We’ve discussed what an insight is and the technical side of delivering insights. But who uses insights, anyway? How can insights best be shared across enterprises? In this episode, Host Ian Cook, CTO of Seek, discusses this in-depth with his Seek colleagues, Andrew Kim, JP Gressman, and Sean Cline.
We’ve discussed what an insight is and the technical side of delivering insights. But who uses insights, anyway? How can insights best be shared across enterprises?
In this episode, Host Ian Cook, CTO of Seek, discusses this in-depth with his Seek colleagues, Andrew Kim, JP Gressman, and Sean Cline. They dive into the different people who use insights and how best to share those insights with them, providing examples from the CPG space.
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Key Quotes:
“If we can surface those insights faster and they're actionable, that means you get to enjoy simple things, like a cup of coffee in the morning. Or it could even just be, hey, I have the weekend with my family and my kids, knowing that the work is taken care of.” - Andrew Kim
“Don't be a button pusher, be an analyst. Analysts don't get paid to push buttons and quote unquote run reports. They get paid to dig probe, ask a million questions, lots of whys, right? Five whys before you get to the end of the question period, ask a bunch of questions, uncover, probe, dig, create an insight, not a report.” - Sean Cline
“Tying it into a company objective or solving an urgent and expensive problem, that is ideally how you cut through the noise.” - JP Gressman
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Show Timestamps:
(01:56) Who uses insights?
(03:07) Are insights used at a certain level within a company?
(05:08) The spectrum of technical skills of end-users
(05:46) What makes an insight usable across a company
(06:31) Working with BI tools
(10:40) Examples of digging to find impactful insights
(19:20) Examples of actions taken from insights
(25:50) Obstacles analysts face when trying to act on insights
(35:50) Last pieces of advice
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Sponsor:
This podcast is proudly sponsored by Seek, the leader in cloud-based creation and delivery of industry-focused insights.
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Links:
Connect with Host, Ian Cook, on LinkedIn
Connect with Andrew Kim on LinkedIn
Connect with JP Gressman on LinkedIn
Connect with Sean Cline on LinkedIn
Follow Seek on LinkedIn
Ian Cook: [00:00:00] Some days it feels like we're drowning in data, but data isn't knowledge, data without context, or the expertise to understand it. It's just something eating up storage space in your warehouse. That's where being insights driven comes in. Insights driven organizations focus on generating actionable insights rather than just collecting and munging data.
Insights drive better decisions. Welcome to the insights factory.
Hello, welcome to the insights factory. I'm Ian cook CTO here at seek and your host. We've made it all the way to episode three, episode one. We had an introduction to the idea of insights, what they are, what defines them, what people can do with them. Episode two, we dug in much more into the technical side of insights, the ability to move data around and get something useful from it.
And deliver it to the people today, we're going to talk about who those people are. And to talk about that, I've got three [00:01:00] great people with me here. First is Sean Klein, industry principal at seek who you met during episode one. Got Andrew Kim seeks VP of solutions. Andrew's a recognized figure among top data professionals for his work with Alteryx and Tableau.
And having top accreditation in both and just known across the industry as a terrific guy. And then JP Gressman seeks insights, product manager. JPG has been a Sikh OG starting in the days when it was Sikh data, helping some of the best known CPG brands create real change by harnessing their data.
Gentlemen, thank you so much for joining me today.
Andrew Kim: Thank you for having us.
JP Gressman: Yeah. Thanks, Ian. We're really excited to be here.
Ian Cook: All right. So as I mentioned, what we want to talk about today are the real and possibly the most important part of this chain of insight from data all the way to end use. So really insights are only useful when people act on them, but I want to know more about who that someone is.
So let's start with Andrew Kim and say, when you think about [00:02:00] insights and the people who use them, what kind of person do you get in your head and what kind of people have you worked with in the past?
Andrew Kim: That's a great question, Ian. So I think a lot of the times, we think that it's going to be somebody that's super technical or tech savvy, right?
