Data Driven Leader Series - Episode 1 - Disco

We have been running the Data Talks on the Rocks series interviewing founders and creators of open source technologies. Now it's time to dive deep with our users. Rill's customers are data driven leaders coming from a variety of industries from ecommerce to technology. One thing they all have in common is a passion for understanding data and gaining insights. 

In our inaugral episode of our Data Driven Leaders Series, we interview Kat Tomlin, Director of Business Operations at Disco. Disco is an AI powered commerce media network that connects millions of shoppers with personalized offers across our exclusive network of eCommerce brands and consumer apps.

Kat and I dive deep into the following topics.

• Kat's day-to-day role - how analytics plays a crucial part at Disco to be a data driven organization 
• Metrics - how do you identify what metrics matter, then how do you actually define the metrics 
• Short live demo - Kat walks through Rill's fast dashboards and highlights her favorite features 

I’ve noted some of my favorite highlights below:

How well are we taking this moment and monetizing it for our advertisers and for that placement for our publishers. And really honing in on the effectiveness of that interaction. How can we improve that? And so to your point, Mike, it's definitely all about getting key stakeholders across the business aligned to this metric, a very core understanding of not only what it is and how we define it, and how we measure it, but why it matters.

What we found was some stakeholders had an understanding of checkouts being a load of our ad, and that really kind of correlates more to an impression. Or isn't a checkout an opportunity for our ad to be shown, even if it's not. And so understanding how different people are interpreting this metric that we're trying to define, and then having that discussion. Oh, that's an interesting take that you have, and kind of meshing those things, and really getting down to the core of what is truth? Which is, I feel like a question that will never ultimately be answered. But, we're constantly striving for having that understanding of how we've come to define this metric very specifically, I think that is key. 

We were using tools previously that were too overwhelming for our Disco people, and that ultimately led to a lot of misunderstandings about our data, and not feeling confident in going in and finding answers. So ultimately, I think that is what you should prioritize when using data driven tools is making sure that it's usable for your stakeholders. And when you're evaluating tools, is this something that you, as a data driven person, are inclined to understand. But can you put yourselves in the shoes of a non technical user? Will they be able to go in and not only understand the tool, but ideally use the tool? We want to create power users across the organization. Rill has absolutely been a game changer for us in that regard.

It makes my day when I get a link or a screenshot from a pivot table that they've built in Rill and I think I came to this conclusion. Kat, can you confirm that I'm understanding this correctly, you know. Have I used the right metrics? Am I comparing this to the right date range? Just asking those questions to validate what they've already gone in and built rather than say, Okay, I saw the graph, and I took away my understanding. And now I go on my merry way. But it just fosters and almost forces the function of curiosity, which I think is ultimately going to lead to a richer understanding of the data and the story.

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Check out the video interview and full transcript below.

Michael Driscoll: 

Good morning. I'm Mike Driscoll. I'm the co-founder and CEO of Rill Data. And today, for our Data Driven Leaders series I'm excited to have Kat Tomlin, Disco's Director of Business Operations, joining us. Kat, welcome to the show!

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Katherine Tomlin: 

Thanks so much for having me, excited to be here.

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Michael Driscoll: 

We were talking before we started the session a little bit about your journey in analytics. You started out in customer success, now you're the Director of Business Operations at Disco. Before we talk about your specific role at Disco, I'd love just to hear in your own words, for those who are not familiar what does Disco do? We know that you are transforming checkouts into AI power and growth is what Disco says on its website, but maybe for the audience, and those who are listening. Tell us a little bit about how Disco helps customers and kind of the animating vision for the business.

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Katherine Tomlin: 

Yeah, absolutely to take it outside of our website terminology, Disco is building a post checkout advertising network. Essentially, what that means is, we are reaching consumers at high, intent moments of shopping, which is right after they've checked out. They're excited. You get that high, you know your orders on its way. And we want to prompt with really relevant recommendations about what they may need next, or in addition to what they just bought. So ultimately, our customers are both those consumers, the end consumers interacting with our advertisements, but more directly the advertisers themselves. So the brands that we're working with to display these ads, and then also the inventory. Those post checkout moments on D2C. Commerce brands are looking to expand beyond that across the web. So we've definitely got a lot of different players here, but really honing in on that customer. Experience is key for us.

