What is that thing growing? This is going to be an action-packed episode all around B2B marketing measurement. I would even consider extending the concept to B2B go-to-market measurement even further as things continue to be a lot more integrated inside of GoToMarket. A lot of companies are running hybrid motions with either product-led and sales-led and partnerships and independent sales prospecting.
A variety of GoToMarket motions that need to be measured holistically, need to be able to be able to be looked at as an entire company. We see marketing getting a lot more involved in independent sales prospecting when it comes to email writing and outbound optimization and looking at that data. Sometimes partnerships fall under the sales department, other times ecosystem and partnerships falls under the marketing department. You can see there's an evolution about what's happening in GoToMarket to be a lot more holistic across the entire team, combining all the different GoToMarket teams.
Look at board diving into that. Today our guest, Charlie and Chrissy, the founders of CS2, are leading GoToMarket operations firm. They've coined the term GoToMarket operations. We actually worked on that together several years ago.
To really hammer home the point that a lot of things that happen in marketing operations or historically have happened in marketing operations extend much further into sales force administration and looking at the deal flow across sales. I think the expansion from marketing ops people are very well positioned to take over this new role and go to market ops and look at everything holistically, which is very different than having a RevOps team that does sales commission plans and territory planning and things like that. Looking forward to diving into the founders of CS2, they work with some of the leading B2B SaaS and tech companies in the world like Gong and Drada and Salesloft and have a lot of experience and very complex mature GoToMarket teams and also very great logos and great companies. Charlie and Chrissy, really happy to have you back for this live event.
We had a podcast that was published about three months ago that was a leading podcast in terms of views and listens. Really excited to have you back on the show for this live event. Hey, it's a lot of us. We're going to have you here and then we're going to go through a set of topics and questions.
If you all have specific questions relative to your situation or you want something that we talked about, you want us to dive deeper and you're going to clarify, feel free to drop those in the chat straight away. Stephanie's going to queue them up when we get into questions. If you feel comfortable coming on live, we'd love to have a lot of discussion with you. Otherwise, you can just say, Stephanie, could you ask this question and then we'll queue that up and she'll ask the question for you.
And so with all that, let's get into it. I think the first place to start, Charlie and Chrissy, specifically in your experience, you see a lot of companies and a lot of companies and how they set up their data. You see companies that are doing it really well. You see companies that when they come and start to work with you, they have a lot of issues.
And so we'd love to sort of just talk through what are some of the most common missteps that B2B companies make when it comes to their measurement, their data, their marketing, automation, and sales force infrastructure. Yeah, I can kick this off. I think the first one we really see is quite a high level. It's not so specific around the data models, but it's more around their general approach.
And under investing, I think it's going to be easier than it is. And not really trying to build a scalable data model, one that can be repeatable, one that they can grow with, one that can give them accurate data. And then, due to that under investment, like Chrissy said, we're working with startup scale up B2B tech companies. Once they get the first couple of rounds of funding, they start to grow.
Our R's going up, business getting more complex. They kind of missed that opportunity when they could have set up that foundation at the beginning really well to scale with them. And as the business gets even more and more complex, it gets more complex to start to implement the right tracking, the right reporting, the right data models. So then as they start to get to that point where the board's asking a ton of really complex decisions, they've got marketing team asking on a decision and sales team, now there's a lot of pressure on trying to get the data in a reportable format and data that everyone can trust, but they don't have it.
So then that's when we often get brought in to try and fix everything and get everything working. But I wish the message would just get out there to start earlier to build that foundation before you need it, because the time to build everything, the time to get everything working in CRM, marketing information, all of your tech stack is a lot quicker before that complexity starts to compound. And then a lot of the issues around being able to do like EO, B, reporting, culture reporting, when you start to implement new models and now your data starts from that date, it starts to create all of this friction points for companies that just want to know what's working. And I can't tell you how many companies we start to work with that really big, lots of funding, hundreds of millions of funding, and they can't answer simple decisions about what's working for their marketing programs.
It's crazy that they can even get to that point, like great for them. Got to that point without being able to answer those decisions. But then we get brought in as a massive rush to try and get the new CMOs that just come in. We need to answer these questions, like, any questions to say to Mal, and then we now have to build a long, pretty long time roadmap to help them get there because they hadn't invested in it earlier.
I think also to add on to Charlie's, like, even if they haven't invested that much into their reporting, they might also be looking at their measurement in the wrong way. And a lot of the time, issue across the revenue team, we see a lot of time goes back to lack of alignment. And like a unified data model, which we're talking about today can really be a key alignment maker across the revenue team to get at them looking at the data the same way to be able to report on all of the channels where they're getting, you know, demand from and sharing that set of data across the whole team that they can align on. I think a lot of you have probably been in meetings where sales is coming with one number, marketing is trying to show their influence and it just creates these conversations where it feels very disjointed or marketing has just been reporting on MQLs.
But they're not actually looking at, you know, how can we tie this demand that we're creating all the way back to revenue, which is where sales is usually focused or hanging on the hook. But really the whole revenue team, like many of you are forward thinking, you know, to mention folks and marketers and know that like it's all about revenue for the whole team to achieve against. And that's kind of the key gold metric. But to date, that hasn't been that way.
Like a lot of the times it's just the lowest, easiest hanging fruit from a measurement standpoint. And historically that's just been kind of MQLs, which is kind of arbitrary and can be created with whatever definition they want, where sales is kind of looking at pipeline and revenue and it's not that tie all the way through. So that's a big issue that we see for clients when we come on board and we try to come in and say, well, we need the alignment to be better. Let's have a set of data that the whole team can rally around and use to make better decisions, also just to see what's the performance and not having to have different numbers that they need to fight each other over in board meetings and so forth.
