't sell that
Speaker 1: data to competitors, we don't do anything with it. It
Speaker 1: just belongs to the brand. So what the brand could
Speaker 1: do something with it? They if they put it into
Speaker 1: their d MP. By that point it's completely anonymized. I mean,
Speaker 1: we're GDP are complying across every single state of America
Speaker 1: and Europe, right, and it doesn't really lead back to
Speaker 1: the publisher. An interesting m article that just came out
Speaker 1: from saraficher Um inter Axios Trends Reports that is calling
Speaker 1: this the summer of ad tech UM acquisitions. Right, So
Speaker 1: you alluded to obviously data Rama being purchased by Salesforce
Speaker 1: for over a hundred million dollars. You've got Medium IPG
Speaker 1: obviously one of the largest holding company groups, acquiring Axiom
Speaker 1: for over two billion dollars. This obvious is going to continue.
Speaker 1: As you're talking on and you're describing your kind of
Speaker 1: thought and vision is what's happening in the the industry is
Speaker 1: where you're positions and knowing that agencies UM you need
Speaker 1: this support. Do you see not moving to a data
Speaker 1: agency that happens to make products. We currently have kind
Speaker 1: of a customer success data and our team that does that,
Speaker 1: and they've worked with your teams and hopefully helped interpret
Speaker 1: some data. But we're thinking a lot about how do
Speaker 1: you scale that, how do you actually even bload that
Speaker 1: up to take it to the next level UM, And
Speaker 1: so we're thinking about UM either building our own high
Speaker 1: touch consultancy around UH, you know, analyzing data not just
Speaker 1: our data, but maybe other first party data that we
Speaker 1: can bring into our fold UM or creating an ecosystem
Speaker 1: around notch and just saying hey, guys, here's our data.
Speaker 1: You know, do you do you want to go analyze it?
Speaker 1: And maybe we work with Canada or a center, you know,
Speaker 1: the kind of more services heavy players um. And so
Speaker 1: that's something that I'm very actively thinking about and we'll
Speaker 1: probably you'll see something from us over the next six months.
Speaker 1: What what is the industry missing? Like? Where are we
Speaker 1: We're so data obsessed to the point of just complete saturation.
Speaker 1: I mean we have people come and talk about data
Speaker 1: in all different types of ways on this show. What
Speaker 1: is the thing that we're not seeing? I mean, you're
Speaker 1: from your vantage point. I always say, you know, there's
Speaker 1: there's this weird paradox where you have too much data
Speaker 1: and very little insight. And I think the what's missing
Speaker 1: is that we're just taking data sets that have already
Speaker 1: been collected and banging our head against the will trying
Speaker 1: to make them, you know, talk to each other. When
Speaker 1: the truth is, things are changing so fast online that
Speaker 1: if you're not collecting data in real time and using
Speaker 1: it for predictive insights, then you're already behind. Like if
Speaker 1: you're still trying to figure out how the data sets
Speaker 1: of six months ago fit with each other, and you know,
Speaker 1: forget it. So I think what's the point that we're
Speaker 1: missing is kind of forget the data sets you've already collected.
Speaker 1: Just try to focus on how you streamline data collections.
Speaker 1: You can action on the stuff that the audience is
Speaker 1: trying to tell you. Now, that's I was just going
Speaker 1: to say to you, this is about insight forward, go back, right,
Speaker 1: it's data back. It's insight forward, which to me means analytics. Right.
Speaker 1: It to me means actually going back to your roots.
Speaker 1: It's math. Yeah, I think it's actually marrying research analytics
Speaker 1: in real time, which has not really been possible, and
Speaker 1: we're trying to obviously do that through what we do.
Speaker 1: And by the way, this is the other thing that
Speaker 1: I don't even know if it's going to blow up
Speaker 1: the Internet, probably not. But I don't really believe in
Speaker 1: benchmarks and indexes in today's world. I've just seen brands
Speaker 1: define their notions of success so radically differently, and content
Speaker 1: is not only valuable, but as I mentioned, it's multivariate
Speaker 1: and its success. I feel like people should just kind
Speaker 1: of forget that and focus on who we are, what
Speaker 1: is our bottom line, and how do we get there
Speaker 1: through marketing? Right, Because everyone's talking about the single source
Speaker 1: of truth, and what you're saying is there is no
Speaker 1: single source of truth. You have to start going forward
Speaker 1: into insights versus worrying about these made up benchmarks well
Speaker 1: that we've created that aren't really actually benchmarks. Right. Data
Speaker 1: is directional. Of course, people have made data their strategy. Yeah,
Speaker 1: you know, and people are like this says this, so
Speaker 1: we must do that. And he's opposed to like, where
Speaker 1: where Why aren't we critically thinking about these things in
Speaker 1: aggregate and then borrowing from culture and borrowing from you know,
Speaker 1: influence and being able to kind of create as opposed
Speaker 1: to dictate. I think it's very different. I also think
Speaker 1: it changes the mindset and the organization of a marketing
Speaker 1: team all of a sudden, puts you into like thought
Speaker 1: sprints almost versus these long drawn out plans that make
Speaker 1: no sense. Data decks that are over a hundred pages.
Speaker 1: Give me the executive summary, what is the negative information?