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Michael Driscoll: 

What are some of the brands that Disco is most proud of serving that you can share with folks?

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Katherine Tomlin: 

Yeah, absolutely. I started at customer success when I came on to Disco. So I had the privilege of working with so many of our great brands, and the ones you see every day. Some that come to mind that we really love working with would be the Caraways of the world, Dagne Dover. If you think about going into New York City and Soho, you're seeing all those Disco brands and their storefronts. And so it’s been really exciting to be on the forefront of some very popular rising. Ecom D2C brands across the space, and plenty more in addition to those, too.

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Michael Driscoll:

Let's talk about some analytics. Of course, you know a business like Disco. It's you guys are an always-on platform. Internet retail e-commerce never sleeps. Let's talk a little bit about your journey into analytics. Again starting out in customer success, now leading business operations as Director of Business Operations at Disco. Maybe kind of a day in the life of Kat Tomlin. How Do you use data day to day? How does it inform decisions? What does it look like when you wake up in the morning? What are some of those analytics tools that you're leveraging to succeed and and thrive at Disco?

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Katherine Tomlin: 

Absolutely. I've definitely had a unique journey as it comes to analytics and data throughout my career having started in customer success. Really the interest kind of generated from curiosity, and just not being satisfied with the top level answer, or just the top level number. And needing to know more to better help my customers. And so just kind of like the relentless curiosity is what drove me to where I am today, leading business operations, inclusive of business intelligence at Disco. And I have really found a passion for that. And so in my day to day you said it best, Mike, ecom never sleeps. First thing in the morning, opening up dashboards right, seeing how we performed throughout the night. You know people are shopping in the middle of the night, too, or on the subway home, or whatever it may be. 

A lot of my day starts with looking at those benchmark dashboards, our main KPIs. Is there anything that has been alerted to me, or flag that I really need to hone in and jump into. And making sure there's consistency there. That's really how it kind of sets up my day, because my day can look very different if we've got an alert, a fire drill. Something is going awry, and so that kind of can then dictate my morning, or even potentially, the rest of my day. Or if we're all status quo, then it allows me to kind of have confidence in really jumping into those additional projects. That can take the rest of my day. And so that's really where Rill  has been super helpful and just truly starting my day in the Rill tab.

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Michael Driscoll: 

I think a lot of us talk about, of course, the benefits of being data driven. And tracking uh core metrics in an organization. Maybe let's talk about metrics. One of the things, of course, that we feel strongly about at Rill, and we hope that philosophy is shared by Disco is really making metrics the core of an analytics strategy, not necessarily making dashboards. But saying, okay, let's see if we can all agree on a set of key metrics, key operational metrics that we track. Hopefully, you know, not hundreds, but a handful. Maybe just talk about at Disco to the extent you can share, what are some of those operational metrics? Kind of leading indicators that you look at to assess the health of the platform. First, maybe how do you know the identity of those metrics, and then maybe talk about the process of how organizations agree on them. The definition of those metrics. How is that sausage made in a sense of how you decide what those metrics are that should matter. So you're not tracking so many. But also, how do you agree with the definition of a key metric? That's taking into account a lot of different pieces of the puzzle.

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Katherine Tomlin: 

Yes, we've had a lot of conversations about just this. I think, being in advertising, you know, there's plenty of metrics. There's no shortage of metrics that we can be looking at. Just the standard ones, impressions to speak to our scale, CTR,, how our customers and consumers interact with the Disco placements. But where Disco has really kind of grown to address this? What we're deciding to be our KPIs, what do we care about? What is the story we're trying to tell, or the question we're trying to answer, whether it be a daily cadence weekly, monthly, quarterly, yearly. What is ultimately the thing that matters most, and we'll answer that question or tell that story for us. Which has led us to kind of the conversation of our KPIs. 