So yeah, we're going to get back to a couple of the other common missteps that people make. But before that, I want to make a little diversion just based on your answers. Like, what are some of the key questions that companies want to answer that they're not able to answer just to give the audience some context around what type of things should we be able to answer what should we have the data to be able to answer? And then I'll also note that it becomes very unproductive in go-to-market team meetings.
We have a pipeline meeting between sales and marketing and SDRs where each of those departments is looking at different data and then you're all reporting on the data. What happens is that you end up making no decisions. Basically, the whole meeting is set around not trusting the data that's presented. It becomes very unproductive and the human capital is invested on.
And then the decisions that are made downstream. So just get back to the question of the topic. What are some of the key questions that companies are trying to answer that they need to answer that you help them get to? I think the biggest one when we start speaking to CMOs, which is usually the kind of key buyer we first talked to, but they're really just trying to get the funds for how is everything that we're doing from a go-to-market strategy in our campaigns and so forth.
How is that turning into revenue? And like I said, they don't have that tied back all the way through. And they want to know what channels also then down from that, which channels as well as campaigns are actually the biggest sources to convert. So when we think about it, like they're sending over different leads based on different actions that they're taking to sales or SDRs and they want to know how are those converting, should we invest in those areas.
And so I think that to start, just being able to tie what they're doing all the way through to revenue, especially see that at a channel and campaign level as well is typically what we hear first. Charlie, come on out onto that. Yeah, I think whenever I think of we think of what I'm going to get into this a little bit today, like different stream of monometrics and multi-touchribution. When it comes to funnel metrics, it's three major types of fundamentals I care about volume, conversion and velocity.
So when you're looking at the bias ages and the signals that you're sending over to sales, so that signal could be a partner of the barrel, it could be a marketing signal, like intent or demo request or whatever signal you're sending up to sales, how many are we getting? What is the conversion rate through all of your buying stages to pipeline and revenue? And how quickly are they moving through the funnel? And that can then tell you which signals are the best to send sales.
So you're not wasting sales time. So many companies out there send everything to sales. The conversion rates through the funnel are like 1%. And now that's 99% of the time of sales is completely wasted and there's sales and not a cheap resource, so you shouldn't be wasting that time.
So try to understand what signals we should be sending to sales to be able to have the highest likelihood of conversion, the highest likelihood of 10% of revenue. That's one piece of it. And then understanding the full buyers journey, so this is more of the multi-touch side of analytics where we're trying to track all of the different touch points across the buyers' journey and then use data models to analyze them. You might want to use a linear split, attribution model, or you might want to look at influence conversion and we can go into some of the imposing cons of all of these.
And attribution can definitely lead to Australia and they're crystals about that a lot from the multi-touch side. But just really trying to understand, we're doing all of this marketing and sales activity, like what actually is working. Particularly now, like back in maybe pre-2020, but particularly over the early couple of first years and 2021, where budgets were really high in companies like a lot of the companies in our industry were getting hundreds of millions of dollars of funding and just throwing a load of money at everything. Maybe they've met us so much, like if things were working or not, but now growth of adult cost is gone and efficient growth is the new trend.
We actually need to know what's working. Are we getting ROI and marketing investments? Are we actually wasting sales time in terms of the signals we're sending to sales? And what shall we actually continue to invest in and what should we cut?
Yeah, and then almost as importantly, we have to be able to know what isn't working. It's important to know what is working, but it's also really important to know what isn't working so we can cut it out because marketing dollars are spent to do things that don't work. SDR time is spent. Sales is working opportunities that close at a very low rate.
And when we think about efficient growth, the cutting out the things that aren't working are the quickest way to start moving in that direction. I also want to double click and just note this concept of volume, conversion, velocity. I think that's a really important thing. Multi-touch attribution is not going to be able to help you get those things.
We need to be looking at multi-touch attribution as one tool, one thing that can give us insights and is good at certain things, but then having a pipeline architecture to be able to measure volume, conversion and velocity in a different way. Before we get into actually how to do this, just want to talk through what most of the companies do today. Most of some people might do that as a best practice that is starting to become outdated in this era. So what are most companies doing today so people can understand maybe I'm doing this right now, maybe this is what I think is the best thing, and then we'll pave the way about the future.
And then also also trying to highlight the flaws or the drawbacks to what most companies try to do to get this done today. Yeah, so I mean, there's so many things here. But one thing I think might be interesting to start with is about pipeline architecture like you talked about when you're trying to get volume, conversion, velocity data from the signals that you're sending to sales and then you're able to see conversion rates from that point and then obviously sales efficiency through the final etc. So back when we first started to do it, about 10 years ago, the data model that everyone was using for this and one context here is that all of our clients use Salesforce as their CRM is what we call this siloed linear data model for pipeline architecture where you're tracking all of your data around the signals on leads and contacts.
And then as your leads turn into contacts, turn into opportunities, some you've got some data on the opportunity of objects in Salesforce, you might have some data in campaigns and sell in the Salesforce and you have all of these data silos to show you that that bias journey from when this is what someone did like a demo request from a Google ad that went to sales, worked them and then moved them through the sales stages at this velocity into pipeline into revenue and that just is impossible to report on when you're tracking everything in these different data silos in your CRM. So and that used to be the way everyone did it. So like many people in this call, you may be seeing when you do this, you have to, you're trying to get a full view of your final data and your pipeline data and you're creating like a lead report and a contact report and an opportunity report or you're trying to stitch all of this data together in Excel. Like my new favorite term is Excel Impix, which is like doing all these backflips and crazy stuff in Excel to just like get one view of just like how all of this is working and maybe, you know, pre series A and you don't have much budget to do things fine.
