Transcription
All right. Well, I know everyone's still flowing in, but welcome everyone here from cloudy San Francisco. I'm Sheri Johnston. I'm the chief operating officer here at Winning by Design. Um, so thank you guys all for joining. We really want to make sure we make a a incredibly relevant and thought-provoking session today on the go-to-market index.
Um, the go-to-market index is going to be a way to really measure your go-to-market maturity. So more than just the diagnostic that we've offered for a long time, it's going to be an additive lens into really understanding your go-to-market capabilities with the context of your growth stage um and a way for you to align your team around really how to improve and drive that compounding growth that we're all um after.
I'm joined here by Dr. Dan Patterson, our chief innovation officer. Thank you for h for joining Dan.
Hi Sher, great great to be here and uh welcome everybody. I am very excited about sharing um this uh new innovative and honestly what I believe to be a very powerful concept. So uh yeah, looking forward to it.
Awesome. He's the uh the brains behind the go-to-market index and brings a really amazing combination of your academic background as well as your operator experience and I know you're going to share some uh real-life stories into how this uh this this comes to life with your own experience. So very cool, excited to hear it.
So before we dive into uh the content and the agenda and get started, as Dan and I were preparing for this, we um we had a fun kind of analogy to describe the go-to-market index and make it uh at least in our minds a really tangible into a way to think about it. So maybe you guys can all contribute to chat um in, you know, how many of you besi besides Dan and I use a health app, whether it be the Apple Fitness or Strava or Whoop. Maybe put in your favorite one into chat. Um if you do utilize one to track your your health goals.
Well, we here at Winning by Design. Oh, I see lots of good ones. Strava, you're you're a Strava man, Dan. That's exciting. Um I I'm more just the uh the good old Apple Watch girl, but Apple I see some some uh fellow Apple folks in there. So um you know this is a really interesting analogy to where you know we all have health goals just the same way we have growth or retention goals. Um but the the health app really helps us understand if we're trying to lose weight or we're trying to get to that seven-minute mile. um that isn't really helping us tell us if we're not getting it getting there yet. If what what's really helping us is understanding the underlying reasons that might be causing that. So looking at our heart rate variability, looking at uh all the underlying heart rate monitoring systems, our stress level, our recovery level, our sleep quality that really feed into those overall fitness goals. And what we really want to do with the go-to-market index is provide you that same underlying performance insight into why how you're going to get to that growth rate and retention rate u and not just looking at the the end goal. So um exciting analogy for uh or I think a really relevant analogy to the go-to-market index really being kind of your your own Strava or Apple Watch fitness app um that uh that you can utilize to to kind of help track your project process on uh progress on maturity.
So so um yeah, Dan, I'd love to kind of you know walk into the agenda um that we're going to cover today. Um and and before I drop uh hand it off to you, we are um going to be going over, you know, scaling versus compound growth, kind of how how we think about those, introducing the go-to-market index itself, how it's calculated, really how to apply it. Um, and and then going into what I'm most excited about as well is some example case studies and really how we're bringing it to life and how you might use this in practice. So Dan, before I hand it off to you, what is what is kind of one thing that you want to walk everybody to walk away with today?
Sure. So, you know, we're going to be talking about the concept of uh compounding growth. And the reality is to achieve and sustain compounding growth is really difficult. It's it's very very hard. And so, the reason why we developed the GTM index is it it's a way of quantifying how mature and honestly Sherry, how capable are we as an organization to achieve that that compounding growth. Um ju just to loop back on your your Strava analogy. Um first of all it's been a while since I've uh I I've managed a seven-minute mile as you mentioned. Um but I've been tracking I I'm a runner and I've been tracking my running performance I think since I was about 10 years old. I literally had a little paper notebook when I was a kid and I used to write down the times for my for my 5k runs. Those times they're the result and output of my training. So, in order for me to improve, looking at my output and the times doesn't actually really help me. What I need to do is pinpoint why my fitness is what it is. And you know, the the the Strava fitness index or fitness score for example is a is a measurement of how fit am I and then I can peel back the layers and look at is it because of sleep etc. So the GTM index concept is exactly the same. Um so I think it's important to understand this is not a measure of um monetary revenue growth. We already have that today. We can plot our ARR over time. Typically that's an S-curve. That's the result of our performance. What we really need to understand is what are the drivers that are influencing that that performance and that's honestly the gist and the purpose of the GTM index.