And actually fine. There's a lot of business users that are engaged and individuals who have subject matter expertise and may not necessarily have, um, the technical skill sets or honestly, even managers that just. Don't have the time, right, to build some of these things out, but understand directionally where things need to go.
And maybe they have some new hires, right? We had the great recession or sorry, resignation that just passed. So with all of those resignations, you have net new people that are coming in and we want to make sure that they get up to speed quickly. So maybe you just recently got a promotion or looking to expand your team.
And what we're seeing is [00:03:00] those individuals really looking to upscale and increase their efficiencies. Excellent.
Ian Cook: Sean, you've been in the industry for a bit. When you think about these teams, are they at a certain level in the company? Are they across the company? Is it a specific person within the larger group or do these insights have to live beyond just someone, one person looking at a dashboard?
Sean Cline: I think a good, good insight actually lives across the entire organization, specifically on a retailer team, living here in Bentonville, the teams are structured kind of in a way that includes analysts, sales and replenishment. Category, a sales function, if you will. So salespeople and then category managers, category advisors, and then executives.
But in all stops, all groups use insights to do something for their part of the business. traditionally analysts were the ones, at the start of this, this kind of this groundswell of data and analytics and insights. Analysts were the ones that [00:04:00] traditionally harvested the data and did something with it and then presented it, for example, to a salesperson today, as I said, all of those functions have some level of accessibility to the insights to do something with JP.
Ian Cook: You worked with a lot of companies to turn these turn data into these insights and then get them. Promulgated across the company, like Sean was saying, who do you find yourself working with mostly, at least, you know, at the certain level of the company or the type of background
JP Gressman: we have worked with, big brands down to, you know, emerging brands, where, kind of to Sean's point,
We're working directly with, the analyst and the business users and, trying to, create value and, understand really first, what are the pain points and questions, they're commonly trying to answer, right? where do we plug in and, help move the needle for an analytics capability that, maybe they're early in their journey, harvesting that data, or maybe they're, they have a, a more [00:05:00] sophisticated footprint of analytics, in the organization.
And we just come in and, kind of helping things along and, uh, it's awesome. Oh, that's great.
Ian Cook: Is there a common background you often deal with in terms of skill sets?
JP Gressman: Yeah. So skill sets, it can be anyone from someone that's just working with Excel and spreadsheets, someone that is on the receiving end of those spreadsheets, right.
That are just focused on the numbers. It could be folks that know SQL and Python or, more of the technical person. So it kind of can vary depending on who you're working with.
Ian Cook: Oh, that's great. So for the kinds of work that to do to generate these insights, we often see analysts and people like you said, that are technical, but if they do need to live, like Sean said, beyond the, that particular group and across the company, what does that insight have to do and have to look like for people who might not be as technical to use them to be able to move that across the company?
What do we need to see out of them? Yeah. So,
JP Gressman: so first thing that comes to mind for me is like, [00:06:00] Using common visualizations, right? Or, or, or things that are easily digestible, right? Like, whenever we're using a B. I. tool, using bar charts, big numbers, things that convey the information very clearly and pointed that way, you know, it kind of helps, increase your data literacy.
Get everybody on the same page. And that can be, anyone from the business user, to the sophisticated analyst that can, everybody can like rally around the same information. Andrew Kim,
Ian Cook: I think, uh, JP brought up something that you're particularly familiar with, which is these BI tools. Is there a, way that you've worked with these tools to make sure that audiences get a sense of what it is you're trying to convey?
Andrew Kim: Of course, I mean, we, I would say a majority of my career, especially on the technical side, has been geared around the self service analytics tools, right? So if we use those in quotes, so any of the ETL, right, ELT platforms that exist, and then visualizations, obviously, [00:07:00] JP and I are probably OGs with Tableau.
Right. And then power bi. I remember back when it was in beta and only took like 1000 records. So as we kind of talk about, some of these aspects, what you'll notice JP was highlighting clear visualizations. I mean, everybody loves a good Sankey every once in a while, right? But our objective here is to make sure that they're actionable.
