# Mach 1 + Taiga DataData in the Day-to-Day — Webinar Part Two

https://www.youtube.com/watch?v=Kl8kIFWYZYM

[00:00] Here we go.
[00:02] All right. Hello everyone and welcome to
[00:04] part two, Taiga and Mach 1, a day in the
[00:08] data in the day-to-day. So, this is part
[00:10] two of a series that we um started
[00:13] working with Alan Meyer and Dave Linder
[00:15] at Mach 1. We had a really great session
[00:16] about a month ago and for anybody who
[00:19] did not attend that and wants a copy of
[00:21] that, we are happy to send that on to
[00:23] you. Um, so we are excited to get going.
[00:26] And what we're going to do is first of
[00:28] all I will
[00:31] let me click my
[00:34] there we go. Let's do a little intro of
[00:35] the speakers. Um while we are going
[00:38] through the presentation there is a chat
[00:41] feature. Please feel free to drop in any
[00:43] questions you have. We can either get
[00:44] we'll be moderating that throughout the
[00:46] course of the session and then also can
[00:48] um answer some of those live at the end.
[00:51] So let's go ahead and start with the
[00:52] speakers. So um myself I am Jenny Hill.
[00:56] I am head of customer success here at
[00:58] Taigga and um I have the pleasure of
[01:02] introducing our speakers. We have Alan
[01:05] Meyer, CEO at um Mach 1 and also Dave
[01:09] Linder who's COO. They're going to speak
[01:12] here about all the great things that are
[01:14] going on at Taigga and we also excuse
[01:15] with Taiga and Mach 1 and we also have
[01:18] John Oakley who is CIO and one of the
[01:20] founders who I'm sure most of you have
[01:23] met. So welcome gentlemen. We are
[01:25] excited to get going.
[01:30] All right. So, um, one of the things
[01:33] that, um, uh, Morgan from our marketing
[01:36] group just dropped in the chat is the
[01:38] link to the first session. So, if you
[01:40] don't have that, you can find it there
[01:41] as well. Um, but what we're going to do
[01:45] is a little quick recap of what we
[01:47] covered in part one of this series. So
[01:49] we talked all about how Taigga connects
[01:52] and standardizes data across your
[01:54] systems. That was the main focus and you
[01:56] have the little photo here, a picture
[01:58] here that shows us here in the middle
[01:59] and all the different connections that
[02:01] we provide. We also touched on the
[02:04] problem of data fragmentation. So there
[02:07] is a tremendous amount of data and
[02:09] information that you all are um
[02:12] presented with on a daily basis and
[02:15] you've got to make sure that data is
[02:16] clean and you want to be able to to get
[02:19] it quickly and be able to react and make
[02:21] decisions quickly using that. So we we
[02:23] covered that topic and then Allan went
[02:26] through um talking about AI and how he
[02:30] utilizes AI with Taigga and using to to
[02:34] conduct his business and all the great
[02:36] things that he's been doing with it um
[02:38] since he has been a client of ours for
[02:42] from just about the very beginning. So
[02:44] we we appreciate that.
[02:47] Um, all right. So, that's the quick
[02:50] recap and today we are going to actually
[02:53] talk about the, you know, what do we do
[02:55] daytoday? How does, how do Allan and
[02:57] Dave use Taigga on a day-to-day basis
[03:00] and then, you know, g get you all
[03:02] through some steps of what you can do
[03:04] next and how you can maximize the
[03:06] information that you're getting out of
[03:07] Taigga.
[03:09] So Alan, I'm going to turn it over to
[03:12] you for a second. Um, before we get into
[03:15] the specific use cases, would you mind
[03:18] sharing, you know, setting the stage for
[03:20] those who may not have been on the first
[03:22] call, talk a little bit about the
[03:23] relationship that you have with Taigga?
[03:26] Yeah,
[03:28] thanks Jennifer. Uh, so Alan Meyer, CEO
[03:31] of Mach 1. I don't remember when I got
[03:33] the title of CEO. I think my dad came in
[03:35] and told me I was CEO like in 2017 or
[03:38] something like that, but the um Bill
[03:42] Ivers sent me a spam email and um
[03:46] usually don't reply to those, but I
[03:49] called Bill up and we had a phone call
[03:51] and um this was way back when I don't
[03:54] even know what year this was, John, but
[03:56] you guys had told me at that time that
[03:59] or something,
[04:00] we were working with some people that I
[04:02] don't even think are in the industry
[04:03] anymore, but We are trying to basically
[04:06] we have all these data points and all
[04:07] these different sources and we are
[04:10] trying to crack the egg of trying to
[04:12] pull all this data into one central
[04:14] source and then be able to take that and
[04:16] bring that out to store level because
[04:18] there's one thing is having the data two
[04:20] if you're not getting that downstream
[04:21] where the people are making the real
[04:23] world decisions on what causes the
[04:26] revenue to go up or down at the end of
[04:28] the day if I know it you know I I'm a
[04:30] very small part of a very big company so
[04:34] We'd been trying to do that uh for
[04:36] multiple years, me and Dave, and tried
[04:39] and failed. And uh Bill said he'd have
[04:43] me live with my first site within the uh
[04:45] within the week. And I mean, almost as
[04:48] just a Pepsi challenge because I didn't
[04:50] think there was any possible way that
[04:51] was going to happen. Uh I said, "Sure,
[04:53] I'll let's let's try this." And I think
[04:56] two days later, we had a live feed of
[04:58] our transactions from our point of sale
[05:01] into a web portal for us to manipulate
[05:04] and look at it in any way we want. And I
[05:06] mean, it was one of those oh wow moments
[05:09] for for me personally. And so we ran it
[05:11] as that pilot for a couple months, took
[05:13] it out companywide, I think by the end
[05:16] of the year, so like two months later.
[05:18] And um watch Tega evolve uh over these
[05:23] many years. And one of the things Bill
[05:26] sold me on then, which is still true
[05:27] today as much as it was then, it's one
[05:29] version for all. You know, it's not,
[05:32] hey, you have to do this upgrade. No, by
[05:34] the way, you know, write me a big fat
[05:36] check if you want this upgrade. It is
[05:39] someone some other TE customer on the
[05:41] West Coast comes up with some great idea
[05:44] and all of a sudden I open up my TEGO
[05:46] website and hey, look, they they've
[05:48] created something and I didn't even know
[05:49] that was being created. and that's a
[05:53] constant theme of our relationship with
[05:55] Tega over these years.
[05:58] Thank you. We appreciate it. Um, now
[06:02] let's let's get Oh, John, I'm sorry. Did
[06:04] you have
[06:05] John? I was just gonna say like I really
[06:08] appreciate the the background there,
[06:10] Alan. It's like you know as as you move
[06:12] through your journey of you know you
[06:14] know getting to like maturity with being
[06:16] data driven you know you start with that
[06:18] that whole you know you know
[06:21] do I have the data where is the data
[06:23] it's here it's there whatever and then
[06:25] and then it's it's really cool as we
[06:27] kind of work with our customers you know
[06:29] over the years that they get to the
[06:30] point where they're just you know it's
[06:32] it's not about that anymore it's more of
[06:34] like okay I know I have this I want to
[06:36] use this in this unique way or how do I
[06:38] go and and apply this with that over
[06:40] there to solve this problem and and it
[06:43] just really helps us to be continually
[06:45] innovative on on our part as well to
[06:48] have that relationship with all of our
[06:49] customers like you guys.
