Mike Rizzo: [00:00:00] All right. Hey everybody. Welcome back. It has actually been quite some time since I’ve had an opportunity to do a no bullshit demo and I am really excited to have the two guests with me today from, uh, a team that we work with [email protected], the Growth Native side of things, but they also have a really cool product that we’re gonna get into really soon.

Um, but as we get this thing rolling, as a reminder, the no Bullshit demo series is especially built for all of you to understand exactly how a product could be beneficial to you and your organization. It cuts through all of the BS, so you don’t have to talk to the sales reps as lovely as these two humans are, and the rest of their team, you do get an opportunity to learn about their products without having to talk to them.

And then you’ll see how awesome they are and you’ll want to go talk to ’em anyway. So with that, I’d like you to meet our two guests, Arpi and Teran, uh, Arpi. Teran. Please introduce yourselves and then we’ll dive into the first question.

Taran: [00:01:00] Absolutely. Thank you Mike. Um, I’ll go first. Um, my name is Theran. I’m the, the founder, CEO at Growth Natives.

That’s the parent company. And, uh, today we are gonna be talking about Dick Growth, but, uh, before founding, uh, growth Natives, I was a career marketer for good 20 years. Uh, three XCMO and, uh, uh, you know, really started growth natives with a single objective of helping marketers achieve better results. Um, and then one of the, the challenges, you know, I had faced while being on the other side of the fence, along with our pit, who’s been my partner in crime for over 13 years now, um, you know, was how do you measure marketing effectiveness and, you know, how do you, uh, achieve better results?

Arpit: Okay, perfect. So, hi, this is arp. I had the product management for day growth, uh, over 18 years of experience across marketing, [00:02:00] data and analytics. And more recently the whole AI bus. So catching up with that as well, uh, both personally as well as on the product side. And really excited to go through this series of no BS demo.

Mike Rizzo: Awesome. Well, thank you both for joining. Uh, I’m excited to dive in. I’m sure the audience will be happy to learn more about what you’re up to. Um, I’ve actually had a sneak peek before, so I’m excited to see where it’s, uh, where it’s gone since the last time we talked, actually. So, so with that, let’s dive into our first question.

Please tell us what is the product, what does it do? Why does it exist? Where did it all come from?

Taran: Oh, so, uh. You know, uh, Mike, I’ll take a stab at it. See, like I was mentioning earlier, it was a lifelong problem that I had been trying to solve, um, you know, as a marketer. And, uh, you know, and I have talked about it for almost a decade, and we actually started working on it about four, four years [00:03:00] ago.

Um, and really it is the, you know, the ever evolving challenge of attribution. Uh, you know how marketing has become even more complex, um, you know, with every. Um, ever so evolving and then, um, the, the ability to hone into, you know, what’s working, what’s not. That was number one. Uh, number two was really, you know, how do you continuously refine, um, you know, your targeting your GTM motion and then, um, you know, how do you make insights that are buried into deep into the data, uh, available.

Uh, for random use and quick use, basically. So, you know, without having to depend on, you know, technicians, business analysts, data scientists, to really dig insights from the data so that you can actually quickly pivot and make decisions. That was like, you know, kind of the, the, the, the baseline premise. And [00:04:00] obviously with the evolution that we’ve seen in the last three years with, uh, the LLMs and ai, uh, you know, we’ve.

Also pivoted the product to be, um, AI first. Um, we built a number of agents and utilities within what was the original platform to actually bite-size it so that it can be used in a far more effective and, um, quick results, uh, fashion as well. And by the way, the, the product is called Dig Growth, and the website is dig growth.com.

Or do you add anything?

Arpit: Yeah, the only thing I’ll add is, uh, this one mission that we have is, can we make the whole data and AI useful for marketers? Because it can be very complex, it can be very intimidating, but can we simplify it and simplifying it is, is a complex task and we are on that mission.

Mike Rizzo: I love that.

Yeah. Well, attribution’s a hot topic in, uh, all of [00:05:00] marketing and go to market, but definitely a hot topic in the Mo Pros community and, uh, marketing ops.com, our events and the like. So, uh, I’m excited. So let’s dive in. This is your opportunity to share the product and show it off. Um, tell us a little bit about why marketing operations go-to-market operations professionals are going to be using your product and ideally maybe show off like two or three.

Key highlights of where it really shines.