One that immediately comes to mind is, we're big on acronyms here, but our PC. And so what that stands for at Disco is revenue per checkout. How well are we, taking this moment and monetizing it for our advertisers and for that placement for our publishers. And really honing in on the effectiveness of that interaction. How can we improve that? And so to your point, Mike, it's definitely all about getting key stakeholders across the business aligned to this metric, a very core understanding of not only what it is and how we define it, and how we measure it, but why it matters.

Why does this matter to the leader of customer success? Why does this matter to the head of sales, to the head of engineering? And why does it matter for all of Disco to ultimately have a core KPI to hone in on. When you have that investment and understanding from all different stakeholders and leaders and individual contributors across the company, you're rooted and grounded in that understanding. And so you kind of have agents all over the company looking at this metric and able to call out things that may be surprising. So that's very, very topical of one that we're looking at constantly. And then speaking to impressions and just scale, we're always looking to grow. The number of eyeballs and add more to the network that is Disco, and get more opportunities to convert and just being able to chart that and look at that over time, and really hone in on how we can kind of continue to optimize for scale is another major KPI for the business.

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Michael Driscoll: 

What are some of the challenges around metrics like RPC and impressions? Maybe I'll throw one out there, and I'd love to hear if there's some others out there that you've dealt with. One of the things we hear about from our customers and users a lot is discrepancies in metrics. We see there's discrepancies not just between a platform that we work with, like Disco, and maybe their partners, who might be tracking things on both sides of a fence, but we often even see discrepancies inside organizations. You know, they've got multiple systems and multiple ways to count things. And so maybe there's a question about how Disco worked to resolve discrepancies, or think about what is truth? Which is always a big hard question to answer. Are there others? What are some other challenges that you've dealt with when working with being a metrics driven organization?

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Katherine Tomlin: 

Yep, I think it's first starting with addressing assumptions. If we use RPC as the example here, revenue is pretty straightforward. Everyone should have a good understanding of where our revenue is coming from. How does Disco make money right? But the per checkout piece. The checkout piece is actually a very interesting take. Because what is a checkout for Disco? How are we defining that? How are we building that metric even in our infrastructure? And actually addressing assumptions across different stakeholders at the business.

What we found was some stakeholders had an understanding of checkouts being a load of our ad, and you know that really kind of correlates more to an impression. Or isn't a checkout an opportunity for our ad to be shown, even if it's not. And so understanding how different people are interpreting this metric that we're trying to define, and then having that discussion. Oh, that's an interesting take that you have, and kind of meshing those things, and really getting down to the core of what is truth? Which is, I feel like a question that will never ultimately be answered. But, we're constantly striving for having that understanding of how we've come to define this metric very specifically, I think that is key. 

And then to your point about discrepancies, we deal with those all the time. I think sometimes people that are in the data all day. It can be a big red flag, but I like to think of it actually as a really good learning opportunity. Why could there have been this discrepancy? What are we potentially missing out on, or missing an understanding? Something we come up with a lot is with our customers, you know. We're grading our own homework in a lot of ways. Our customers are grading us as well. Any discrepancies that we can understand from the data they're looking at and their platforms, whether it's Google Analytics or a 3rd party kind of attribution source. How can we understand things that we can improve upon to ideally reduce those discrepancies. But also to optimize. There's so much opportunity, I think, in a discrepancy that can be really positive, and a good takeaway and a learning both internally or when working with customers.

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Michael Driscoll: 

One of the conversations we had over the last year was for many E-com platforms, the biggest few days or week of the year. I guess it used to be Cyber Monday was the day that everyone logged on, I guess, went to work and logged on and did their shopping.

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Katherine Tomlin:
Yeah, exactly.

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Michael Driscoll: 

Back when Internet access somehow wasn't available at home. You know Black Friday is just as big of a day for Ecom, if not bigger. Tell us a little bit about those few days of intense activity that happened at a place like Disco, and maybe again, about how analytics plays a role. Just kind of paint a picture for folks like I have in my mind almost like, this idea of a command center right. Where Kat Tomlin and team, the business operations team is like manning the consoles. But maybe just help us understand. I think that sort of is a very vivid example of where kind of real time metrics and real time analytics can be such an important part, because so much activity is happening on your platform during that period. Maybe tell us a little bit about that experience, and then maybe this is segue into kind of where Rill fits into Disco’s analytics stack. But maybe I'll give you a chance to talk about that very crazy week of your life. 