And if you ask, or if you've ever exported, Sankey data. Right. That's not exactly the cleanest data to work with. So that's part of the objective here, right? With insights at large is making sure that they are actionable. So you immediately can pull it up, can take that information. And as we discuss that, right, I think there's two key pieces here.
There's was self service analytics. Your objective isn't just the action that happens at the end. It's the speed at which this data moves. There's always [00:08:00] going to be a gray area that exists, right? So what are those actions that we can take where we're communicating back within the business, to showcase and highlight?
Hey, here's what that percentage is. Maybe it's 5% where we're not Sure, what the information is either trying to tell us, right, or it's not lining up perfectly. And that could be from a recent acquisition, maybe some aspects where there were returns right around some product. And as we're looking at those transactions, we need to be able to highlight that.
So whenever we share it with our leaders, we're 95% certain here, right? But we've already identified and we're working on that other 5%. We're not going to hold up the entire business or making decisions, for something that we may be aware of, or maybe a recent development, if that makes sense,
Ian Cook: it does.
So Sean wanted to get in here on one of these points. Go ahead, Sean. Yeah,
Sean Cline: just thinking along the lines of who's using what and at what level. So if you think about an analyst, an [00:09:00] analyst is the one who's paid to dig and uncover kind of root cause what's happening. why am I out of stock? Why was my forecast off?
Why, where, and, and then how do I fix it as that data story? Turns into an analytic, into an insight and rolls up the true change, if you will, in one of these retailer groups, as it gets to the executive levels, call it a salesperson, a director, senior director, VP of those teams, those chunks of data become bigger as JP mentioned, the big numbers.
So a lot of times they live in pictures and colors. And lines and things like that. So it's very visual because that person, that executive on that team needs to look at a, something, a sales status or, or, or in stock percent or something like that, and be able to tell very quickly where the business is, the health of the business overall, because they're responsible in aggregate for certain parts, it's the analysts who are going to look at that date item store level type, and again, I'm speaking specific to retail [00:10:00] CPG.
But they're living in that date item store level environment where they've got to be very granular and they're going to see all the nooks and crannies and they're looking at numbers where some of the executives are looking at colors and pictures.
Ian Cook: Excellent. Thank you. So one of the reasons I got hooked into working in data in general was the kinds of things that I think people who find insights.
Like to chase. So this, the person who's digging and looking forward is suddenly having that realization and being there and seeing like this, there is something truly interesting here. I'd love to go around the horn here and hear examples of when you've worked with people that have come across this or some particular moment you remember where somebody you're working with or what you were working on just turned into a great insight or the.
You could see the light shine on somebody's face when you presented them with this to say, here you go. And you just realized that they went, that's what I've been looking for. Let's start with JP.
JP Gressman: We've got a few of these, but thinking about one in particular, you know, working with the, an emerging [00:11:00] brand and the soft drinks and just a soft drink industry, small analytics footprint.
And, uh, we were able to come in and, you know, work with, A new data provider that they had just, gotten piped into their, data warehouse and through some of our insights, we were able to surface, new distribution opportunities for them, right? Because being an emerging brand, one of your key objectives is, Hey, I want to get my product in more places.
Like, how can I get this in more stores and more importantly, stores that will, you That my product will do well in that are a good fit for my product or brand. And so we were able to, derive an insight from that data that highlighted, multiple, hundreds and thousands of distribution points for them for a single product, which actually resulted in a 10 million opportunity.
Ian Cook: Distribution points in this case, just, for the audience are places where things are sold. Like, so every time a, a one particular product shows up at a store, that's a, that is a [00:12:00] distribution point. So you're talking a ton of stores across a ton of
JP Gressman: products. Right. And this in particular for this insight that we developed, it was, that was for one single product across stores that did not have it.
And so, before we were able to, to help create this insight, that information was otherwise buried in the data, right? It was there. We just created the value for them, highlighted that. And, and that's, you know, that's a real number, right? Like when you show people that that number, and then to Sean's point earlier, visualizing in that.
Like that's where it gets super exciting. And that's where you start seeing people get super excited about data and being fans and promoters of insights in general, because, it leads to good things for the business for, all the way down to someone's individual role. Right.