But like I said, you start to mature and you actually need a robust dataset that everyone can trust. You can't be doing Excel Impix in this unrepeatable way to try and get this full final data and stitching everything together. Sometimes I'll try to stitch together in their BI tool, which is okay. But again, you're stitching in a place outside of the operators system as in the CRM where there's still some reporting requirements in CRM.
So then gets back to Chris's point earlier where sales then pulls the report out of Salesforce, marketing is pulling a report out of their BI tool. They both come to the board meeting with different numbers because the data is being manipulated outside of CRM. So we can go into kind of what to do in a lot more detail, but really the high level here is trying to unify the data into one reportable objects. So you have a record to show you the journey through the final report, as opposed to pulling all of these disparate reports and trying to stitch everything together outside of CRM.
So we can go into a lot more details there, but high level, that's the some of the issues that have been kind of propagated over the last 10 years and a solution to that particular issue. Yeah, I think adding on to the unification, it's not just like the data model, but also unifying the different signals or like channels that you want to report on. So I think historically today teams have been sold this like serious decisions or Marketo has on, you know, up a funnel in the stages and kind of show it very linearly. And a lot of that starts with MQL or LEAM.
Part of the reason why we try to stray away from even calling it funnel, even though everyone in the market still call it funnels, it's hard to fully stray away from it, is the way that we would suggest the reporting is not just looking at an MQL. We actually re-turb it and call it sales ready because we want to ensure that you're capturing in your data, just all of the points in which demand can or someone can enter your like pipeline. And so a lot of the times the data will be missing out on outbound and the funnels that could be started from there, they'll be missing out on partner. So deals being sent from partnerships or your channel and potentially even other sources, maybe they have a whole PLG model, but all that data kind of lives in a disparate system or they haven't figured out a way to blend that into their architecture.
And so only seeing things also started from MQL, you're missing out on a huge part of your business, they're not actually able to then report on all the way through to revenue. And we see that a lot where marketers are just looking at it through that lens. And as we know, people can enter the funnel even just on opportunity, but we still want that included in the reporting and we can dive into some of how we do that. Yeah, just to sort of recap some of the challenges or what people try to do today.
Number one is cross-object reporting. So you take a campaign member report and then try to match it with opportunity data and you're looking at five different objects trying to stitch it altogether. That's one issue. Another issue is that people try to just basically try to copy all the data through.
So they take a lead object and they'll copy the data onto the contact object when it converts and they'll copy it onto the opportunity object when it converts and that has its own challenges. And then lastly, and I want to talk through this in more detail because I actually see a lot of mature companies fall into this trap, which is effectively trying to use multi-touch attribution as a single tool in order to achieve this outcome where you don't get velocity conversion or any of that type of data through multi-touch attribution. So we'd love for you to sort of highlight the key challenges with this method specifically because I'm not exactly sure how we got here, but a lot of companies are totally skipping pipeline architecture. They just basically have their sales stages and then multi-touch attribution for marketing.
It causes constant downstream issues. We'd love to get your perspective there. I think the reason why people do that is kind of a notable reason that they might be the wrong way. But they're doing it for the right reason.
I think what reason why people go a bit off the deep end with multi-touch is and not want to set up just the basic pipeline architecture that's going to help them is that with pipeline architecture, when you think of it as purely a attribution model, which it is because if you think about attribution, it's just saying like one thing led to another and we're trying to relate this thing to this sales outcome, right? It can be slightly multi-touch, but it is more on the single touch side of things. So when you think of the way that we think about it is we're tracking the signal that leads to what we call the tipping point for sales to reach out. So like if you're like demo requesting a paid ad like a hand raiser or an event or a partner or product MQL or PQL, and that leads to sales being to follow up.
And then if you're then using your pipeline architecture only to then say, okay, this is what I want to invest in, that is going to be some bias in that because it's going to say, well, these are the signals, the one signal that led to the sales outcome. And often there's certain signals that could be more correlated with that stage of the buyer's journey for someone to reach out. So that is like one downside in it. It is a simplification of the buyer's journey, kind of like anything is.
But the thing is, they kind of throw the baby out with the bathwater and go, well, the buyer's journey is so complicated, having this more single touch model is useless. But I don't agree with that. I think you have to try and simplify. I'm thinking about it.
I'm thinking about it with data model. It's like the George box quote, all models are wrong, but some are useful. It's like, yeah, we're just trying to create a representation of reality to help you make decisions. So you're saying, yes, this one touch point or this one signal and its relationship to the sales cycle.
But then when they go into the multi-touch side, so they go, okay, I don't want that one to get multi-touch, you're saying every signal potentially in the buyer's journey has a relationship to their sales cycle. So they go completely to the other end of the spectrum where it now says everything is working. So one thing that we always see between the two different types of pipeline architecture multi-touch is quite hard to set up like for some of the complexities that we talked about, but it does give you a more simple data model to help you analyze because you're looking at one signal, you're looking at the sales process, you're looking at conversion rates to the sales process, which is not the part of it. That haven't even really mentioned where it really helps you just optimize the sales process and any bottlenecks within that sales process regardless of what that signal is right.
If you're seeing a low conversion rate between meeting the pipeline or pipeline to close one, that might not even be related to your marketing activities. That might be related to sales training issues, sales and movement issues, process in your sales process. So that pipeline actually gives you all of that data to help optimize that. Then the multi-touch side is going to show you all of the touch points across the buyer's journey.
