Pod Insights MarTech Podcast ™ // Marketing + Technology = Business Growth CMO and Former Meta AI Marketer on AI’s Reality Check About this episode Most companies struggle with stale data and delayed dashboards. Noha Rizk, CMO of Incorta and former Global Head of Marketing at Meta AI, explains how live data infrastructure drives real business impact. She details how retail chains optimize inventory waste through real-time promotions, manufacturing companies save hundreds of thousands through live factory floor analytics, and finance teams make faster trading decisions with immediate market data. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
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0:53 Speaker 00 Welcome to the Martech Podcast, a member of the I Hear Everything Podcast Network. In this podcast, you'll hear the stories of world-class marketers that use technology to drive business results and achieve career success. Here's the host of the Martech Podcast, Benjamin Shapiro.
1:15 Speaker 04 54%. 54% is the increase in data analytics and customer insights investment last year. Data is food for the AI revolution because it creates the content needed for personalization, which increases business performance. Here's the gap. Most companies are relying on their stale dashboards and ad hoc reporting. The question isn't if you can use live data, it's how you think you can afford not to. So how do you build systems that deliver insights and AI-powered actions in real time? I'm Benjamin Shapiro and joining me today is Noah Risch, who is the CMO of Encorta, a unified data delivery platform delivering live detailed data from all of your systems
1:58 Speaker 04 of record with built-in semantic intelligence, faster time to value and no complex ETL. And today Noah is going to share how you can harness live data and AI to unlock an immediate impact. Noah, welcome to the Martech Podcast.
2:14 Speaker 05 Thank you for having me, Ben.
2:16 Speaker 04 Excited to have you here. It's an honor. It's a privilege and you're you're sitting in a very interesting spot in today's AI revolution. In Corte, one of the sort of leaders of the industry in making your data live and actionable. So why do so many organizations rely on delayed dashboards still when live data and AI power? So why do so many organizations rely on delayed dashboards when live data and AI can power decisions instantly?
2:48 Speaker 05 Great question.
2:50 Speaker 05 It's a great start to this conversation. I think ultimately, why do most people stick with systems that aren't necessarily ideal as the world evolves and they can do more? I think we can start by saying that people are just committed to legacy systems that they've invested large amounts of money and time to putting together.
3:14 Speaker 05 And changing those systems is difficult. Sometimes it's also hard to change user behavior. I mean, we were just joking around in our planning session today that our finance team continues to use, and I will not mention names. You know, a finance system that majority of the companies use that is just not working for us. You know, and they don't want to change it because they're just used to using it.
3:41 Speaker 05 The modern data stack ultimately is actually quite broken. It's fragmented, it's slow, and it's costly. Legacy processes require staging and conforming data to like outdated models and making reporting kind of backward. And often enables, often makes businesses look at obsolete
4:00 Speaker 05 obsolete points of information. What's interesting in this moment that we're living is that a lot of companies are scrambling to build AI on top of these outdated stacks, which makes solving the core connectivity and context problems, I think, ever more pertinent.
4:16 Speaker 04 The story you're telling is a migration problem. And, and I think that that is not new in the technology world. AI feels different. It feels bigger because it is not changing a migration that is on top of the existing stack. Like you said, it's, it's going back to the foundations and the understanding of data processing cleanliness. Right? What data we look at, how quickly we can respond to it.
4:46 Speaker 05 Right.
4:47 Speaker 04 And I do think that you're right. The story is a, a human problem, not a data, not an infrastructure problem.
4:54 Speaker 05 It's a little bit of both. I mean, if you think of the early days of the internet, and I think you and I are old enough to remember those days very, very well.
5:02 Speaker 04 Don't date us both, come on.
5:05 Speaker 05 Sorry, I have to do it. But, uh, but, uh, but like in the early days of the internet, I remember when we, you know, we, like it, it used to be, it almost looked like a DOS system. It was like links HTTP and that was the only way that you could use the internet. And then all of a sudden, um, browsers came on to the scene and everybody wanted to build websites, right? But the websites were being built the way that we used to build brochures for our companies, like physical brochures. They were just like pretty, static. We weren't really utilizing the very power of the tools that were becoming available on the market today. And, um, and so we were using these rails that suddenly became available, that you could build something that's visually pretty,
5:47 Speaker 05 that is cheaper and easier to access than actually going out and building a brochure that is easier to change the information because brochures were, once they're printed, they're printed, right?
