Your Personal Automated Rev Ops Team | Todd Abbott from InsightSquared

If you work in sales for an enterprise company, you probably have an expert revenue ops team providing you with engaging visual insights into how you succeed, why you fail, and in what areas you should be focusing your attention.

And if you don’t work for an enterprise company… you might find yourself gazing blankly at Excel spreadsheets, wondering what all these numbers mean. This was the experience of Todd Abbott. After working for a large company with excellent resources and support, he found himself in an entirely new situation.

“I went to a startup that didn’t have that, and I was lost. And so that’s when I got exposed to InsightSquared many years ago, simply searching the web, like we all do, looking for a solution to a problem I had.” He couldn’t have predicted that he would go from customer to CEO.

On this edition of UpTech Report, Todd tells the story of how he joined the company, and explains how InsightSquared auto-generates revenue intelligence with interactive reports, forecasting, and even coaching through the use of a virtual assistant.

More information:

As CEO of InsightSquared, Todd Abbott is on a mission to extend the dreaded 16-month tenure of CROs by improving forecast accuracy and delivering deep analytics and insights that pinpoint exactly where and how revenue teams can improve execution.

Abbott has three decades of experience leading go-to-market teams at multinational corporations including Cisco, Seagate, Avaya and most recently Mitel where he served as executive vice president of sales and services.

InsightSquared helps B2B companies take control of the revenue journey. CROs and revenue teams partner with InsightSquared to turn data gaps into opportunities—and wins. Here’s how.

Using machine learning, we automatically capture, integrate and analyze every revenue activity throughout the sales process giving you unprecedented visibility and predictability into your pipeline and quarter.

Then we calculate your winning deal profiles, clearly identifying what great looks like, when to coach, where to drive improvement, and how to create long-term competitive advantages. InsightSquared is your blueprint to revenue growth.

DISCLAIMER: Below is an AI generated transcript. There could be a few typos but it should be at least 90% accurate. Watch video or listen to the podcast for the full experience!

Todd Abbott 0:00
If your value prop is resonating with the customer, the customer is going to respond to your reps, emails, they’re going to schedule the next meeting, they’re going to look at your attachment. But the moment your value prop doesn’t resonate, they’re going to make a personal decision to not invest any more time.

Alexander Ferguson 0:22
Welcome to UpTech Report. This is our apply tech series UpTech Report is sponsored by TeraLeap. Learn how to leverage the power of video at Today, I’m joined by my guest, Todd Abbott, who’s based in Boston, Massachusetts, and he’s the CEO of Insightsquared. Welcome, Todd. Good have you on.

Todd Abbott 0:39
Great to be here. Thank you.

Alexander Ferguson 0:40
Now Insightsquared, your platform is a revenue intelligence platform, you’re focused on forecast accuracy around sales and marketing KPIs. So for those out there, if your CRM or you just responsible for revenue at your organization, this might be a platform you’re going to want to check out. Now, Todd, help me understand kind of where the genesis for Insightsquared began? And how has that evolved of the challenge, the problem that you set out to solve?

Todd Abbott 1:07
Yes, we started 10 years ago, and it was really about serving mostly the small and medium business segment, companies that didn’t have the big ops teams or don’t have the big sales or revenue ops teams, to be able to give them out of the box dashboarding capability on their funnel, and on their sales processes, their execution. You know, we are all very graphically orientated. And so the ability to tell stories and to identify where to focus as a leader, a lot of times, you know, metrics and dashboards help us steer us to where to go focus on the ground. Yeah, you were saying

Alexander Ferguson 1:46
earlier, we were talking about earlier how the visual, we like

Todd Abbott 1:49
visuals, it really having that dashboard, something to look at is so helpful. You know, it goes, it goes back to our early upbringing, if you think about your early children’s books, right, I call them what the big animal picture books, that doesn’t really change. I mean, our our, our mental acuity obviously grows and develops. But that need to be able to see the big animal picture to understand, or to back up and reinforce the words of what it is you’re presenting is extremely powerful, right. And the challenges is that those big animal pictures on the revenue processes, were all fundamentally driven by an ops team that would be this export of your Salesforce data, put it into a big Excel file, do a bunch of manipulation to be able to get the insights report metrics, to be able to report out to boards or to know what to go focus on with your team. It’s a was a very cumbersome process. And so the beginning was to basically replace that export Excel manipulation and to create those insights out of the box.