Basically, depending on the model you use, I tell you everything's working and doesn't give you just one core data set to help you optimize the signal that you really send to sales because that's the stuff that's either wasting sales time or not and all of that sales process side. So you end up with decent information to help you to see relationship between different touch points and revenue, but it's not showing you the relationship between those touch points and the actual sales cycle, which I think is the core piece that you need first before you start playing around with multi-touch attribution. I think the key takeaway here is this is not a binary decision and it's not either or which many people make it that way. It's having different things set up for different purposes and being able to use them in specific ways, pipeline architecture to be able to measure from signal all the way through the sales process and optimize each stage of the process and optimize the marketing activities and the marketing budgets that you use to create signals, which by the way is most of the marketing budget, and then using multi-touch attribution, self-reported attribution, other forms of customer research to look at all the things that happened before, the signal and try and look at that data to say, what are the things that are influencing our buyers to move in market and treating them as two independent processes rather than trying to blend them together, which almost every company does right now.
And so with that, I think it's a good transition point to start looking at the new model and how companies could look at that. And then after that, we'll get into some tactical tips and implementation. I want to make sure and just be conscious because I see questions coming through in the chat that we leave at least 15 minutes of time for questions. So let's move into sort of like what your view of the future and the current new best practices are related to this topic and when it comes to B2B, go to market measurement and marketing measurement.
The approach that we're talking about today, I think we talked about a unified approach or unified go-to-market approach. And I think to get tactically, what we build in such as where clients is we're using within CRM to prefer most of our clients thoughts, Salesforce, we can probably emulate that in different CRMs, but using a custom object to be able to track a journey or buy a journey, starting from when their sales ready all the way through to revenue. I think I saw some questions kind of still kind of like, is this multi-touch attribution, is it not? And so I think first we're going to talk about kind of our core place where we suggest to first measure because a lot of marketing teams are like, where do I start?
Do I start with multi-touch attribution? Do I start with hyper-architecture or historically that's called funnel metrics? It's basically like tracking people through the funnel. But the way that we approach it is just a little bit different.
So it's more unified compared to what has been done historically. So we would start that like what Charlie talked about, what looking at tracking and starting the funnel when someone becomes sales ready, which could be a signal or source that then is when someone should get followed up with by sales or starts to get followed up with by sales. So that could be marketing has demo requests that come through. They want, they say that those are now sales ready, so much to follow up with them.
That can be the signal, a very simple signal and basic and something that you all are probably sending over to sales. And then being able to track that on the custom object, we can see how people are flowing from stage to stage. So that could be from sales ready to working. We try and keep things very basic too in terms of our stages so that the whole revenue team understands like how people flow through your pipeline architecture.
So working all the way to that meeting booked to a pipeline opportunity and then close one revenue. And then we'll also be able to take that source data from where that tipping point or signal was like the demo request, which also could be from a certain channel like paid search, but be able to track that on the custom object record. And the reason why it's beneficial for custom object is we don't have to stitch the leave in contact together. We also have that history.
Someone can have multiple journeys that they've taken. So you're not overriding historical data and all of that lives in your CRM. So you can report back even two or three years ago if you have this running long enough. And then another source that maybe don't get included are say like outbound.
So sales actually decides, oh, I saw that in the news, this company had new funding. I'm going to find a contact and go reach out to them with a custom message. Our infrastructure that we've set up, once that person gets moved to a working stage, that would also then be included in your reporting and you'll be able to track that journey all the way through to revenue. Same thing with a partner deal.
If an opportunity gets created and there's a contact associated, we'll track that through the funnel. And so because of this, you'll have this one data structure or architecture that the whole team can get insights into from channel to the outbound, what you're doing from outbound to inbound marketing events, so forth. But from what started and what sales is falling up on. So the difference between multitouch like Charlie talked about in this is it's more of a getting a sense of what your sales is working or what you're saying over to sales and how that's flowing over through to revenue.
Multitouch is just an understanding of also additional touch points or even pipeline acceleration. But bottom metrics are key because not only do they give you insights into that, you can see bottlenecks for the business. You can see what's not converting from sales ready for marketing to a meeting book. Is there a stall between meeting booked opportunity or the meetings you're generating just core?
And not only does it get you those insights that marketers want to know of like, how are things that they're producing getting turned into revenue, it also can give the whole revenue team insight into their whole sales process, which is and you're creating sales process with the team while you're doing this. So that's why we think it's great for marketing teams to start with this model first and then maybe venture into multitouch attribution. Yeah, the thing I want to make really clear here is this. The different data here is that the old way is track all this data on the standard objects in sales, what's going to be called somewhere.
What we're saying here is you need to abstract the pipeline data, so the signal and pipeline data into a different object, so a different data layer to be able to unify that data in one place. So you don't have to run the lead report, the campaign report, the account report, all of those reports and then do Excel Olympics. Unify that data in CRM and the custom object. Your team still uses the typical object, your sales team is the working leads complex accounts and opportunities, but you're now reporting on a different object which unifies the data and it really allows you to have this cyclical buyer journey working within that data model because when you're only looking at opportunities, contacts and accounts, you can only really report on like lead source like the first journey through the funnel or the last journey through all of these stages.
As everyone knows, right, like a lot of times you might send over some sales, sales might work them, they kick them back to marketing and there's going to be constant, you might just send someone to sales multiple times before it actually gets to close one. And if you're reporting and all of your data architecture is on the standard objects, you're just going to be overwriting all of that data and you're just going to be losing a lot of the data. I mean, some of you have clients when they have it, they might have like a hundred meetings in January and then over time that number actually in the bar chart goes down because someone's come through the cycle and had multiple meetings and they've been reporting on it using the content data. So abstract that into a different data layer as like in a custom object and then you can have a one to many relationship between the person's journey through all of your sales stages and the actual person themselves.