[06:51] Yeah. One quick use case, John. uh it's
[06:54] not on this list but there is another
[06:55] big partner that we bring our data to
[06:58] you and I think it's just a perfect
[07:01] example of I mean how you guys are as a
[07:04] software but I think more importantly is
[07:05] just who you are as a company and a
[07:07] culture and a team because there is one
[07:11] database that is notoriously difficult
[07:13] to do anything with that I will not name
[07:17] but I talked to John's brother and John
[07:19] and said man it'd be great if we could
[07:21] pull this into Tega and I think at the
[07:25] time I don't even know if you guys knew
[07:26] the platform but it literally was well
[07:30] let's just jump in and figure see if we
[07:32] can figure this out and I think it was
[07:34] like 10 days to two weeks later we were
[07:36] pulling live data from that platform. I
[07:40] honestly thought that was never going to
[07:42] happen. So it's um it's very much a hey
[07:47] let's see if we can figure this out type
[07:49] of relationship.
[07:51] Yep. And we're we're doing new
[07:53] integrations like that pretty much day
[07:55] in and day in and out every month. And
[07:57] so, you know, it's a point of sale here
[08:00] or it's a loyalty program or it's a, you
[08:02] know, labor management system. It's it's
[08:04] all about being constantly curious about
[08:06] how do how do we give you a full picture
[08:11] and and u we appreciate Ellen you
[08:13] bringing us to you know ideas that
[08:16] you've come up with that we've been able
[08:17] to work towards and and help you solve
[08:20] you know solve the problem for speed
[08:23] things up. So for any clients you know
[08:25] any ideas you have you may think it's
[08:27] crazy it would never happen bring it to
[08:28] us because John loves a challenge. I
[08:30] love the
[08:32] Yeah, to you know, a lot of the stuff
[08:35] that some of the new things that are in
[08:36] Tiger are as a result of John and Allan
[08:39] working together to and Dave um as well
[08:42] to to get it integrated for other people
[08:44] to be able to use. So, we really
[08:46] appreciate that. All right, let's get
[08:50] into
[08:51] the first use case. So, we have um a
[08:55] number of them that we're going to go
[08:56] through. I think we have, let me check,
[08:58] like four or five um use cases. If we
[09:02] run short on time, um Allan and Dave
[09:05] have been kind enough to to agree to do
[09:08] a part three if we need to. So, we want
[09:10] to make sure we spend enough time on
[09:11] each one of these. So, if you have some
[09:13] questions that come up um regarding each
[09:15] one of these use cases, drop them in the
[09:17] chat. Let's answer them and we'll, you
[09:19] know, if we have to do another part,
[09:21] we'll do that as well because we want
[09:22] you guys to to see all the benefits. So,
[09:25] let's start with the first one. And
[09:26] Allan's going to talk a little bit about
[09:28] this. Um, this is a, you know, a
[09:30] dashboard. You know, what does Allan do
[09:33] at the beginning of every day? What are
[09:34] the big things that you check? Allan,
[09:36] and if you wouldn't mind sharing with us
[09:38] a little bit about what your day-to-day
[09:40] looks like with Taigga, that would be
[09:42] great.
[09:43] Yeah, absolutely. So, if you kind of
[09:45] look at, um, you know, my role in the
[09:48] company, I'm mostly looking at things
[09:50] from a 10,000 foot view. Uh, Dave is the
[09:53] COO. So he is he's running all
[09:56] operations and he's the buyer for
[09:59] everything non-food service and nonfuel.
[10:03] So all the different category management
[10:06] and the granular details uh that is
[10:09] definitely going to be Dave's uh
[10:10] umbrella of expertise for me. What I do
[10:13] every morning is I want to see how our
[10:16] stores performed
[10:18] uh the previous day previous day that it
[10:21] rolls into a month. So I'm looking at
[10:24] transactions
[10:26] broken out inside outside percentage of
[10:28] loyalty inside sales fuel gallons truck
[10:32] diesel gallons uh I'm looking at how
[10:35] these stores are trending
[10:38] relative to the other stores in the
[10:40] company and I'm looking at how the
[10:42] company as a whole is trending over
[10:44] time. So that is pretty much the first
[10:47] thing I do every morning. Um the other
[10:51] thing that I do pretty much every day
[10:52] too is like I said Dave's the buyer for
[10:55] everything uh other than food service.
[10:59] So u the food service rep uh reports
[11:02] directly to me. So I'm so I kind of
[11:04] think of myself a little bit as the Dave
[11:05] lender for food. So I'm looking at what
[11:08] Dave does for the other categories. I
[11:10] kind of do that on a food service basis.
[11:12] So I'm looking at how each category is
[11:14] doing. So whether that be breakfast,
[11:16] lunch, uh we have Little Caesars, how
[11:20] the categories did, how they're doing by
[11:22] location, how that trends over time, and
[11:24] how it compares store to store. That is
[11:27] that is pretty much how I start each one
[11:29] of my mornings.
[11:33] Awesome. Um all right, thanks for that.
[11:36] And now we're going to get into any if
[11:39] anybody has any questions again, please
[11:40] let us know on that. Um then the next
[11:43] one we're going to get into is Well,
[11:47] I'd love I'd love to hear how Dave
[11:49] starts his morning, too.
[11:52] How do you start your morning?
[11:54] Uh, John, very very similar to uh what
[11:57] Allan described. Uh, now, one thing that
[12:00] I'm probably looking at uh is going to
[12:02] be the the monthly promotions that we
[12:04] have going on
[12:06] and seeing how they're trending, seeing
[12:07] how they're performing. Uh the other
[12:10] thing that I that I really like to look
[12:11] at uh I spend a lot of time in brand
[12:14] detail and product scorecard
[12:17] and the brand detail uh a lot of what
[12:20] I'm looking at in there especially when
[12:22] you're doing uh negotiations with
[12:24] vendors and manufacturers
[12:26] is what is their percent of market
[12:28] share. uh vendor is uh they're going to
[12:32] show up and they're going to tell you,
[12:34] okay, here's the latest Neielson report
[12:36] for your area. And there you go,
[12:40] Jennifer. That's exactly what I'm
[12:41] talking about. So, you can see in that
[12:43] slide where Monster Energy has a 34.68%
[12:48] assuming that is uh the energy category.
[12:52] So, so a lot of times I'm looking at
[12:54] that because that can change uh quite
[12:57] quickly. So,
[13:01] So that's a lot of what I look at on a
[13:03] daily basis and a lot of it is just um
[13:07] also dictated by what's going on. I mean
[13:09] today, you know, I'm working with Dart
[13:12] and they requested velocity for some
[13:15] propri proprietary cups that I wanted to
[13:17] get a quote on. Within a couple strokes,
[13:20] I had that information and was able to
[13:22] send it over to them. So, it just the
[13:25] date pretty well dictates, but it's so
[13:27] easy, so convenient. In a couple of
[13:28] keystrokes, you can go grab whatever
[13:30] data that you're looking for, export it,
[13:33] and send it out.
[13:35] So, Dave, when you when you're meeting
[13:37] with a vendor, um let's say they're on
[13:40] site, do they do you actually show them
[13:42] what's in Taigga? Do you show them those
[13:44] numbers or do you have those in your
[13:46] back pocket and you're just referring to
[13:48] them? Um and then what kind of reaction
[13:50] do they give you when you're when you're
[13:53] Yeah, that's a great question. So, today
[13:55] most of the vendors that I meet with on
[13:57] a regular basis, they already know that
[13:59] I'm using Tiger. They already that I've
[14:01] showed it to them. Um, so it's almost
[14:04] expected for me to pull that up during
[14:07] during a meeting as we go through and
[14:09] discuss uh whatever it is that they're
[14:11] presenting.