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Katherine Tomlin: 

It is and it seems to be growing, and I think you touched on it really well there Mike. Historically it was, you know, people lining up at midnight at the Target and Walmart to get their TVs right? And then Cyber Monday comes in. And actually, now, what we're even seeing is the entire month of November is shopping month. Whether it's preparation for those days. But brands are starting to introduce deals even earlier. So it's what we call our Super Bowl, because it really is. You're spot on with the command center. It is all hands on deck. You know we've got people manning the ship if you will at all hours during those key days especially. When it comes to analytics, it's everything. It's where we start with preparation. Even now I'm starting to talk about Black Friday and Cyber Monday.

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Michael Driscoll: 

Wow!

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Katherine Tomlin: 

Preparation for those days which is wild to think about. But of course it'll come up quickly. But really, where it starts is understanding trends. What can we anticipate going into Q4, the month of November, these specific days, and that all goes back to years prior. We are seeing shopping habits change year by year of course But we can get a sense of what we can anticipate from the data that we've collected over the past 4 years that Disco has been around. And so we know when those trends start to start to go up. And that's really where we start working alongside our engineering teams. You're gonna see a major influx at these hours at these times. We need to have all of our systems ready for that influx of traffic, right? We can't have anything going down. And so to be able to empower the technical teams and their preparation for those days in our infrastructure is huge. Then when it comes to the day it really is just a refresh of the data, and all of that goes back to understanding in the preparation of what we expect to see. What are those benchmarks? How are we pacing towards those benchmarks? Is anything looking different? Positively or negatively. Is there anything that we need to jump in on? Any red alerts? Having alerting set up so that you're not having to really waste time coming to a conclusion of, is something off here. Have that set up before, so it just can tell you, and that you're then able to just immediately jump in with your team and address a potential problem.

It's at the core of everything we do. And then, if you think about. The week has passed. Cyber Monday is behind us. The work doesn't stop there. And as my team knows, it's then all about the retroactive understanding of did we hit our metrics? What went great, what went wrong, you know? Where did we ultimately net out here and aggregating all that understanding for Disco as a business. But then, of course, for our customers. And giving them that understanding of great, you guys crushed your bBack Friday and Cyber Monday with Disco, and we can show you here, and how that growth has looked. Not only for this year, but years prior as well.

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Michael Driscoll: 

What are some surprising, without talking about any individual brand that's on the Disco platform, maybe what are some surprising…You talk about benchmarks and expectations, for you Cyber Monday, Black Friday kind of November shopping. What are some surprising insights that you saw, or you have seen over the last 4 years that you know unexpected, that maybe shifted what those benchmarks might be for next year. And again, maybe talking in a way that doesn't reveal any one customer or any one of your brands. What's changing in the world of Ecom that stands out and is kind of evident in those metrics that you track?

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Katherine Tomlin:

So many really interesting things, I think, especially from last year, that we gathered. But with these customers being Ecom brands and thinking about the consumer shopping habits. There's a lot of preparation that a consumer will have going into these peak times. They know they can expect a deal, they want to get the best deal. Are they going to get the best deal by being a part of the Loyalty Club and kind of trying to chart out and anticipate when will they make the purchase?

And something that we found last year that was really interesting was, we saw, of course, a ton of scale and influx on Black Friday, and a ton of impressions and engagement click through rates with our advertisements, but we're also tracking the end result of a conversion. An order being placed. We saw a lot of add to carts on Black Friday, but then consumers are actually waiting potentially till a better deal on Cyber Monday, then they were converting. So it was really interesting to see the habits. Throughout the weekend of just kind of getting in the mindset of the consumer. And it can really vary based on our customer, too. If you think about you know, a high priced product that takes a lot of thought and consideration. You don't buy a mattress every day, let's say, for example. And so you want to make sure you're getting the best deal versus potentially a skincare product that you're running through, and you're continually going back to. And you're a loyal customer too, but you want to get the best deal at the best time of year.