So when sales recycles them or kicks them back to marketing and then they become sales ready again, you know, overwriting a data, just create another record in that data table in the custom object, data table. So now you can retain both journeys. So it's a really important kind of different way of thinking about how you're capturing the buyer's journey that particular using sales for CRM. I've definitely had that happen to me before where like in January report, we had a hundred demo requests in January, but then April rolls around and all of a sudden I'm looking back at our January data.
Now we only have 90 demo requests and then halfway through the year. Now it's down to 80 because what's happening is that the people that had a demo requested something else, the data got overwritten. So it starts taking the historicals back and it puts all of your historical data out of whack. It's like the probably in my opinion, the number one drawback of using this method.
If anyone's ever felt that, it's real. I want to highlight two other key points for marketers and demand-gen people. When we have this data available, it allows us to level up from being a marketer to being a business person by being able to look at the entire sales funnel and say, Hey, the conversion from working to meeting booked used to be 6%, but now it's 3%. If we don't fix this, then our pipeline of revenue is going to go down by 50%.
If we don't have any more volume through it and being able to diagnose issues across the entire go to market, not just looking at our specific marketing campaign or channel that we own. I think there's a big opportunity to level up as marketers and become real go to market operators and go to market professionals. Lastly, I want to highlight this point of the unified model that today, they're sure we have a lot of first party things that are typically viewed as marketing, like form fills and event data and PQL data and event data and things like that. But also what's happening to go to market in a majority of the time where sales is reaching out, sales now is using third party data.
They might be pulling data out of zoom info. They might be getting data from trust radius. They might just be looking at an S1 report or some financial data and reaching out to their enterprise accounts. They might just have a target account list of 50 accounts and they're just going through and recycling those accounts no matter what the signal is.
We need to be able to have insights around all those reasons that sales is reaching out, not just the things that we got sent from marketing. With all that, I actually think that we could go into more topics, but I actually think the most effective and the most valuable for people would be to transition to questions at this point. I'd say what's transition to questions, let's get tactical. Hopefully we can have some back and forth and help people if you're trying to implement this right now.
If you try it and haven't been able to get it to work or if you're facing a specific challenge or want some clarification, everything is open, we'd love to help you as much as we can. Thanks all for being here. All right. Thank you.
Thank you. Thank you. Thanks for having me on. I'm in a public place, which is why I don't have my camera on and I apologize if the audio drops.
My question is about Dynamics 365, which must be used by Microsoft Partners because Microsoft tracks the partners performance remotely. One issue that we've encountered include the fact that it's not particularly sophisticated CRN and when I wanted to implement tracking such as that, loss of C and so on, that was literally just not possible. The answer that I could get out of my colleagues was, you know, you're going to have to do this and you're going to have to. The secondary issue is that because Microsoft gauges the colour of their partners by the percentage of SQLs for SQLs, what ended up happening is we wouldn't enter contacts into CRM unless we were confident that they, well, we wouldn't enter leads, I should say, unless we were confident that they would close.
So I'm not justizing my employer for this, it's too understandable. What they did was they ran a parallel CRM in HubSpot and we called it a marketing CRM and then we had a CRM for Dynamics 365. You can imagine what conflicts and friction that entailed. So is there an answer to this?
Is my question to the hosts? This might be considered a niche use case, but there are thousands of Microsoft partners around the world, so it'd be interesting to know what your angle is to this. Thank you. Yeah, it's not as niche as you think.
I'll pass it over to Charlie and Chris to get away in, but yeah, this is not the first time I've heard about this. Well, first off is great to hear another British accent. So thanks for your question. Honestly, first off in full transparency, all of our clients are Salesforce, so we're definitely not dynamics experts.
The one thing that I would curious to get Chris to get Chris to get on this though. I don't know if your question here is that they don't do it out of the box or it is impossible to do it at all. So I'm curious if potentially there would be a way to actually configure Microsoft dynamic to be able to do this or was the feedback that you got. It can't even build a custom object.
We can't even build automation to track progression through the final and not a dynamic expert to make it easier to build it. I think that's one thing that I would like to add to that. I think within dynamics you can create custom objects. If you're looking internally to build out the similar infrastructure we have, it could be possible.
You would need someone to figure out how to build out the right way. But essentially what you do is once you have the data on your lead record, you can then start associating a custom object record that you can tie data to at a single point in time and create the rules for that. I would say the question that you had around or the comment that you had around the expectations or being gold on the performance from MQL to ask you all and that being low so you're only entering the leads when you know it's going to close. I would say this is a common thing.
Oftentimes we come into organizations and from sales ready or MQL to meeting booked or to opportunity pipeline opportunity created is quite low. But actually this is even separate than our measurement approach but we like to just go in and identify your definition for who should be sales ready and look at which signals that you have been historically sending to sale just might not actually be intense signals. And so you can't wait to just keep nurturing those people, educating them but actually waiting for when there's a true buyer intense signal that's been done to send them, deem them as sales ready and that alone that improvement which we've done for clients has raised their double tripled their conversion rates and they don't actually miss out or have an impact on revenue. I think that's even the thing that happens time and time again, companies like kind of pull back, they clawed back because they're so tied to this concept of volume and they're being on volume and that's a big problem too.
