Interview: Filling the Consumer Data Decision Gap, with Jascha Kaykas-Wolff, President at Lytics

TRANSCRIPT

Peter:

Welcome to unpacking the digital shelf, where we explore brand manufacturing in the digital age. Peter Crosby here from the digital shelf Institute in this DTC era. First party consumer data is increasingly becoming a brand's most valuable asset. The hard part is how to draw the line from that data to the right action in marketing merchandising and product innovation, Yasha KC us Wolf president at consumer data platform Lytics joined Robin me to outline how brands can fill the consumer data decision gap and turn your data Lake into revenue and loyalty. Yasha. Thank you so much for joining us. Uh, you you've spent a career at companies who have massive connections to, and therefore massive data about consumers. Can you talk a little bit about those lessons that you bring to work, uh, at your customer data platform company Lytics?

Jascha:

You bet. Peter, I appreciate being here. Rob excited to have this conversation with both of you today, and maybe I'll just give a little bit of context around what I've been doing professionally, and then we can kind of jump straight into, I think, some of the trends that I've seen and how I've brought some of those trends into the work that we're doing at Lytics. Perfect. I spent the last 20 plus years and in the technology world in software companies in particular and, um, the kind of success that I've had and kind of the relationships that I've built over the years have really been because of two threads that have been pulling on professionally. One of them is around kind of acknowledging that businesses are always required to think about what they need to do next, like how they have to evolve and transform. And there are really two critical components of that.

Jascha:

One in my opinion, is a focus on how do you improve processes within an organization? Um, back about 15 years ago, I started experimenting with agile as a practice inside of marketing organizations and product organizations, as it was kind of becoming in Vogue and engineering teams and have kind of pulled that thread along the way. The second is acknowledging that businesses transform along with the technology that helps them do it. So investing in kind of go to market technologies that help businesses understand better who their customers are, how they can build a deeper and more interesting relationship with them. That kind of journey in those threads have taken me from places like Yahoo, where I joined in the late nineties, kind of at the infancy and the kind of internet becoming a practical vehicle for business. And, um, often to Microsoft for myself and a couple of my colleagues started what's now Microsoft store.

Jascha:

So helping a business like Microsoft at the time, 30 plus years as a through channel business, uh, transitioned into being also a direct business. And then over the last definitely years, I've spent a lot of time working in startups, um, focused on how do you kind of take more and more customer data, improve processes and create better experiences for customers can culminating in the that I took at Lytics about a year ago as our president or I'm responsible for all of our go to market operations. And I think I kind of hit on it in this kind of theme for me professionally, but I think fundamentally every business is dealing with the same general problems, challenges, opportunities, however you want to frame it. And that's the kind of, we have been progressively shifting the way that we work starting back in that mid nineties, where the internet propelled us to think about how our business could exist online.

Jascha:

And then software started to eat every process that existed, which is making it an opportunity for every single business to evolve the way that it creates a relationship with customers. And, you know, fundamentally every business has to think about how it improves its priorities and improves communications to be able to be effective in serving their customers. So I think from a professional perspective, I get really excited about experiences at big companies and small company is all focused around how do you create a better kind of go to market operation? How do you serve better your customer? And I'm really privileged to be in an organization who software helps businesses do that. So I take a lot of my practical experiences as a business leader and take them as an operator into the way that we work at lyric.

Peter:

Yeah, it's so interesting because the, the, when I joined, uh, and started working with brand manufacturers, that word customer has such a different meaning for them. Uh, their customers are the retailers and distributors that they sell through, but what's changed. And so, and the consumer is, is who this end consumer is. And in the last few years, that shift has started where they become truly omni-channel where brands now have more and more opportunities to sell directly to consumers and sometimes through branded sites. But there are more ways that consumer data is starting to come into their organization and in order to be able to do something with that, uh, you have to be able to understand that manage access. So that's why we're so excited to talk to you. Like, so when you think, when you work with brand manufacturers in the context of their data platforms, are you hearing about those sorts of trends and, and how are you engaging with them?

