Marketing leader roundtable: Fueling your growth with data in 2024

Transcript

Gabrielle Stafford: We're really excited to be kicking off our first summit with a roundtable discussion on how marketers can fuel growth with data. 

 

I'm a career marketer. I've worked with marketing one way or another for pretty much my entire career. And being a marketer and working with a product that makes life so much easier for marketers is really, really awesome. But one of the reasons that I really love my job at Supermetrics is that I get to talk to marketers from lots of different sectors - agency marketers, retail and eCom, B2B marketers, the list goes on and on. And it's really the passion and the depth of the conversations that we've been having with our customers, particularly over the last 18 months, as we've been evolving our product offering that have literally inspired this event today. 

 

We are so happy to have you all with us for the event. So, it's the end of October, and I know a lot of us are deep in 2024 planning. I know every second meeting on my calendar has 2024 as the title. So, we're going to start our roundtable by bringing some awesome marketers together to chat about the big trends that are keeping them up at night as they plan for 2024.

 

So, let's invite our speakers to the stage. Let me introduce our awesome panel. We are so lucky to have them. We're calling them our “super panel” because we tend to super in a lot of words.

 

First of all, so excited to have Dorothy Advincula with us today. Dorothy is literally the “duchess of data.” She has spent time with Apple and Samsung. She's now with Uber where she heads up audience insights and measurement for advertising globally. 

 

Dorothy's specialty is integrating insights and synthesizing data sources to influence business strategy and decisions for the world of brands. Welcome Dorothy, we're delighted to have you. 

 

Next up we have Jessica, previously with Wayfair and Rakuten and now with Graduation Alliance. Jessica has a wealth of experience in multi-channel digital marketing, media planning, strategic analysis, and team management. She has a track record of over 17 years and has consistently delivered successful outcomes by leveraging data driven insights. So, super excited to have Jessica with us too.

 

And last, but definitely not least, we have Josh Cottrell-Schloemer. Josh is a performance dashboard specialist who has worked on projects with brands like Google, Microsoft, Lego, Gatorade, and Philips. His newsletters on TikTok teach people how to build better worksheets, using the Overlook design features in Excel and Looker Studio. 

 

So let's kick off. When we talk at Supermetrics to marketers about the things that keep us up at night, there are three really strong themes that we have seen coming up again and again. And these are data sprawl, more channels, more tools than ever before. It's becoming increasingly difficult to see the wood for the trees.

 

I think if you take just one look at Scott Brinker's marketing technology landscape map and it's over 11,000 tools, you get a feel for how fragmented the marketing data and tool technology space is. So that's number one. 

 

Then we hear as everybody else, a lot about generative AI and how we as marketers are going to figure out how it's going to help us do our job better, and ROI and efficiency.

 

And for me as a career marketer, this is something that has been top of mind for me, my entire career. Same for all, I think, the other people on our panel, what has changed recently is the increasing loss of signals. How do we handle measurement where those explicit attributions signals are increasingly going away? And we need to take a more holistic approach. 

 

Why are these things important? Why do they keep coming up when we have these conversations with our customers? Densu, who is one of our customers, predicts that global ad spend will reach $725 billion in 2023 and it will be worth over a trillion dollars, that's 12 zeros, by 2027. But despite this, Gartner say that 86% of companies are still struggling to understand marketing's impact.

 

A lot of them are looking at it as a cost rather than an investment. So there's a lot of money being spent, and there's a lot of tools being used, but marketers are still finding it really difficult to join the dots. So that's why we're here today. So, as we've said, it's that time of year again, where many of us are deep in our planning cycles. Starting with Dorothy.  As you put together your plans for 2024, what are the things that are keeping you up at night? 

 

Dorothy Ann Advincula: That is so resonant right now, which I was just in the thick of planning the other day in the last few weeks, right? But what keeps me up at night and also what got me out of bed this morning other than this session is, what do I do with all this data?