Taran: Absolutely. Uh, if we can fire up, uh, so basically, you know, the, the number one challenge is marketing data sits in so many systems. Uh, you know, you’ve got your ad platforms, you also got your website. You also got your marketing automation platform. You’ve got your CRM system.

You’ve got like, you know, all these, uh, different channels where interactions are happening and a lot of times you’re not able to stitch. That, um, you know, the customer 360 degree view, the prospect 360 degree view, uh, the number one thing you know, the product does is [00:06:00] we built connectors and integrations wherein it literally is a drag and drop where you can actually integrate your website, your marketing automation system, your, uh, CRM, uh, and bunch of other places where the data could be residing.

And even if you have. A CDP, like snowflake or any other data solution, you can actually directly connect, uh, dig growth with that as well. That’s like really about the stitching. And then once you stitch that data, you’re actually able to, you know, uh, we have a lot of prebuilt reports. The one that we are seeing on the screen is actually the CMO dashboard.

This, uh, very simple report, but very complex data engineering behind the scenes. What it is telling you is, you know what? Is the total number of closed one business that you have in terms of number of deals, as well as the average deal size and the total revenue and the marketing spend. You see there’s like, you know, 2.6 million [00:07:00] in uh, total revenue.

Um, and then how much of it is being attributed to marketing? How much of it is influenced by marketing and how much of it was sourced by marketing? And then. The two little graphs on the top, the marketing ROI, and the, the, the cost per opportunity are actually, again, trend graphs over time. Uh, and then everything that you see here, you can double click and do it for more detail, basically

loading up.

Mike Rizzo: Yep.

Taran: And while it’s bringing. The, it reported. So what the, what this level of reporting does is it’s really ideal for CMOs. It’s also good for, um, you know, the, the heads of mar, you know, head of, head of marketing ops, um, people who are managing paid, uh, and this advertising, this gives you like, you know, what the revenue, what the goal was, [00:08:00] how much of it has been achieved, and how the trend line works.

And then really down to the, the deal level. You know, where, you know, which are those deals which are actually influenced by marketing, which are the ones which are sourced by marketing. Uh, and a lot of depth in that, uh, since we are on it. But let’s just show like the performance report as well, that tells you like how granular we can go, uh, with the data.

So we’re basically able to attribute revenue down to the keyword level. So obviously at the highest level is the channels. You know what? Whatever channels you are using today for your marketing, you know, be it, Google, LinkedIn, whatever are the, the, the sources and then, you know, campaigns that you’re running, which campaigns are bringing in how much revenue, and then revenue by ad group and his, the [00:09:00] revenue by keyword.

So really like this, obviously the keyword revenues for Google and Bing search. So from all the way to the, the click to the revenue and the, you know, the, the, the biggest challenge marketing ops and the CMO faces today is nobody cares about vanity metrics anymore. Nobody cares about how much traffic you drove.

Nobody cares about, like, you know, how many leads you got, the boardroom and the CEO want to see. How much impact have you had on pipeline and revenue? And more and more it is becoming revenue. And now like I, we’ve also seen the evolution of the role of the chief revenue officer. Again, the whole objective is that marketing and sales need to work as one team chasing the revenue goals for the company.

So this gives you like a very high level view of like, you know, how the, uh. The, the product, how deep the product can go. And then there’s a bunch of other reports [00:10:00] that come ready with it, um, that are turned on day one of use. And then there’s also many, um, possibilities in customization, customizing reports as well.

Uh, this again, is a, another one of the reports that is pretty key. Um, this is like in the full funnel report. Rarely do you get to see, um, a report where. You are actually able to see the entire funnel in operation, both sales and marketing. So, you know, at the top you see like the leads, the m qls, the um, the sales accepted leads, and then as things are moving into the sales pipeline from discovery to engagement to close one.

So this really gives you a complete view, and again, everything is double click away. Yep.

Arpit: Can you talk about the cohort, uh,

Taran: as

Arpit: [00:11:00] well?

Taran: Yeah. The last report that we will show, um, because I think we wanna talk about other stuff as well, is really like, cohorts are extremely important. Um, you know, especially when the time to value is a longer, um, time, space. Space. You know, for example, like. Large events and trade shows, companies that tend to spend a lot of money at large events and trade shows, there’s no instant ROIA lot of times it’s just, you know, uh, beginning of conversations and then over time you wanna see like, you know, how, um, was that event, uh, successful or not.