Ian Cook: All right.
Thank you, JP. Andrew Kim, how about
Andrew Kim: you next? Yeah, you know, it's funny that we're talking about this because I was just watching the preview for Wonka. So like Willy [00:13:00] Wonka and the Chocolate Factory, right? And I, I told my wife, I said, first of all, I'm going to have to use this in a post because it, that's one of my all time favorite movies is Willy Wonka and the Chocolate Factory.
And I think there's that moment when they walk into his world, right? And you really start to see things unveiled and just the full breadth of color that exists. And whenever. Insights are shared right with someone or they have that impact in their life. Things just become real, right? It like the breadth and the dreams that you had really become tangible.
And I think the first time I experienced that was really within consulting. and the reason I say that is because not that I didn't have the experiences prior, working on client side work, right? we, Bill. built some amazing things [00:14:00] at universal, and healthcare and e commerce. The difference is, what my friend Emil said to me is when you join consulting, it's basically three years of work in every one, right?
So you're drinking from a fire hose. And all this time, I realized that there was a. A piece missing for me where I just enjoy helping people as you're kind of talking about you and there's all of these use cases that JP is talking about where not only can you find value in terms of dollars, but in consulting, it was the first time where I had a customer actually give me a hug and he was like, you gave me my weekend back right?
Like every day. Yeah. Every month I actually lose a weekend because I'm spending the entire weekend working on this report that has to be out on Monday, right? And I look at that and [00:15:00] honestly, it really tears me up a little bit because thinking that you can have that impact on people's lives, right? Like, hey, work's gotta get done.
We can talk about how technology is advancing things, but it's also advancing expectations. Right. So as we look at that, if we can surface those insides faster and they're actionable, that means you get to enjoy simple things, right? Like, like a cup of coffee in the morning, or it could even just be, Hey, I have the weekend with my family and my kids, right?
Knowing that the work is taken care of.
Ian Cook: Wow, that is, that's great. I have never been hugged for the work that I've done, Sean,
Andrew Kim: I'm here for you, man. I really do
Ian Cook: appreciate it. I would take you up on it. But, unfortunately, our arms aren't quite that long. But Sean, how about you? Any hug
Sean Cline: stories? I have been hugged, but just more on the sales side than the, uh, than the analytics side.
But, I do have kind of a A fun story to share around this analytics piece that we were just talking about examples of aha moments [00:16:00] thinking about forecast POS forecast and forecast variance. So that variance is a measurement between actual sales to what is being forecasted to be sold. And one thing that many retailers will measure is this, this high level variance.
So. Your percent of sales to forecast. If you're in a 95 range or so 95% range of actuals to forecast, it's okay. We don't need to do much. Typically it's about a 15% swing before people start to really freak out. So if you're at 95%, you really kind of don't dig into it and look for opportunities there. Well, we ended up doing that.
And so, one thing that we found was the. The products that were being, the units that were being sold in stores were actually selling out before the replenishment order was coming in. So in a week long measurement, we didn't see the out of stocks on a daily basis. So again, everything looked okay. Sales were good.
Sales were moving up. And what we understood, what we kind of understood from, from. Running out of [00:17:00] inventory on a Wednesday, for example, and the replenishment order would arrive Friday or Saturday, we had two to three days of auto stocks. So what we were able to do and replenishment is really driven off of forecasts.
So if your forecast is low, it will only replenish to the forecast. And you can't go above that forecast unless you exceed. In reality what that forecast number is so what we're able to do is have a long conversation with our counterparts in the replenishment team at the retailer and talk through let's try to find how high is high so they allowed us to push inventory outside of the replenishment into specific locations that we felt.
We could, we could push sales above what the forecast was, because again, the forecast was stifled by the actual sales. If you only have six on the shelf, you can only sell six. So we were saying, what if we put eight on the shelf? Or what if we put 10 or 12, how high is high? So we were able to take. some of these, these, location sales, store location, sales up 30, 40, 50% in some [00:18:00] cases.