 

If I had all the time in the world and I'm sitting in this gold mine of data, different signals, and then kind of like still trying to figure out how best to make that integrated with all of the other signals in the world. That's what keeps me up at night because no one likes a good challenge, right? I think it's always what stories are we still missing out on being able to tell? It's not just about the marketing story. But stories that need to be told, they're about experiences that people have, which will lead us to like create better products, create better processes and all of that, right?

 

Like, so yeah, I guess the main question that keeps me up at night is what do I do with all this data? 

 

Gabrielle Stafford: So same question to you, Jessica. What is keeping you up at night as you're heading into your 2024 planning cycle?

 

Jessica Moore: If I had to pick one thing, it would definitely be reducing our CPA. We look at a cost per lead. We have education that costs enrollment. so as far as our ROI, that's what we measure, expanding into organic opportunities. What that means for us, to piggyback off what Dorothy just mentioned, that just means more data, right? More data points and not losing sight of our true north.

 

So what is that cost per enrollment? How many enrollments are we getting? How many lives are we changing to our mission? At the same time, we need to plan for earlier key performance indicators in the funnel. So that's what I'm focused on. And of course, getting leadership on board with that. They're focused on enrollments and how are these students being retained?

 

So that's very early on in the funnel is what these early indicators, engagement rate on social or YouTube, right? These slightly newer channels and metrics that you have to look at the full funnel.  

 

Gabrielle Stafford: Great. And Josh?  

 

Josh Cottrell-Schloemer: I think for me, and I come at this just from a different perspective as somebody who's primarily doing content creation, I see this kind of growing rejection of design, communication, and quote unquote soft skills in the data and analytics space. 

 

I see this gap growing between the data folks and the actual operators in the business growing wider. Not coming closer together like you would think it would be. And it's really serving nobody as those two get further apart. And we need way more people in this space that are going to bridge the gap, multidisciplinary people who can fill that space in between the growing data sprawl, the confusion around ROIs, the kind of stuff you were mentioning in those lists of main concerns. I think they're really directly tied back to that growing gap that we see. 

 

Gabrielle Stafford: Yeah, I love that point because data is a tool. It's a tool to help us do our jobs better. And as Dorothy said, we want to as marketers create amazing experiences for our products for the way we're promoting and in a sector like education, we're just actually actively changing lives. Data is a tool to help us do that. 

 

And I think Josh, the fact that we need to bring data together with people to make it work is a really, really interesting way to kick off this discussion. So let's keep going. Dorothy, you've worked with some of the world's biggest tech brands and even for those huge tech brands, data has been used as a rear-view mirror traditionally to review past performance. What is trended? What is our benchmark? What's our week on week? Our month on month? But in the new era of marketing, looking at past outcomes isn't going to be enough to get the ROI that we need and stay ahead of the curve. How are you using data in your function today to stay ahead of the curve? Any tips for all the people listening to us right now? 

 

Dorothy Ann Advincula: Yeah, I think what has happened is, there is a need to shift the mindset, right? So as you mentioned, there is a reporting measurement aspect of marketing analytics where we're looking at historical and then forecasting for trends and everything.  But to Josh's point where there is this divide between design and actual data, I think the reason for that is we forget that there are people behind this data, right? The data that we are analyzing is about people and the data that we are serving and learning is for people. So I think we need to shift the mindset about who we are representing from our data, but also at the same time, who are we thinking to educate with our data?

 

So it doesn't mean that I think in the historical days, you're always thinking about decision makers. And people who hold the first strings, basically, and that's who you present your data to, right? At the end of the day, as the world democratizes its data functions, you can't influence the product marketer. You can't influence the actual product person and even the engineer, right? How do you inspire them? How do you give them the right data to potentially rethink their strategy? So I think when we shift the mindset into more people based, people first, it definitely changes how you would look at data in general.

 

For me, when I started looking at use cases or user personas for the different data sets or even the different data tools that we are trying to bring forward, that it blew my mind that an engineer would need something else from me. Whereas the data scientist would probably want something else to write like so it's about recognizing that.