Jascha:

Yeah, well, I'll say that I think universally, everything that you said Peter is right, like businesses are evolving. We all have to think about the omni-channel relationships that we're establishing and building the questions that are consistent from startups, from manufacturers, from B2B companies, historically, as we're transitioning into B to C or B to C companies, they're all fairly, they're all fairly consistent right now. And they kind of look like this, at least in my experience in all of my engagements, like first and foremost, pretty much every business right now, if they are not completely tuned in or asking the question to themselves, who is my ideal customer and what are the attributes of that ideal customer. And it's as simple of a question as it is for us to say in a conversation like this, it's actually quite complex for a business to be discreet and saying who my ideal customer is, because when you identify who your ideal customer is, it starts to say no to other initiatives that you could potentially put in your business.

Jascha:

So that kind of first question that pretty much every business as they're contemplating, what they can do now that they're asking is how do I focus the organization on a specific kind of customer? And the reason that that question is so important leads directly into the next set of questions that every customer that I engage with of our customers that let X, but also in the industry, every question they're asking is what is the ideal customer experience that I can build for my ideal customer? And this is where Peter, I think our industry, the way that we work, no matter what kind of business we're in is being transformed most quickly. Like we now have the ability to create an ideal customer experience and be responsible for it soup to nuts. And that just hasn't been the case, even over the course of the last two decades, as technology has become more and more available to us, it's the tech that's made that possible.

Jascha:

Absolutely. And fundamentally like the technology has made it possible. And then we have seen bellwether examples of great consumer experiences that are setting a consumer expectation that consumers can get that from every single business that they work with. So technology becoming enabled, creating kind of bellwether examples that are the kind of first and best in class in creating a digital experience principally, and then that expectation being drawn into every single business experience that you have when you think about our day-to-day lives, I'm sure Rob and Peter, you have some sort of cable TV provider, right? And then you have on demand, uh, you know, over the air providers. And then you've got cell phone services that are kind of intertwined with them. Each one of those businesses are all thinking about, okay, I've now have technology and consumer expectations that have changed. So how can I better my experience? This is the exact same conversation and the exact set expectations that are playing on every single industry.

Rob:

So what's interesting about the world that we're in now, is it just playing on something that Peter said a couple minutes ago, if you're say Coca-Cola in the years, 2000, you use the word customer within your offices to refer to Walmart. And your ideal customer profile are companies like Walmart that have massive distribution and a ton of foot traffic, and you use a brand and trade marketing to drive foot traffic into Walmart stores. So when you've got, when you've got a new product launch, um, when you do national campaign planning and management, your, you know, the Walmart's your customer, and you're trying to be a good partner to, to their, to their customer, right? You, I mean, you care about the end consumer, but they're not, they're not your target and you don't have a database about them and you don't, you're not like tightly segmenting who they are.

Rob:

And you don't know who Rob is. You just have like a broad swath of information about a class of consumer of which Rob might be apart. And this con this experience shift that you're talking about and, and the business that Lytics is in is personalizing and, and really enabling these brands to get analysis more on the Rob level and less on the Walmart level. So, so, so it's like, if you're, Coca-Cola using the word customer to refer to Rob, is, is the shift. And then how do you actually reason about that? So, so can, can you talk a little bit about that? The, the, the data and the analysis and like understanding the customer?

Jascha:

Well, I, I think it's, it's a, both Rob, to be honest, like if you're a business that sells through, uh, different channels, like you still have to be conscientious of the fact that part of your customer population is a business. Oh, sure. Yeah. But there's a new expectation that exists now that consumers, the end consumer, which many people in many different businesses just called the customer now, um, also has an expectation. They can build a relationship with you. So that, that kind of business, that the Coca-Cola's of the world and CPG companies of the world and retailers of the world, that those businesses are now being challenged with. How can I understand the capabilities that my organization needs to have in order to be able to create, maintain, and most importantly, deepen the relationship that they have with their customer, both the channel and the end customer.

Jascha:

So the capabilities that businesses I think are struggling with Lytics sits in the middle of the space, but we'll just talk about kind of the industry at large, the capabilities that are typically most required are kind of first and foremost, the business, having the processes and the talent that is able to have the conversation that we're all having right now, how can I distinctly understand who my customer is? How can I define what I want the experience with them to be? And then the second piece is understanding the technological capabilities. I'll dig into that in a little bit and I'll do that. I don't want it to sound super self-serving just because of where I thought it worked, but I will say that the technology has come so far over the course of the last decade. That the way that we've been considering understanding capabilities in a business have evolved massively.