5:58 Speaker 05 But we didn't actually utilize them the way that we could have built on the internet rails, which is make them clickable, make them a bit more usable, maybe gather information from them and then eventually use them as payment rails. And I think there's something about this moment that we're living today, where people just want to slap AI on top of their existing infrastructure without really thinking through, is this infrastructure actually going to help me maximize what I can do with this new technology?
6:24 Speaker 04 I just had this conversation today. We're revamping the way that we do our communication for our production business. So look, you'll get emails from us a week after the interview airs, asking you to share something. And it used to be a process where we had somebody go and type out an email. And then we created templates that were replication of the emails that we were manually typing out. And now we have AI, which can do a better job. Right? And so naturally, the thing that we gravitated towards was let's take our existing email template and let's have AI fill it out. As opposed to, hang on, let's stop and think about the problem we're trying to solve first. And with the current toolset, what is the most effective way to solve the problem?
7:09 Speaker 05 That's right.
7:09 Speaker 04 Instead of duplicating and automating, it is fundamentally rethinking. That requires so much brain power. It is, it is exhausting. AI is so tiring to use right now because the migration can be really painful. So let's talk about a understanding that pain, figuring out what is worth it. When you think about the migration to a real time data infrastructure, what's the right way to do it? Where does it fall down? How do people get from here's my static dashboard to dynamic data that's relevant today?
7:47 Speaker 05 Yeah. I think real time data infrastructure, as it stands today for most of the tech stacks that exist, is very costly and extremely time consuming. It's, it's actually really time consuming to ensure that the data is not lost. And that, and it is much harder to query your data on top of live data than it is on static. And so a lot of the migrations aren't happening because companies are worried about that loss of fidelity.
8:23 Speaker 05 And so, you know, it falls short. Eventually, the total cost of ownership becomes really, really high because you're trying to account for these things. And the ROI starts to like, the math doesn't compute. Like, you don't have a significant amount of ROI. And I think the right way to do it in the current moment is that you have to really think through where in your organization there will be incremental value. There's this concept of incrementality, which was drilled into us when we were at Meta, which is like, where is the incremental value to what I'm trying to change or what I'm trying to do? And if I really think AI is an interesting moment and I might be able to deploy it for improving certain processes within the company, or improving how we do business, or even improving
9:08 Speaker 05 our ability to make decisions, I have to think where, what are the pockets within my company where this will actually give me incremental value so that this ROI starts to compute? So that that pain of migration starts to become worthwhile because you can very quickly in your head do the math and say, yeah, like, this migration might be difficult to imagine, the brain power that it takes for us to just sit down and map out that workflow, or figure out, you know, where, which pockets within my company I can really benefit from having access to this live data, for example. And where the impact of live data is significantly higher than, you know, I can probably live
9:50 Speaker 05 with, you know, data refreshes once a day or once a week, and it's not necessarily going to have a huge incremental impact on me running my business. I think that's where people fall short. They just don't do the homework enough and they don't really pick the pockets or the areas where there will be that incremental value over time.
10:08 Speaker 04 I'm glad that you talked about ROI when it comes to revamping your data stack because I do think that the Common answer is what we're doing is good enough. And well, live data will get me from my daily refreshed reports or my weekly refresh reports to my real time always on. But I'm not making decisions in real time. So where's the ROI, right? Other than real time data? Nah, that's a nice to have, but maybe not necessarily mandatory. There are other capabilities that come with a refreshed AI-driven data tech stack. So talk to me a little bit about the live data capabilities?
10:51 Speaker 04 And how are they driving higher customer engagement and conversions? And things that actually drive real ROI?