So you can actually create events based on time, which right now we are looking at the monthly, but you could also do like channel, you could also do events source. Um, so for, for example, in this case, what we are looking at. Is a lot of times, you know, with longer sales cycles, you might spend $50,000 this month and you might not see any ROI for the next three months.

’cause your sales cycle is average five months, six months. But this tells you [00:12:00] over time that, you know, when did you achieve break even? Like, what was the ROI, what months did you achieve, uh, break even. And then also the overall ROI from the spend in that month. So as you can see, uh, uh, you know, the, the ROI.

Is is clearly depicted for spend in a month. What was the multiple that we got in?

Mike Rizzo: And this is based on, uh,

Taran: sourced

Mike Rizzo: one.

Taran: Marketing sourced.

Mike Rizzo: Marketing sourced, and then closed one.

Taran: Yes. Revenue. This one? One. Yeah. So full end-to-end stitching of the data. These are the leads that marketing got by spending x.

This is the current ROI and it also tracks ROI over time as this, um, you know, as time lapses, the ROI keeps increasing, basically

Mike Rizzo: would love to have, [00:13:00] uh, in case the feature doesn’t exist. Uh, would love to have an alert that says, you know, boop new ROI analysis available for the event you hosted four years ago.

Taran: That’s, you know, and it’s a, you know, you spend so much on events, but then every time you want to go. Um, you know, either, uh, participate in an event or exhibit at an event. The number one question is, you know, what did we achieve last year? Or like, you know, we’ve been, we’ve been exhibiting for the last five years and have we gotten any ROI?

So this is like, you know, a great example of that. Um, like I was talking, we’ve built some AI utilities into the, um, the product as well. The, the one we are showcasing right now. This is, uh, you know, AI first lead scoring. So we’ve always seen you, and this should be very exciting for marketing ops professionals because, you know, we are always challenged with, Hey, you know, you have to score the leads before you send it to sales.

And they have to be like, you know, well worth it. And the con continuous [00:14:00] battle between marketing and sales. But you know, the sales leads are not sales ready. And so this actually saves a lot of that pain. Uh, what it does is basically it creates. Um, leads into three categories, A, B, and C. And the way it is doing is it’s actually looking at the entire historical data that is available, running an AI model against it, and then looking at leads as they’re coming in and predicting the win rate that you can expect against each one of those leads.

So the grade A leads, you know, the, the prediction for the closed one, it’s predicts a hundred percent, but, um, in reality the wind rate was 99.27%, which is very accurate. So. You know, there, the, the opportunity locked about 8.5 million. As you can see, that was, um, you know, retrospective, looking at what the model had predicted as a leads and the ones getting closed as, um, A leads.

If you go to grade, uh, grade B or grade B, [00:15:00] it’s, you know, again, in this case, the predictability was. 59.18% of these leads should close. The actual win rate is 64%. And, uh, as you can see, again, a fair amount of, um, you know, value logged. Um, and, and the nice thing about it’s, it actually grades them the moment they come into the funnel.

So it doesn’t wait for any, it does evolve with time based on behavioral and uh, demographic. Data. But if it feels very compelled based on the AI model that this could be a winning lead, it actually categorizes them a, uh, right upfront. And obviously grade C will, will, will be performing very poorly. So, you know, if the model is a hundred percent accurate or close to a hundred percent accurate, it would be worthwhile that, you know, the moment you see an A lead goes directly to sales.

So there is no. [00:16:00] Need to qualify it through, like, you know, has this person visited the website so many times or have they opened emails or have they actually logged into a webinar or not? It actually is giving them a very, very clear picture. What we also do is we actually, um, do a side by side comparison with the static, um, models that you have, be it in a, you know, a market or a HubSpot or like, you know, anywhere you built a.

Um, it actually does a side by side comparison for each lead that is coming in. And you know, here you can see that the A category leads the MA score was not even existent basically. So the marketing attrition was not even like recognizing it to be, but these were all worthy leads basically because, you know, the bias based on the digital body language.

Can be too much at times.

Mike Rizzo: Yeah. [00:17:00] And a lot of the human, uh, derived sort of signals, um, just lack like ability to pick up everything. Right. Um, and so I, I, I can see the value in having, uh, an AI assistant that’s, you know, navigating multiple inputs. Uh, I think. If I was a marketing ops professional coming in to now talk to you after this demo is done, uh, and you don’t, I don’t actually want you to answer this question now necessarily, but.

I think I would want to understand, you know, how, how can I interact with that AI model? What is it basing those decisions on?