Jascha:

And this is where I think a lot of businesses are struggling to keep up. So I think traditionally, if I were trying to answer these questions of what are my capabilities, to be able to create, like maintain a, deepen a relationship with the customer, and I've got processed and they believe that I've got the right talent, and I'm looking at technology, I'd really be looking at two kinds of technology investments that are going to support processes and talent. One of them is going to be around organizing all of the information that I have about my customer. And we've been hearing about customer three 60 projects for years.

Rob:

Yeah. Take data lakes for everybody.

Jascha:

Pick, pick the flavor of technology that exists. I think part of the challenge there, I think 20, 20 hindsight, we're, we're benefited by it. But part of what we see now is that the promise of just together, all of the attributes of a customer in a data Lake, didn't actually help enable the processes and the people to be able to create a better experience that establishes, maintains and deepens the relationship to the customer. The second part of the investments that businesses were making, this is all that capability is we're in the technologies that helped craft the customer experience. Let's call this go-to-market tools, MarTech stacks, they'd be investing in advertising platforms to run better. Programmatic advertising. They've been investing in CX tools to create a better web experience. They'd be investing in a channel tools and CRM tools. I can manage my email better. I CRM better.

Jascha:

And truthfully, there's been an entire industry built up. If you watch any of those, LUMAscapes around just this. It's like look at the 8,000 plus different companies that if you pick the right technologies, you may be, you can piecemeal them together and you can create a good customer experience. The thing that is really, really important, I think for all of your listeners to take away is that those capabilities are needed capabilities, but they don't fundamentally solve the problem that businesses have to solve for when you're trying to connect together, the technology is the people, processes and talent. And that is really specifically what I would call the decision gap. When you have information about a customer sitting in a data Lake, and you've got a bunch of marketing tools to be able to create and craft an experience, what you call that Rob, which is really important is that we have to create an experience for you, Rob, or ups or me.

Jascha:

And the scale to do that in is like mind numbing, right? If I've got hundreds of thousands or millions of customers, if I'm Coca-Cola, and I've got all that information in a data Lake that I've been building up and building up, but I've got all these great marketing technology tools, how big is my team going to have to be? And what kind of processes I'm going to have to put in place so that a marketer can make a decision about how to customize an experience that's relevant for you Rob right now. So that decision gap, the ability to connect together, that customer data into the experience is where a lot of businesses are falling down. And it's the area where I think more and more technology is going to solve the problems that that gap has exposed for businesses,

Peter:

Right? You to do that scale that we're talking about, it has to be an automation and technology and AI thing,

Jascha:

AI and ML have to play a part in it. And I mean, we think about all, you've all been involved in advertising and different parts of your career, and maybe spend lots of money down to like we got better and better at advertising as marketers because of the kind of popularization of programmatic advertising using AI and ML to better kind of tune the performance of our advertising programs. And what we were willing to do there is trust that the AI and the ML could help us perform better. As long as we created all the conditions of what the right goals were at the end. So the same concept that exists in programmatic advertising a goal, a journey that you want a customer to go on to get to that goal, and then utilizing AI and ML to help you make decisions at a scale that we just can't do as humans is where this big shift is starting to happen. That's solving the decision gap.

Rob:

So w let me, this is a lot of information on technology. I'm going to try to try to pink the different pieces and correct me if I get any of this wrong. So there's, there's several different components that you're talking about here. Component number one is collect the information about all the possible customers in one place to begin with. And this is the, this is the data Lake problem, right? And, uh, there's all kinds of challenges there. And I think a lot of the manufacturers that we talk to have it, that's not, that's not like a solved problem for them. They've got initiatives there. They're still trying to do data capture. It's not that you know, it's not a solved problem. Um, separately, you've got your experiences that you're delivering to the shopper. So some of those experiences are through third parties, like a Walmart or an Amazon.