[14:13] But the the sometimes I will even do an
[14:16] export and send it to them. Uh for
[14:19] example uh just here not very long ago
[14:22] uh when you look at the nicotine pouch
[14:24] category
[14:26] Zen had owned that market for quite some
[14:29] time and there was a big switch in the
[14:32] market about three months ago. Well
[14:34] before Taiga that I don't know when I
[14:37] would have realized that. So when I'm
[14:40] able to go in and look at that on a
[14:42] regular basis and then all of a sudden I
[14:44] can see, oh, there's a huge shift in
[14:47] market share, then that changes that
[14:50] conversation with both of those vendors.
[14:52] And it also creates a conversation for
[14:55] store level when it comes to ordering
[14:56] and making sure we're not running out of
[14:58] product.
[15:03] the uh John,
[15:04] you have any
[15:05] can I add something real quick to our to
[15:08] our original conversation on the AI
[15:10] stuff? I think this and Dave, you you do
[15:13] this way more than I do, so I'd love
[15:14] your thoughts on it, but you know, uh
[15:17] food service is one that we've really
[15:19] done a lot of deep dives on AI and it
[15:22] works really well because we have about
[15:25] 10 12 menu items at 12 stores. When you
[15:29] get into the categories of what we're
[15:31] looking at here with the energy and all
[15:33] the different flavors and and the
[15:35] monstrous amount of sales, when when we
[15:38] start trying to push data like all of
[15:42] our energy sales into an AI agent like
[15:46] Chat GBT or Claude, it it can get
[15:50] overwhelming and it can take forever.
[15:53] So, um, to just take the time, me and
[15:57] Dave have been messing with this, just
[15:59] to take the time stamp of of all of your
[16:01] energy sales for all of your locations
[16:03] for a month. I mean, you will use your
[16:06] usage limits on AI before it ever
[16:10] finishes your first question. So like
[16:13] for what Dave is accomplishing here,
[16:16] um to try to take that amount of data
[16:19] and push into AI a would take forever
[16:21] and it just it's a very arduous process
[16:23] where with you guys you just make a few
[16:25] clicks and it pulls up exactly what what
[16:27] you're looking at in front of you.
[16:31] Awesome. Thank you.
[16:33] One question for you, Dave. like what uh
[16:36] what's the what's the most like
[16:39] off-the-wall interesting question you
[16:41] were able to answer with you know just
[16:44] within Taigo related to vendor
[16:46] negotiations like you know anything ever
[16:48] come up where you're like like I don't
[16:50] even know where to find that or or where
[16:52] that could be and
[16:54] like just curious and and did we hit the
[16:57] mark on it or
[16:58] right right I mean I I think for me it's
[17:01] it's uh being able to deep dive. I mean,
[17:05] just like the slide that that you have
[17:07] up right now. I mean, you click on
[17:08] Monster Energy and then it takes you
[17:10] over to products scorecard and now
[17:11] you're you can you can go as far down in
[17:14] the rabbit hole as you want to go on
[17:15] those things. I think I haven't um to
[17:19] answer your question, I've probably been
[17:20] more surprised
[17:22] from the actual data to be honest to
[17:24] where you think that this product is is
[17:28] performing at this level and then when
[17:31] you start diving into it and again it's
[17:33] just a couple of clicks and you've got
[17:35] that at your fingertips and now you're
[17:38] able to see uh and I'm gonna go back to
[17:41] the Zen Boilo scenario. I had no idea
[17:44] that that transition had happened. I
[17:46] mean, that was huge. I mean,
[17:48] yeah, it was
[17:50] when when you're
[17:51] Yeah, I remember you came in and showed
[17:52] me that, Dave.
[17:53] Yeah,
[17:54] that was definitely an aha moment.
[17:57] And and again, I mean, uh, pretigga, I
[18:00] mean, when I was pulling scan data, uh,
[18:03] it was very, very time consuming. I I
[18:06] was uh asking one of the employees in
[18:10] the office to pull the data for me and
[18:12] then to try to get it in the format to
[18:14] where I could use it the way I wanted
[18:16] to. And now that is that has changed so
[18:20] dramatically. And uh not only from
[18:23] sitting in here working with vendors,
[18:25] but then also at store level. I mean, I
[18:28] I can't tell you the last time I was at
[18:30] store level that I did not pull up Tiger
[18:34] for something.
[18:36] Um, now it could be salesreated, it
[18:39] could be uh transaction related, you
[18:42] know, when you're looking at uh, you
[18:44] know, how many transactions did we do at
[18:46] the drive-thru at this location? You
[18:48] know, you have a store manager saying,
[18:49] "Hey, I need more hours. I need more
[18:51] hours." And then you pull that up and in
[18:53] a couple of seconds, you're looking
[18:55] right at the data. And maybe they do and
[18:57] maybe they don't. Uh but that's probably
[19:00] for me the biggest thing uh is just the
[19:04] surprise of the data that you can you
[19:07] just get that shock value sometimes.
[19:10] Yeah. You mentioned the Zen Vo it still
[19:14] fascinates me like those categories like
[19:16] that for pouches and then you know the
[19:19] energy drinks and stuff like it just it
[19:21] just takes one you know influencer to
[19:25] say you know I you know I only drink
[19:28] this you know XYZ you know flavor of
[19:32] Bloom or Alani or whatever and all of a
[19:35] sudden sales just go complete opposite
[19:37] direction what you thought it was going
[19:38] to go. So
[19:40] yeah, I mean
[19:41] it is
[19:42] really really matters in those
[19:44] categories to have that timely data to
[19:46] be able to be nimble with it because
[19:48] it's just it's wild how how easily it
[19:50] moves with the wind.
[19:52] It is and you know anytime I meet with a
[19:55] vendor, you know, one of the one of the
[19:56] especially a new vendor, one of the
[19:59] first questions that they'll always ask
[20:00] is, "What can we do for you?" And I
[20:03] always have the same answer. I'm looking
[20:05] for a partner. I'm not looking for a
[20:07] relationship with a vendor or
[20:08] manufacturer. I want a partnership. And
[20:12] you know when you look at Red Bull and
[20:14] this is a prime example uh when you look
[20:16] at their market share and how they have
[20:18] grown over the last 18 months. Well,
[20:21] that is that is and I'll give a shout
[20:23] out to our new rep. I mean that is the
[20:25] partnership that he and I have created
[20:28] and the new promotions that he and I
[20:30] have put together. And now I can pull
[20:33] that and I can show him the trend of
[20:36] okay this is where we started and this
[20:38] is where we are today and look at how
[20:40] much your market share has grown
[20:43] and John it's amazing how much they are
[20:47] willing to participate with you even
[20:50] more so they do want more do more
[20:53] promotions and be more aggressive. So
[20:56] if you're able to reciprocate with them,
[20:58] like give them that data, help them to
[21:00] be better, and then they're going to
[21:01] bring it right back to you. Like it's to
[21:04] have those vendor partners engaged, you
[21:06] know, being data driven with you is just
[21:08] amazing. Yeah,
[21:09] it is.
[21:10] Yeah.