Alexander Ferguson 2:53
So a lot less manual effort of having to create those insights. Now, your your experience over the past years has been as a CRM, so you’ve been intimately involved in this problem. That’s right.

Todd Abbott 3:06
I mean, I, at one point in my career, I had left a company where I had some 20, sales ops people that were doing all that work for you creating all those reports and the analytics. And I went to, to a startup that didn’t have that, and I was lost. And so that’s when I got exposed to Insightsquared many years ago, simply searching the web like we all do, or looking for a solution to a problem I had. And I was awakened to the fact that there were systems out of the box that I didn’t need that big obstacle. And so I’m sure that an immediate need for me, and I’ve been a customer ever since.

Alexander Ferguson 3:40
And then you’re like, all right, when you saw opportunity to join the team, and now lead as CEO, you saw the core value of what it could provide. Now some of our conversations we dug into is the fact that, you know, current models right now are not designed for salespeople to both see the insights and make it easier or nice for them to have to give the data because it’s like a manual entry and who wants to waste their time or spend time the big money on your salespeople to just do data entry?

Todd Abbott 4:10
Does that right? That’s right. I mean, if you think about the beginnings and the orientation of CRM, at the beginning, was from 2530 years ago, was to be able to get the data off of the reps personal device, because when you lost a rep, you lost all the context and what they were working on. And so companies at that point needed to take control of that. That information was not the reps information, it was the company’s information. And that’s how CRM started. We get that data into the system. And the point there is it was never really designed for the rest. It’s it is evolved to be a critical components in virtually all sales, tech stacks, but it’s really morphed into a database and a workflow engine system. It never really had Dress the lack goes usability and territory management for the rep. And so consequently, keeping information current in the system, it’s a burden to the rep, there’s no return on investment from updating the system, and keeping everything up to date. And so consequently, what happens is, most reps are spending a few hours the night before updating the information before you get in to do a forecast and final review. And inevitably, they don’t get it all. And there’s always things that are not up to date and, and sales ops spends a lot of time chasing people down to get the data so that the CFO can have the good data set to be able to present to their CEO or to their board or for their for their monthly business view, etc. You never seem to have enough data to be able to forecast a forecast

Alexander Ferguson 5:47
accurately, you need that much data, but you never seem to get enough because the people who are inputting it, as you say that the system wasn’t built for them, it was built serums were built for just have it as a database because the people needed it. So it’s like a challenge all the way up the line. That’s right. And if you think

Todd Abbott 6:04
about what what is the data in a revenue process, and if you think about it, kind of in the terms of a manufacturing support, it has become so fine, too, because there’s so much data as products go through to address quality and efficiency. If you think about that, in the revenue process, like we don’t have the data, and what is the data, its engagement, its customer engagement. If If your value prop is resonating with the customer, the customer is going to respond to your reps, emails, they’re going to schedule the next meeting, they’re going to look at your attachments. The moment your value prop doesn’t resonate, they’re going to make a personal decision to not invest any more time. If your value prop resonates, they will invest they will schedule the next meeting, they will open your email. But how do you tell today, if that value prop is stopped resonating, and the deal is gone, cold or potentially even dead, you have to basically rely on the reps judgment and what they will share with you in that interrogation called the forecast model. Right. And so that’s where the model is broken. And it wasn’t until I got exposed to some new technology for the company here as I was doing some consulting having left my last year, okay, that my eyes got open to there’s now technology to get that engagement data in without burdening the rent, because you can’t burn the rep with any more administrative work. So let’s

Alexander Ferguson 7:29
let’s dig into that a little bit more. So I think you said in about 2019, that Insightsquared, you guys acquired a company that had a unique machine learning model, dig into that help me understand that that technology, how it works.