 

Different types of people with different needs and different ways to understand the data will need that specific insight expressed in many different ways to be able to do that.

 

Gabrielle Stafford: I think we find that an awful lot in Supermetrics, and that's why we have destinations as disparate from Google Sheets right through to BigQuery or AWS, because different users have different needs. Josh, when you and I chatted, I think we had a very similar kind of conversation and you talked about something that I found very interesting, which was big data and small data – which I think is another version of what Dorothy has just said. So I think it might be really interesting to hear your interpretation of that concept and why you think it's important for marketers to use both of these kinds of data. 

 

Josh Cottrell-Schloemer: Yeah, the way we talk about data has gotten, I think, a little bit out of touch recently. It's BI tools, it's data lakes, it's data warehouses, whatever the flavor of the quarter is. And I think of that stuff broadly as anything that requires you to go file a ticket with whoever specializes in it. You get something back a week later. There's an issue with it. You have to file another ticket. You get something back a week later. That kind of stuff. And that's just getting more out of touch with like the actual day to day decision making that happens.

 

I think that happens in large part because some of the loudest voices in the data space right now are folks coming out of tech. They are implying that you need to have terabytes of data that you're loading into a machine learning model somewhere and then visualizing a BI tool. I mean, it's important. It's best practice. It's great if you're working with that kind of data, right? But the vast majority of folks out there aren't in that space. Tech is a little bit of a bubble in the way that it works with data. Honestly, what I've seen across dozens of companies, the vast majority of data work is small data sets, copy pasted into Excel or Google Sheets or wherever with some manual work being done on it to clean it up. It's messy, it's not automatable, and it's typically focused on answering a very specific question from somebody just operating day to day.

 

And there's a lot of power in saying that out loud and just getting it out there. Kind of pulling back to where people are actually at right now. 

 

Gabrielle Stafford: I love the emerging theme of this talk. It's data for people, data to make people's jobs more efficient not data for data’s sake. So Jessica, Graduation Alliance, just to explain what they do and provides pathways from heights for high school graduation for young adults and adults…you can probably explain it better than me, as I have probably just demonstrated. So maybe start off with a little bit of explaining about what you do. But when we chatted, we also talked about this centralized, decentralized approach, and that you use both Google Sheets and then, I think it's a BigQuery. So tell us a little bit about that journey, because I know you started with Sheets and moved into the more centralized approach. 

 

Jessica Moore: Sure, Graduation Alliance, our biggest population are the youth and adults. So it’s a high school diploma. It's different between the youth and adults. We, of course, do advertising towards adults, right? We foster college and career exploration. So that's the differentiating factor too, which you get your high school diploma. And you have a resume builder, you have that leg up as far as an industry recognized credential. 

 

So as I mentioned, there is a lot of data. I completely agree with you, Josh. I mean, there are queries that I pull that I just cannot pull from Power BI. That's how we've leveraged BigQuery. It's pulling it into our IT department, which has developed a really great dashboard for us. So that's how we use it. It's Power BI.

 

We do have big data. So what does that mean? Big data is looking at our product life cycle. So that is something that is more robust. However, to your point, Josh, yes, there are not going to be queries, all have or reports or dashboards. I'm going to put a request in for I.T to build out. And I love pulling raw data in Excel. There's nothing better. I still keep my hands in the data. It's important to do that. So we definitely took it on as a Google Sheet product Supermetrics. That was because again, as I mentioned, it's nice to have control as a marketer and to pull data when you need to slice how you need to. You don't have to wait for anybody else to provide that.

 

I think as marketers, we're used to being innovative and savvy and the Google Sheets product had limitations as far as data visualization. I think that was the biggest piece, providing it to leadership, telling the story, like you mentioned, Dorothy. I mean, these are people, they're audiences. Just like they are with our leadership when we're having to present or pitch, hey, let's do this BigQuery product instead of Google Sheets. And here's why. So that was definitely our first step in the Google Sheets product. I was able to create a cost benefit analysis to say, okay, based on this, I'm saving this much time. 