Taran: Right, right in front of you, Mike. So, so the, uh, so the question is very appropriate and timely. Um, there you go. So we’ve also built, like, you know, our own, um, GPD, it’s called Diggy, GPD, and we have a mascot for it, which happens to be my dog.

Um, it’s called Scooby. So I

Mike Rizzo: love

it.

Mike Rizzo: I love it.

Taran: So you ask, um, [00:18:00] you know, dig, g, b, d, any question, and it actually turns, uh, you know, runs SQL queries in the back and brings back data. So, and it don’t, just doesn’t give you results. It actually gives you, uh, you know, recommendations as well. So, like, I think the question, what was the question that you asked?

Um.

Arpit: So we just checked what is the spent by channel. If you are running like multiple channels, it’s always good to track the monthly spends. So it’s looking into that trend and then it also gives you some recommendations. And then each of the questions, uh, it also auto suggests what is the comprehensive report that we already have that you can double click on.

Taran: But let’s ask the question, you know, what are the top three channels?

Arpit: Yeah, so top three channels based on pipeline is what I’ve asked.

Taran: Perfect.

Mike Rizzo: I love it. And earlier when we were talking, you were saying that um, the index of the information is heavily predicated on your taxonomy and sort of your use case, which actually I think is [00:19:00] wonderful for marketing operations professionals to understand because that does mean that there’s a degree of control over the system, right?

Yep. And so oftentimes we find ourselves in tools that are sort of like. I don’t know. Um, pre-established, right? They have a, a point of view, so to speak. Um, and we’re, you know, I think Salesforce is a great example of right, of that, right? There’s, there’s leads, there’s contacts, and there’s accounts, and then inevitably you start customizing things to try to fit the way that you want it to work.

Um, and the more control you have over that taxonomy, the things like your UTM sources, your lead sources, your campaign naming conventions, um. Those things just make it that much more tangible. And so if your system allows for a marketing operations professional to establish those, uh, foundations, the way that they expect to be able to do reporting, it means that they’re not conforming to the system so much as they are adopting.

It’s

Taran: completely configurable. Like it’s completely configurable. It, [00:20:00] um, you know, like I said, when you initially set it up, um, you. Do all that through the drag and drop capabilities that we have. So the moment we connect the system with your sales force, it actually pulls in all the, the fields that you have, the custom fields, anything that you have done in there,

Mike Rizzo: that’s awesome.

Taran: Um, and then all you do it is map it to the, the, uh, you know, the map, the, the system to reflect that. And it uses the exact same taxonomy. It uses the same nomenclature. So you see what you actually have in your Salesforce or in your marketing operations.

Mike Rizzo: This is awesome. Yeah. And so then you can go back into these, these systems and ask these questions, right?

Um, it understands what you mean by total pipeline, by campaign, because you have it structured that way, right? Yep.

Taran: Or

Mike Rizzo: you know, the top, top whatever, three deals it looks like, uh, you pulled [00:21:00] on the last report there,

Taran: so that’s

Mike Rizzo: awesome.

Cool. Um, well,

Taran: I think that’s a little bit about the product. I mean, we can keep going. There’s like a lot of depth in there. Um, but I think we can, um, just stop sharing now and get, get some reaction from you as well as like if you have any other questions for us.

Mike Rizzo: Perfect. Yeah, no, thank you. That was, uh.

Super helpful. I saw other screens that I’m sure our community members are going to want to dig into. There was an entire marketing ops section, uh, that we’re gonna have to unpack one of these days. But, um, when we, when we head down this list of, uh, no bullshit demo questions, the next one in line is to talk about what integrations you are seeing most frequently used in dig growth.

Um, so you talked about the broad scope of integrations, right, that are possible. Uh, but what do you find is sort of the most common set of integrations that users are

Taran: the, the most common ones when it comes to ad platforms? Because, you know, [00:22:00] we primarily work a lot with the B2B uh, companies. Mm-hmm. Uh, the product also built in a way that it’s more B2B first, uh, to be very honest.

So it does, it can do B2C work, but it was built for the B2B mindset ’cause that’s where we came from. So, you know, the most common integrations that we see, Salesforce. And HubSpot are the number one from the CRM side. Um, from the marketing automation side, HubSpot, Marketo, um, Salesforce Marketing Cloud. Um, and uh, on the ad platform side, we see Google, we see Six Sense or a BM.

We also see a lot of LinkedIn, which is, you know, Google Channel for, um, B2B marketers. Uh, these. Are the most common, uh, from a, um, you know, ad and interaction mapping.