I think the, I think on one item, the result was like a million dollars in annualized sales that we wouldn't have had, had we not been able to one, identify that, that insight, that, that thing, that aha moment, like, Oh, we're running out of stock early before the replenishment order comes in.
it also drove us to adjust our replenishment settings to bring inventory in earlier. So we, we were able to then add a second delivery to the week. We didn't have that gap. So we pushed forecast up, we pushed sales up, we, we tweaked our, our replenishment or our, logistics patterns, reorder points, things like that.
It was just a great success. And, and it was funny while I didn't get a hug, although, it would've been nice, everybody was like super excited because wow, we've just. We didn't sell any more product. We didn't have to go to the buyer and ask for more distribution. We uncovered something that allowed us to drive more sales into the chain.
Everybody was happy. Even, even the retailer folks were happy to like, good job. That was great. Yeah, they get to make, they get a cut off it. Right? Absolutely. Absolutely. As we, [00:19:00] as we mentioned in, in the first episode, it's really a game of real estate. So the higher we can push that return for them, the more they're going to like that product, that brand, that person, that company,
Ian Cook: that's terrific.
So you're starting to get at something that I was. I'm interested in, we've mentioned this word actionable a number of times and we take action on insights. And that's what makes an insight different from other things. But we're interested in the people who are doing that. So the people have to take an action, just for context, what kind of actions do people take from the insights that you've provided?
What can they go do? What is the actual step that they can take next? So maybe Andrew Kim, if you can think of some examples first.
Andrew Kim: Yeah. So as we look at what actions people can take, I think one of the biggest ones is obviously going to be conducting orders, putting that into a POS system, right? working either with your manufacturing team, right?
So it could literally be, with A plant, right? So as an [00:20:00] example, I've worked with large bread companies in the past, bottling companies as well. We'll typically have this, we just passed the 4th of July, right? So great example where, you really have a peak season, where you're anticipating increased volumes.
Other components that we typically see in relation to this are, going to be, let's say now that we came off the 4th of July, is there maintenance and stuff on the equipment that we need to, have in mind? When can we go ahead and set that? And then even workforce planning are some other aspects at play here, right?
That become crucial. and that's just to name a few. I know that J. P. and Sean have. Have quite a few other examples. I won't steal all their thunder
JP Gressman: here. Yeah. Yeah. So, so one that comes to mind and, when you think in terms of, staying in stock and supply chain, the metrics or measures, however you want to call it, the on time in full, is your order on time being delivered on time?
Is it in full, is everything that you ordered there? we [00:21:00] have an insight in particularly where you can, Take action and improve your carrier network, right? Like you can look at who's bringing shipments, where you can see, you know, who has great performance, who has opportunities to improve, everything from the regional to the local level.
You can target. And, improve your retailer relationships by being, you know, having better performance there. working with retailers to, and carriers to, to understand what, are the root causes? Like, are these local carriers seeing some sort of common weather issue or routing issue for the shipments that you can tie back?
and we can actually show you in an insight, right? So you start with the insight. And consuming the information and that leads to the action of going to have the conversation with your local carrier to say, Hey, here's what we noticed in the data, like, what can we do together to, improve this, it's something that's, super, super important, as we're designing insights, that we carry that, [00:22:00] Actionability through
Andrew Kim: JP, you're, talking a little bit here about everyday decisions, right?
And you brought up weather. So I really want to hear you share your story about, weather situations where, it's more of an ad hoc request, right? Like everybody's on the ground. What are some of the insights that we even have today that kind of highlight? Let's say some of the seasonal opportunities that exist or things that are one time occurrences, right?
That actually impact a lot of individuals and a lot of products. Yeah,
JP Gressman: so we actually have, a really amazing severe weather. Sales insight, right? And so, it's been really cool to be able to, partner with weather data providers and point of sale providers to actually build something where you can see the impact of something like a hurricane, right?