Very cool. Well, excited to dive into it. So let's um take a step back before we take a step forward and dive into the the GTM index. So the com the the the concept of compounding for me is incredibly interesting. You know 15 years ago um generating revenue uh in a software business was fairly straightforward and also I think fairly linear. We would feed the top of the funnel in the form of an input. We would convert leads to opportunities through to to deals. That's our output and that essentially would be the the end of the journey and then we would rinse and repeat. Well, as we all know, things have got a little bit more interesting in the world of SAS in that the point of sale instead of it being the end of the journey is really now the beginning of the journey because we can leverage that initial acquisition, that initial piece of revenue, and we can compound it through both what we call retention and then expansion on that initial initial sale. And so there is a huge opportunity today to leverage this mathematical concept of compounding. That's the good. The bad is it's very difficult to optimize all of the inputs and the levers within our GTM organization that are required to achieve that optimization. And um I I know I've shared this with you before Sher, but I have a funny funny phrase. Is I call I call the uh the concept of this compounding math magical because yes it's driven by science but there is just something so special to this in terms of the opportunity but again we have to get it right.
Exactly. So the benefit and the reason why this is mathematical is the the three revenue sources acquisition acquisition feeds retention. Retention then feeds expansion. Expansion then in turn feeds retention. We end up with this feedback loop. Um the result of that is it's it's a nonlinear it's actually exponential in terms of benefit. So we generate this if you like this momentum this flywheel and what we're trying to constantly understand is which of those three revenue sources should we be focusing on the most to not just uphold that momentum but actually increase that momentum which is acceleration which can which translates into compound revenue growth. Now again, all of that is is good and well. ARR growth and and the ability to to compound is highly sensitive to the balance of those three revenue sources. And so having a mathematical or a quantitative means of measuring the goodness, the health, the maturity of our ability to drive those three revenue sources in my mind is is absolutely invaluable. And again that was one of the drivers behind uh developing the GTM index.
So early warning indications of a compounding stool. I put this slide in here Sherry because again in my mind success is all about pinpointing root cause so that we can address root cause of failure. So if we were to look at this example um ARR graph here on on the slide the yellow line is showing um revenue over time and on the face of it when I look at this everything is quite healthy that yellow line is increasing all is looking well now while growth is increasing what that line doesn't show is how are things changing over time and so when we look at um the first derivative which is essentially looking at the rate of change. What we can see here in the dotted orange line is yes, our growth is actually increasing. It's growing up until about you'll see here about 2022, but then that growth starts to decline. Now, why that's so important is there is a time lag with regards to the momentum of that ARR growth as we showed in in the previous slide. And so yes, we may still be increasing our revenue beyond 2022, but because we are slowing down as represented by the orange dotted line, we are eventually going to stall. And so this in my mind is the first step in understanding do we have control over that compounding growth. This tells me we have a problem, but it still doesn't get to the root cause as to exactly what is that problem.
Now, with regards to um the three revenue sources, first of all, on the left-hand side of the bow ties, we we call it we focus on acquisition. And I don't think it's it's certainly not a new concept. Ultimately, we're trying to minimize dilution from top of funnel through to that initial point of acquisition. That that's very well known. I think less understood though is the fact that on the right-hand side of the bow tie when we start to look at retention and expansion. What we're trying to do is align those entities. There's no point in having a very healthy acquisition machine if we're falling short on our ability to retain and likewise expand. And so it's this concept of having a balanced factory um that again is so important to to compound growth. And so today at Winning by Design we have the concept of the revenue architecture which comprises six models. The GTM index is layered on top of those six models. And in fact what we do is we look at we critique and we score each of those each of those six and that then helps drive or or build up the overarching GTM index.