Mike Rizzo: Yeah, that makes sense.

Taran: Then you also have offline data sources. The [00:23:00] product is enabled to have offline data sources. For example, if you went to a trade show, got back a list, you can upload it and you can also upload the budget and the cost for all the other channels.

It pulls all the cost metrics from there directly through the APIs. So what are we spending on Google every day? It’s incoming in all the, um, you know, the, the click data, the, the performance data, all that is coming in. Um, and then obviously we do integrate with your website as well because we do want to make sure that, you know, we’re capturing the organic side of the house as well.

One thing we haven’t been able to show today is we do a very good job of, um, organic, uh, capture as well. You know what?

Mike Rizzo: Oh, that’s great.

Taran: What keywords. People are searching for and how is it attributing to revenue? And the last thing I would say is all your social channels as well. Um, so you know, YouTube and um, uh, Facebook,

Arpit: LinkedIn.

Yeah. Yeah. [00:24:00] And then, uh, like one thing I would like to add is, uh, there’s another pivot that we have, uh, basically added recently is more on the data enrichment side. So we pretty much also integrate with all the major data vendors, be it a Polo ZoomInfo contact out, and then we are sort of also helping clients and enabling the whole waterfall approach in terms of refining their data set, ensuring that.

We have good, uh, data to enable segmentation and filters. Um, because if your data has a problem, then I mean, those beautiful dashboards will not reflect the reality. So that’s where we, we thought that’s a good time to get into that as

Taran: well. So that’s an excellent, uh, you know, that’s an the, in the evolution of product, there are two things that came out very clearly.

A lot of times the, the CRM data is not, um, complete. So, you know, for example, if you’re missing industry field for half your [00:25:00] opportunities, then you really cannot do much of, you can’t tell which industry industries you’re winning in. And then what it does is it also, uh, you can never, uh, be sure about your ideal customer profile.

You know, which industry, which sub-industry, and then the depth within that. So that was like the, you know, as we were implementing this and customers were looking at this data and they were asking. We actually solve that problem as well, wherein we’ve built this waterfall model where one, we do grade your quality of data in the CRM and marketing automation platform, like how good or bad it is, what are the gaps?

And we can help you close that through, um, the AI first tool that we have built. Uh, uh, and you know, the other thing we did, uh, Mike, we actually reverse engineered the lead scoring models. To help you identify the ideal customer profile as well. So, ah, nice. So, you know, we, [00:26:00] we use the same logic to say, you know, who, um, obviously once they become a lead, we score them, but even otherwise, if you, let’s say you gave me a list of like 50,000 companies, that is your total addressable market, and you wanna understand, you know, which ones should I really go after?

Um, the, the science is very accurate on that as well. So that has been something that we’ve been, and again, that data gets fed back to your a BM activation, your sales teams to prioritize those accounts. Uh, so it’s a win-win situation across the funnel. So like, you know, the, the, the big evolution that we had once we actually started understanding, you know, what’s working what’s not, was that if we really wanted to help our customers be better at targeting.

We have to give them a complete picture and not just based on, you know, 50% completeness or 60% completeness, which is what we see on an average.

Mike Rizzo: Yeah, I don’t know that I’ve [00:27:00] seen a ton of, uh, I don’t know that I’ve seen a ton of, um, platforms that, that go kind of end to end like that. Right? Like oftentimes we encounter, you know, attribution, analytics, uh, that are solving for some complex things, um, but aren’t necessarily trying to.

Help sort of backfill data and enrich it. They leave that up to other, you know, providers. Um, and then even on the post, post consumer side, right when they become a customer of yours. Uh, if you’re making sure that it is updated and enriched, um, then it only advises the models so that that grading that you were showing us that A, b, C can get.

Taran: That’s again, so it feeds, so it feeds the entire purpose. You know, one is as people are coming in, who’s the best prospect? But also if you’re wanting to look outside the world, you addressable market and say, you know, who should I go after? Um, because there’s obviously an outbound motion as well that, you know, can be powered through.

Okay.

Arpit: Can I add one more use case that we solved?

Taran: Yeah, absolutely.

Arpit: So we didn’t stop there. We actually have started working on the activation. So [00:28:00] we have always heard of hyper-personalized emails and they end up with high first name. We are actually come up with a true hyperized email that is backed by everything that we know about the company and the person and that tool.

Oh, nice. Any, any number of lists that you have.