Todd Abbott 7:41
Yeah, so at its core is a is a machine learning engine that basically sits in between your CRM system, and your email calendaring system, because engagements now are all digital engagements. And, and so what you want to be able to do is capture all of the engagement with against all of the contacts in the opportunity to count the email. Now, the first thing we realize is is that reps are terrible about adding contacts, and SES and overlay reps are in the works. So what we’re finding is that most, you probably have about 30% of the contacts that your team is engaging in on a deal in the CRM system. So the first thing we do is go sweep your emailing calendaring system, and identify all the contacts your teams have engaged in over the last 15 months, and and put those contacts into the CRM, we then go back and whip up all of the emails back and forth meetings, file shares, attachments. And so we’re now able to capture all of the engagement. And if you think about a sales process, whether it’s a lead conversion, a new business, or renewal or process, a sales process is about a series of meetings, and converting the customer to the next meeting, until you can finally get enough of the people and decision makers to make a decision. Right? And so a sales process is about what’s the engagement to get to the next meeting. Was the meeting good? Did they stay engaged? What’s the engagement. And so now the systems can keep can give you a quick assessment is this deal on track against an activity profile for a winning deal, and identify when something goes off track? Very promptly. And so what you want to be able to do is identify that and alert the rep to force directed, to be honest about maybe that meeting didn’t go as well, because I can’t get the customer back. And he hasn’t scheduled the next meeting. And so he or she needs a different approach to be able to get them. Right. And so that’s the key to the system engagement.

Alexander Ferguson 9:42
Engagement is the the the data points that you’re looking at is how often are people opening the emails and looking at that now when you’re the machine learning algorithm that you acquired and now I’ve integrated into your system basically looks at the two data points of whoever you emailed, and who they opened as well as what Calendar invites. So that’s how they it knows who’s an ideal contact that should be inputted, it looks at the calendar and says, Oh, you met this person. So that must be a data point. And so let’s pull the email,

Todd Abbott 10:10
very gross level, yes. But then you then take it down to the next level and say, for you engaging, and how many people are you engaging with? So the system has an open ended architecture to identify, as we look at all of the activity in the engagement over time, and numbers of contacts, numbers of questions to all of the things that go into a meeting, you know, when this se is engaged as an improved win rate when the product manager is engaged as an improved way, way. So we’ll we’ll look at all of the analytics as to who is engaged, when how often what’s the frequency, at what level, and we’ll be able to identify what’s the portfolio of activity that best gives you an opportunity to win based upon history. And so what’s critical is you get all of the activity, not just the reps there. But because when we put this system in, what we’re typically seeing is that the amount of activity we put into the CRM record was up by a factor of 10. Now we think about that much data, and 70% more contacts, there’s no way operations can do exports, and try to figure out what those insights are. Right? So we’ve, we’ve taken so much more data into this into this process, that you’re going to have to have a machine learning model, because you also want to be able to slice that data to say, I want to know what the conversion rates are by Rep. by geography, maybe you’ve got different lines of business, it’s going to be different for enterprise sales than it is for commercial sales, and you’ve got different lines of business. So you need a machine learning engine that’s going to give you what is the sales process really look like in each of those segments to be able to know, am I doing individual coaching on these two reps? Or do I have an overall sales execution, sticking points in my sales process where win rate goes down when this competitor gets engaged, or when rate goes down when the zero doesn’t come in until meeting 10? Like, that’s the level of analytics. That’s where we’re going to write. And that’s that’s the science of sales, like we’ve been living in the art of sales to a great extent, where a lot of personal judgment has been applied, going into the quarter, right. And that’s what that’s what metrics are, that’s what certain ones have been, they’ve been historic. And they helped me as a leader, apply better qualitative judgment, for my experience, they helped me feel more confident make better decisions, actually, tomorrow’s dashboards are going to be much more predictive. I can give you a good case in point, we all go into a quarter a month with new I have enough fall coverage to make the number because I typically convert 30%. So I mean, three times. And you may have had three times coverage for the last four or five quarters. But when you go into that quarter, and you have three times do you know what the quality of that flow is, because if you put enough pressure on the team to give you three times coverage, don’t get your three times covered. But how do you know going into this quarter that it might be a different portfolio, right? When I didn’t have all of this analytics, some of what a deal looks like. And I can benchmark every deal on funnel, now you can start to do predictive forecasting, where I would submit to you that tomorrow, you’re not going to be thinking about final conference, because you’re going to rely on analytics from the system to say this funnel might be two times this quarter, but it’s a higher quality, I’m in good shape. There might be three times but it’s in worse quality. And it’s actually not enough. That’s where we’re going.