Very cool. So that's super helpful. I think we all 100% agree that the that three trifecta is our our end goal there of acquisition, retention and expansion. Um but what we really need to know is you know where do we track in those six models kind of equivalent to our um our sleep and our health and our eating habits etc. um that really feed into those um those revenue rates that that can create that compounding um effect. So, so Dan, now I know you're going to kind of walk into we understand, you know, how um what the goal is and what the underlying elements are. Um tell us a little bit about more how the go-to-market index works and and how it will tell us those underlying performance rates.
You you bet. So um again just to to to sort of start out um in many ways the GTM index is it's an evolution um on top of uh our existing what we call a a GTM diagnostic. So for many years now and we've conducted tens and tens and tens of these we've been able to do a deep dive and diagnose the health and maturity um of your your GTM organization. The challenge we've run into is when presenting the results and the information back from that diagnosis, um, some of the the the interpretation is somewhat subjective because there are so many data points. And so the GTM index is now consolidating all of those results and all of those uh, data points into an overarching index. And I think Sher, it's really important and and this is something I'm super excited about. The index is actually, in all honesty, it's more than just an index. It's actually a three-dimensional entity because yes the the the primary dimension is the index. It's a 1 to 10 score. So the example here 4.3 is actually the average of what we're seeing um in the industry today. We we've benchmarked and stress-tested the index. Secondly though the second dimension it it will not only come back with a numerical score it will actually come back with a narrative diagnosis as to why the score is what it is. And most importantly, the third dimension, it even comes back with recommendations. So, it's not just telling me that I have an issue, it's telling me what I need to do in order to resolve that issue.
Yeah, that's that's that's super important. And, you know, go going back to our our favorite analogy totally correlates too, right? Um, in terms of being able to have that data set to benchmark against, of course, for the most part, we want our uh our our rates to improve. Our heart rate variability is a good example to improve against our own score. However, it is always something to that's very helpful to know sort of where you land compared compared to your age range. But in this case, sort of where you land uh within with your um maturity based off of your uh your ARR.
Exactly. And so there's multiple ways we can benchmark and compare our score to either peers industry standards. I think one of the um the benefits we have is because we have so many years of executing these GTM diagnostics, we know what good is. And so we've actually established a target trajectory, a target score that you should be marching to based on your your current uh revenue number or or or ARR. So at the bottom here, Sher, there's actually an example of um four organizations that we've not only uh worked with on a GTM D diagnosis, but actually run the GTM uh index. So from left to right, um the Acme or here scored actually worse out of all four organizations, just above four uh 4.3. Um, I think what was so telling here was the index came back and diagnosed the fact that the score was what it was and compounding growth is going to be a huge challenge was because their engine was still highly input dependent. So, it's still tofu dependent. Um, they they were under uh underfocused on the right-hand side of the bow tie with regards to retention expansion. Um, Azimoff slightly higher but still below five. um was interesting in that the diagnosis that came back actually predicted future state. Remember the the chart that we showed with the the first derivative and and the deceleration. The index was smart enough to not just diagnose current state but actually suggest a significant decline going going forward. And again in my mind telling me my state today is interesting but what I really want to know is if I continue as is what is my future state going to be and that's exactly what I did there in that second example. Um Raptor was interesting as as it showed a 6 and a half more importantly they're right on the crest of achieving the holy grail of approaching compounding. It's sort of this almost this sort of self-perpetuating flywheel concept and then Wasatch close to seven um was doing significantly better and expansion was absolutely um holding the fort in terms of achieving that that compounding. That was an example of um four organizations with the score behind the scenes or or to to go a level deeper Sherry the scoring mechanism is actually a 1 through 10 scoring mechanism and it's actually a if you're if you're interested it's to actually one decimal point to support those those scores. Um we have uh characteristic definitions as well. So you can see an example here anywhere from unstructured through structured all the way through orchestrated and then ultimately autonomous um if indeed you can you can achieve that uh that score of 10. And um this little bit of the sausage making slide like what's behind the scenes in terms of exact definitions behind each score. That's that's helpful.