Rob:

So let's leave that aside. Let's leave it. Let's talk about experiences that you have control over. So let's say Shopify Shopify site, and then with Shopify, you've got an email marketing system like Clavio or something like that hooked up. Right. So then, so you've got, um, your DTC team and your marketing team are running Shopify and Clavio. And then you've got a data analytics team. That's trying to get some of the shopper data and toss it into the data Lake. And your observation here, what you're calling the decision gap is that the data and the insights that are potentially available to be unlocked in the data Lake, the, the way that you unlock them is ultimately to express them through, for example, Clavio emails. And there's no way to do that right now. And especially because you're talking about like millions of consumers and, and it's just sort of like, there's no way to do that right now. And so people just aren't. Yeah,

Jascha:

You've articulated it very nicely. I think that the, like the nurse or that exists in the industry is that there is a belief that if we finish that three 60 data project, or we finish that MarTech roadmap that we're going to be done, we've we have what we need. And the reality is that the industry and the expectations that consumers have are outpacing both of those two initiatives. And this decision gap has grown even more because the expectations are so high that you're going to get a great experience, and Peter's going to get her a great experience. And I'm going to get a great experience when we get an email from Clavio, from side company, and it's going to be different.

Rob:

Yeah. Molly Schoenthal has this vision in, in the, in the post COVID times of the future, when people are traveling and going to hotels again, where she walks into a Marriott and the sheets are the exact type of sheets that she likes and the fridge, the exact type of soda that she likes and so on and so forth, and like the experiences for Molly and, you know, Marriott has, I don't even know tens of millions of people, uh, or hotel nights that they have to plan for every single year. And the only way to even have a remote chance of doing that is exactly what you're talking about. It's that sort of anticipate anticipatory, um, experience that can be delivered at scale through AI, but also requires all kinds of other execution muscles throughout the company to be coordinated, to get right. But imagine a little bit, sorry, sorry.

Jascha:

I was just going to say, imagine the transporting experience if they were actually able to deliver that. Right. I mean, my, my only point that I was going to make Rob is that maybe I'm a little bit older, but that story that you just told is the exact same story from 15 years ago that I heard about period, and I'm not discounting that progress hasn't been made, but quite frankly, technology hadn't caught up to the promise and consumer expectations just flat out weren't there. I mean, like in the, in the last several years, like we've become so accustomed to using services like Spotify and Netflix, and even Amazon, that it's hard to comprehend that we're not going to get that same experience when we go to a Marriott now, like there's a, there there's enough cultural distinctive differences over the course of the last decade that I think it's practical, that that could happen. And we have an expectation that it should. And we're disappointed when it doesn't.

Rob:

Yeah. It's, it's so interesting to think about that. It's, um, like web van in the nineties exploded, but Instacart is like web band, but at the right time where there's enough technology to support it, uh, Benedict Evans linked to a YouTube video from Westinghouse, I think it was a Westinghouse advertisement from the late fifties, early sixties called, uh, the Westinghouse electric home of the future. And it's exactly a smart home. It's like, it's like, if you've got Amazon Alexa and you've got Spotify, you've got a speaker, you've got all this. It's like, that's what they're advertising, but 50, 60 years ago. Um, and, and only now are we, are we getting there? It's so, you know, I guess to your point, Marriott could have had this idea in 2005, but without AI, without, um, you know, basically cloud computing and scalability that enables you to collect all the data without all this sort of stuff, you have no prayer of actually getting it done. You know, I'm still waiting for the Jetsons. You know, I don't know where

Jascha:

Look at it. I mean, it's all coming, right? Like the, I think one of the big learnings that I'd add on top of that, Rob, is that we, like, we just now flat out, expect our experiences to be better. Like if somebody was a thought leader a decade ago saying, you know, when I show up, I get my warm cookie and I get the right sheets and the right pillow. It's a thought leader said that we're going to do that. And now, like I turned on my radio and I'm getting the music that I like. And I expect when I show up at the hotel to get all the things that I like as well. Like we just, we, there has been so much advancement in CU consumer experiences because of like these bellwether companies, like the Spotify as in the Netflix is the world that we just expected everywhere. So it's our responsibility as business leaders to make sure that we're developing the best customer experience we can because our consumers expect it. Our customers expect it.

Peter:

Yeah. I would just say, I'm not getting the music that I like. I'm getting the music that my daughter likes. It's like show tunes all day long. My Spotify throw off my own heart. Well let's well, uh, yeah, so let's, let's dig into the gap cause I'm pretty sure most of the people listening to this that are, that are dealing with this problem will understand what you're talking about, which is I have all this data. I have all these systems. What the hell. So, uh, talk to me about what is changed, uh, and how you guys describe filling that gap. Yeah.