Alexander Ferguson 13:36
The The idea is that all this data is collected automatically is being able to go through the calendar in the email. So you don’t have to rely on the salespeople or them having to spend their time waste their time on this. It comes into the dashboard, which gives you the nice pretty metrics of visuals that you can look at and be able to make more educated not gut decisions based off of this data. But you’re starting to describe a world where this could provide suggestions on where it should be. Is that actually implemented? Is it saying hey, this this funnel better be aware based off of this data, it actually needs extra coverage? Or are you having to look at that as a as a CRM and make that decision. Shiro

Todd Abbott 14:15
didn’t believe my team when they told me this to be perfectly honest, because I’ve been doing funnel coverage for my whole career. But I will tell you now after over the last six months, I don’t focus and don’t care what funnel coverages because the system gives me a predictive outlook as to what this funnel is going to deliver. And so on an overall gross level, it’s much more accurate than any funnel coverage metric that I might have

Alexander Ferguson 14:44
that fine tuning of how it determines how well the the funnel coverages is working and based off of those interactions, right? We talked about this earlier, or you’re mentioning it’s looking at how many open engagements etc until it turns into a sale so it knows what it should look at? How often is that? Is that changing dynamically? Are you having to manually go in and adjust things? It’s a really good question. So

Todd Abbott 15:07
the first thing we do, which is very unique is we come in, we’ll go back and augment all of the CRM records for the last 15 months, the deals you one closed one and close loss. So we already get a running start as to what your engagement profile looks like. And we can deliver that typically in about three weeks. That that that machine learning model is then run once a week going forward. And we and it’s a revolving five forks, we believe that five quarters is more than enough, if you go longer. sales processes and dynamics change so much that you want to keep it as fresh as possible, we could actually make it longer if you’ve had, if you’re a six to nine month sales process. 15 months wouldn’t be enough. But in general, 15 months covers the vast majority effect, nobody’s asked for more yet. But we have the ability. So we get into Running Start. And then what happens is, is that as you close out more opportunities, as you have more engagements against open opportunities, we’ll rerun the model once or once a week. So it’s continually being refreshed. And you can start to see things move. And I can also look at, show me this my what the sales process look like, since March when COVID? What was it before? What was it after? How is it evolved? So you have the ability to start to look at this in different in different buckets, depending upon what a business issue you’re trying to solve.

Alexander Ferguson 16:32
Now, one of the things we were talking about in a previous setup chat before before interview here is getting multiple silos on on this. So marketing, sales operations, be able to get them all on here, help me understand why would you want everyone on one system? versus everyone just has their own system that they’re working? They’re providing the CRM CRM to the data?

Todd Abbott 16:55
Well, it’s a great, it’s a great question. And it’s something I’ve dealt with I know many of my customers deal with is that, you know, we have grown up in a world where each function has its own text on its own, and therefore its own output relative to our version of data. I refer to it as you know, BYOD, bring your own data. So when you get into the monthly business view, the quarterly business view, marketing has its state, Product Management has its state sale, that’s his finance as as theirs. And oftentimes, the CEO is trying to sit there and figure out like what the data is don’t match. Like, there’s a story from this function, there’s a story from this function, and to a CFO, I submit, that’s the sales function has more dependencies cross functionally than any other function, I need leads at the front end, I need reality analytics, from a product standpoint, I need finances input, revenue ops as input. And yet, I’m often struggling very much as a CFO, the most important in between marketing and sales, like the common debate, I’ve given you a lot of leads, you’re not your guys aren’t following up, you’re giving me a lot of leads, but they’re bad leads. That’s why they’re not following up. And this goes on in every call. And so what you want to be able to do, and this is the trend around the revenue ops function that we’ve seen over the last few years, is you’ve got to break the silos and think of things not functionally not a mark on stack stack, the sales tech stack, but a revenue loss. And the analytics that you want to bring to the table has to ground the cross functional interpretation, and focus. So great Case in point, if I know that, that there are certain inflection points in my sales process, then I can see when a deal is dead, or dying, I need to be able to bring other functions to the table to say, help me with the marketing material with sales pitch to talk track to get that customer back. How do I AV test different versions or if marketing is saying, hey, I’ve got this great content, your guys aren’t using it. Oh, actually, I can see where it’s being used. And it’s being used with the team that is not converting, or conversely, if my best reps are using it. Now I’ve got a built in mechanism for marketing to go to the rest of the sales function and say, Look, these guys convert the most this team doesn’t, then you’re not using this material, like here’s the difference. Same thing with product, right? I’ve had many cases where product thinks that they should never, you should never lose with their product. Well, if I have the data that says, Well, actually, when this competitor gets engaged, our win rate drops in half. I have the data to say, Hey, I’m hate to tell you but we have a competitive positioning challenge against this competitor. Help me and let’s identify a plan. And then let’s see if the conversion rates improve week over week by trying different techniques, different contracts, different material, right? So it is a great way to bring the functions to the table, grounded in data, where are we going to focus to help our sales teams like I always tell people it’s not myself because They report to me as a CRM, like this is our sins. And let’s identify where they’re struggling and what we’re going to do about it to help them improve and have the closed loop analytics to be able to determine are those plans having an impact on them?