Yes. So, but again, this is all driving towards um root root cause. And um as a as a 10-second uh interlude here, I actually watched this movie with my daughter over the weekend, and it made me it reminded me that um success is all about pinpointing um root cause. And again, the index is more than just a score. It's actually going to help us figure out where we need to focus our our efforts. In the case of uh Raiders of the Lost Ark, they were focusing their efforts in the wrong place.
All right. Well, that's amazing. Um I love to uh hear about, you know, kind of sort of how now that you've established kind of what the go-to-market index is, how we go about um looking at the framework, you know, tell us how a bit more detail and sausage making around how it is calculated.
Okay. So um the engine can be broken down into five steps. So on the screen here we have what we call our ranking system. So we have our scoring uh by row and then the column Sherry are the criteria that the AI engine uses to critique each of the individual models. Remember we don't we don't do top-down. We do bottom-up in our calculation and then roll it up to the overarching score. So we look at the revenue model, the data model, math model, operating model, growth model, GTM model. Each of those models have different criteria against which we we critique the maturity and the health um of the organization. Now step one is first of all we need to understand the characteristics of the business. And so in the case of uh customers where we've already conducted a GTM D, we have the luxury of inherently understanding all of the data points that we need from them. Um and so that that's a relatively straightforward process. what we're working towards is automating the extraction of that information and characteristics of an organization actually without having to do a GTM D. Um so that's that's another very exciting area that we're using AI for um sort of AI-driven interviews if you like to extract the necessary information and then once we have that information the AI engine applies this ranking system. It scores each of the six models separately as um step two and three. We then take those scores and they are combined into the overarching GTM index.
Very cool. Um to give us a little bit of a example, you know, what might a company's score be in one model like for instance the the data model?
Sure. So let's let's touch on a couple. So data model and maybe uh revenue model. So, um, let's say a company hasn't yet, um, adopted the the the bow tie model. Uh, they're still very much funnel focused. So, focusing on acquisition and not so much on retention expansion, that would that would score a two on the data model. Um with regards to for example revenue model if if there's still uh non-standard discounting in place either by product or by even by seller then that would be classified as a three and characterized as having operational debt. So each of the models can carry their own uh score with regards to maturity or immaturity.
We have a question from uh Wendy. Um you know is the model based on data or is it based on answers to interview questions combination of both?
It's a fantastic question. So it's actually the latter. So it's a blend of qualitative and quantitative um information. So the qualitative is largely uh interview uh based and the quantitative is typically the result of the diagnosis that we do during a GTM D.
Very cool. And and and without getting well I'm going to say without getting into the weeds and now I'm going to get into the weeds. Sherry the engine the AI engine is self-learning. So over time it's actually and it's already starting to do this. The criteria that we have in our ranking system. the engine is coming back based on the results and self-adjusting either the questions in the questionnaire for the qualitative or giving more weighting to the quantitative as well.
So this one's really kind of the money slide of uh giving you that that score. Tell us a little bit about what we're seeing here.
You bet. So I describe this as a tornado chart. So it is a um a chart that based on the score of each model it essentially ranks those models. So from top to bottom we can see that the operating the revenue the growth model are all um relatively healthy and and mature and sufficient. Uh conversely the data model, math model and GTM model in this example are less mature and less appropriate for where we're at in our stage of growth. So, two things I guess. One, first of all, those individual elements get summated into the yellow overarching index. I think more importantly though, this is our it's our to-do list. Uh we focus on the uh the poor first or the the less mature. Um and and this reminds me um you know my my career has largely been in project management Sherry as you know and there is a a very well-known and respected concept called critical chain and the analogy in critical chain is that if you have a a cub scout uh troop and they're they're out on a hike then you put the the slowest hikers not at the back of the hike but at the front. First of all, it keeps everybody together and secondly, the faster hikers at the back encourage the slower hikers to push at the front. And that's exactly what this tornado chart is uh is is designed to do. So, we focus on the uh the less mature. One thing with that though, as we address and resolve and drive up the score on, for example, the data model, the math model, and the GTM model, we it's an iterative process. We need to recalculate the GTM index because the the relative scores of each of these will uh the tornado will essentially re reprioritize as we as we improve.