[21:12] So, so that actually that flows nicely
[21:14] into the next I know we've covered a
[21:16] little bit of this already, but when it
[21:17] comes to category management and
[21:19] planagrams and all those types of things
[21:21] and you know, working with your vendors,
[21:23] they're they want as much space as
[21:25] possible, you know, how do you how do
[21:27] you work with them to get there? And
[21:29] then specifically here, um you guys have
[21:32] a Kate's kitchen example that that's
[21:35] mentioned here to share. If uh Dave, if
[21:37] you want to talk through that
[21:38] specifically, we would we would love it.
[21:40] Well, the Kate's Kitchen would be
[21:42] Allan's uh category.
[21:44] Oh, Alan, you're gonna talk about that
[21:46] one?
[21:46] Yeah, let me let me drag Kate's kitchen.
[21:49] So, uh what what are we looking at here?
[21:52] Let me zoom in here.
[21:54] So, this is a this this is really
[21:57] talking about planagrams and um making
[21:59] better category decisions, management
[22:02] decisions.
[22:03] Yeah. So this is I'm looking at unit
[22:05] sales by day of the month
[22:09] and basically broken out by category. So
[22:12] uh yeah this is where we spend where I
[22:16] spend most of my time within when you're
[22:18] talking about item level analysis
[22:22] department. So,
[22:25] what one of the big things like if you I
[22:28] don't know how easily everyone can see
[22:29] it, but when you're looking at the unit
[22:30] sales, the average basket, time of day,
[22:32] item sales by hour, monthly sales, item
[22:34] frequency, all those different tabs,
[22:36] each one of those gives you a different
[22:38] way of presenting your information. So,
[22:42] when you're um looking at this and it
[22:45] gives you a great visual of what does
[22:49] any one item uh make up for that entire
[22:52] department. So, when you're talking for
[22:55] Kate's Kitchen in our lunch program, uh
[22:58] the the pink item there is the double
[23:01] cheeseburger. So, pretty easily there,
[23:04] uh that visual shows you that double
[23:06] cheeseburger makes up a large percentage
[23:08] of our overall sales. And you can see
[23:11] how those trend from day to day to day.
[23:15] And one of the things that TEG has done
[23:19] really well right from the beginning is
[23:21] the ability to
[23:23] manipulate your time window. So whether
[23:26] I want to look at just yesterday,
[23:28] whether I want to look at this week,
[23:30] last week, month to date, quarter to
[23:32] date, year to date, most of that is all
[23:35] just hyperlinks. It's literally two
[23:36] clicks of a button. And then you can
[23:38] kind of expand out the time and then you
[23:40] can kind of drill down the time. And
[23:42] then stores. Uh, for us, we have 25
[23:45] stores, 10 with Kate's Kitchen. You can
[23:48] start breaking that out all the way to
[23:50] everybody that has it. You can break
[23:51] that out to any one store. You can do a
[23:53] group of stores. It's a series of three
[23:56] or four clicks. And I mean, I don't know
[23:58] how much money spends on servers. Uh,
[24:02] that cannot be a cheap number, but the
[24:04] speed of which this continually updates,
[24:08] you know, you're not sitting there. I
[24:11] mean, I've grabbed I'll grab if you
[24:12] looked at further down on this, which I
[24:15] know you can't hear, but if
[24:16] hypothetically if this was the actual
[24:17] website and you scroll down and you have
[24:19] a timestamp and you want to grab the
[24:22] timestamp of any one individual item,
[24:24] now that is where I kind of use it for
[24:26] the AI side of Tega.
[24:29] I've grabbed the time stamp of every
[24:31] item, year to date, and a series of
[24:34] flags, whether that be loyalty, whether
[24:36] that be margin, uh whether that be cost.
[24:39] And when you hit submit and you hit
[24:42] email, I mean, it is it is insane the
[24:46] amount of data that Tega can send me on
[24:49] a link to an email from a year-to-day
[24:52] data of every time somebody purchased
[24:54] one of these things. And it's really
[24:57] just an extension of whether we wanted
[24:59] to start playing with some of the links
[25:00] on here and never take it out of the
[25:02] ticket portal. It it is as fast as you
[25:04] can maneuver around the portal.
[25:09] Awesome. Thank you. John, do you have
[25:11] anything to add on here about how the
[25:13] how the management?
[25:14] Yeah, John, you want to talk about how
[25:16] much you spend on servers?
[25:17] You don't have to stop.
[25:21] This is to let you all know we're rais
[25:24] It's It's less than less than my
[25:26] daughter's spend on shopping, but I'm
[25:29] just
[25:29] John's building a little data center
[25:31] over in Cincinnati.
[25:32] Yeah. No, it's uh I mean it's really all
[25:35] about the design of the of the of the
[25:37] data model that like you know how and
[25:39] why we can do it that way. We um you
[25:43] know our team has been doing this sort
[25:45] of thing for going back 20 years. We've
[25:47] been through all the different
[25:50] historical iterations of of data data
[25:53] warehouse data lake data lakehouse
[25:55] design and stuff like that and and we we
[25:57] just we know we know how to how to build
[26:00] the sandwich the right way. So like and
[26:02] we're going to keep keep advancing those
[26:04] tools to make sure that they're um you
[26:06] know keeping up and as fast as possible
[26:08] on the latest tech just like we do with
[26:10] everything else. So, um, we're all about
[26:13] giving you answers you need now.
[26:15] And that might be year-to- date data
[26:17] like you're talking about there or you
[26:18] want to see like what has happened in
[26:21] the last two minutes and you know, we
[26:23] want to make that happen for you.
[26:26] Thank you, John. All right, we're gonna
[26:29] go now and Dave mentioned this a little
[26:32] bit when talking about Red Bull and
[26:33] promotions, but Dave, if you could talk
[26:35] a little bit about um promotion analysis
[26:38] and and how you use Taigga in regards to
[26:43] to setting up different promotions.
[26:46] Sure. Um
[26:48] what used to do and how you've changed
[26:49] it, something like that.
[26:51] Sure. Um so you can see uh as you have
[26:54] there selected the the 2026 April sign
[26:57] program. So one of the things that we do
[27:00] uh is we take uh how we go to market uh
[27:04] for each specific item. So it may be a a
[27:08] two four or it could be a a bogo uh or
[27:12] buy one get one for a dollar and then we
[27:15] try to uh go to market different ways
[27:19] and then we're able to come back in and
[27:21] pull the data data out of the promotion
[27:24] tracking and then we can compare that
[27:27] and then make a decision. Okay, so when
[27:29] we run this promotion again, what is the
[27:32] best way to go to market? And so that is
[27:35] something that um we're doing a deeper
[27:40] dive into as we speak. Uh we recently
[27:43] just uh hired a new marketing
[27:46] merchandising manager and so she and I
[27:49] have been working on that and that is
[27:50] that is something that that is a
[27:53] personal goal of mine to try to do a
[27:56] better job of evaluating that data and
[27:59] adjusting accordingly.
[28:07] Very very cool. John, do you have
[28:10] anything about how promotion analysis
[28:13] works from the Taigga standpoint or the
[28:15] back end?