Trying to time the purchases was a very interesting correlation that we saw with our scale as well, and kind of inversely correlated, which was very interesting, and not something that we anticipated seeing. So it was a big learning.

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Michael Driscoll: 

Interesting. So just to play that back there is an inverse correlation between the size of a purchase, and maybe the length of time between when it was added to cart, when it was kind of consummated, I guess.

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Katherine Tomlin:

Yeah, or even just the inverse correlation to the number of eyeballs we saw in advertisements. To the action of ordering. And so you would think those to be pretty synonymous, more eyeballs, more people buying. But not necessarily, and it's really kind of speaking to the intent time of that purchase for the consumer, which is why post purchase is so interesting. The moment for Disco to hone in on, But yeah, definitely, was something that we were monitoring, but the hypothesis was proven otherwise, which is always something.

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Michael Driscoll: 

If that didn't happen, what's what? If everything happened exactly as you expected? You wouldn't need to track any of the stuff.

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Katherine Tomlin: 

Boring. Who wants that? That's so boring. We don't want everything to be exactly as we expect.

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Michael Driscoll: 

Right. So talking about analytics tools. Obviously we’re here to talk a bit about how Rill fits into the Disco ecosystem, but maybe more broadly. What are some of the…I guess some folks out here are often listening to a discussion like this to help understand how data fits into their role, their organization. If you were giving advice to a colleague that just started as the Director of Business Operations at a non competitive player in e-commerce, what are some of the tools that you would recommend that are kind of bread and butter, and obviously, we hope Rill is one of them. But maybe what's in your toolkit today, as you do your job and what would you recommend that others kind of essentially need to perform their role as a data driven operations leader?

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Katherine Tomlin: 

Absolutely, I mean, Rill, definitely, primarily comes to mind. I think what has been so impactful about utilizing Rill and in this role is evangelizing our data across the company. I think an Achilles heel that I see sometimes in organizations is you have a data team, or you have a data person, an individual and the understanding that the data isn't evangelized. People don't feel that agency, whether you're an SDR or you're a software engineer, to feel like they have the data and these understandings at their fingertips.

We were using tools previously that were too overwhelming for our Disco people, and that ultimately led to a lot of misunderstandings about our data, and not feeling confident in going in and finding answers. So ultimately, I think that is what you should prioritize when using data driven tools is making sure that it's usable for your stakeholders. And you're evaluating tools, you know, is this something that you, as a data driven person, are inclined to understand. But can you put yourselves in the shoes of a non technical user? Will they be able to go in and not only understand the tool, but ideally use the tool? We want to create power users across the organization. Rill has absolutely been a game changer for us in that regard.

In addition to that, as much as you can integrate and pipe into other tools, whether it's your CRM. Of course data is never in a silo, ever. It correlates to everything. And so, as I've been talking about our customer performance, we're using Rill to display customer data that needs to be integrated into our CRM. Our sales team needs to understand metrics for their prospecting, their pipeline. Everything that you know revolves around Rev Ops as well. So the integrations are also key, not to mention data warehouse, Snowflake, so many different tools to be able to integrate.

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Michael Driscoll: 

What are some of the…you talk about evangelizing data in the organization. Sometimes that term of art that's used a lot, data democratization is a word that a lot of folks throw around. And and then another buzzword is self-serve analytics. But when you know where, I guess again, to that element of surprise, like what has surprised you? You said that you've worked with other tools that maybe were too overwhelming, and maybe that that ended up creating some friction to their adoption. Who are some of the groups of users inside of Disco that surprised you, that were actually kind of leaned into being a little bit more data driven. I often think that as humans, we really are all, you know, kind of naturally data literate, right? We all kind of think about the world. We all have a module in our brains for adding numbers, and so it shouldn't be surprising that you know anyone in an organization should be able to work with data. This is not something that's just the high priests on some data team to use. But in your experience, what groups of folks at Disco surprised you in terms of their adoption of something like Rill?