And you can follow within an insight, follow the pathway of a hurricane. up the East Coast to where we're showing [00:23:00] in an insight that pathway correlated with, sales surges and measuring that and showing replenishment quantities to Sean's point earlier of how critical replenishment is, this helps CPGs and retailers if you leverage this, an insight like this.
plan better order in a smarter way, order in a more predictable way. When you have, it's hurricane season, you know, that's coming. I know that I get a three X sales surge during hurricanes in these stores. Like that is taking, for the next one, taking action on the insights.
Sean Cline: And along the same lines, JP, that's a great point.
So pre pre hurricane, there's a lot of stock up. I gotta, I gotta buy certain things to be ready for the storm or to hunker down somewhere, or I'm leaving home. So I've got to, pack a bunch of peanut butter and jelly sandwiches or something post storm. There's cleanup there's a lot of construction that happens so that that supply chain changes from, stock up, I'm going to [00:24:00] hunker down to.
Okay. The storm came through. There's damage. There's people coming back home. I know if you think about laundry detergent, sales of laundry detergent go through the roof after storms like that because Everything's been flooded. All their clothes are ruined. They got to wash them I know some of the bigger brands will will roll in with big semis that are full of washers and dryers So people can have a place to do their laundry
JP Gressman: sean just to pile on to that you know, I think that the coolest part about having that data and actionability is like the fact that we can do it at, at a store item granularity, right?
So we can tell you which stores need to have how much product, before or after a storm. and I think that's really being able to do that, at that level is, is really transformative. when using something like this,
Sean Cline: yeah, 100%. I mean, think of the value that a company like a smoker, for example, in advance of a hurricane, the value that they bring to the retail partners when they're able to tell [00:25:00] those partners, here's what we see coming, here's how severe we think it's going to be.
Here's the net effect on our products, and we're going to help you manage. the load in the stock up purchasing and then the replenishment through the storm, and then we'll stage product in a safe place. So post storm, we're able to push the peanut butter in because people are going to again, make peanut butter and jelly sandwiches, but it's a partnership, high value piece to the partnership, not to mention it's real life too, right?
Ian Cook: Exactly. I want to make sure we're not making this a little too clear cut. Some of the people who have to work on these things and who get the insights have a lot going on during their day. One of the ways that have been that people have put information in front of them are just some dashboards and reports, and you get tons of these things.
But Andrew Kim mentioned he had somebody who was doing something on the weekend. Probably because the rest of the week was so full of other things. I'm interested. What are those things that these people face day in and day out? And what are [00:26:00] some of the, the things that keep them from being able to truly act on these insights?
Andrew Kim: Ian, that's pretty straightforward. I mean, Dave just did this post where he talked about meetings, right? And he did how Spotify is working to kill some of those. That was a great post
Ian Cook: from him. Sorry, just to be clear. You're referring to David Hannibal, our chief product officer posts on LinkedIn.
So anybody who's listening, go take a look at LinkedIn. He refers to a great post that talks about the cost of meetings.
Andrew Kim: Yeah, it was an incredible post about the cost of meetings. I think it said something like 600, 1, 600 is the average cost for it was like a 30 minute meeting and it doesn't surprise me.
Yeah. And as we talk about those meetings, those meetings are about topics that I had mentioned. Hey, the data we need that other 5% right? Where did it go? How can we clarify some of those components? And when I look at who the clients are from an action [00:27:00] ability perspective, it does need to be bidirectional, right?
And do you have a A party that has vested interest there, right? Which is why here at seek we like using third parties because they want to make some money, right? So they're going to make sure that data for you is as clean as possible. And then from an insights perspective, right? Being actionable. That really is the key, because if it's not, it doesn't drive any value, right?
Which is that proliferation that you're talking about in regards to dashboards. And we're creating things because they look pretty. And really, that's where we, JP is king of this, right, in terms of refinement and driving it back to where it's, it's simple, right? The KISS principle. Hey, this is a simple dashboard and it's intended to be so because it needs to be quick to understand and quick to action on.