[28:16] Well, I guess just like what we've
[28:18] discussed on the other things, you know,
[28:20] we try to give you that drill down
[28:22] ability to get all the way down. So, you
[28:24] want to see, you know, down to the
[28:25] individual offer and individual
[28:26] transaction. um we can, you know, get
[28:29] all the way down to the nitty-gritty
[28:31] detail of, you know, a specific
[28:33] promotion, but then also backing it out
[28:34] and say, okay, you know, how am I doing
[28:36] on twofers, how am I doing on on this
[28:38] promotion across stores and all those
[28:40] sorts of things. Um, it's one caveat I
[28:43] would kind of add to it and a big
[28:45] challenge with promotional data is uh on
[28:48] the point of sales side of things, you
[28:49] don't necessarily always have all that
[28:51] context there. you know, you've got your
[28:53] your loyalty provider and you're setting
[28:54] up loyalty offers in there and then
[28:56] you've got everybody offers going
[28:57] through your back office to point of
[28:59] sale. You know, it's really nice to have
[29:02] a system like Taigga where all that's
[29:05] combined in one data model together. And
[29:07] so if you've got, you know, your loyalty
[29:09] provider providing offer activity and
[29:11] you've got your point of sale within
[29:13] Taigga, that's all in one system
[29:14] together. So you can see apples apples
[29:16] across loyalty promotions, non-loy
[29:18] loyalty promotions and all those sorts
[29:20] of things down to that offer level
[29:22] detail um in that same interface here
[29:26] that that Dave and Allen are talking
[29:27] about.
[29:31] Perfect.
[29:32] All right, next topic is a big one for
[29:37] everybody in the industry. Loss
[29:39] prevention. So we know shrink is a huge
[29:41] challenge for sea stores and we would
[29:44] like to get some examples from you all
[29:46] about how you're using the data that you
[29:48] see in Taiga to help minimize that
[29:51] issue. Um I know I was just in on
[29:53] training this morning um where we were
[29:55] talking through loss prevention and it's
[29:57] always an an eyeopening
[29:59] module to go through um for lots of
[30:02] different reasons when people are
[30:03] actually able to see you know what's
[30:05] been manually entered what's been
[30:08] scanned you know what different cashiers
[30:10] are doing things like that so if you all
[30:12] could go through uh this particular
[30:15] example is the date of birth transaction
[30:17] tracking um and how tiger flags that if
[30:20] you want to talk a little about loss
[30:22] prevention. Um, that would be great.
[30:27] Dave, you want to start?
[30:29] One of you can
[30:30] if you want to go ahead and go. I mean,
[30:31] this was actually Allen's idea and this
[30:34] has been a a huge timesaver on the LP
[30:37] side. So, uh, I don't want to steal your
[30:40] thunder here. I'll let you go ahead and
[30:41] speak to it.
[30:42] Yeah. And and this is Allen's idea that
[30:44] everybody is getting the benefit of too
[30:46] because we came up with
[30:47] the versionless software that's a very
[30:50] great use case. I agree. So, you know, I
[30:53] don't know when, you know, time uh
[30:55] evades me sometimes, but I don't
[30:57] remember how long we would uh go from
[30:59] this, but before we had this, you know,
[31:02] it was a series of
[31:06] I guess one thing I would state uh we as
[31:10] a company have set a policy that
[31:12] everybody has to give their birth date.
[31:14] I think it seems like it's becoming
[31:15] pretty standard in the industry. So
[31:17] whether you're 12 years old 12 whether
[31:20] you're 25 years old or 80 years old, you
[31:23] need to give us a birth date to purchase
[31:26] an age restricted item. And one tendency
[31:31] for when people when our associates
[31:33] aren't um uh following that policy, they
[31:38] tend to for speed do the exact same one
[31:41] repeatedly. And it could be a 11 one one
[31:44] or because the associate is the one
[31:46] entering that birthday. They could
[31:48] either I it'll be a repetitive number.
[31:51] So
[31:53] when once we um
[31:57] implemented the policy of asking for the
[31:59] birth date, we very rarely ever failed
[32:02] age restricted stings, but it's not that
[32:06] they went away. And time and time again,
[32:09] we would fail a sting. And time and time
[32:12] again it was the associate entering
[32:13] their own birthday or entering 1111
[32:16] whatever it was. So we started spot
[32:19] checking uh through the register
[32:22] receipts trying to find that pattern and
[32:26] trying to find a birthday pattern of a
[32:29] company uh that has 400 employees buying
[32:33] age restricted items. I mean it's the
[32:36] definition of a needle in a hay stack.
[32:38] it's, you know, you want to try to fix
[32:40] it, but you it is almost impossible to
[32:44] try to identify trends like that in a
[32:46] large database. So that's when we went
[32:49] to TEGA and very similar, it's very
[32:53] similar to the first thing I brought up.
[32:54] Hey, we have this problem. How can we
[32:57] find a solution? And John, I'm going to
[32:59] let you explain the solution, but this
[33:01] is what we do. it it spots these trends
[33:05] and we can at a moment's notice find
[33:08] these. Go ahead, John.
[33:10] Yeah. So, it it really just you know you
[33:13] know addressing the problem talking
[33:15] about like you know you know people are
[33:17] creative and and um you know trying to
[33:22] be efficient in what they're doing and
[33:23] they're you know they'll put the same
[33:24] thing in over and over again. you know,
[33:26] it might be the same day of the year for
[33:28] their birth date or whatever whatever
[33:30] today is 18 or 21 is or 25 or whatever
[33:33] whatever the whatever the policy marker
[33:36] is and then and then um so being able to
[33:39] catch that across the different
[33:41] permutations of those those birth dates.
[33:43] It might, you know, know they might
[33:45] enter um, you know, their their birth
[33:48] month and day and the year for for 18
[33:51] for, you know, tobacco and then and the
[33:54] year for for 21 for cigarettes and
[33:57] sorry, for for alcohol and, um, you
[34:00] know, being able to cast that across all
[34:01] those different things. Um, we kind of
[34:04] took those ideas, uh, in our discussions
[34:06] with Allen and kind of went talked to
[34:08] some other partners as well and then and
[34:11] then built up a whole dashboard suite
[34:12] around it. So there's there's ones that
[34:14] are focusing on you know specifically
[34:15] age restriction um you know you know
[34:19] catching bypasses you know maintaining
[34:21] scan rates all those sorts of things
[34:23] depending on what what your policy could
[34:25] be
[34:26] and then put it in a nice easy package
[34:28] where where you can build an operational
[34:31] plan around that for you know this is
[34:33] where we need to introduce more training
[34:35] this is uh that uh this might be
[34:37] something that's a DM activity it might
[34:39] be a store manager activity it might be
[34:40] both levels and then you've got um the
[34:43] DM referencing that and in the
[34:46] performance appraisals for the store
[34:48] manager. So trying to make it so that it
[34:50] works at those scales. Um once we
[34:53] started going on that then we start like
[34:55] okay well what other what other ways can
[34:57] we um help um to you know address these
[35:01] operational training needs. So now today
[35:04] we've got a whole suite of uh dashboards
[35:07] and reports that are all centered around
[35:09] uh cashier performance and and uh
[35:11] associate level performance. Everything
[35:13] from integrating your labor information
[35:15] and you know how how many refunds they
[35:17] do, payouts, all that sort of stuff that
[35:20] you know we've taken as additional
[35:22] feedback as you know we just get you
[35:24] know recursively go back through and try
[35:27] to say how how can we make this better?
[35:29] How can we provide better information to
[35:32] the operations teams so that they can
[35:35] find these needles in hacks?