Amazing. So Dian I have a few questions coming in if you're open to taking a few. Um yes Edwin has a question around you know how long does the uh full assessment take and is it repeatable for seeing improvements over time which I know uh you and I are both excited about that last part.
Yes. So um the first question within the question with regards to how long does the initial diagnosis take um a typical um deep dive GTM diagnosis that is facilitated by us um is typically order of magnitude about 4 weeks. um we are working on a um self-diagnosis but it's not a fully fledged GTM D diagnosis is specifically a GTM index diagnosis and the goal there is um within 20 minutes of interaction with you we should be able to glean sufficient information to run the the index. Um the second part of the question pertaining to um ongoing I think performance tracking beyond the initial baselining of the index. I think this is actually more valuable than the initial scoring because knowing that today I score a 4.3. It's interesting and you're going to pinpoint for me where I need to improve. I think most importantly over time is my 4.3 getting higher or is it getting worse? And is that trending towards the target of good or is it trending away from the target of good. So, so yeah, this is very much designed to be an an ongoing uh perpetual assessment.
Yeah, just again back to our health analogy. You got to keep tracking it over time to see that uh the compounding growth. Um well, I'll take I'll ask one more. We have quite a few coming in then I'll let you continue. We can uh keep keep going. But um hey let's see here. Vlad asks are the interview responses analyzed by AI or by a human? Um are the interview Oh so simple answer. Um they are interview sorry they are analyzed by the AI engine that is behind the GTM index. Um and that gives benefit of uh speed. It also the AI engine has the luxury of um knowledge of prior assessments and so it has an incredibly strong understanding of context again pertaining to what should be good or what is good but yeah it it is AI-driven for sure.
Thank you. So, we've talked quite a bit, Sher, about the the quantitative score of the index. Um, and I think we we alluded to the fact that, you know, this is a three-dimensional uh entity. So, here is an example where we have a spider diagram showing the quantitative score, but to support and defend that score, we have a narrative pertaining to diagnosis here on the right-hand side. So depending on um the criteria that was uh that either passed or failed the engine will actually return diagnosis. So um for example our math model here is scoring a four. Um we have a clear growth formula defined but that growth formula hasn't yet been applied to specific GTM motions. Um, conversely, no data model scoring three. There's a framework in place pertaining to the bow tie. Um, but data is siloed and uh um action items are hard to uh hard to achieve. So that's the second dimension. And then thirdly, and again I think this is the the this is the real value. We score, we diagnose, but then most importantly the engine's able to to recommend. So again based on the scores here by model we can see recommendations. So uh for the revenue model we need to focus on harmonizing packaging and pricing. Uh we need a centralized data taxonomy for the data model. Uh we need a unified operating cadence for the operating model. So on and so forth. So, someone telling me a, you know, that I that I score X in Strava, okay, tell me why. That's the diagnosis. And now tell me how I need to improve. Do I need to do I need more sleep? Do I need to train more on on cardio? Do I need to go down the gym and bulk up? That's the recommendation piece.
That's awesome. So, this really gives us that road mapap for an action plan of really what to do about it next to get those scores up and matured to to get to that growth rate and retention rate expansion rate that we're after.
It's fantastic. And then again, because this is a community-based uh knowledge base, we're able to track what drives good. And so over time those recommendations are actually going to become even more uh pinpointed and I believe more more valuable because we can benchmark against what has historically been successful. Fantastic. And, you know, this is also taking into account uh many many of our um, you know, hundreds of diagnostics that we've done in terms of what we see as a successful maturity stage throughout.
Very cool. Um all right well I know um next section you're walking through is you know when to apply this. Um tell us a little bit about uh h how do we go about applying it?