Taran: Yeah. So again, nice. Leveraging the same data. The the next step of evolution was really creating hyper-personalized content that can be served through emails. And again, we’ve already built the integration, uh, with HubSpot and tested it. Um, we’re, we’re building one with Marketo now.

So where, you know, completely hands off once, uh, we know who, um, you know, the, the ideal customer is and who. What segment they belong into and what prospects, I’m sorry, the ideal, uh, customer profile, what segment they fall into, and it’ll create content relevant to that segment. Um, based on the, the knowledge we have fed the model, your website, you know, your documentation, your [00:29:00] everything that you have from the product side, it leverages that to create hyper-personalized nurture tracks and activates them in, in the marketing automation platform.

Mike Rizzo: That’s wonderful. It’s a, it’s a core tenet of a, uh, high quality AI forward, uh, platform these days. And I cannot tell you the amount of conversations I’ve had with some, uh, providers lately where, um, that is missing at the moment. The context piece is missing. So it’s exciting to hear that from you.

Taran: Yeah, and I think the reason the context piece is missing is because none of the marketing automation platforms has that level of, you know, context depth.

Because they’re only looking at part of the data. They’re not looking at the, you know, they, they don’t have access to the entire, uh, um, big set of data that is coming from the CR M. Sometimes they’re integrated, but like, there’s so many other places that the data is coming into, uh, dig, growth.

Mike Rizzo: Yeah. Yeah, absolutely.

Well, this has been helpful. Let’s keep, uh, rifling through. Yes. A handful more [00:30:00] questions here, so I don’t my

Taran: questions, so

Mike Rizzo: That’s okay. So, uh, let’s talk a little bit about the average size of your customer. Um, who typically is, uh, signing up for dig growth?

Taran: So, I would say companies that are, there are two qualifiers.

One is really like, you know, if you’re not spending at least 500 KA year on advertising, I don’t think we would be a good fit for you. So, 500 k uh, of annual paid advertising budget all the way to, um, close to you, 200 to 300 million. Um, paid advertising budget and, uh, from a revenue perspective, if you are north of 5 million and above, you would be a good fit.

So either of those two situations. Um, but, but if you are like, you know, north of, you know, 5 million, but you don’t spend at least 50 k on advertising every month, then the VA you [00:31:00] won’t get the as much value from the tool as you would if you were spending that much.

Mike Rizzo: Yeah. Yeah, that

Arpit: makes sense. All those, and just like we tweak the product a lot, uh, even our existing ICP, we realize that agencies are also really good fit partners for us because a lot of them don’t really focus on analytics and for them it becomes additional revenue stream.

And then they can actually, uh, white label us as well.

Mike Rizzo: Nice.

Taran: That’s always the product is we’ve enabled it to, you know, if, if other agencies want to use it to. You know, serve their end customers, they can as well. So Nice. Uh, you know, and, and they can white label it to their branding users as a, you know, standard tool of choice.

Just like they’re used to using, um, you know, data Studio or any other, um, product in the past. This really becomes kind of the default product for them.

Mike Rizzo: Love that. And, uh, tell me a little bit about the average time to complete an [00:32:00] implementation.

Taran: Good question. It is a. Um, I would say one, it depends on your complexity of how complex you are, but the minimum I would say is two to three weeks to start seeing.

In fact, some of the data you start seeing within 48 hours of us plugging in. Um, but you know, all the configuration and everything that needs to be done takes about a week, maybe two weeks, and then you can start seeing, um, so between two or three weeks, if you are a basic, um. You know, we call it like Salesforce, HubSpot, Marketo combination website, and then you have like another standard channels and not a hyper customized, uh, Salesforce.

Um, it would, right, you can start seeing value in like two to three weeks if you have a lot of customizations on your end. We have to map to those customizations. We have to. But some of the reports, even in the complex situations, you will start seeing reports [00:33:00] in under one week. Um, and then we’ll keep growing the library.

Um, in most complex cases, what we have seen is anywhere between 12 to 16 weeks, so. Okay.

Mike Rizzo: Nice.

Taran: Yeah. Nice.

Mike Rizzo: Yeah, I mean, well, that

Arpit: actually covers a lot of the custom reports, the ICP analysis, so you know, all the depth in the platform, so

Mike Rizzo: Right.

Arpit: It’ll cover all the aspects in that. Yes.

Mike Rizzo: That makes sense. Awesome.