[35:38] Yeah, I think this is a perfect example
[35:40] of actionable data. You know, there's,
[35:44] you know, you guys aren't creating
[35:47] anything that I could not get access to
[35:50] at some point in time, but it's before
[35:54] you. It's unusable data. It might as
[35:56] well have been unusable data. So it's
[35:59] packaging the packaging the data in a
[36:02] presentable for format. You know where
[36:05] are the opportunities to make real world
[36:08] change in your company that this changed
[36:12] because of something that we worked with
[36:14] with you. So, you know, I have a lot of
[36:17] conversations with people on our
[36:19] industry
[36:21] specific around TEGA, but mostly around
[36:23] data management and uh and they they'll
[36:27] ask me like, "How do you do this? How do
[36:28] you do this?" And I and I always say,
[36:30] and this is how we kind of work with
[36:32] Tega is don't try and come up with the
[36:34] solution. Just tell them your problem.
[36:37] And that's what we did here. I didn't
[36:38] know that Tega was going to figure it
[36:41] out in this format. I just told him what
[36:43] we were doing and how it wasn't working.
[36:45] It was John and Tega that said, "Here, I
[36:48] think we can take care of that."
[36:52] There's no doubt the efficiency that
[36:55] this has created both internally and
[36:57] then the accuracy of of the data that
[37:00] we're looking at. I mean, it's just
[37:01] night and day difference compared to
[37:03] where we were.
[37:07] And and that's one um one thing that
[37:09] I'll go off script here for a second
[37:11] about regarding the data accuracy and
[37:13] and maybe John you can weigh in on this
[37:16] but when we start with new clients um
[37:18] oftent times well I shouldn't say
[37:20] oftentimes uh most every time in the in
[37:24] the process of doing the QA
[37:27] between you know once we connect Taigga
[37:29] to your point of sale and your price
[37:31] book we go through a QA process and we
[37:33] want to make sure everything's mapped
[37:35] correctly and at that point we often can
[37:36] find some discrepancies between
[37:39] possibly, you know, what comes in
[37:41] through point of sale versus what's in
[37:42] the, you know, how price book is set up.
[37:45] And um it can be alarming, but that's
[37:48] that's the point of of this is getting
[37:50] to accurate. Yeah. Part of the process.
[37:53] It's um it's getting the accurate data.
[37:55] So, there may be things that you don't
[37:57] realize are going on. We had one big
[37:59] example with a client that was using
[38:01] theirh car wash information. they were
[38:03] getting just the data that the car wash
[38:06] company was providing them. But then
[38:07] when we actually got it hooked up to
[38:09] Taigga, we could see that that wasn't
[38:11] those numbers did not jive and that you
[38:14] know opened up another conversation of
[38:15] of what you know what's the truth with
[38:18] the data and what data is accurate. So
[38:20] John, would you mind talking just a few
[38:21] minutes about that that process and and
[38:24] what that entails and why it's a little
[38:26] painful, but it in the end it makes it
[38:29] makes all the difference.
[38:31] Yeah. Um, so we're all about trying to
[38:35] make sure you've got accurate data as,
[38:37] you know, complete as possible. And so,
[38:40] um, we don't just look at like, you
[38:41] know, pulling from like a single source.
[38:43] Maybe it's transactions or whatnot.
[38:44] Like, we're trying to make sure that we
[38:46] can validate it against alternate
[38:47] sources to make sure that there's a
[38:49] consistent whole view of the truth. And
[38:51] so um sometimes depending on where you
[38:55] know you're starting from there there's
[38:57] different building steps along that way
[38:59] to make sure that it's uh um you know
[39:02] everything's complete. You might have
[39:04] you know previously been working with
[39:06] information that was only granular to uh
[39:09] monthly or weekly or whatever or only
[39:12] spec uh specific to you know a handful
[39:14] of categories versus transaction level
[39:17] item level detail offer level detail.
[39:19] when it comes to say something like
[39:20] sales. Um we um you know we put a lot of
[39:25] effort into you know that process from
[39:28] the very beginning to make sure that
[39:30] we're getting you to a full and complete
[39:32] set for your data. being honest with you
[39:34] about where you are and best practices
[39:36] on how you could improve on that. So
[39:38] that you know maybe you didn't have that
[39:40] level of detail a year ago and and we're
[39:42] starting you know at you know that point
[39:45] but we certainly want you to be as
[39:47] complete as possible so that when that
[39:50] you know when those issues come up and
[39:52] those those opportunities come up where
[39:54] that data is very valuable to you, you
[39:56] can act on it um versus having to tell
[40:00] you that well you just you don't have
[40:01] that data point. So, um, we, uh, um, we
[40:05] go through a very extensive process with
[40:08] all of our customers as we're, as
[40:10] they're coming on board to make sure,
[40:11] you know, price book matches up with
[40:12] with sales and sales matches up with
[40:15] labor and sales matches up with loyalty.
[40:17] And, uh, if you had a historical source
[40:20] for sales, making sure that matches up
[40:22] to what's coming off your point of sale,
[40:24] because, you know, some sometimes people
[40:27] have multiple point of sale systems and
[40:28] maybe one wasn't going in there before
[40:30] and stuff like that. So we we like to to
[40:33] really put a fine tooth comb to those
[40:34] things because we want to make sure that
[40:36] uh um you know everybody's data is not
[40:39] perfect but it doesn't mean that we
[40:41] can't uh work together to make it so
[40:43] that you get more perfect every day.
[40:47] Yeah, John, I uh I think that is a very
[40:50] underrated part of your company and very
[40:52] unappreciated just in general as a
[40:54] concept where you know typically the
[40:57] larger the data size the more likely
[41:00] there may be from errors and not only
[41:03] are you dealing with a large data sample
[41:06] you're also dealing with that data
[41:07] coming in from multiple avenues and I
[41:10] remember when we first started with you
[41:12] guys we were constantly hey double check
[41:14] double check and so we pulling up. We're
[41:17] with NCR and so we pulled up the
[41:19] register receipts and we'd be spy
[41:20] checking and doing all these things and
[41:22] eventually you build that level of trust
[41:24] to the point that very rarely am I
[41:27] double-checking at this point. But, you
[41:29] know, if you can't trust the data coming
[41:31] in, then at the end of the day, you
[41:32] didn't accomplish anything. So, uh it
[41:35] took a little bit for us to build a
[41:37] level of trust with that with you guys.
[41:38] Uh not anything that you did. It's just
[41:41] I mean it's a crazy how much data is in
[41:43] there. And then now that you've got that
[41:46] level uh now we are trying to build that
[41:49] level of trust with AI. So it's it's
[41:53] crazy how often we have to spot check
[41:55] what AI spits out and I'll say I mean
[41:58] the the food service one that Jennifer
[42:01] had pulled up earlier. I'll look and
[42:04] it'll say it sold 12 cheeseburgers at
[42:07] this store in one day and I'm like well
[42:09] I see that I sold 16. And then it'll
[42:12] look and say, "Never mind. You're right.
[42:15] Yeah, I I thought that was
[42:17] cheeseburgers, not double
[42:18] cheeseburgers." Literally, I mean
[42:20] something as
[42:22] And so when you start losing that level
[42:24] of faith, then you're not going to do
[42:26] anything with the data,
[42:28] right?
[42:30] And then, you know,
[42:33] even though at some point, you know, we
[42:34] build up trust and you're not looking,
[42:36] doesn't mean we stopped looking. So,
[42:37] yeah. Exactly. Well, you better keep
[42:39] looking. I'm I'm trusting you to look,
[42:41] John.