 

Each report for 18 programs would take me 30 minutes each to pull from disparate areas. So, within 30 seconds or 10 seconds, right? That's an easy sell. So that was awesome for us that I.T leadership said absolutely. I.T is like let's just get this done. So it's really awesome to be able to go in there and look at a dashboard.

 

But the same token. I get what you're saying Josh and also Dorothy. I mean, they are audiences, quickly tie it back. AI means that creative is constantly being fatigued. You have to constantly feed the beast. We have an in-house designer. We're lucky enough to have an in-house designer. I work with her. She loves data. She loves the numbers when it tells her how to change a video or banner or landing page. So, it's a great point, Josh. We're needing you guys more and more, and we need to include you in those conversations collaboratively.  

 

Gabrielle Stafford: Great. So, I think we've all just mentioned storytelling and how important it is to use data to tell your story as a marketer. And as we've mentioned already, some of the signals we have used for the last 10 years are going away. Last click attribution, even multi click attribution, it's not going to be something we can do. And that's not possibly necessarily a bad thing. But Dorothy, working with data and that storytelling part is becoming more important. How are you dealing with the increasing pressure marketers are coming under to justify all the money that we're spending and tell the story about how it's positively impacting the business?  

 

Dorothy Ann Advincula: I think this is probably going to be unconventional wisdom. In my case, it's making the decision makers or even just leadership people that we need to influence realize the problem at stake, right? And when I say this is, for example, probably in the case of so much data, we need to integrate the data, make it come home so that we have a singular source of truth somehow or else it's all going to be all over the place. So sometimes when you say that and they're not in the day to day of the actual disparity in this data, it's so hard to decide on that.

 

But then if you show it to them in a way that…sometimes my readers would come to me and they will say, I don't understand why we have 5 million dashboards that we have to pull one single data from. And I use that as an education point. It is because this is the problem and this is the challenge, right?

 

And then I would get asked, what does it take to solve that challenge and I would say, well, it would require a lot of investment, obviously, but then there's a return because, I don't know that someone like you needs to be spending so much time trying to find this information. So that already became an ROI of one from that same person, and you multiply that across. 

 

So I think it's really more helping people or helping your stakeholders understand the challenge that could be blocking their own success, because I think at the end of the day, ROI is dependent on that outcome. So it's like the ways to the outcome are blocked because here is the challenge, and that usually helps open up some of that investment and curiosity. 

 

Gabrielle Stafford: I think that's super interesting. And Jessica, when we chatted, we talked about the fact that you use a lot of on and offline channels with very different measurement tactics and therefore ROI for you particularly can be a little more top of funnel. Can you talk to us a little bit about that and how you go about it? 

 

Jessica Moore: Sure. I'm going to throw out a buzzword “attribution” when it comes to looking between channels. And that can definitely be a point of analysis paralysis. Looking at offline channels, it's best to look at it holistically. How is our entire program performing for us? We're looking at programs, right? How is this campaign performing in its silo.

 

But holistically, are we increasing our leads and then our enrollments? What's our true norm? Is that happening? It's also important to have a control. So, doing tests is a great way to get buy in from leadership to say, we just have a limited budget, we're going to focus on this geographic area and everything else will be the same as far as media mix where we're advertising, doing organic. We're not going to change anything, but in Cincinnati, we're going to do Uber ads or Uber car top ads. That's a terrible example because I think they're only in Phoenix and certain locations. So we did a test on Uber car top ads. It did help us. We saw lift. We do these tests in silos to look at overall lift. Was there brand awareness from that? 

 

We have product specific pages. So Arizonaadultdiploma.com, that's where we drive our traffic. However, when they hear about us, they hear about Graduation Alliance. So we have attribution there, right? At the end of the day, did the Arizona program see more enrollments, more students that were happy, right? That's  our goal.