10:57 Speaker 05 Yeah. Look, there are certain functions that lend themselves and certain probably verticals that lend themselves to that more than others. From my experience within Corda, this is where data is their oil, right? Like they have to live and breathe the data because every extra piece of information could have significant impact on their bottom line. So verticals like retail, supply chain, manufacturing and finance. Some of our customers, you know, let's talk about in the food retail business. Just figuring out, you know, one of our customers has, I don't know,
11:40 Speaker 05 3,000 physical branches across the nation. And just figuring out what items are moving in their store? At what times of day? And being able to optimize for waste? Like just that, that singular line item? And live data helps them optimize that waste because then they can push promotions to geo-source people within the neighborhood and make sure that these items are actually consumed versus thrown away. And that they're, it's almost like a revenue maximization. Even if they discount those items, it's better than completely not selling them and then having a significant amount of wastage. It also helps them.
12:24 Speaker 05 Uh, move items from one branch to the other. Um, if there's more demand in one branch versus another, almost in real time. It's like this branches, you know, there's more of item number one being consumed and in this branch, the other, and then they can do swaps. It's a logistical nightmare to be able to do that without it being live. So, and, and, and this customer isn't using us across the board for all of their data and all of their analytics across the company. They are using us in, in areas, they've done the homework, we've done the homework with them. Like we, we often go in, we have conversations and then we advise, we're like, this is probably an area where you could benefit a significant amount from using your live data and running analytics or querying your data on top of it. And other areas perhaps not so much.
13:08 Speaker 05 So I think, um, in verticals like retail, supply chain, manufacturing, you know, we, we, we speak to companies that aren't that large, but every cent counts in hundreds of thousands of dollars and in terms of their, um, uh, revenue and their cost of manufacturing. And, um, and having real time information about how some of this stuff is being used on their factory floors, again, across the nation, especially if they have like multiple branches, uh, creates huge amounts of savings. Um, so there are verticals that do lend themselves to that a little bit better than others. Um, I just got a message from CVS yesterday. I was thinking about this on my way home.
13:50 Speaker 05 I got a message from CVS yesterday saying, there's a surgeon like flu in our neighborhood. Please come in and get your flu shot. That's an example of how like, you know, if you're using live analytics on, uh, how many people are getting sick and coming in and buying flu stuff in your store. And you can then use that to push people to come in and get their flu vaccine.
14:15 Speaker 04 It's funny because when I get those notifications from CVS, I am assuming that that store is not performing well. So they are purely just sending the message because sending a you should get your flu vaccine gets a certain number of people into the store, whether there is a rise in flu or not. I think it's a marketing ploy because when you go into that store, you come out with multivitamins, you know, baby wipes and some Cheetos. Right? If the message was, we've seen a 75% increase in the sale of Tamiflu this week. A flu epidemic in your zip code is coming. You should get vaccinated.
14:57 Speaker 04 I would probably respond a little bit differently than, hey, have you thought about your flu vaccine? I was like, oh, this seems like better data. When you were talking about the grocery store, it's funny because you're like, all right, real time data. Yeah, I want my data to be always updated. Sure, it's logical. I don't want to pay for it. And I don't want to go through an entire infrastructure migration to get my reports an hour earlier. But you use the grocery store example of optimizing your throughput, right? Reducing your waste. You mentioned finance, right? You know, optimal signals of when to buy or sell. I'm thinking of stock traders, right? Yeah. Manufacturing or let's take retail, right?
15:38 Speaker 04 Customer experiences, improving what's actually available on your site based on your inventory in real time seems like something. Nobody likes the experience where they get an ad for a product. They go to the website and the product isn't there. So there's multiple different examples of where live data really does have positive input, better margins, better customer experiences. Where doesn't it work? Where doesn't it matter? Where is the old what you were doing before fine? Are there industries where live data doesn't make a difference?
16:12 Speaker 05 Um, it's a great question. I'm in the business of where it does. So it's hard for me to think of the negative.
16:18 Speaker 04 Well, who are you working with, right? I think that's a fair.
16:23 Speaker 05 That is.
16:23 Speaker 04 Obviously be transparent, but you know.
16:26 Speaker 05 Yeah.
16:27 Speaker 04 Sure, you work with all the grocery stores. Great.