[42:42] Every every single day, you know, we're
[42:44] pulling the real-time transactions.
[42:46] We're validating against the daily
[42:47] audits. If there's an alternate source
[42:49] we're connected to, say like a your back
[42:52] office market basket or something, we're
[42:54] validating against that, too, and
[42:56] running audits against those things. And
[42:57] so, um, we uh we take a great deal of
[43:00] care with uh with data integrity.
[43:03] Yeah. And that's fantastic. I mean, if
[43:05] we had to double check your data every
[43:06] single time, then that is be able to get
[43:09] anywhere. Yeah,
[43:10] exactly. That and so but you take it as
[43:15] with anybody else. You have to earn that
[43:17] trust and you you guys have absolutely
[43:19] earned that level of trust with my whole
[43:21] company.
[43:24] Thank you. That means that's that's
[43:26] awesome. That means a lot to us. We
[43:27] appreciate it.
[43:30] All right. So, we are getting close to
[43:34] about 10 minutes before the top of the
[43:35] hour. Um, we were able to get through
[43:38] all of the use cases that we had listed
[43:40] in this presentation. Um, this last
[43:43] slide just talks about how Taigga runs
[43:45] in the background of of a lot of
[43:47] different things. Performance reviews,
[43:49] we uh, waste reduction, labor planning.
[43:51] Is there anything on this list, Dave or
[43:53] Allan or, you know, John, that you would
[43:56] like to go into a little bit more depth?
[43:59] um this is this would be the time to do
[44:01] it and then we'll start opening it up
[44:02] for some questions
[44:05] if there's
[44:07] any of these you want to talk about that
[44:09] you've used or
[44:11] uh yeah I mean I can speak to a couple
[44:13] then you guys can take her John or Dave
[44:16] uh for me the
[44:19] in the bottom is kind of where uh I
[44:23] think labor is the biggest
[44:27] um correlation to profitability in
[44:30] anybody in our industry. So, and that
[44:34] can go two ways. If you're underst
[44:36] staffed, that can cost you as much money
[44:39] as if you're overstaffed. But the the
[44:43] amount of data you have to comb through
[44:46] to make sure you're properly staffed.
[44:48] One, it takes forever. Uh two,
[44:53] it's if if it takes you forever to do
[44:55] it, you you can't do it very often. and
[44:57] we have so much seasonality. What blows
[45:00] me away with our business is to me
[45:04] there's two things that are more
[45:05] critical to the success of our company
[45:07] than anything else. There's labor and
[45:09] what do we sell our gas for? And it is
[45:12] insane what percentage of our industry
[45:15] uses their gut to determine both of
[45:17] those evaluations. And that just that
[45:20] blows my mind. I mean those are the top
[45:22] two KPIs for it. So specific to labor,
[45:27] you know, we have about half our company
[45:29] in selfch checkout. We have about half
[45:30] our company not on selfch checkckout.
[45:33] Both of those models, we use your
[45:36] platform to gauge how well our labor is
[45:39] used. So the number one responsibility
[45:42] that a our associates have is taking
[45:45] care of our customers.
[45:48] We generally speaking treat a customer
[45:51] buying a 12-pack of of beer, a soda, or
[45:57] a bag of candy as the same customer
[46:00] because it's that's the same
[46:01] interaction. Typically, it's the same
[46:03] length of time. The what they're buying
[46:05] in the number of products generally
[46:07] isn't going to vary that greatly. So
[46:09] what we do is look at transaction counts
[46:12] and
[46:13] we had before we would pull those in
[46:16] before you guys. We would pull those in
[46:18] in mass. It would take somebody over a
[46:21] day to pull those in. We come through
[46:23] those and we would start manipulating
[46:25] the schedule to to kind of match our
[46:28] associates with when our customers are
[46:29] there. Uh and then before that we would
[46:32] let our store managers determine their
[46:33] own schedule, which I mean I think
[46:35] that's an absolute terrible idea. So, if
[46:37] anybody wants to speak to that, they're
[46:38] more than welcome to call me, but uh let
[46:42] the data tell you what you want to do.
[46:45] Um and then waste reduction food
[46:47] service. So, for me, once we got
[46:49] production,
[46:51] um the production data points in and
[46:54] then you can create the sales from the
[46:57] from Tega and you got your item cost
[47:00] from Tega, it can tell you everything
[47:03] you want under the sun for food service.
[47:04] I mean, we can we basically once we are
[47:07] able to grab a time stamp of our
[47:09] graband-go program, just that time
[47:11] stamp, Intega basically gives us a
[47:14] full-blown P&L that I can run at any
[47:17] given time I want for any location I
[47:19] want over any time over any time period.
[47:23] So,
[47:26] awesome. Thank you.
[47:27] Very impressive.
[47:28] Dave Dave or John, anything else before
[47:31] we go to questions? Oh, I can speak to a
[47:34] couple. I'm on to uh
[47:36] the labor planning. Uh in addition to
[47:38] what Allan mentioned, uh for me, it it
[47:43] really shows up at store level, as I I
[47:45] mentioned earlier, when you get to store
[47:47] level and excuse me, and you're looking
[47:50] at
[47:52] possibly the the standards not being met
[47:54] or the appearance of the store not being
[47:56] met. And usually one of the first
[47:58] excuses that you get is, "Oh, we've been
[48:01] really busy." or you know, we don't have
[48:03] enough labor to do what you're asking us
[48:05] to do. So, it's very easy to pull up not
[48:08] only the the sales information, but as
[48:10] Allen spoke to, more importantly, the
[48:13] transaction numbers and you can look at
[48:15] the transaction numbers and you can you
[48:17] can quickly tell because you can go all
[48:19] the way down by hour and you can see
[48:21] exactly what was taking, you know, okay,
[48:24] all right, well, you were correct. you
[48:26] you were probably underst staffed here
[48:28] or no data says you did have time. Um
[48:33] and the other thing I'd speak to would
[48:34] be the novelty planning and that's
[48:36] something that's really been a big focus
[48:37] for us here recently. Um and trying to
[48:41] uh historically we we've not done a very
[48:44] good job of
[48:47] creating u in our own price book on how
[48:51] to drill down the specific skew. So,
[48:53] we're in the process of of streamlining
[48:55] that. And then Allan and I have been
[48:58] talking about, you know, how we're going
[49:00] to move forward with our novelty and and
[49:02] being able to better assess the data
[49:05] that we have because when when it comes
[49:07] into novelty, I mean, obviously, it's
[49:09] high margin, but a lot of it is very
[49:12] trendy. So, if if you're if you're not
[49:16] there when it starts or if you stay too
[49:19] long, then you're missing out on
[49:21] opportunities. So, so being able to
[49:23] streamline that and be able to pull the
[49:27] data that we can actually act upon, uh,
[49:30] like I said, right now that's a that's a
[49:32] pretty high priority for us.
[49:36] Awesome. Um, all right, let's get in.
[49:39] Thank you, Dave. Thank you, Alan. Let's
[49:41] get into into some questions. So, please
[49:44] please drop those in the chat. Um, we
[49:46] have a couple that have come through
[49:48] already. So, I just want to I'll direct
[49:50] these to the right to the right folks.
[49:53] Um, the first one, this would be I guess
[49:56] this could go to any of you, but let's
[49:58] let's push this to I think Alan, how
[50:01] about this is for you. Um, the question
[50:04] is, we we are very new to Taigga. We
[50:06] want to get the most bang for our buck.