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Katherine Tomlin:

I can do this because I was previously in customer success. But I'll aggregate it to our go-to-market team. I completely agree, we all have a level of data literacy, but I think what I say is are you data hungry? Are you really curious, or are you looking to just find a graph?. And so I think you use that buzzword self-serve. And I really like to differentiate when I'm presenting the tool, training anybody up on the tool or helping them with an analysis of understanding. This is self-serve in the sense that I'm empowering you to go and utilize this tool to answer your questions, or potentially even come up with more questions and keep diving and keep wondering. But in no way do I want this to be a tool that you go and you have a graph delivered to you. You see 4 numbers, and you take away an understanding. I want it to be so much more than that. And I'm really big on the storytelling of data. So, yes, my go-to-market folks have been so awesome in their adoption of Rill.

It makes my day when I get a link or a screenshot from a pivot table that they've built in Rill andI think I came to this conclusion. Kat, can you confirm that I'm understanding this correctly, you know. Have I used the right metrics? Am I comparing this to the right date range? Just asking those questions to validate what they've already gone in and built rather than say, Okay, I saw the graph, and I took away my understanding. And now I go on my merry way. But it just fosters and almost forces the function of curiosity, which I think is ultimately going to lead to a richer understanding of the data and the story. And the answer that everyone's coming to with the tool.

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Michael Driscoll: 

What someone recently told me that I chatted with is, they said, you know, I'd heard about Rill, and some folks that I've worked with told me, check out this BI tool Rill. And their initial response was, another BI tool? They're like, Oh, man, I just don't have the energy to try another BI tool. And you know, I think that probably a lot of folks maybe even within Disco, where you say, Hey II want to tell you about this BI tool. That there’s this other BI tool that we've now incorporated. How have you? I guess. 2 questions. What's different, what has been different or differentiated about Rill, that you've been able to maybe overcome that  Resistance to like. Oh, no! Kat is trying to convince me to try like yet another BI tool. You know, stop pestering her for answers.

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Katherine Tomlin: 

Yes, what I found to be the most effective, because I understand that it's another tool. It's another change. Speaking to when we transitioned over to Rill, the way I approached it was to prove it. See it for yourself, and so my team will probably laugh. But I gave homework. We're gonna be having this training on a new tool called Rill. And you're gonna have 3 questions that I'm presenting you with. Each of you will share your screen and they're gonna laugh because I put people on the spot sometimes like that. But it was so fun to see their eyes light up with how intuitive it was to use. Very tactical questions that I actually altered by team or by individual that were relevant to them. And kind of presenting hey, how many times have you asked me to see the impressions for your set of customers? Explain to me from this very high level introduction that I've given you to this tool. How would you go about that? And just seeing them be able to navigate, whereas before it was almost terror to go into previous tools. I can't do this. I don't know how to do this. It's not delivered to me on a dashboard. And really seeing that that kind of light bulb moment go off for everybody at the company, and then feeling empowered too. Oh, I can do this. I've got more questions actually, than the homework I was assigned by Kat. And kind of taking it and immediately running with it. We saw such great immediate adoption across the company, which was very helpful, as the one trying to get that instated.

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Michael Driscoll:

Well, uh kudos for you. Maybe we'll take a page out of your book and try to emulate that. That is the Rill educational seminar approach.

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Katherine Tomlin:

Yes, rewards are always good, you can add in a reward that might help too.

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Michael Driscoll: 

In another life you could have been a middle school. Uh, algebra teacher. Well on that note, and I thought one of the things we chatted about was obviously we don't want you to show any of Disco's internal dashboards. But there was a demo dashboard that is free and publicly available. It does have some advertising like data in it. I just thought if you were willing to pull that up and maybe just share, you know again, maybe in that vein of like, you know an exhausted member of your team is saying like, Oh, Kat! What is this thing? Maybe in your own words you could just give us a little bit of a tour, and of how you think about this tool Rill kind of what are some of the features that you appreciate. And you know this is gonna be kind of an elevator tour. You're caught in the elevator with a colleague, and they're like, Oh, no, Kat! You've cornered me. What is this thing Rill, and why should I care?