 

So, analysis paralysis can definitely happen. I think it's a matter of just catching yourself if you go down a rabbit hole. It can happen.

 

Gabrielle Stafford: I think it's so easy to go down a rabbit hole when you have so much data. You've all said it one way or another. Remember what you're trying to measure, what you're trying to achieve, how you're trying to impact your business or the people that your business serves.

 

And I think that's really interesting, especially from my American tech days where it was all about the data and not about the people. I love adding people into the data. But let's shift gears a little bit. So, we've mentioned AI a couple of times, but I think we need to talk about it a little more just because it's been in the news so much recently. I read a Forbes article a while ago that said the top performing companies are more than twice as likely to be using AI for marketing.

 

I think the same article mentioned that McKinsey said that sales and marketing is the single biggest AI function, or the single biggest function that AI can positively influence. Possibly because of the amount of data we all work with. So, I know that we have a lot of marketers who are very different points in their careers with us today and registered to listen on demand, and I know that some of them are worried about their jobs despite the fact that the U.S. Bureau of Labor Statistics estimates that marketing job demand will actually go up 10 percent higher than other sectors. because of what AI is going to do. 

 

But I know, Jessica, you had a conversation with one of your more junior staff members along these lines recently. Can you share a little about that, and your views on it for those in the audience who might be concerned?

 

Jessica Moore: Absolutely. My background is in paid search. So, with Google especially, we see enhanced campaigns that were rolled out over a decade ago. That was definitely something in our industry which I was not thrilled about, I'll admit it. I liked being able to reach people and bid based on if they're on mobile, tablet, or desktop.

 

So, that was definitely a great example of…I'm so glad it happened because I could not scale. We could not scale if we were sitting there looking at, okay, did Joe fill out a form on mobile or desktop? No, the journey started on mobile. Because of this, we've been able to evolve and now look at mobile first, and really capture people where they are in the funnel.

 

It's been a huge impact to content creators as well. From my perspective, we need more. As I mentioned, AI is just gobbling it up. We have to feed the beast. That's content definitely. So, that's where that's been an impact for sure. 

 

We have internal copywriters. I hear the challenge from the AI perspective there when you're just allowing blog articles or ChatGPT, for example, the language AI platforms to just do everything. You still need to train it. You still have a brain that's involved. 

 

What I explained to our copywriters is it's a tool for brainstorming. It gets the conversation going. If you're staring at a blank page for a blog article, let's get some ideas flowing. By no means should you copy and paste. But that gets your head into the audience. How you're segmenting, how you're segmenting out. You have the persona cards. You can focus on that now and be a lot more strategic. You have a whole roadmap of content. So that was my takeaway. 

 

And the last little piece is AI has been around for over a decade. Sorry, over a century. So one of the earliest, and this is 80 years ago, was the mouse. And this was done by Bell Laboratories. And it was a mouse in the maze, a robotic mouse. And it was the first machine learning example that we have to date. So it's really just snowballed since then. And I think our, our careers more interesting. 

 

Gabrielle Stafford: I think so too. And I think we're always going to need people. And we're always going to be selling to people, so I don't think any of that is going to go away. But AI is fed by data, Jessica, as you said, it feeds the beast. Data feeds that particular beast, and marketing departments outside of large enterprises aren't going to be able to employ specialists. It's not just data engineers or data specialists, and neither should they need to. But as AI becomes a larger part of what we do in marketing, it's likely that they'll need to understand data and how that works with AI a little more. Josh, this is more in your wheelhouse. What's your view of this one?  

 

Josh Cottrell-Schloemer: So the way I see it right now, and just my experience so far has been that AI is probably going to eliminate a lot of the formulaic and repetitive work in this space. My perspective is obviously data focused and reporting focused, but across the board, this is true.