Sure. And again we've touched on this somewhat in in some of the previous discussion but um don't think of the scoring of the index as a one-and-done. Think of the initial uh analysis as setting our baseline score. So the this is actually an example based on a customer that I'm personally working with at the moment, Sherry. They're about a $200 million ARR organization. They're they're um looking to grow to 500 in the next 3 years. So as a baseline, when we ran the analysis today, they're scoring 5.5. Um, now that 5.5 can be looked at within context of where they should be based on the fact that today they're a $200 million ARR organization. So the the target horizon, the the score they should be achieving today is actually seven. The reason why they're falling short is primarily because of an immature both revenue and GTM model. And so the customer in question has been able to focus on driving or or improving and addressing the shortcomings in their revenue GTM model as they then start to move forward. So over time and this is example data. This doesn't pertain to the customer in question. They are going to be able to track not only their score but how the score changes relative to prior period and also then how that score compares to that target horizon. And we are driving towards quarterly updates. We believe that gives us sufficient uh check-in points to to generate a line that is meaningful enough to proactively course correct.
Very cool. Yeah. Well, um now so thank you for for talk. We have quite a few questions. I'm going to go through a couple of them before we go into this next section if that's okay Dan.
Sure that would be great.
Right. Um so Rob has a question that you know this seems to focus on maturity of the models but is quality of the models and the metrics and the metrics they move.
Great great question. So the ranking criteria does also look at um metrics. So for example in in the the bow tie model we have uh conversion rate time in stage and volume um they are taken into account with regards to uh the scoring. Yes.
Very cool. Um and then Edwin asks you know at which stage of going into a new market would you start making a go-to-market index?
Gosh. to going into a a new market. So, a new market would demand potentially new motions, new GTM strategy. Um, I would score as is today based on the existing markets and then I would rerun the model but plug in our new target market and see what the score came back with. And if it was significantly less, I would use the recommendations to prepare myself for entering that new market. I think you would have to run two scenarios that are sharing and compare the two.
Gotcha. Okay. It's almost like we would interview or diagnose based on current market and do the same thing then for the proposed new market and compare the two. Right. Makes sense. And then last one before I'll let you continue. um you know how does how do you think about it differently between kind of sales-led versus product-led growth for for this analysis?
Um great great question. So we are um we're still evolving the what we call the the the ranking matrix. Our thinking is depending on whether you are a high touch, low touch, PLG, SLG, some of the criteria are arguably different. And so what we're thinking is that that ranking matrix um will probably not probably will evolve over time and there may be slightly different iter uh slightly different versions of it depending on whether you're SLG or PLG. Um I I'm thinking with my technical hat there ultimately it'll be the same ranking system but the as part of the interview process the model will be smart enough to know whether you're a PLG and an SLG and then apply slightly different criteria to to uh to the scoring.
Great. Thank you. All right. Well, tell us a little bit about the uh growth guidance.
All right. So, growth guidance, one of my favorite favorite topics. So, at the uh impact summit in San Francisco in May, we introduced the concept of growth guidance and we are embedding the GTM index into that concept. So growth guidance is essentially ensuring that we align our revenue aspirations with our ability to execute our GTM plan. Um so the way this works is we first of all look at future state from a top-down perspective. So we start actually sharing on the right-hand side and let's say we have an aspiration that five years from now we want to be uh a $500 million ARR organization. So what we do is we work backwards in time. So this is the blue dotted line. We work backwards in time. That gives us a aspirational growth trajectory. Now that's all very well. However, how on earth do we achieve it? Well, step one is we can actually and we we we reverse calculate what is needed from a demand perspective. So from that strategic aspiration we can calculate what is needed from our GTM organization from a a volume perspective from uh a balance of acquisition, retention, expansion, what is needed to achieve that blue dotted line. Now step two is then to diagnose and analyze what is our capability today as a GTM organization. Now by doing that bottom-up we can then compare it to the aspirational top-down and more often than not there's going to be a delta which is the delta between the blue line and the white line on this slide. That delta is what we need to improve it. It's for example, it's more inputs. It's more volume. So that the little bars at the bottom there, Sherry, with the blue uh sub-bars, that is extra stuff we need to generate. Well, again, that doesn't tell us what we need to do in order to achieve that extra stuff. Well, we know the GTM index does that because it pinpoints root cause and tells us the recommendations. So we can apply the GTM index to the strategic blue line, the top line, and we know the deltas. We know what needs to be improved. And the GTM index diagnosis and recommendation dimensions will come back and tell us what we need to invest, when we need to invest it, and where we need to focus. What that does, it drives up not necessarily the blue line. The blue line is what it is. It's an aspirational curve. What it does, it drives up the probability and the confidence of our capability line, the white line, because in a perfect world, we want that white line to to marry up with the blue line. So, we're using the index to drive the two together. Well, to drive our capability to match our aspirations.