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Katherine Tomlin: 

Well, it's funny. There's plenty of times that I have it up on my phone in an elevator with our CEO. And we're looking at data that is very relevant. When I think about Rill and initially coming into the tool. And you know, even seeing other people at the company utilize it. 
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What immediately comes to mind is this explore view that we have in front of us here. There's plenty of times where you're not trying to understand maybe a trend or see data over time. You just need a number. You have that number here, it's immediately accessible to you, but you have more than just a number displayed to you. Let's use advertising spend overall. You can see the trend over time. Great. This initiates an immediate question. What happened between September 13th and September 15th. I want to dig into that. And you can immediately kind of with this usability be able to do that and expand. And ultimately understand. Oh, that's right. We shipped this improvement. We can see that here. And so it just kind of follows again, it's all about intuitive nature, of how you're gonna interact with the tool. I have a question. I want to learn more. I'm going to click into this for Disco. Of course, when we are looking at our customer base, and we want to see an individual or a breakdown of, let's say our top spenders. We can get that understanding here. Advertising spend overall by a customer base. Here in this example, domain names. And then you can click in individually. Oh, wow! Really interested about Hyundai.
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I mean, can we just for a minute talk about the speed. Like have you ever had a tool load like that. That was the number one thing that was, I truly didn't believe when I first saw it coming onto Rill. But it just kind of takes away so many clicks, so many steps, so many questions for an individual. Where do I go and filter? All of that's available. But it's right here. And it follows just a very again I've said it, but intuitive path of thinking when interacting with the tool. 
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But I will have to say my favorite part is going to be the pivot function, I mean, when I again, early on in my career you're using. I'm using pivot tables all day every day, and probably started by not loving them, and then have grown to absolutely love them. And so for me being able to easily slice in data here. If we use that example of by day, the ad spend overall, and we want to see what changes you can add in the hour as well, or if I need a breakdown of the advertiser name. For this example hre we can see that the advertiser name given by day, you could just so easily craft this pivot table in real time. Add to it, change it,, and get it in a view that is getting your answer right here right now. And this is really the part that I go back to. What is the question you're trying to answer? You know, how are you trying to answer that? And it really enables the thought process of well, what do I want to choose from here? You can see it all available. Oh, are impressions actually a better way to look at this? Or I'm curious, do they correlate? Do they not correlate?Being able to see that in a pivot view, and then, of course. If you need to be sharing you can easily export and access outside of Rill itself. But obviously just right here.

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Michael Driscoll: 

Okay, I'm gonna pause while we've got you saying, some nice things, and uh maybe we'll end. I know we've got a few minutes left. I won't let you have to share your screen longer, but I'll say any other features in particular. You talked about pivot tables. The  speed we're, of course, very proud of that speed. That's obviously a massive investment in the infrastructure to make that possible. But are there any other features in particular that stand out? And I won't lead the witness, but in terms of your day-to-day use that you find yourself reaching for in the product.

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Katherine Tomlin: 

Yeah, I've said it before is this combo alerting. How many times have you been reactive to a problem potentially that is as a result of stumbling across data points that lead you to believe. Oh, gosh! Did something go wrong? And how many times have you wished, wow! That would have been great if I had been notified of this alerting and the feature to set those alerts up was one of the first things that we enabled with Rill. When we got all of our data integrated, we knew exactly what we needed to be proactively alerted on. Historically, we were very reactive. And that was delivered straight to our email, straight to the Slack we use. We live in Slack, right? So many people do. And that allowed us to solve issues in such a quicker time period and a turnaround time. A lot of times alongside a customer in ways that we weren't able to before. So the alerting feature has been hugely helpful.

Also just all the recent things that Rill has been shipping. I think that'sone of my favorite Slacks that I get every couple of weeks is a new feature update from Rill. Your canvas dashboards, pretty graphs are absolutely applicable in so many instances.

So being able to stand up what we call our command center, where we have all of those main KPIs and able to visualize it in a way that we need to for a given metric or breakdown in the more typical, pretty graph structure that you're used to in other BI tools has also been incredibly helpful in additive. To the explore and pivot features that were already readily available.