Very helpful. And, um, let’s keep going here. So tell us a little bit about your pricing model and whether or not there’s set up costs. Now you don’t have to necessarily quote a price ’cause this video will live on as an evergreen asset, but you can give us a sense of how you price the platform.

Taran: Yes. So there is, uh, certainly the initial setup.

We want to do it because we want our product to, uh, not be shelfware. So, you know.

Mike Rizzo: Yeah. Yeah.

Taran: So we’ve,

Mike Rizzo: there’s been a few of those in our lifetime as

Taran: working

Mike Rizzo: with professionals.

Taran: So the, [00:34:00] the, the way we priced it is that it’s really based on the amount of advertising you’re doing, um, because that’s the, you know, like we’ve tweaked them, you know, the pricing model.

Um, we looked at what competitors were being. We also looked at like how typically SaaS companies are pricing. We were. We didn’t wanna price it by seat, we didn’t wanna price it by What we did is like, you know, companies have a marketing budget, they’re spending a certain amount on marketing. We will like, you know, tier it based on how much they’re spending.

’cause that’s the ROI they’re trying to measure. Um, so, you know, um, for a, so it, it grows, um, based on the, we have tiers. So if you’re spending, you know, anywhere between 500 k. To a million on advertising, there’s a price, then a million to, uh, you know, 3 million and then three to five, and then five and above.

Um, and, [00:35:00] and in situations, if it is more than 10 million, there’s like some custom pricing involved, but otherwise it’s pretty standard. Um, starts, I can tell you what it starts at. It starts at, um, you know, $500 a month. Mm-hmm. Um, if you’re somewhere in that range of, you know, the, the, the 50 KA month spending, um, the first.

Implementation, uh, there is a charge. Um, we really gotta do it at cost, so it’s about 2K to begin with, um, the initial setup, um, if it’s a base tier and not very complex, then you only pay is like in the monthly fee. Um, and within that monthly we also include, uh, five to six hours of our analyst time. So if you ever need a custom report.

That can be done. If you need support, we are available. Um, support is free. But even for customizations in every tier of pricing, we include a certain number of professional services hours.

Mike Rizzo: Okay. Awesome. That’s [00:36:00] great. I love the scalability of the pricing model as, uh, hopefully as your performance gets. You know, uh, the, you know, the platform performs along with you and it’s telling you where to go spend more and you’re making more money and everybody’s winning.

So that’s, um, okay. So we’ve talked about pricing model. We have just a couple questions left. Um, who would you say on the internal team side of things, like which of our internal team members and stakeholders are needed to get fully implemented? Um, do you think that it needs to sign off, uh, sales marketing?

Like who do you think is sort of most involved?

Taran: So, you know, we’ve, the reason we do the implementations, we really don’t want, um, that we go and ask for like, you know, a lot of time from the teams internally, but like from a buy-in perspective, obviously, like, you know, the, uh, if, if there is a CIO’s office, I think they would probably wanna sign off on this.

But we have also seen where they have not, uh, you know, it really is, um, the marketing [00:37:00] ops and the CMO, that’s usually the, the deciding team. Um, you know, lately because we, you know, made the product such that it’s becoming closer to sales, so now we are also seeing a lot of involvement from sales ops, especially since we launched like, you know, the ICP analytics side of the house.

Um, and then also product marketing teams. Um, they are also like, you know, getting involved, uh, from an input perspective. We need, uh, inputs from the sales ops people because we’re gonna make sure that we understand the, the, the waterfall that they’re following. The marketing ops team, um, and then the, uh, paid advertising folks.

But in all, like for a simple implementation, we don’t need more than, you know, 10 hours of input from, uh, your teams. Basically the customer teams. Um, we do all the heavy lifting and then ongoing basis, it’s pretty low maintenance, uh, from a, um, you know, the, the IT [00:38:00] team’s perspective because we are doing all the hosting and managing everything.

They

Mike Rizzo: great.

Taran: Um, you know, the end users for them as well, we are making to, trying to make this product as easy to use as possible. Uh, and especially like, you know, I think in the first month or two they would probably more involved ’cause they would want, like some customer reports, they might want like something tweaked.

But once those reports are solidified, all they have to do is come back periodically. And, and get access to those reports. And then with Dig GPD, they can get deeper insights, um, just by prompting their questions.

Mike Rizzo: Yeah. Yeah. The age of ai. Oh man. Things have really changed. It’s so wonderful.

Taran: Well, what used to take weeks, literally like, you know, the questions we were asking DGBD about, Hey, you know, what’s my best performing channel?