Jascha:

Yeah. Well, let me, let me share a learning. That's been really fascinating since I joined Lytics and I'll give a little bit of context as well. So I spent the last half a decade prior to joining analytics as the chief marketing officer at Mozilla Firefox and Zillow Firefox is a fascinating organization, uh, governed by not-for-profit and very much focused on, uh, creating the best experience so that consumers can be in control. Customers can be control of their internet experience and part of what I learned there. And I think this was a bit ahead of the curve that I think we now broadly understand is that like, if you want to create a deep and meaningful relationship with the customer, you have to take really good care of them and taking good care of them means taking good care of their data, right. And that kind of internet existed.

Jascha:

And a lot of MarTech tools existed with it. I mean, this is a to analogy, but they were kind of like Hoovers. They just sucked up as much information as they possibly could with a hope that at some point it sat in the data Lake and you could make sense of it and it would be useful. And that's a really, really, really bad way to build a trusted relationship, to just take everything that you possibly can without somebody knowing, and then decide that you're going to give something back to them. At some point in time, it's going to benefit you as a business. So we came up with this kind of concept that you had to always give back to a consumer, something of equal value to what they give you. And the first thing that a consumer gives to you without explicitly saying, I'm giving it to you is information about what they care about by their behavioral patterns.

Jascha:

So they're kind of the panacea in the three 60 customer data world. And this is a juxtaposition to that concept is that you collect absolutely everything. And until you had absolutely everything, you can't find the right information to be able to make the magic growth equation that makes your business work well. And Lytics kind of what I have found is that over the course of several years, the team has been running a longitudinal study, looking at what data actually informs the ability for a business to give back something of value to the customer in the form of a personalized, customized experience, the vast majority of the data necessary isn't data that is asked for, or extracted from them in a kind of, um, uh, kind of malfeasant way it's data that comes because of the behaviors that somebody shows you when they show up at your website.

Jascha:

So let me say that in the most concise way that I can, you can create an amazing personalized customer experience by just paying attention to how your customers use your first party data, your website experience, and the content that you produce. Like it is not about collecting as much as possible, as much as possible. It is about focusing on the right kind of data. And that right kind of data is not intrusive it's behavioral data first. So that was the first big learning that I had. So when we think about right now, what we should be considering, if we're a business first decision that you should be making is what's the value exchange that I'm going to create for my consumer, for my customer. And what's the best way for me to do that. And I'll tell you that my recommendation to every single business that I talked to, whether they're a customer of Lytics or not is to not try and collect as much data as you possibly can, but to focus on collecting the right data.

Jascha:

So that's step number one in trying to solve this decision gap, focus on the right data. The second is acknowledge that you probably have a massive set of customer experience opportunities, problems that you want to solve for, but you will get traction in a business. You can make good for progress in the business. If you focus on a really simple concept that just doesn't show up a lot in the world of marketing, instead of creating a set of goals and decisions that are based on goals. Like if this happens at this time, then do this where you can focus on is creating an ideal journey for a customer that achieves if a journey has completed the business objective of the goal of the business. If you think about creating a consumer experience that just looks at behavioral data and tries to help a customer get to an end goal, by experiencing a few stages in the journey, you can start to attack this decision gap problem in a really, really tangible and simple way.

Jascha:

So we, I sit in a category called the customer data platform category. The customer data platform category, I think is challenged with it can be a be all end, all it can solve all kinds of different problems. And my recommendation as somebody who's listening to this podcast is to really start to tune in on identifying. If you believe you have that decision gap that exists in your business to start to think through what the ideal customer journey is that you want a customer to go through. So they achieve a goal on behalf of the business, and then to start to construct the kinds of behavioral data collection that are going to be necessary to create personalized experiences through that journey. So I would say maybe another way to say it is you don't have to boil the ocean. You really can start with a very focused approach and you don't have to collect all the data in the world to start to solve the decision gap that exists in your business.

Rob:

Right? So it's interesting the, the, the journey element there, um, two thoughts popped to mind for me, one is Seth Godin says the purpose of marketing is to cause change. And I think a lot of marketing has, has had the, the purpose of marketing is to drive another transaction. And w what you're talking about is, is a reframe of what the marketer is trying to achieve through this, uh, through, through looking at behavior and driving behavior and walking somebody through a journey. And there's something that's very pleasing about that because it's not about selling something it's about like, what's the change in somebody's life that, that you're trying to push with this product, right? They, they, you know, how, how are you going to improve them to succeed? Um, so I, I like that point of view, it's a fee. It feels like a very generous point of view from, from a brand to be operating from.