Speaker 1: I need to move on? Let's go. I think data
Speaker 1: has two functions. The first one is you put it
Speaker 1: to use immediately through plant forms that can harness it
Speaker 1: to better target audiences both with creative and distribute media. Right,
Speaker 1: or the second is its surfaces three to five main
Speaker 1: insights that you can go and think about and act
Speaker 1: on in your next campaign or next strategy. But anything
Speaker 1: in between. If you're talking about you know, even tempage
Speaker 1: data decks, I don't think it's you know, I don't
Speaker 1: think it's realistic. Are you only digital in terms of UM,
Speaker 1: Like online media is notch? Can you notch work on broadcast?
Speaker 1: You know, linear, other screens, vot T voice, those types
Speaker 1: of places. So now we're going to get to like
Speaker 1: how we take over the world answer. So we started
Speaker 1: off with literally just paid custom content. We then expanded
Speaker 1: into content syndication, then expanded into own content, then social
Speaker 1: so we actually can capture first pretty data on a
Speaker 1: couple of the big walled curtains, which is crazy UM.
Speaker 1: Then expanded into email, and now we're expanding into being
Speaker 1: able to measure podcasts as well. And so we're building
Speaker 1: towards understanding what is being said in a podcast and
Speaker 1: then trying to basically figure out how do we meet
Speaker 1: the customer or the audience in a way to ask
Speaker 1: them for their actual feedback but also study their behavior.
Speaker 1: So very much working on that very much, thinking about
Speaker 1: O T T I think there will be a day
Speaker 1: when we figure out how to do offline as well. UM.
Speaker 1: But yeah, the entire philosophy behind it is do it
Speaker 1: in a way that has the CMOS kind of interest
Speaker 1: in mind, and do it in a way that collects
Speaker 1: first party data only. I would love to see the
Speaker 1: temperature scale on this show. You'll be the first customer
Speaker 1: the beta test. I don't know how Atlantia feels about that,
Speaker 1: but like it's coming at you Atlandia. So and uh,
Speaker 1: we're gonna flip to our game that we play. It's
Speaker 1: called kill by d I Y what would you kill?
Speaker 1: What would you kill? What would you buy? What would
Speaker 1: you do yourself? Not? Not something else? So I would
Speaker 1: kill any form of transactional advertising, to be honest ist, UM,
Speaker 1: that would be mine, and that includes display, but it
Speaker 1: includes a few others. UM. I absolutely hate being met
Speaker 1: with the um the really impersonal advertising of a brand.
Speaker 1: So I persume a massive believer in content, I would
Speaker 1: probably buy most of our competitors and create a larger
Speaker 1: consortium that has a lot of power to compete with
Speaker 1: the Oracles and Adobes and salesforces of the world. And
Speaker 1: I'm actually actively thinking about that so hopefully they can
Speaker 1: hear me say that. Um, And what was the last one?
Speaker 1: What would you do yourself outside of notch? Oh? Well,
Speaker 1: can I answer something radically different that's not at all
Speaker 1: in this space. That's what we like. Well, So I've
Speaker 1: been thinking a lot about how professional women need UM
Speaker 1: a brand that cuters, like a clothing brand that cuters
Speaker 1: to them, UM in a way that you know, enables
Speaker 1: them to look amazing and high powered and sassy and
Speaker 1: awesome from seven am to the eleven PM when that
Speaker 1: dinner ends. And so I've been thinking a lot about
Speaker 1: the fact that I don't really have those types of clothes.
Speaker 1: I have to go like our shoes that are comfortable
Speaker 1: but also kind of show my very spunky personality. So
Speaker 1: I've been thinking a lot about like what brand would
Speaker 1: do that? And I would probably do it myself. And
Speaker 1: thank you so much for coming, Thank you for having
Speaker 1: this was so much fun. If people want to get
Speaker 1: in touch with you by your product, get more of
Speaker 1: your insight, yeah, reach and at k n otc H
Speaker 1: dot com. And it's been awesome much thank you, Thank
Speaker 1: you on a fun conversation, and there's so much more
Speaker 1: we like just hit the tip of the iceberg. So
Speaker 1: we're definitely going to have you back. I think we
Speaker 1: should do like a much bigger round table too with
Speaker 1: some of these folks who are thinking differently, talking differently
Speaker 1: about data. And I love that she said she wants
Speaker 1: to put together a consortium of ad tack that could
Speaker 1: go and compete against the oracles. I think that actually
Speaker 1: is the new attack, that meaningful attack, and we've got
Speaker 1: to like strip away this like old skin of ad
Speaker 1: tech and actually kind of raise the people up who
Speaker 1: are thinking like Ada and who are doing things that
Speaker 1: actually make a difference and have a vision around what
Speaker 1: marketing of the future is going to look like and
Speaker 1: how it's going to speak and get back to their
Speaker 1: consumer needs the way we need to get back to
Speaker 1: our consumer needs. Yeah, I love the idea of creative
Speaker 1: and strategic application of data. I mean, I think, you know,
Speaker 1: data for face value in the numbers is certainly valuable
Speaker 1: and I think we will continue to need it, But
Speaker 1: the interpretation and how we use it is obviously where
Speaker 1: I think we all collectively agree to it needs to
Speaker 1: go so big. Thanks to our friends and family at Panoply, Dana,
Speaker 1: our producer Matt Turk Andy Bowers, Jacob Weissberg, and all
Speaker 1: of our friends and family in Atlantia. We'll be back
Speaker 1: in two weeks. Full Disclosure. Our opinions are our own