You and I talked about this and as a as a former operator and uh whatnot, kind of having that growth guidance help you sleep better at night knowing you're on the right track and getting closer and closer to uh to achieving the uh the targets that you're that you're going after. So, very cool. And um no, I've been a uh a three-times operator now. I've worn CEO and founder hat uh in in all three of those instances. Um, oh how I wish I had the GTM index uh in in a prior life. Um, what I've done here is I've actually retrospectively analyzed uh the journey of um my own organization and then as that organization got acquired and was part of a much larger parent organization. What I wanted to do was critique that journey and see if it matched my own uh my own experience during that time. So I've chunked it into essentially three phases of growth on the left-hand side. Um this was our uh this was our startup phase Sherry. So essentially 0 to 10 million. Um in all honesty we were just trying to escape Earth's gravity. We we were just trying to launch that rocket ship into into space. It was it was largely brute force. Um we we had some informal models in place but uh we we certainly didn't score very highly on the GTM index. In fact, the model came back and scored as as a two representing high risk, which was extremely ironic given the software offering that uh we built to get us to $10 million was actually a uh a risk assessment software tool. What I didn't realize at the time was if we'd have focused more on the compounding part of the business on the right-hand side, we could have actually accelerated our growth um by by about 25%. So it was more of a time improvement than necessarily we would have got to the $10 million eventually at some point. So so it was a it was an acceleration time uh eye opener I think for me once we then became part of a larger organization um I I was part of a journey going from then the 10 to$100 million um we had highly we came in and were highly inconsistent with regards to our revenue model as well as our GTM model with regards to the larger organization. The index came back with a 3.5. And not only that, it came back with a recommendation that my gut already knew and that was um we delayed full integration post-acquisition. Conversely, if we'd have actually accelerated that that integration in in you know taken our own business unit into the larger organization, uh we would have been able to again compound a lot lot faster um than we actually did. Um, so those two indicators were uh sort of hindsight where we could have done better. I think what's really exciting is today the the parent organization in question um is actually now marching from 500 to a billion dollars. Multiple business units, incredibly high maturity across the six models. They're scoring an 8.2 on uh on the GTM index. I think not only is this sustainable growth, they are absolutely uh nailing it with regards to to compounding. So you know the index not only reflects shortcomings on the left-hand side, it reflects goodnesses as you can see on the right-hand side as well.
Very cool. Yeah, some great great real-world examples. Um well, we have a few minutes left for questions which is amazing. Um so I will you know pepper you with them Dan in just a second but I know there is a lot of questions related to you know what the go-to-market diagnostic report with the go-to-market index included looks like um I I have shared that there is some example um reports on or examples on our um our page our website page but it is a really good question so u I'll look at that for a followup and see if we can give some more insight into what some example reports look like to give give some better um better understanding of that. Um and don't don't hesitate to reach out if you you know we're trying to get get as many questions as we can answered right now, but if there are specific ones to your organization or we don't get to them. Um here is Dan and I's direct emails. So feel free to ping us directly about um the advisory service or uh a GTM D or or just questions um on this presentation. Um, very excited to have such a great audience on here, but let me go to a few questions if you're up for them, Dan, to uh to give to give the audience some other insight.
Yeah, let's let's go for it.
So, um, Andre asks, you know, what is the cadence for the qualitative interviews? Um, in terms of quarterly, monthly, how often?
Um so the the qualitative interviews um and indeed the quantitative diagnosis the cadence um we are recommending a minimum of quarterly which is the advisory uh chunk on the right-hand side of the slide. So yeah quarterly.
Excellent. So you can kind of monitor your your uh compounding growth, your your index scores and see with a reasonable time to improve them between uh between measurements. That makes sense. I think the quarterly gives us enough um time with regards to again you know everything is is lagged in in there's a there's a huge lag between extreme left in the bow tie and and right hand side quarterly I think is uh granular enough to highlight that we have a a stall or a potential stall and more importantly fix it.