Alexander Ferguson 20:15
When it comes to be able to, there’s a plethora of data that you’re both generating that there’s data elsewhere. Can you speak to any type of integrations or API’s or any type of connections as far as the flow of data?

Todd Abbott 20:27
Yeah. So I think what you’re going to find is that today, what we have is, we have all the analytics relative to the electronic engagement, we have conversational intelligence to be able to transcribe the video or audio calls and do analytics on what was said in the meeting. And how effective was that meeting? I think the next step is to start to bring content, like being able to understand when you send material out as free material to a customer, do they open? Where are they spending time on the material. So if you’ve kind of think about it in this way, if I send out a some pre material, and I know that the customer has looked through it, and has spent serious time on the value prop slides, and also cost and it’s forwarded it three times, I’m going to craft that meeting completely different than Converse that with I’ve sent it out, the customer looked at it one second, one second, one second stop halfway through, they didn’t really look at it didn’t fold it, Danny, I’m going to craft that meeting agenda completely differently, if I know that. And so if I also now want to know, when I convert that meeting, how is my team using the presentation material? Where are they spending time on the ones that convert the most focusing on value, and the ones that aren’t going right to price? So knowing how the content is used in the meeting, and then now I’d go after the meeting, I send out more material, or looking at the material? Or did they not even open the email, right? So being able to bring out the customer and your team is using content in the sales process? in that meeting, conversion is the next step. And then on top of that, is to be able to start to bring attempting, how is the customer engaging on the web? are they searching you? are they searching relative data points in the value prop of the technology that you’re selling? And does that engagement in searching of what they’re looking to go get educated? Because everybody is spending time educating themselves on on unmet we know that, but But what are they searching and does the search pattern change after the meeting versus before, which is going to be another indicator of how good of a meeting that was. And so we’re quickly becoming are coming to an era were the science of sales, being able to have data in real time analytics to look at it deal by deal and crop the red to say you might have thought that meeting was good. But look at these characteristics. It wasn’t and you now need to figure out how to get that customer back. And oh, by the way, I’ve got two plays run play beat because that’s going to be the best one I miss ICP. That’s where we’re very close to getting in this in this next era of revenue operations.

Alexander Ferguson 23:11
I’m going to stand for this revenue intelligence, a platform or mindset. Does this like sit on top of for the marketing, product marketing or sales folks, if they’re using Salesforce or HubSpot or Marketo? Or depending on their size? Is this just sit on top of one of those the integrate? How does it How does it It

Todd Abbott 23:31
sits on top? It’s not trying to replace your your, your marketing management platform marchetto or part about or worse or? Worse or HubSpot? Yeah. And or your customer success platform? Right. So those are the platforms that really are you think about Marketo and Salesforce is their data engines. And so we’ll take feeds from both of those systems into that machine learning analytical approach, and be able to put that into the dashboards, to be able to make it actionable, real time, right, like one of you able to look at a funnel, I had four deals that went through meeting for, I want to be able to look to see as a manager as a rep. What so they can

Alexander Ferguson 24:13
also log in and see these insights like who uses this type of what you’re dealing with. Right? So