 

When we talk about small scale data analytics, so if you're writing SQL queries all day long, you probably should be concerned right now because the need for that is just going to decrease, but it's also a really big opportunity, and I think the opportunity lies in this idea that any kind of AI, whatever it is, requires a training set. That's how they're created. The training sets don't and absolutely can't include the context of how an individual team or org is run and all the nuances within that. So I think the skills that won't be replaced All are kind of going to tie back to what we call soft skills. And I keep putting the air quotes around it because soft skills has a negative connotation. It shouldn't. It is so critically important and just becoming more important. So understanding your audience, understanding how to keep them engaged, telling them a compelling story and a story that speaks to their lived experience, that's where we as small businesses, or if you're just somebody getting into the space of marketing now, I think that's where we need to shift our focus because getting ahead of that now and developing those skills now is going to pay massive dividends as AI starts spreading and getting more of a foothold in this space. 

 

Gabrielle Stafford: And I think we're all aligned that it definitely will spread and get a foothold. It has already. And many of us have been using it for years without even realizing it in a lot of the ad platforms. But Dorothy, how are you and your teams thinking about using AI to help you be more effective into 2024?  

 

Dorothy Ann Advincula: I think I echo that AI is not a new concept, right? We've always done machine learning in the past or all of these automation and scaling processes, which technically is AI. But the way that we're thinking of it at large at Uber is how do we make this an engine for us to be more efficient and more effective? So it's really about how do we use AI to get into more of that developer productivity and then how do we reduce costs on other things so that we could invest on more things. So I think a lot of the context that Josh was mentioning, our time is better spent on something else or our resources are better spent on something else.

 

In my mind, it's almost like AI is not just generating content. It's actually about generating resources that can get pretty tapped out. So it's not a replacement to who we are as people, but actually an enhancement to what we are able to bring to the table. So that's kind of like how we're looking at it. 

 

I just had a really good conversation with one of the analysts on my team yesterday, and he was saying, I don't know that editing this query is a good use of my time, but I could do this XYZ that I think could help us optimize how we should be thinking about creatives or different brands across our platforms. And I said, yeah, that's right because that's actually a larger impact than actually editing queries. So I think in that sense, it actually did dawn on me that we need to move away from more of this tactical skillset based things into more of how we empower our humanness in a lot of this context and big data analysis? 

 

Gabrielle Stafford: I think empowering humanness and people has definitely been the theme for the day. And I know it's something very close to my heart because I think all the data in the world is not useful unless you can use it to make things better. That's what it's there for. So we do have a lot of questions coming up in our Q& A panel. 

 

First question, how can a small team of one web analyst/SEO in our business deal with the challenges of GA4? We didn't touch on this but I know we have talked about it in Supermetrics all year long with the channel challenges of GA4 to reduce data retention and the ability to do year on year comparisons after the change when they don't have the resources to figure out how to use BigQuery and SQL. Anyone want to talk about your GA4 experience? 

 

Jessica Moore: I love that question. We're doing it right now. We do year over year and it's not apples to apples between universal analytics and GA for what I have been doing is using it as a direction. When I look at year over year, I have two columns and try to visualize it for you guys now.

 

I look at, did we see increases at the same time? So is it new? New Year's is a big time for us with education. Summer is as well. So did we see the same percentage and that's when I'll break out like an index that's using that raw data. I'm not going to have I.T do it for me and Power BI, but that's how I do it. It's not an exact science, but it's really just looking at trend lines and comparing those versus looking at unique users between GA4 and Universal. 

 

Gabrielle Stafford: Yeah, and I think even in Supermetrics, we've taken a very similar approach. It's not apples for apples. But if you're looking at this data all day long, you know what your trend line is and you know if it has changed significantly. So it's just creating a new baseline and deciding what that is and comparing baseline to baseline. Anyone else want to take a stab at that one or should we keep going? 