Amazing. James asks um which there's a few questions around this um on you know at what stage does and and I think this applies to just the go-to-market diagnostic regardless of the index but um would would this make sense like at as a startup stage does this make sense to do or at what what ARR level should they be at before doing a diagnostic?
Great great question. So we've defined uh 12 breakpoints with regards to um a growth stage. For me I think it just goes back to this slide. I would have benefited personally in the zero to 10 million dollar mark knowing I wouldn't have tried to achieve you know the the 10 out of 10 but if it helped me pinpoint my my weaknesses and mathematically helped me I would have I would have used it even in uh late startup mode.
And similarly you know in terms of applicability is uh a few questions around is it does it make a difference between mature markets like the US versus EMEA you maybe you can talk to whether the um whether it addresses worldwide companies.
So I don't think it um I don't think no I don't I don't think it matters. Um, the reason why I'm pausing in my thought is I think you could actually apply this at a motion level. There's no reason why you couldn't apply this by geo as well. Um, but it's certainly not specific to uh um, you know, mature markets versus immature markets. Yeah. If anything, I think it actually helped.
Sorry, Sher. Oh, I was just going to add to it that yeah, I you know, as we we know the go-to-market diagnostic library that we have has especially a lot of EMEA companies included in it. So, I think they're very well represented. Um the other question kind of similarly around applicability is is um a few questions from you know how do how do you think about this and this this definitely applies for just doing a general go-to-market diagnostic but um for if if you have completely different uh segments of customers you're doing you know you have mid-market versus enterprise you recommend separate ones versus versus no um
yeah yes 100%. So I I would again bucket that into or I would bucket into for example uh different GTM motions, different business units, different geos. The um the slice and dice is this is going to sound ridiculous. It's nothing more than slice and dice. I think just understanding out of the gate what we need to slice and dice by is because you want that to be consistent when you do your ongoing uh performance analysis.
Very cool. So actually next time you run this webinar Sher I think I'll do a better job of explaining you can apply this index to any any level of the organization. Yeah. No no worries. Um and yeah one more categorization question and then I'll uh but yeah you Jerome asks this is categorized by ARR. Um is that always the case and and is there um is there other criteria besides that?
Um so today the index is geared towards driving ARR and more specifically is it are you truly nurturing and driving compounding. So yes it is an ARR based uh mechanism today.
Great. Um and Ron I know you're a avid Winning by Designer. I'll um get to your question of um he asks can you explain the AI aspect of the diagnostic is this a product or in this case a part of the service?
Oh great question again in version two of this webinar I should add that up up front. So this is a um a product that we have developed internally at Winning by Design that is then um offered as part of our GTM D uh advisory uh offerings. So this isn't something that uh you would um buy from an app store and and and uh either host or subscribe to a cloud instance yourself. This is something that we've built internally at Winning by Design. Um and then it's part of our advisory practice.
Yeah. And just to differentiate R, I think you're aware we we do have uh consulting services around implementing AI in your own go-to-market which is different than in this case we're we're leveraging AI to help provide you more um insight into an existing product of ours uh on our end. So, but understandably a little confusing between the two. So, amazing. Um, let's see. Let's take one more question. Um, VC asks, you know, how many hours days is required for the interviews?
Um so if you're partnering with us on a GTM D, that interviewing process, the interviewing process to glean the GTM index is part and parcel of what we already do with regards to interviewing. And that that's over it's over sever a couple of weeks elapsed time uh or or calendar time. um but it's probably no more than 10 hours uh true interaction time. But again, as we move towards more of a self-service precursory analysis where um the AI engine will actually uh through a brief interview glean the necessary information to do the analysis. Um again, our goal is to get that down to certainly less than 30 minutes.
Amazing. Very cool. Well, um, you know, happy to stay on and answer any questions for people who want us to go off mute. Um, but I really appreciate everyone joining today. Um this was really interactive. Thank you for all the questions. Um fantastic. And again, if we didn't get to yours or you have additional ones, don't hesitate to reach out to