Todd Abbott 24:19
what you want is the insights to be given to the rep you want to be able to have this system, the machine learning will keep track of everything in the restaurant, and it’ll prompt them. Alright, I don’t prompt them on the health of the deal. It we’ll call it we’ll call Bs, if you’re forecasting the deal, and you haven’t had any engagement for the last three weeks. And that’s a typical with the activity profile. It’s going to call it out to you and it’s going to force you to take accountability for what’s in mind. Right. So the key thing here is what you want to do is give the insights to the rep and drive accountability, but not with the added administrative burden, right because reps already feel like there’s too much administrative burden keeping Salesforce update. Well, we can now do is keep track of all of the data hygiene items in the funnel. This deal just went past it, and found that rep to say, hey, went faster yesterday, let’s fix it. Now, I’ll pull in the fields from Salesforce, you don’t have to log on to Salesforce, open the tab, find the field, I can pull in the specific fields that need to be updated. If it’s a, it’s past due or you had a meeting didn’t throw in the next action won’t pull in the relevant fields. And so we take the administrative burden off the record. So now you can hold them accountable. And you don’t go into a system into a funnel or forecast review frustrated that this is in the wrong place that wasn’t updated. And so I can pump them on sales, process hygiene, and that interrogation that I used to do as a sales manager. Like it, you have everything the customer should this being committed. Or conversely, you’ve got to deal in upside, right? Because we all spend a lot of time trying to figure out what in the upside is really real. Because that’s like the big graveyard, lots of opportunity to check reps, put everything in there. And if you have any risks, how do you figure out what’s really well leveraging machine learning, we can graphically point you right to the deals that have lots of engagement, high confidence to close, you don’t spend any time on the noise. In fact, the system can kind of prompt the rep to say, This shouldn’t even be in this quarter’s funnel, you haven’t heard from them two months, either it’s not a deal or push it but get it out of the phone.

Alexander Ferguson 26:26
I love it. Love it looking forward here. And if you were to provide word of wisdom, insight tip to sorrow in in today’s world that we’re in and headed into when it comes to data intelligence and be able to make smart, accurate decisions and forecasting. Just what kind of insight would you provide?

Todd Abbott 26:47
I think you have to embrace the fact that predictive analytics is, is going to be the difference maker for for success and non success. We technology has been a huge advocate to efficiency and productivity improvements, we haven’t really been able to do it, especially in the nonlinear sales process. But there’s been a lot of good technology in the inside sales EDR the sequencing systems. But in the nonlinear sales process, we’re still very much dependent upon, like, my best advice, and my revelation is trying to get the reps that give you accurate data, either the characteristic is a waste of time, I’ve tried them all, you will not. And if you think you’re getting data, you’re only getting 10 to 15%, you’ve got to get the data in and you need to be thinking about an architecture that’s going to give you the insights without burning yourself out. So the science of sales have come in a huge way technology is now enabled. And so my best advice is embrace it. Because it’s going to get you out of some of the busy work, like no manager likes doing interrogation and inspection. It’s, it’s the least stimulating aspect of your job, like get out of it. Let’s go focus on on having the system do all that. And you can get back to coaching and getting a set of analytics that will give you a much greater degree of predictability. Because the way you survive as a CFO, if you’re going to have a bad quarter, you want to know it in week one of the quarter. It’s, it’s when you go into the quarter anxious, like oh, I’m little bit more exposed. And you’re hoping because you know the expectation is x. And if x doesn’t happen, that’s when your your jobs in jeopardy. And so knowing that the beginning of a quarter, wait a minute, something’s different about the fall, something’s changed. And being able to actually see the data that allows you to stay ahead of the curve and align the cross functional team to what do we do with that?

Alexander Ferguson 28:45
That’s where the the power of technology can shine is being able to take a lot of data act faster and enable you to do the job that you hopefully enjoy doing and are good at better than just the manual data entry. And tracking. Well, I appreciate the insights, no pun intended on the name of it, what you’re able to dig into in this conversation. For those that want to learn more, they can go to right, and then be able to get a demo is that what’s a good first step for them to take when they get there? Yeah,

Todd Abbott 29:18
I mean, definitely go to the sites where you can get some more material and request the demo, you can certainly feel free to reach out to me you can hit me up on LinkedIn or send me a direct email at

Alexander Ferguson 29:34
And you you understand ciros painpoints intimately so you can talk one on one definitely when it comes to that. Thank you again for your time, Todd and everyone. You go to for the full interview and you head to their website for more details. Thanks again for joining us, and we’ll see you on the next episode. That concludes the audio version of this episode. To see the original and more visit our UpTech Report YouTube channel. If you know a tech company, we should interview you can nominate them at a Tech or if you just prefer to listen, make sure you’re subscribed to this series on Apple podcasts, Spotify or your favorite podcasting app.

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