They're being gold on how many MQLs can use and we have these huge SVR teams and we need to feed and that cycle continues to happen and they're still wondering why they're not meeting their goals but they can say, hey, I delivered on what the executives told us to do. So I think as go to market operators, it's a big thing that you can do is to show even if you scale back that volume and improve your conversion rate, how little of an impact it can have on revenue and actually you might find improvements because you're focusing sales on the true intense sources, you're not wasting their time in places where they shouldn't be. And also from a buyer, if you're not ready or you're not really showing like active and tenderly intent, you don't expect to then have like a demo or go straight to sales. You want to probably do some research on your own and turn communities chat with people, have a really awesome newsletter, do events like this where you're sharing expertise.
There's so many other things you can do to convert those into sales ready leads but yeah, we're handing over those really early. So I think it's a lot of education there and also some improvements that you can do up front. So maybe not wait until there's a deal that's going to convert but maybe thinking through and looking out, okay, but what actually is some other intense signals that have converted to date that we can prioritize as well but take the rest out and send them once they actually are showing in time. I'll just add a couple of notes from my perspective, one man's opinion dynamics is definitely an inferior CRM compared to the other available tools, especially for mature businesses.
So it's unfortunate that some companies get forced to use it. I also say that I know for sure companies that are deep Microsoft integration partners that still use Salesforce as a core CRM. So I'm not sure if they're using Salesforce as a CRM and they just have an integration built purely to push data from Salesforce into Microsoft to meet those requirements. But I think depending on how much you need this, that's a potential opportunity.
I mean, if you do that to use Salesforce, not HubSpot for a variety of reasons, but that could be a potential workaround as well. I don't know exactly what they're doing, but I do know for sure that companies that are deep Microsoft partners still use Salesforce CRM. Awesome. Thanks so much Tatiana.
We're going to bring Nancy on next morning. This is one of my favorite sessions every week. So thank you for sharing your advice. Just this morning, I was approached with an exciting opportunity to build a partnership as I go to market channel for a company I very much admire.
Obviously, the measurement will be the contribution to having a dedicated partnership person building up a partner ecosystem will contribute to revenue growth. So I was wondering if you can give some advice how I should go about to make sure that we have the proper infrastructure to track and measure partner. Partner referrals. Yeah.
Okay. Yeah. So much from infrastructure from just a partner perspective and we'll focus on the pipeline architecture side of it where you're probably either going to partner portal set up or there's partner referrals and partners are sending you deals and you're collaborating on those deals. So our recommendation is that you don't do what a lot of companies do, which is you have some of your pipeline architecture workings like marketing in a certain way and then you build this whole new tracking model and method for the partner side where maybe only looking at opportunities or you're only looking at like you create like a whole new custom object just for partner deals or you've got a partner portal kind of running its own thing over off in a silo.
That's where the unification comes in. So kind of the nuts and bolts of it would be what we talked about before with the custom object. So having a new data layer that we're introducing for our pipeline architecture and utilizing and our typical buying stages to be able to track partner referrals like you would, the marketing MQL or like you would a product qualified lead or like you would sells outbound. So when Chrissy mentioned when we think about the start of a final or start of pipeline, a lot of companies think it's just MQL, but we like to remove that.
That's just one type, right? So we call that stage sales ready and you have a partner referral sales ready at that stage. And then we're talking about before the custom object. When someone hit the partner refers, we would then have automation to then create the record in the custom object and then that lifecycle or that actual record related to that partner referral would be tagged as a partner referral at sales ready as opposed to a marketing MQL sales ready for like a demo request.
And then you would use the normal buying stages for sales unless there are like a lot of like nuances that you might have to consider. So then when you look at your reporting, you can just pull the reporting for this object and go, okay, what am I conversion rates for partner referrals versus marketing MQLs versus sales outbound. And all of that is built in that same unified data set so you can compare and contrast and you don't have to maintain a load of infrastructure in another part of your CRM reporting a different way. Everyone's singing up the same hemsheet and it's utilizing the infrastructure that you have.
I'll add on to that where a common problem we see too is that from the partner side, they're thinking about sometimes it's like a company based like, oh, hey, there's this company that's showing interest and that could be really hard because it's just like, oh, it's just an account. Like how are we blending this into our data? So I think the more you can work on kind of facilitating the actual person tied to that company, that could be that point of contact, the better we have a client though where sometimes they don't know yet. Like there's a period of time where they know that there's a company that is interested in their product, but they haven't nailed down the buyer.
I think it sounds like Calciona has this problem too. And what we'll do is that we'll wait until the contact role is then added to the opportunity and we'll still then include that in the custom object reporting. And so, and for everyone, if someone skips the stage, I think that hasn't been clear. We'll back date that data, which sometimes can show some velocity kind of issues, but really your send an opportunity.
There was zero velocity between the other stages, but that also helps without having lumpy data. That's a big also difference that some people have where they're like, oh, I'm talking in MQL. And then to SQL's I'm like, why is my meeting an SQL number bigger than our MQL number? Like, it should, how can I do a conversion rate off of that?
Well, it's likely they're not kind of back dating that data. So that's the whole thing. Like someone can join in at a certain stage, but we're still then back filling the rest of the data and that signal that started that was still got the credit. So I think the more you can tie these partner roles back to a person and get that captured up front, you know, we're partner type companies.
We send over deals. It's way better when we can send them the right person to follow up with. And there's likely is a person there. But if there's not, then if once you add the contact later, our method that we do would still be able to capture that once the contact role is associated.
So I think that should be another one of your requirements. If you need that level of sophistication to account for that, I'd like to follow up with you on this topic a little bit after the webinar today. Is that okay? Of course.