16:29 Speaker 05 I think in every industry, there is a pocket where live data does matter. When I first stepped into Incord, I thought, oh, you know, maybe one of the verticals is like education, like, why would that matter? I couldn't conceive, sitting at my desk, of why would education, well, like, why would that be a sector that we would go after? And then to my surprise, I found that we actually have some really big university brand names as our customers. And as I dug into it, I was like, what's going on there? It's got to be the Office of Finance or something like that, right? And then lately, we've had a surge in demand from the education sector as education is moving more towards ed tech.
17:09 Speaker 05 And being tech-enabled in terms of how you're teaching and relaying contents to your students. There is suddenly this surge in need and understanding student outcomes and student behavior in almost real time. And then adjusting instructions, curriculum, grading systems. Like, there's, it's hard for me to imagine a space. I think technology is just becoming so pervasive in so many different fields that I, sitting on the fence, would not have imagined. And I think this is the real unlock for me is what's really important about live data is it unlocks creativity. Like, it truly unlocks human creativity
17:52 Speaker 05 and their ability to understand the possibilities. Business users, I think, across all industries and all spaces need to have the ability to ask unlimited questions so that they can explore how they can improve their roles and jobs and do their work better. And so there are, I would say, instead of saying there are spaces where it doesn't lend itself, I would say there are low-hanging fruit and like, it makes total sense. And you can immediately map out the ROI in spaces like retail or supply chain or manufacturing or the Office of Finance. But there are other spaces where there isn't a direct. R Y, it's almost like there's a correlation that work is improving in this space,
18:36 Speaker 05 but there isn't like, I've saved X amount of money, or I have improved my margins by X amount. And I think education was one of those examples that I was pleasantly surprised by, and I would never have thought.
18:47 Speaker 04 I want to play devil's advocate a little bit, where, yeah, everybody should have access to this data so they can ask all of the questions that they want. And in theory, and you're probably right, but I think that there's an argument to be made that being able to ask more questions of your data isn't necessarily always a good thing, because you can get analysis paralysis, right? If I have access to live data and I haven't taken the time to go through the exercise of, what is my North Star? What are our KPIs? What are the metrics we are always going to look at? You can go down a rabbit hole and get lost in data. So how do you balance this idea of real-time insights
19:32 Speaker 04 without losing control over what really matters in your business?
19:37 Speaker 05 I think that's why I was saying creativity, right? There's a difference between, this is starting to feel a little like hot takes. I don't know if you've ever followed that social media account, but it's like, your hot take is, you know, asking more questions is not good. My hot take is, you should be able to ask away as many questions as you want. And here's the thing. The creative process requires enough space for you to go into rabbit holes and noodle and get lost a little bit. And then aha moments really come from that.
20:12 Speaker 05 And I think if you close that door and you become too rigid and like, I'm only measuring certain KPIs, or I'm only, I'm closing that door to curiosity. There are predefined things that matter to us, but then everything else, maybe, you know, giving too much choice and too much room to rabbit hole into asking questions is going to create a paralysis. You might miss some really important bits of nugget. Like, I can give you an example. When I first joined Meta,
20:42 Speaker 05 Five, six years ago, I joined in a product called Commerce. Like, at the time, the product org was Commerce and Payments. And we were in charge of launching, like, you know, Marketplace and shops and all the payments that happen across our apps. We had like thirty-three different payments use cases across our apps. Not all of them were consumer-facing. But, um, and we were trying to scale Marketplace, which is a C2C. Like, you're selling, you know, it's kind of like eBay. You're just selling stuff to other people like you predominantly use stuff from your home. And, uh, and to scale it, it wasn't one of the largest, you know, services that were available on Facebook app at the time.
21:25 Speaker 05 This is fun little project as most things on Facebook, you know, started. But it wasn't like what it is today, which is one of the largest drivers of daily active users on, on Facebook app. And to go from that to that, if we weren't open to asking questions and experimenting and having deep access to information and telemetrics and understanding how customer behavior is happening as they're using this product, we, we wouldn't have had an aha moment where it's like, oh, crap, COVID just hit. That completely kills the C2C model where I'm knocking on your door and I'm buying your old bike from you, from your garage, and you're knocking on my door and you're buying my roller blades or my surfboard or whatever.