And you need to be able to explain it to your executive cleanly, right? Or a buyer. So that way, collectively, you can make the decisions. [00:28:00] Efficiently, and drive value for your
Sean Cline: and if I could just jump in on that one, too. So if you think about to the weekend work, somebody's compiling data, creating reports, doing things on a Sunday tools, position a lot of those folks, those, users of data, creators of analytics and insights that they give back the weekend, right?
A lot of times those weekend, that weekend effort is because. Their counterpart at the retailer has a Monday morning meeting that is very standard where they, go over what happened over the weekend and last week. And so most key, most strategic partners in a, from the brand community has to provide a recap to their buyer by a certain time on Monday morning.
So it is a, I live here in Bentonville and it is a standard. Sunday nights, people start to pull data that don't use third party data providers. They will start to pull data and run their ports so they can get it to the buyer by a certain time on a morning because if they don't, that buyer [00:29:00] goes into an executive meeting unprepared.
And it's, it looks poorly, it reflects poorly on the brand who didn't provide the insights, the, the information. So speed is key, and also the day of the week is key for that information. Monday being the most important. So a k you hit, you hit it on the head with that, giving people their weekends back and it's, it has a lot to do with that.
Getting that information dis distributed to the right people on, you know, first thing Monday morning. I also think
Ian Cook: it's really interesting what AK said about the amount of money per these meetings and if the meetings are about this, then you can start talking about just how much internal money you're saving by having insights at hand and avoiding meetings, avoiding overwork, avoiding time on the weekend.
So these things. Actually have a cross company impact to the way the work is done and the way the company functions if it's done, right? So the question that becomes is how do you do it right for the individual? What [00:30:00] has to be there to make it more useful for them to think about it this way? Cut through all of the other noise that's going on.
What can we do to make them very clear and almost like an alarm bell going off saying, watch me, watch me. JP, take a swing at that.
JP Gressman: Yeah. I think it's tying it into a company objective or solving an urgent an expensive problem. I'll call it right. That is ideally how you cut through the noise and how we design insights at seek from my perspective is, it's highlighting the.
Annualized dollar opportunity that you get by expanding to these stores. It's sizing up the the number of out of stocks, right? it's making those things very clear and transparent and digestible across Again, going back to the roles anyone from the day one entry-level analyst to the ceo who who is also interested in in those numbers, right?
And so, I think that I have [00:31:00] like design opinions and like, how do you know what sizes and shapes and things you use to actually translate that information, but it's, it's really about solving that urgent and expensive problem, showcasing that, and then, having it kind of be centralized for
Sean Cline: folks.
I wish we could take that thought and put it into. Like an analyst brain or a, or a CEO's brain, because that is, you know, the good analysts are that, that I'm, that I'm familiar with are the ones that stack crank the opportunity. Oh, TIF on time and in full, there's companies around town here that do business with Walmart.
Let's say they're a 900 million business. That's huge. 30 million in fines. That's huge. That's material. So of course that's going to bubble to the top. And then you start thinking about all the other revenue driven opportunities. All the way down to like item store level replenishment. You know, you have to attack those biggest to smallest.
Andrew Kim: Yeah. Thank you. And as we're looking at those opportunities, I give Steve Wexler the credit [00:32:00] here for this one and the big book of dashboards, bands. So big ass numbers. Uh, so forgive the cursing here, Ian, but. Basically, I think it's worth it, as we talk about those big numbers, that's what JP was mentioning and Sean's mentioning in regards to, Hey, here's that number.
That's scary, right? And maybe it feels a little bit like an insurance tactic, right? Like we're trying to say, Hey, you've got 10 million in fines, 30 million in fines that you're paying. Same is true even in the healthcare space, right? That number is scary. and whenever we look at it, we say, okay, well, how can we save lives?
Save, find, save. Time, right? All of these things. And then we're trying. We're all motivated to solve that one piece, right? Which is why whenever you look at, control towers that we're building out or different executive level dashboards. That's their focus, right? From a key objective perspective is how do we [00:33:00] make that number move directionally where we want it to?
And there's all the little pieces underneath it that are there to support it. And our key is okay, we'll start you off with that band. So we all have an idea of what the opportunity is. And as soon as you open it, right, JP, if someone opens one of the insights, literally, they're going to have that, let's say in a few days or a week, right?