[50:08] Where would you tell us to start? What's
[50:10] which one of the use cases has the
[50:13] fastest payoff?
[50:15] Uh well I think and that's so hard to
[50:18] answer without talking having a
[50:20] conversation. I think it's like where do
[50:23] you see
[50:24] part two when they call you is would be
[50:26] part two
[50:28] the you know the uh the one thing I I
[50:31] just did a um a education session a
[50:35] speaking session not too long ago and
[50:38] and they said what is the what is the
[50:41] number one thing you would urge anybody
[50:43] in your industry to do and to me it's
[50:47] about networking so you know all of us
[50:49] in our industry one, you everything you
[50:53] do is within your own ecosystem. So if
[50:55] you don't branch outside of your
[50:58] company, how could you possibly have any
[51:00] context to what's good, bad, or
[51:01] indifferent? You know, I I would tell
[51:03] you labor, but the person that wrote
[51:05] that question may be the best the best
[51:08] at managing the labor costs in the
[51:10] industry. And maybe I want to talk to
[51:12] that person and say, "Holy cow, how did
[51:14] you come up with with that format to to
[51:17] implement that policy?" And to me, what
[51:21] I strongly urge them, then then they're
[51:23] welcome to call me. And it's it could be
[51:25] a thing where we just start bouncing
[51:27] ideas back and forth and what we're
[51:29] trying to do is identify our problems
[51:31] together and then we start sharing best
[51:34] practices. Um,
[51:37] I think couple things about our
[51:40] industry. One, we it's amazing how far
[51:43] behind from a technology perspective our
[51:45] industry is relative to other
[51:46] brick-andmortar retail. that will never
[51:48] fail to blow me away.
[51:51] Two, we are the most helpful industry
[51:55] that I guarantee you other industries
[51:57] and other retail channels don't actively
[52:00] try to help their peers out. And that's
[52:02] because we're so fragmented, you know,
[52:04] and we're mostly a family business type
[52:06] of industry. So I think first and
[52:09] foremost before I would say any one
[52:11] metric or any one KPI I would figure out
[52:14] where you struggle in the industry
[52:16] relative to your peers and
[52:20] um I did I'll just give my example this
[52:24] not so I did study groups as a peer
[52:27] group and we started sharing certain
[52:29] financials and you compare that to your
[52:32] um peer group and there was some things
[52:35] that I thought we were really good that
[52:36] that we absolutely sucked at. So, that
[52:39] was definitely a oh man type of moment
[52:42] for me. And it can be study groups or it
[52:45] could be just having those conversations
[52:47] and see where you differentiate
[52:48] yourself.
[52:49] Y
[52:50] that's great. Great advice.
[52:54] John, you look like you're about to say
[52:55] something or you
[52:56] No, I was just I agree on all points
[52:58] there like like
[53:00] like
[53:01] take it introspectively like go go
[53:03] network with your peers, you know,
[53:05] figure out where you feel that you're
[53:07] struggling at and chances are it's it's
[53:11] a problem that other people have some
[53:13] advice on and there's this is a very
[53:15] helpful industry.
[53:17] Exactly. You know, John, I always tell
[53:19] everybody everybody was trying giving
[53:21] each other hands up. So, created a
[53:24] single thing in my life. I've never I
[53:26] never did anything. I just said, "Oh
[53:29] man, that's a good idea. Let's go steal
[53:30] that guy's idea." But then here's an
[53:32] idea I got here and you do that and then
[53:34] we're all better together.
[53:36] Yep.
[53:37] And we can help facilitate that, too. if
[53:39] you, you know, as we and we've done that
[53:41] before as you're working with us. If
[53:42] there's something that that that is
[53:45] particularly worrisome for you or you
[53:47] want to get better in and we have a
[53:49] client that is really doing great in
[53:52] that area, you know, we we get I have
[53:54] yet to have anybody say no, they won't
[53:56] talk to you. So, we can connect you with
[53:57] our clients as well. You can talk not
[54:00] just about the Tiger stuff and how you
[54:02] utilize Taigga for those issues, but
[54:03] even just um different connections in
[54:07] the industry. I know that that Allan
[54:09] just uh we had connected him with one of
[54:11] our other clients recently and they had
[54:14] a they were able to hit it off at one of
[54:16] the shows they were at. So stuff like
[54:18] that we're definitely here to help
[54:20] facilitate as well.
[54:22] Yeah. I I
[54:24] I think one thing real quick, I don't
[54:25] think you should think of TEGA as a
[54:27] vendor customer relationship. I mean I
[54:30] think you should truly think of as a
[54:31] partnership. So uh they're not just a
[54:35] data software company. They're there to
[54:37] try to identify where your weaknesses
[54:39] are at and and figure out a way to make
[54:43] them better. So, I strongly agree or go
[54:47] reach out to TEG and say, "Hey, I'm
[54:48] really struggling with this. Do you have
[54:50] anybody else in the industry that may be
[54:51] able to help me out with that? They're a
[54:54] great connector in that way.
[54:57] Awesome. Thank you." Okay, we have one
[54:59] more question we have time for and
[55:01] anything else that's here we'll we'll um
[55:02] email out to you. But this was for John
[55:06] specifically for customers that are
[55:08] early stage. So this is you know along
[55:10] the same lines and this is not from the
[55:12] same person. Um what do you see as a
[55:15] mistake that people are making when they
[55:17] get started and any you know advice on
[55:22] looks like it's any yeah biggest mistake
[55:24] people make when they're getting started
[55:25] with Taigga. What can we do to help
[55:26] prevent that from happening? So they'd
[55:28] like to know
[55:29] what some of the roadblocks are and
[55:32] how to get around this. probably the the
[55:34] biggest one is jumping in too fast and
[55:38] and too far out. Um there um you know
[55:43] when we implement a new customer partner
[55:46] on on Tiger there there's you know a
[55:50] whole process that we go through with
[55:52] you to try to try to help you kind of we
[55:54] call the walk before you run type of
[55:55] approach. It's really important to
[55:58] follow that process. A lot of folks when
[56:00] they first see some of the things that
[56:02] are in TA that are way way out there as
[56:06] far as like things that like you're
[56:08] going to be capable of doing, they kind
[56:09] of tend to get distracted by those and
[56:11] they miss on a number of very good quick
[56:14] wins. Um, so I guess that it's, you
[56:18] know, I'd say probably the biggest early
[56:21] misstep is getting too excited and then
[56:24] running off onto a tangent instead and
[56:26] and missing some things that could have
[56:28] been uh really great for you. So
[56:31] thank you. That's my big spiel that I
[56:33] give at the on the kickoff.
[56:36] Follow the process. All right, we are uh
[56:39] just over the top of the hour. I really
[56:41] appreciate it. uh Mach 1, Alan and Dave,
[56:44] you guys uh done tremendous job here and
[56:46] we we appreciate the partnership that we
[56:49] have with you all. Um for all of those
[56:51] who are attending, if you have any I
[56:53] think I have a let me make one more
[56:55] slide here. If you need more
[56:58] information, that's our my contact info
[57:01] and John's, excuse me. And we will also
[57:05] be sending this out to everybody that
[57:07] attended or signed up and look forward
[57:10] to hearing from all of you and getting
[57:12] to work with everybody and and making
[57:14] this industry move in shape. We're
[57:17] excited. So, thank you everybody.
[57:20] Thanks.