 

Josh Cottrell-Schloemer: One thing to reinforce that is just this idea of accepting when things aren't one to one, that is really important. We like to try to force it, like a square peg, round hole kind of approach. It just doesn't work. When things don't match, they don't match, and it's okay, but looking at time series data and thinking about it through the lens of what direction is stuff moving in, how do things compare one quarter to the next, is something that you can almost always do if you have that historic data available. 

 

Gabrielle Stafford: Yeah, and I think choosing your right point of entry and your right benchmark, you can pretty much measure anything again. I love this next question, so I'm definitely going to ask it. So what is your view on attribution and marketing in general, and whether it still shows the correct journey after all the privacy updates? 

 

So any of the three of you can take that one. Dorothy, do you want to take a stab on attribution? I think we've touched on it a little in the past.  

 

Dorothy Ann Advincula: Attribution is near and dear to my heart, especially where I am right now, because we are both a platform of exposure and a platform of conversion. So, do I just want credit for all of the ads I ever sold? That's converted on my platform or should I give some of that to TikTok and Meta who probably got to where it was, right? I think for me if the thought about the full funnel journey or the linearness of the funnel, I've always challenged that even from before, because I think the linearness of it depends on what the product would be, right? And then also, the effectiveness of the actual creative, at whatever point in that funnel. 

 

So sometimes, it's not always just because you were in the beginning that you were carrying a lot of the weight. Because maybe people are not really paying attention to that, which is a whole other series of talks. I think there are many variables that we need to think through when we're thinking of attribution. So, the journey is not the same. It never was, is what I will say. I think when you think of it linearly, we might be thinking of it so narrowly as well. And then when we talk about attribution, we also get into like the marketing mix model, multi touch, or whatever it is, right? 

 

Usually how I counsel my peers and our advertiser clients is you need to think through the context of each platform rather than just running them into that one time series of correlations and stuff like that, because you might mistakenly give so much weight to an earlier process because there were more impressions and maybe there was more revenue going on in there. When it could actually be the context of one of the smaller players that actually brought in that thing. I think it's really more like how do we look at data more holistically so that we are weighting the attribution properly?

 

Gabrielle Stafford: So I'm actually being told to wrap this up and there are a lot more really interesting questions. I have scanned through them. I see that there's a lot on GA4. We have a GA4 Knowledge Center on our website. You can also submit some queries through there. If there are more questions here that we can answer, we will try and do it offline in our post event follow ups. So I just want to take a moment to thank you, Dorothy, Jessica, and Josh, for giving us the benefit of your experience at the Summit today. It has been totally awesome to have you all. And if you could say in one sentence, the one thing data and measurement related, you wish somebody had told you at some point and you didn't have to learn yourself. What would that be? And Josh, let's start with you this time because we keep making you go last.  

 

Josh Cottrell-Schloemer: Data doesn't exist in a vacuum. If you are going to drive any kind of growth, you have to understand how your organization actually runs. You have to understand how people make decisions, develop the soft skills, and understand that we are humans. That's the focus for me. So work on the soft skills. It's important.

 

Gabrielle Stafford: Dorothy, same question.

 

Dorothy Ann Advincula: I would say data will always be messy. If someone tells you to clean it up, you're never going to have it. Data is always messy.

Gabrielle Stafford: I love that. Maybe that's the name of our next conference. Data is always messy. So Dorothy, you said something earlier, which is bring your data home, which I was thinking, I see a whole campaign around that, but I was also thinking that could be a conference. And then our third conference can be data is always messy. I love that. Jessica, same question for you.  

 

Jessica Moore: Mine's going to be knowing your product lifecycle. I know that involves digging into the data. It's not going to be exact. It might change. It's interesting. It's important to know this because you can establish early KPIs, key performance indicators to say SEO, that's going to take longer. If you're doing organic social and/or YouTube, that's going to take longer. That's a consideration period, top of the funnel. So think about your funnel, work to get to know that, it’s a great way to diversify your media mix.

 

Gabrielle Stafford: Awesome. Thank you all so much, it has been awesome getting to know the three of you.