Yeah. It's definitely something that needs a little more than like a five minute conversation. Yeah. There's a lot to unpack.
Sure. Thank you. All right. Up next, we've got a couple of clarifications from the earlier topic.
So I'm going to bring Tony on for those. Hello, team. Appreciate the conversation. Just a quick question going back to one of the early comments, Charlie made.
So it sounds a lot like what you guys are advocating for in terms of collecting touch points, right? And using that to service maybe sales and revenue opportunities. It sounds like you're advocating for building an infrastructure that collects all these touch points similar to multi touch attribution, but then instead of just sitting in a box and assigning random weight and significance to these touch points using some sort of machine learning model to do that process better. Am I hearing that correctly?
Or am I missing pieces? I think there's a little bit of nuance there. So I still think what you're saying there's a bit of a kind of a blending of the multi touch and funnel metrics. So yes, we want to capture all of the touch points.
Yeah, that's true. When we're trying to figure out what is the touch point that we're relating to this custom object to be able to track the signal that led to the sales conversation. We're not using machine learning for that. It's just literally like, get demo requests comes in.
We have rules to say, okay, when a demo request comes in, we send it to sales or partner, a fellow comes in, we send it to sales or you might have lead scoring when they hit the certain threshold that we send it to sales and then we're capturing that signal at that tipping point. There's no machine learning having to do that. But the multi touch attribution side, you can use machine learning to try and understand which of these touch points have had the most influence on the purchasing decision. There are tools out there that will try and do that because as we all know, they could be like 100 touch points before an opportunity is closed.
How do we know like what actually influenced the purchasing decision. So then there's different models like the linear model where you would split the opportunity valued by all of the touch points equally. There's what I call lumpy models where W-shaped models, U-shaped models, whatever, where you're splitting the touch points by certain stages in the bias journey. I particularly like those because I feel like if those touch points are just like random points in time, are they really the touch points that influence the buying decision?
Well, like you said, yeah, I mean, there can be machine learning models added onto this to try and understand that. But there's always going to be kind of volume bias in that much distribution data when you're including all the touch points. Right. So, okay, I appreciate that.
So, the next question would be that when you're creating these rules with something to say on an object, then sort of if this is not methodology, how are you determining what those rules are? I guess one of the biggest issues that I see managing me to be clients is they go through this process of collecting really, really robust. How do you hear about us? The different touch points all the way through, right?
And then they sort of make these arbitrary decisions about what these things mean and what leads to going should be and what thresholds. I mean, it's probably one of the biggest things that I have to unravel every time with my clients is how did we come to the conclusion about what these things mean. So I'm wondering, Chris, maybe this is something that you're doing. I'm looking at Pissetto's website and it might be something that is part of this.
But what is the functional process of setting these standards for if this or that throughout the system? Right? Because most of the time I experience the Chris, you touched on this a lot. It's just sort of arbitrary.
We're like, well, they visited the pricing page twice. Therefore, they're qualified. That's an extreme example. Right?
But how do we go about setting these? How do we begin determining the weights and significance of touch points to make these rules that you're suggesting you build into your process? That's a great question. Go ahead, Chris.
If you don't mind, I'll take a swing at this and you can level on because this is exactly what we're working on. So, the first thing is distinguishing a touch point from a signal. So, a touch point is any digital trackable event and there's going to be lots of them. A signal is we're investing sales resources and sale, human capital to be able to follow up on this specific touch point.
So, that's the distinction. A signal has a much different cost basis than just tracking a touch point. From there, you can then go and use historical data and look back and you're not going to be perfect. Most companies have 20, 30% signal coverage if they have good data, but you can look back historically and connect these signals with these sales outcomes.
And you can use historical data to say these signals are not productive. We're going to stop saying that the sales, these signals are productive. We're going to continue to use them. From there, then you have to make arbitrary decisions around these are the things that we're going to send to sales and these things aren't.
Then you're able to track that data over a period of time, 30, 60, 90 or more. Then you have the full data set. You can look at these with the low performing signals. These are the high performing signals.
These are the things that we're going to send to sales. And then as you introduce a new signal, you go through the same process, signal, and then track it over a period of time. Look at the outcomes and make a decision whether it's happening. And then you can just consistently, I think over time, this will be basically a machine that's consistently looking at performance data and making determinations around what signals should actually go to sales based on the outcomes that are being tracked.
The problem today is that companies do not track the data, but do not track the data holistically. So, it becomes impossible to make the decisions purely based on data, which is why you reference it being more arbitrary. Not sure. Question follow up on that.
Because that's a good point. So, you can see that there's a signal that is not a touch point or a high-intent expression of interest. Every signal is technically a touch point. Every signal is a touch point.
So, again, is a signal now multiple touch points or is a signal? Signals one, one signal. Okay. So, give me an example of a touch point that is a signal and a touch point that is not a signal.
You might decide that an e-book download is a touch point that's not a signal or an e-mail open is a touch point that is not a signal or somebody registered for your event is a touch point but not a signal. So, how do we determine those? My clients ask me to make recommendations for lead scoring weights and what is a buying intent signal? What is not a buying intent signal?
How do I look at what's currently happening and say we should look at e-book downloads as a signal, but we should not look at two or more pricing paid visits as a signal? How do we get to that conclusion? One place to start is are they asking to speak to sales, right? Is it a demo request?
Is it a content? Is it a hand-raiser? Like, usually with our clients it's like, if you want to start a summary, just people ask to speak to sales because they're good signals, right? Then you open up this whole big kind of worms which is like, well, all of these other touch points, are they signals, are they not?