Maybe you already know what your fines are, but there may be some other numbers that you aren't aware of, like the inventory that you're missing out on being able to sell. Right? and then as we look at that, if we said, Hey, I want to build that myself, that may take a few months to get the data together.
It may be a lot of weekends and things that you're putting together. That's for one use case, right? And we're talking about a platform that is going to have hundreds of those. So is that something that, again, as we look at this and scale as a community, right, that we should all be trying to resolve the same things?
I mean, that doesn't make [00:34:00] any sense to me. So like, how can we go in and really scale this? And I think that's where there's opportunities for us to help the data community at large. maybe we won't be as fancy as chat GPT, right? but I think we've definitely got, some smart individuals here, right?
With lots of experience, who can really drive opportunities for productivity improvements, right? as well as getting that next promotion, or next bonus, right? That, everyone's eager and looking for so they can help out with some of these inflation costs that are hitting us. And to that point,
Sean Cline: Andrew, if you think about it, every person in this process, whether you work for a brand and you're on the sales team, the replenishment team, you're an executive, you're a retailer, good buyers think of themselves as sellers, right?
they have to consider how is it going to come off the shelf and out the front door through the register. We are, part of this community. We have to think of ourselves as sellers and part of that sales process. We're all in this. To sell more [00:35:00] part of my French. Like that's why we're all doing this.
And that's what we're here. That's, that's what we are here to do. That's what these insights are here to do. That's what the analysts are here to do, et cetera. It's just so more stuff. That's what we're all trying to do. Fantastic.
Ian Cook: Well, gentlemen, thank you so much for this conversation. I've had a fantastic time.
Hopefully you've enjoyed it as well. Before we head out, do you have any last piece of information that you would like to share either to, or about the people who consume insights and the people we deal with every day? Andrew Kim, why don't you start?
Andrew Kim: I think this is my virtual hug to you. Ian is we thank you so much because we literally have, from a go live perspective, free demos for insight.
So you can go on our website, right? And try these out. And there's going to be some fun ones coming out. the JP and I are working on the relate to, to sports, to drinking, right? So that way you can enjoy some drinks on your weekend instead of working on reports. Drinking and sports. Drinking and sports.
[00:36:00] Exactly. That sounds like a fun weekend, right? It does. And, I think the other piece is if you're not seeing anything you're liking, Again, we're, we have a solutions arm that I run, right? And we engage with these self-service analytics platforms, right? And our goal and objective is, again, to help you in your journey, right?
Become more actionable, insights driven. And we have a lot of connections within the, data provider space, right, to be able to do that. Terrific. Gp,
JP Gressman: how about you? Yeah, I, I would just say like, If you're someone listening that, you know, is even just kind of getting introduced to the idea of insights versus reporting versus dashboards, I would just encourage everyone to kind of think about the power of an insight versus just having data.
And what that can do and how you could be someone that [00:37:00] consumes insights versus reports someone that builds insights, versus dashboards in that, it's about taking the mindset of going, Hey, I'm used to working with data or working with spreadsheets or something like that to going, I'm now working with insights and going from data driven to being insights driven is really where.
What I would encourage the mindset around because that's the shift we're trying to push here and we're super, super stoked on it. Excellent.
Ian Cook: Thanks, JP. Sean, you get the final word.
Sean Cline: Man, there's no top in that one. That one was perfect. Like the one it's it's as good as it gets right there That was that was fantastic.
Like all right, then we're done to JP's point. Don't be a button pusher be an analyst analyst Don't get paid to push buttons and quote unquote run reports They get paid to dig, probe, ask a million questions, lots of whys, right? Five whys before you get to the end of the question period, ask a bunch of questions, uncover, probe, dig, create an [00:38:00] insight, not a report.
Ian Cook: This podcast is proudly sponsored by Seek, the leader in cloud based creation and delivery of industry focused insights. Thanks for listening. If you liked this episode, feel free to rate and subscribe wherever you get your podcasts.