Rob:

The second thing that I think is really interesting about that is we, you know, we had a recent conversation, um, about the death of the cookie. And one of the predictions of when you, when you, when the cookie dies of a third-party cookies for cross-eyed advertising and whatnot, well, you know, what are the marketers going to be going to be left with? And the, um, the guests is, well, watch the behavior look at what they do on your site. And that's actually probably better anyway, but it means that you actually have to start treating that as really, really critical data on customer, the way that you segment customers and what you're going to show them next. And, and you've got to be more intentional about journey design. Um, and those are, I think, relatively new concepts for a lot, for a lot of manufacturers, right? I mean, big tech companies have UX teams that designed websites with these concepts in mind. I mean, you were at Mozilla, but, but that's a new behavior for, uh, manufacturers who are building websites to sell products to, to really, to really adapt. And they're doing it at the same time that that scale is becoming massive for them and where they're struggling with other core technology investments, like the data Lake. I mean, so, so yeah, there's, there's a lot to like in, in what you're talking about there,

Jascha:

I think, yeah. I mean, Rob, you hit on a super important point if, if you're in an industry and a business that some of this conversation is feeling like it's futuristic, like we can kind of go back to what we talked about earlier, which is every business needs to identify the capabilities that are necessary for them to be able to create a great customer experience. And that fundamentally is going to come down to the talent that you have and the processes that talent uses, and then that's going to be supported with technology. So I think that there is absolutely a crawl, walk, run approach that every business can take if they believe in the course of this conversation, that all of these factors are coming true for them, that they've got to create a deeper relationship with customers, that they have to understand who their ideal customer is, and that it's their responsibility to create a great customer experience. Um, I, I believe that every marketer, every business leader is going to take the point of view that you have, and that we're talking about here. It is our responsibility first to create a great experience when we do that, we influence change when we influence change, we benefit that person. And by the way, that happens to benefit our business.

Peter:

Gotcha. Can you make this a little bit more concrete for our audience? You know, you've, you've talked the, the sort of the theory of it, but do you have any use cases that would resonate with a brand manufacturer that might sort of bring that to life?

Jascha:

Yeah, so a brain manufacturer may not have any information about who their end customer is. So like a really simple use case is I first and foremost want to know who my customers are. My end customers are a way to validate that I know who my customers are, is that I am getting information from them. Like they've chosen to give information from them to me as the business. And let's say that at the beginning of a relationship can be defined as an affirmative delivery of an email address from a consumer to me as a business. So simple use case and a new use case for many businesses that have not thought about working directly with the consumer. So a use case would be when I promote information about what my product offers, what my service offers, and I bring a customer to my website, as opposed to one of my channel partners.

Jascha:

My opportunity is to understand how they came to me to understand what they are doing on my website, looking at my product products, product information, and in that experience that they're having present them with relevant, personalized, additional content that supports one goal of them starting the relationship with me. So you can choose to solve the decision gap by in this case, acknowledging that success, the goal is an email address has been collected and presenting personalized experiences on the web without knowing information about them, but by looking at their behaviors and making recommendations because of those behaviors. So very simple use case can apply to many different industry, but a very tangible way to think about trying to solve a decision gap. I like the plug from a technology perspective is that you may not even need a giant data Lake. At this point, you can consider using a technology like a CDP, like Alytics, you can deploy Lytics.

Jascha:

That would take a look at that behavioral stream of data. Take a look at your Corpus of content that exists on your website or in your dam, and use that behavioral data in stream to make real-time recommendations to that person. The recommendations are going to be tuned to help that person recognize that giving you an email address is going to be beneficial for you and successful for the business. So simple, simple use case, very tangible way to think about a first step in solving decision gap. If this is a net new addition to your business strategy

Rob:

And the, the difference between, cause I would imagine that's a behavior that without a data Lake, if somebody new comes to my website, I will try to give them a, some sort of incentive to sign up for my email list. What is it about their journey on your website that would make you just a little again, a little bit,

Jascha:

Yeah. Deeper, even more into the weeds. Yeah, well that, that, uh, particularly the use case I picked because it's one that we see much success for many new businesses, many businesses that are new to thinking like this, um, with Lytics in particular. So behavioral data is important because it shows kind of what interests are of the consumer recommendations are relevant to a consumer, to a customer when the content that they're being recommended has a direct relationship into that experience that they're driving. What Lytics does that's relatively unique in our category is that we have a, um, NLP engine that analyzes content,

Rob:

Natural language processing for those, uh, yeah,

Jascha:

The analyzes content across your Corpus of content. So your website, your a the place where you store content, we categorize it. And then we create what are called affinities. So let me draw an analogy. When you watch Netflix, you pick a movie and you watch a movie or a TV show. What Netflix is doing on the backend is they're saying this movie is not just an action movie or an action TV show or a drama. It has subcategories, it's got a morally ambiguous lead character. It has car chases, it has Kung Fu those are content facets. Lytics does effectively the same thing for your business. It looks at all of the different facets and creates affinities groupings of those facets that create better recommendations based on the behavior. So if you Peter show up at a website, you look at product information, you move over to another piece of content that supports product information.

Jascha:

And what lytic sees the AI sees is that you're actually really interested in the technical details of the products because of the behaviors that you're showing. We can find content that exists in your content Corpus that reinforces the technical details of the product purchase and make those recommendations real time in the experience that you're currently having. So that relationship into content content affinity is, and content categorization is a very unique way to solve this decision gap problem. When you're really focused on behavioral data as the core set of data stream that you're looking at. So that's a, that's kind of getting a little bit more down, right?

Rob:

The weeds so interesting at, uh, at, uh, at Endeca and the two thousands, we had, um, a technique on, on facets called clustering where we would, we would basically, we would basically do statistical analysis on search behavior and give recommendations on search based on, based on basic click streams effectively. Like, what are you going to do next year? Here's the thing that you're most likely to do next? Um, once you're talking about, it's really interesting because it's, you know, the whole content and commerce angle has, has struggled to come together in the way that people have been talking about. Um, and it's maybe because it's missing this, this link, right? It's like, how do you actually link extremely relevant content to, um, to somebody in a way that's going to increase conversion rate? Well, you do it, you do it based on behavioral data, but behavioral data is really hard because there's an infinite number of paths across the website. So yeah, no, that's a, that's a very clever technique. I like it a lot.

Jascha:

You know, this, especially because you're talking to the DECA, there are, there are different decisions you can make architecturally that, um, help you process and find relationships differently. And sometimes more effectively in our case, our founders built Lytics on top of a graph database and graph databases are really powerful because they are able to kind of construct relationships in real time or near real time, like really, really, really quickly. But they're also really good at finding context. So like a recommendation that would come from this podcast would be not here's the podcast and all of the podcast episodes. So let me give you the right content for it. It would say here's Robin Robins, background and Robin kind of facets. Here's Peter and Peter's background, Peter's facets and here's mine. And then it would make a recommendation based on all of that information in real time versus, Hey, here's my cluster of stuff, but I say, you should go get, I'm not saying that that's a bad approach. It's just a very distinctive preps that we've taken that that is, uh, I think, uh, uh, available now because of the technology that, that we're, we're able to take the bear and the monkey,

Peter:

And is Lytics able to discover the facets of content or do they have to be cause right now, you know, I, we have to do tagging on our thing to say, Oh, it's this we're making up. But so you're saying Linux is able to go in and

Jascha:

In the business world, there, there are the two important call-outs here. One, yes, this is part of what we do natively. We've been going to kind of content affinity engine that goes out and does all the searching and the NLP and et cetera, et cetera. Um, but the reality is that businesses know their businesses very well. And so they may have a taxonomy or an ontology of the way that they want information to be categorized. And so we can ingest that and think about that as well. So it's, it's an, it's a both and, and scenario. So,

Peter:

Um, what is the, you know, you talked earlier and it's the usual, uh, wonderful three legged stool of people, process technology. Um, let's, let's talk now about sort of the people and process when you come into an organization where this is becoming a need, where it has been a need, but they didn't know they could solve it. What are those issues of people and process that brands need to confront to be able to do this work?