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Michael Driscoll: 

We're glad you're using that, because we certainly know that Disco was one of those customers. They often say the best ideas for your product, and I’m sure Disco knows, is that the best ideas for features come from your users. And so, we want to give credit where credit is due. It was knowing that you had this need for a more traditional dashboard, like a Looker or Tableau that really drove the build out of Canvas. Folks who are interested you can look at rilldata.com and find out all about Canvas.
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I want to end on a topic that everybody is talking about, which is of course, AI. We know that the world is changing under our feet. And of course, AI, of all of the places where AI is having a significant impact, it is in the space of data and analytics. I would love you to hear…I have two questions with regards to AI thatI would love to hear your thoughts on. First is, how is AI actually changing Disco's business? People aren't necessarily using Google to do search anymore, right? They're asking Chat GPT or Perplexity when they're thinking about making a purchase right? It's kind of a a different journey for users in the world of Ecom. Then, secondly, when you think about what you would want. This is maybe a chance to tell us what we should be building, but, when you think about what you want as a data driven leader at Disco. When you're in the elevator with your CEO, and you're talking about the business. What would you want from an AI powered analytics tool? So those are the 2 questions. AI, how it's changing your business, and really, maybe the business of online shopping. And then what you would want from an AI powered analytics tool?

Katherine Tomlin: 

One. Yeah, I mean, I won't give away our roadmap, but we're definitely always looking for ways to integrate into our product itself with AI, and really leaning into that. But I think more applicable is the day to day for individuals with AI. We're a startup. We're a lean team. Everyone wears multiple hats. How can you find those little pockets of ways to make your day more efficient, whether that's deck creation or a follow up email that you're consistently sending. Whatever kind of routine tasks that you're having to do that may have been very manual in the past. You're now able to default and lean on AI, which are readily available, and a lot of the tools that customers are individuals are using. A CRM comes to mind with a lot of capabilities that are being presented to just make your day to day easier, which is always great.
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I think the data component with AI is so interesting. And AI is obviously broad, and it's almost overwhelming. You know that you can do an endless amount of things with it. Where do you start? And for me in particular, that has been an overwhelming task of gosh! Where do I even start with utilizing this with our data? And I think what is really exciting to me is that prompt of, Hey, you've actually pulled this report four different times in the last week in these ways. Understanding that, do you want to pull that again? Prompting you with the questions that might take you a little bit more time to come to. Like, I was saying, with the pivot feature. You know, kind of giving you that nudge or suggesting different ways to look at different data, reading the data, understanding it. Hey? Typically we see you in advertising companies looking at impressions by customers. They want to see it like this. That is what I really see as the next step, and being super helpful again to the piece of not only the people that are in the data day in, day out, but those who aren't as close to it and making it just even more accessible. For them to be diving into and utilizing is really where I'm excited to to jump in more.

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Michael Driscoll: 

This is some great insights, I'll add obviously, we're working hard on integrating AI, you know more than ever in Rill, but to your point of augmenting right? I don't think we're replacing human analysts. I think we're looking at how we can make their jobs easier. And I think in particular folks who may not have the same depth of domain knowledge. You know, you're a power user of a tool like Rill or another analytics tool. But someone who's maybe starting out at Disco may not have the same knowledge of what to look for, and so kind of, there's no accident that Microsoft calls their AI, Copilot right? But someone to sort of be a kind of the sherpa, I guess, on the guide of making sense of data and suggesting next steps for analysis. I think it's very exciting. I think we're all just trying to figure out the shape of that user experience, right? Because there's so many. Should it be a Slack agent? Should it be something that sits inside of Rill kind of like a better version of Clippy, the paper clip. Should it be something built into an app as part of Open AI. That uses Rill’s MCP server.  I think it's exciting times. Obviously, it's fun to be at the forefront of all of this. And you know all building tools that are taking advantage of the innovations that very smart folks out in the world are building.

Kat, I just wanted to say thank you so much for joining us. It's been a real pleasure to have you here as our Data Driven Leader from Disco. We look forward to seeing more success from Disco and from you in the future.

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Katherine Tomlin:
Absolutely thanks so much, Mike. It was a pleasure.