And then where you are in the maturity of your company or the company that I'm working with, you do have to kind of draw a line in the sand and use a bit of common sense and go, if I was the person behind the computer on the other side doing this touch point, would I want to be followed up with sales? And then like Chris said, you create the starting point, it is an evolving thing, you then have the architecture to get the data and then you keep on improving it. What you start with and what you end up with, later could be dramatically different if you have the right data to improve it. Okay, okay, so Chris, don't mean to put you on the spot here, but I just want to ask, keep it going man.
That's a great discussion, yeah. A couple years ago, what I remember of your philosophy was that the only signal, signal, signal is somebody asking to talk to a sales team, at least for complex, to be sass. How has that philosophy changed now that you and the community have sort of gotten more and more into this signal-based selling? Because again, for me, somebody has to functionally work with clients.
It sounds like what we're saying is that there are things that user can do. There's web pages they can visit, essentially, and things they can say in Slack, maybe with keywords, that we can use to surface revenue opportunities for our clients. But we also say that sales cycle is no longer and more complex. The subjective interpretation of funnel analysis insights leads to a fundamental misunderstanding of what compels a purchase decision.
So how do those two things exist and what's functionally happening here beyond revenue attribution, getting added to the future? How do you think about the distribution, getting added to a high-intent expression of interest to create leads going? So first thing is to recognize that it's a spectrum of performance. That a hand-raiser is probably going to be very high in the spectrum, and then other things will go down.
And when you think about performance, that could be conversion, sales productivity, velocity, overall ROI, customer acquisition costs, you have less spectrum of performance where hand-raisers become the highest productivity, typically. But if you look in historical data, many companies, the people that attend webinars, maybe they don't win at 5% like a demo request from signal to win, but you'll win them at 1%, which is a lot better than an e-book download that you win from performance marketing at 0.01% or 1% in 10,000. And so then you have to make a binary decision based on data to say at what point, at what percentage of signal to win does it make sense for our sales team to reach out? Is it 0.8%, is it 2.2%, and you can do that based on your deal size or sales velocity, your allowable customer acquisition costs and other data like that.
And just to recognize that there's a total spectrum and some companies that are saying, hey, we can spend 36 months to acquire a customer to pay back the cost of acquiring a customer might go down to the level where their sales team has to try a thousand times to win one deal. But in other companies, they might say, we need to pay back the cost of acquiring a customer in two months, maybe because they have lower gross margins or for other different business dynamics. And that would most likely force them to only focus on hand raisers. And just to clarify my original perspective, I just think that from a marketing perspective, that it completely changes your mindset when you're focused on getting someone to raise their hand for sales about how you actually execute marketing and not looking for an intermediate point to say, my goal is to get someone to sign up for our webinar.
My goal is to get someone to download an e-book. When you say my goal is to get someone to want to buy to wherever they raised their hand, it just changes the way that you think about and how you execute marketing broadly, which was my original point a couple of years back. Completely great. This is all just marketing too.
Yeah. Are they going to market initiatives like outbound? You want to be able to track in the same day tomorrow outbound and figure out, okay, well, who should sell to be getting outbound and what sequences, et cetera. You might have a product like growth strategy.
So what signals do we have in the product partner? We've talked about partner as well. Those are all signals as well. And a lot of those have the same dynamic where there's like the hand raiser type signal where there's like a true partner referral because their partner has actually found someone wants to buy or the partners found someone who they think they want to buy.
And it's kind of a low intent partner referral that you might want to hand off to an SDR versus AE or have these different business rules behind them. I think also just one final thing to add to Tony for you to think about also to you is to think about fit. And that's where we as a portion to if marketing is trying to bubble up people who are showing signals. I think you think also about fit because a lot of the time if you have a huge amount of people that are showing these signals even raising their hand to talk to sales, there's a potentially good portion of those that might not be a good fit for the company or they don't not show a target account or whatnot.
So you're probably already doing that, but it's like a blend of that too. And that's where you can kind of go down those like maybe other signals where if you have a strong element of fit, then that could still be a good time to reach out and get in touch with sales just because they're just like perfect. You could have a great ABM play across the team to go into that account. So that can be when it's worth it and we'll see better conversion.
Totally totally totally totally. I would love for you guys to I would love for you guys to maybe put together one of these presentations where you walk us through at least a slide by slide of how to build that model from somebody who doesn't have it because again for me it sounds like out of all of this. It's still we have these various touch points that we assigned to account and we need to do some sort of process to determine which are important and which are not and it doesn't involve machine learning. So therefore it probably involves decision making by committee, which is not good.
So there's still a gap in the decision making process here of this philosophy of this approach to measurement like in terms of how to bridge the gap from the philosophy to the functional execution of it that I don't understand and some of my clients don't understand. So I'll tell you what I'll attend an event where you guys can do like here's an example and here's point A and here's point B and here's how we got them there that would be super helpful and something that no one else has taken the time to put together in this space yet. I think we walked in the over maybe we didn't but I think we should walk in a day to do that sometime and make so we were hoping to do a monthly joint event with CS 2 particular around this topic. So stay tuned Tony appreciate you being here.
Hope you're doing well man. Great discussion also where like a couple minutes over so Charlie Chrissy. Thanks for being here. Thank you for sharing your expertise.
Thanks for the people that stayed on a couple minutes over. Hope it was valuable to you. This will get published on the podcast in the coming week or two. You'll also publish on YouTube if you want to re-listen so feel free to reference that if there's a certain point you want to go back and learn.
So appreciate you all being here and we'll see you again for the next event. Thank you everyone.