Jascha:

Well? Yeah, there's two, two issues. One is the people issue people opportunity, and one of them is a kind of a process opportunity. Um, so the first is let's hit the process opportunity first, um, when, when contemplate, how we're going to create a space to make decisions are kind of inertia inside of an organization is that we want to have control. And so the kind of the mindset shift that changes processes comes from this kind of base. Like I'm not going to seed control decisions to AI. I'm just, I don't trust it. So the first kind of step is that we have to introduce the notion that decisions at the scale that we want to get to have to, at some point be assisted by or automated with AI and to get there organizationally, we have to set up the processes that allow for a spectrum to exist in the business so that a marketer can feel like they're in control and be in control of certain kinds of decisions.

Jascha:

Like this is the content that's most effective in driving this particular journey. So I'm going to produce more of it all the way to the other end of the spectrum of these are the kinds of decisions that I'd like to try and automate to begin with because setting up that spectrum that and understanding how you can make decisions on a spectrum is the first process to attack this problem. And it's part kind of understanding where the, I think the emotional center of most teams are and second kind of creating a path to break through that inertia over time. So that's, I think piece number one, the people opportunity is that there's a function that doesn't consistently exist in every organization that I will predict will exist in every single organization. Um, and, uh, I believe over time will exist in every single marketing organization right now is we're experimenting with changing processes to create better consumer experiences.

Jascha:

We have to be dependent upon data science and the analyst function in particular in marketing and the analyst function in marketing just flat out doesn't exist in every single company, the person who understands context of the business and is able to interpret the data that's coming from the systems that we have available to us. So when I'm trying to talk to this problem, I'm first going to talk about the introduction of that decision gap in the spectrum against it, and how to help an organization think about making decisions along that from peer decisions, I'm making all the way through to automate decisions. And the second is I'm going to introduce them into the function of the marketing analyst, which may sit in a different organization right now, but I believe will be a function that every marketing organization will have, which will help inform the marketer. That's running the channel to work better on that decision gap, on that spectrum and support kind of a better understanding of the way that your customer data and customer information is showing up in the customer experiences over time.

Rob:

Yeah, I, I agree. Um, I will quibble the, with what you're calling it because there was always business analyst, um, and statisticians and whatnot, but then they called them data scientists and, and now it's a super attractive profession and they get paid three times as much and they're under high demand. So you should call him like marketing data scientist or marketing scientist, or there's gotta be, there's gotta be a way to make it sound like marketing it up, you know?

Jascha:

Well, I think it's the, I think the position title should be marketing analyst. Like I think that, I think that that is what we should call it.

Rob:

That's what I'm saying. There's room for improvement there. Even some more flourished

Jascha:

There. All right. Well, we can brainstorm that in our next bucket.

Peter:

Well, I mean, there's a lot of competition for that brain these days, and if you're going to get them to come over to our side, we have to make it big in a jacket. Yeah. We aren't throwing money at them, so, yeah, sure. Um, you know, we're, we're still in the beginnings of 2021. Um, what is your view? What would be your closing Clarion call for a companies? You know, what, what are the state they need to be in to be able to move forward on this initiative within, within the year to really make an impact on their organization?

Jascha:

Uh, I'll come back to what I said earlier. If you are considering this conversation interesting in the context of your business, like you are absolutely in the right position to, to do the following this year, like you need to make for progress in creating a great customer experience for whomever. Your ideal customer is you're not going to get there by the end of this year, but you can make progress. So be very smart about how you frame the progress that you want to make. And, and in my opinion, if this year you're a new business, considering everything that we're talking about now, if you pick one goal for your business, you design one journey and you test processes and tech around that one journey. When you exit 21, you're going to have some so much benefit for your business. That is not only going to make progress this year, but also inform a really strong 20, 22 and on, because what we're doing and experimenting with this year is not a nice to have anymore. It's a requirement,

Peter:

A very good call to action to close. So, uh, yeah. Thank you. So, I mean, what a pleasure to have you on and, and have this conversation. And I know it's on the minds of our listeners as direct to consumer just becomes increasingly a more important channel, uh, and has benefits across every channel that they sell on. So, uh, looking forward to, to, uh, you know, the further conversations that come this year of how they make that data, uh, make them serve their customers better wherever they are. Peter

Jascha:

Rob, uh, thanks for having me. I appreciate being here.

Peter:

You have a decision gap at your company. Please share this episode with your colleagues. It's really a great cross-functional conversation to drive for sure. And thanks as always for being part of our community.