22:09 Speaker 05 And COVID hit and no one was allowed to meet anyone, right? So that's one problem. It's like our product's growth just completely stalled.
22:15 Speaker 04 Here in California, nobody was allowed to meet anyone.
22:17 Speaker 05 No, at least, yeah. But like, for, for a short period of time, the majority of the nation, even if they were allowed, they just didn't want to do that. They didn't want to go to your house and pick up your stuff. So we saw usage tank just as the product was taking off. And if we didn't have the curiosity to dig into behaviors and start to see signals that people were trying to find alternate ways to exchange fraud, which meant that it was sticky enough and they still wanted to do this, but were trying to, we wouldn't have said, okay, like, let's provide C2C shipping. Like, let's actually pay for it, subsidize it, provide C to C shipping, which allowed this product to go from, you know, good growth cycles into hockey stick growth.
23:01 Speaker 05 And that came from just us being able to constantly query and question how our consumers are behaving and using this app. So very long-winded answer here is that I think there's room to say creativity is helpful. Sure, it can be a waste of time, eighty, ninety percent of the time, but you can't design for the aha moments. You can't put rigid rails around the aha moments, and I think that's the missing piece, and that's what AI unlocks for a lot of people today. You can ideate on your own, or you can sit and ideate back and forth on ChatGPT or Claude, or whatever, you know, you use perplexity and say, hey, like, I wanna write, I wanna, I wanna create a campaign, or I wanna write a story about X, Y, Z,
23:47 Speaker 05 and then through a back and forth process, you suddenly, you're like, oh, I didn't think of that. Well, that is interesting, and then you rabbit hole of that, and then all of a sudden, you're all the better for it. And I think that's what data, that's what we're hoping, enabling a business user to have unlimited ability to query their business information, because they know best. Me and my role as the person that's in charge of POs for my company, you know, I take care of like the PO, the purchase order process. I know a lot more about that process, and I know a lot more about the suppliers that I'm buying from than probably anybody else in this company. And you can't design that tops down. And the current way that data and dashboards and like analytics runs
24:31 Speaker 05 means you have to think of the questions first, design your entire pipeline to be able to answer those questions or have those KPIs ready. And then if you suddenly come up with a new question, to go back and redesign all of that is really, really hard and very, very expensive to do. So you end up getting stuck, not being able to ask new questions, not being able to dig in more, and not being able to really utilize all the data that your business is generating. To get creative and figure out what's a better way for me to run my business.
24:57 Speaker 04 My hot, my hot take was, is it actually better to be able to ask unlimited questions? What I actually believe is you need both. You need to have a North Star metric, a goal, KPIs, and understanding of business performance.
25:14 Speaker 05 For sure.
25:14 Speaker 04 Then, then, once you see the trend change of the business performance, you need to be able to dig into the data and understand why. And so.
25:25 Speaker 05 On that, I think I hundred percent agree. I'm going to use the hot takes language.
25:29 Speaker 04 Great.
25:31 Speaker 04 All right. We're, hey, we're on the same page. And I think that, you know, as we think about the migration, the idea of having live data and be able to ask questions of your data and get real time feedback and not have to be beholden to the data structures of the past is incredibly powerful tool. It does not remove the need for an understanding of your overall mission, objectives and what goals are and metrics are most tightly tied to them. You still need your KPIs. The live data has helped you, helps you understand why. And that's an incredibly powerful tool. And that wraps up this episode of the Martech Podcast. A huge thanks to Noah Rizik, the CMO of Incorta for joining us.
26:16 Speaker 04 If you'd like to contact Noah, you can find a link to her LinkedIn profile in our show notes or visit martechpod.com. If you'd like to learn more about Incorta, you can visit their website, which is incorta.com, I N C O R T A dot com. If you haven't subscribed yet and you want a daily stream of marketing and technology knowledge in your podcast feed, hit the subscribe button in your podcast app. Or of course, visit us on YouTube where we'll be back in your feed every week. All right, that's it for today, but until next time, my advice is to just focus on keeping your customers happy.
27:14 Speaker 00 Thanks for listening to the Martech Podcast, an I Hear Everything production. Looking to launch or scale a podcast like this one for your brand? Then visit iHearEverything.com.
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