What’s my best performing, you know, how much did I spend by channel last month? Like it would take. A few hours and sometimes, you know, at least like two or [00:39:00] three days for like multiple people working on it to bring that data. Oh, for

Mike Rizzo: sure. And in a bigger org it could be longer. It

Taran: could be longer. It like

Mike Rizzo: gotta put in a queue to the, to the digital team.

Yeah. And then put in a queue to

Taran: analytics mean, let’s say you wanna understand like how much we spend on marketing last your own ad ad advertising. Yeah. If you’re using five different channels, five different people will first report that somebody will collate it and then bring it to you. Basic. That all happens in a matter of seconds now.

Mike Rizzo: I know. It’s so wonderful. Well, um, I do feel like we got through the final questions earlier in, uh, the content. So, um, you know, I got a sense from you all that we don’t necessarily need external agencies or consultants in order to be successful with this product because you and your team are helping to implement it and make it a success.

Um, and if it is, uh, a very custom environment, it sounds like you’re there to support them. So. We do

Taran: have you just, sorry to interject, but we do have No,

Mike Rizzo: you’re fine.

Taran: About three, uh, [00:40:00] four agency partners now that are actually working with us. So they’re using it for their end customers and at times they’re also, um, you know, bringing us in just the like, you know, they’re using the platform and they’re capable enough to implement it themselves as well,

Mike Rizzo: so.

Okay. Great. So if, if it is needed or, uh,

Taran: yeah, if it is needed, there are, there are at least four other agencies that are out there, um, that can help with implementing it.

Mike Rizzo: That’s wonderful. And then, um, we definitely heard about your support options, uh, and the fact that support is included in your monthly fee.

And there’s what, uh, I think you said six hours of analyst time available.

Taran: The basic,

Mike Rizzo: yeah, so the basic

Taran: tier gets six, but like, you know, as you move up the tier, you get up to like 60 hours every month.

Mike Rizzo: That’s amazing. Um, and then, yeah, we, we know generally that, uh, average time to see success in the platform is, uh, potentially as, uh, as quick as two a couple days when you’re starting to see analytics.

Uh, but beyond that, you know, two to three weeks you’re probably implemented and really starting to, to hum unless it’s a [00:41:00] complex, uh, system where, you know, maybe it’s a little longer, but

Taran: Yeah. But otherwise, and the, uh, yeah, and the only other thing I would say is the, uh, um, from a. Um, you know, real ROI point of view, um, it’s really the insights that you’re getting and how you are leveraging them to make decisions.

Um, and obviously like, you know, the, the quantum multiple, that could be very, very high.

Mike Rizzo: Yeah. Yeah, absolutely.

Arpit: And what we have seen is, uh, in a lot of cases, uh, clients are actually, um, asking us to run monthly reports and be part of those qbr. So we are not just shipping reports, we are actually shipping some actionable insights and really getting, uh, up to speed with, uh, with the actual business and their outcomes and things of that nature.

Mike Rizzo: I love that. I love that you’re taking a hands-on approach to helping to provide some key insights. I think, uh, people are lost in the [00:42:00] minutiae, in the dream that AI can replace, uh, any level of human, uh, decision making. But at the end of the day, yeah, I can produce these reports for you and deliver them, but someone’s gotta read it and someone has to make a decision on what to do

Taran: next.

Human, human, human, actually, the human in the loop is now ever more important because now, like you know, earlier, you should. Take a lot of time for a small amount of data to come to you and you have to look at it and decide now you have large volumes of data that is moving in front of you. Uh, you know, and AI is giving you all this, um,

Mike Rizzo: even faster.

Taran: Even faster. So that’s why like the human in, in the loop is even more important now.

Mike Rizzo: Yeah, yeah. I totally agree. Uh, well this has been wonderful. Thank you both for doing, uh, what we love to do, which is the no bullshit demo program of. Uh, dig, growth. And, uh, thank you for, for joining me, uh, Terron. Arpi. We really appreciate you.

Uh, for everybody watching, go check them out, uh, dig growth.com. You’ve got the links, they’re in the [00:43:00] video, they’re in the description, and if you wanna meet them, uh, I’m happy to introduce you to ’em. So just ping me in the, uh, community and we’ll, we’ll make a connection happen. Uh, until next time, everybody, thank you for watching the la the latest no bullshit demo.

Arpit: Thank you. Thank you. Cheers.