[00:00:00] Rosalyn Santa Elena: Welcome to the revenue engine podcast. I'm your host, Rosalyn Santa Elena. And I am thrilled to bring you the most inspirational stories from revenue, generators, innovators, and disruptors revenue leaders in sales, in marketing. And of course in operations. Together, we will unpack everything that optimizes and powers the Revenue Engine. Are you ready? Let's get to it. 

Imagine being 20 years old, fresh out of college with a computer science degree and coming to the United States with an engineering job at microsoft fast forward twenty years and having led product and engineering teams at companies like microsoft, code.org, Zulily and Amperity and now a successful co-founder and CEO.

Well, this is the story of Mona Amahl the CEO and co-founder of Falcon, the revenue analytics engine that is empowering teams to enable daily. Data-driven decisions. Mona shares her backstory in journey with us. In this episode of the revenue engine podcast, Mona also shares the right way to leverage AI and machine learning, to approach data hygiene, and to make data actionable.

Today's. It's sponsored by outreach.io. Outreach is the first and only engagement and intelligence platform built by revenue innovators for revenue innovators outreach allows you to commit to an accurate sales forecast, replace manual. With real-time guidance and unlock actionable customer intelligence that guides you and your team to win more often.

Traditional tools don't work in a hybrid sales world. Find out why outreach is the right solution at click.outreach.io/revengine. 

So please take a listen and make sure to grab a notebook. You'll want to capture these great insights and advice. So super excited to be here today with Mona Amahl CEO and co-founder of Falcon, falcon.ai is the revenue analytics engine that is creating a way to combine machine learning.

And human intuition to empower professionals, to define, understand, and improve metrics that really matter. So welcome Mona. And thank you so much for joining me. I'm super excited to learn more about your journey and what you're building.

[00:02:42] Mona Akmal: Thank you so much, Rosalyn. It's great to be here 

[00:02:44] Rosalyn Santa Elena: Thank-you. So, I mean, you have, you have had just an amazing career, just amazing career journey, leading product and engineering.

You know, companies like Microsoft. I know you are@code.org. Zulilly appar T you know, All these wonder all these amazing companies before becoming a founder. Um, and so before we maybe jump into what you're doing today, um, can you tell us more about you, your backstory and maybe some of that journey that led, led you to where you are today?

[00:03:13] Mona Akmal: Yeah, absolutely. Um, so I think that, you know, um, I, my. Career background looks a little all over the place. I've done consumer, I've done B2B. I've worked in very big corporate environments, also, uh, you know, teeny tiny little startups for-profits nonprofits. And, uh, I would say the common theme that connects all of these is.

For as long as I can remember, you know, I was that kid who had blend out my entire summer vacation, uh, three months before it happened, it was full of activities. Um, I still have a lot of hobbies. Um, the, the common theme, personal life and my professional life. I'm predominantly motivated by learning curiosity and getting better every day.

Um, it doesn't matter what I'm getting better at. It could be, you know, the ability to, uh, bench press more than I had done before, or it could be going on a hard hike or it could be solving a really hard technical problem or, um, Um, managing a very senior person that I've never managed before. So all of those are avenues for growth and pursuit of excellence.

And I think that ultimately defines the choices that I've made in my life. Um, and yeah, as far as how I got. I started as an engineer at age 20 and Microsoft got hired right out of undergrad from Pakistan. Um, and, uh, you know, just continue to lean into my professional life. I get a lot of joy out of work.

Um, and, uh, here we are. That's 

[00:05:00] Rosalyn Santa Elena: great. Wow. Hired right out of college, 20 years old. It's so young, a lot of folks are still in college. I'm just trying to figure out what they want to do. And you were already building things at Microsoft. 

[00:05:13] Mona Akmal: Now, it wasn't a mad leave, work and start work. So yeah, 

[00:05:21] Rosalyn Santa Elena: I love that. Um, you know, oftentimes, you know, when a business gets started, right, it usually starts, you know, it could be a problem that someone's trying to solve.

Um, you know, or there's some type of, you know, just kind of aha. Type of moment, you know, how did the idea for Falcon develop and you know, what was your vision when you first started out? 

[00:05:38] Mona Akmal: Yeah, so, you know, there's a larger theme here, which is, I think one of the biggest rackets out there right now is the modern data stack.

We are all collecting is stupid amount of data, but when you think about a business users interaction with data on a daily basis, it's predominantly. Um, which is not really actionability. It's more about looking in the rear view mirror and seeing what happened. Right. Uh, and so I've always found that incredibly frustrating that we collect so much data, and yet we don't do a lot with it specifically with.

My, um, aha moment was I had transitioned from working in consumer tech products, which are incredibly data-driven because, you know, when you have a hundred million customers, you're not reaching out to them, like calling them up and, and, and so on. Right. Um, you have to do this through data and you have to do them through automated.

When I switched into B2B at Imparity, I, it was a bit of a culture shock for me because, um, you know, as the head of product, I would sit in meetings with sales and marketing and see the disparity in how we were using data, uh, sales, uh, predominantly, like I said, sales and marketing, looking at three to five metrics top and forecast.

The forecasting is interesting, but forecasting doesn't actually help you hit the numbers that you want. It's mostly telling you whether you're going to or not. And so I care about data being used for daily decision-making and operational rigor. And, uh, it seemed like, uh, you know, there was also an interesting trend emerging in the B2B world, which was.

Um, consumerization of B2B application, how businesses are selling the other businesses, product led growth has become a very real motion. A hybrid product led growth sales motions have also become the norm. And that creates an interesting opportunity for a person like me, because I have lived in both environments and I understand the pros and cons of both.

So I was very excited to bring. Uh, consumer analytics data-driven, um, respective to, uh, the revenue function within a B2B businesses. Uh, because my belief is if. Uh, combine revenue, sales, marketing, and product data, uh, and mine, it for insights that drive daily behavior, not just board decks, um, repeatable revenue, uh, and collaboration across the three teams becomes possible.

Right. That was the big thing to go after 

[00:08:33] Rosalyn Santa Elena: you raised a number of really great points there. And one of the things that. And it really resonates with me is around, you know, we all want more data. We all think we need more data. Right. We collect all of this data, but what the real question is, what are you doing with the data, right?

How are you using the data to drive decisions, but also actionable? And as you said, daily, not just looking at it over, you know, over time or monthly or at the end of the quarter, but understanding what's happening in your business. That's really powerful. I love that. Um, you know, kind of speaking about data and used to talk about, you know, AI, right?

If you think about AI, everyone's talking about AI, right. It's kind of used everywhere these days. And sometimes it almost seems like it's used more as a buzz word right. More than anything else. So when you think about AI, you know, what does AI mean to you and how do you approach 

[00:09:23] Mona Akmal: that at Falcon? Yeah, that's a great question.

And, you know, I think, uh, AI needs to be demystified, um, Uh, having built machine learning applications, uh, pretty much starting about 12 years ago. I can tell you, AI is phenomenal at automating things. Humans already know how to do, uh, in doing them fast. And at scale, AI is not good at telling you insights that you couldn't possibly have gotten yourself.

And, um, and you know, to meet this is actually one of the ways I qualify customers. If they think AI is this magical thing that is going to give them the answer is not the Oracle, right? It's just a way to automate things that a person already knows how to do. Uh, then you can start to apply AI and have reasonable expectations of where AI can be helpful, uh, to an organization.

And so practically speaking, how we use AI in Falcon is exactly that. So, um, A few examples. One, um, we do attribution based on, um, artificial intelligence machine learning algorithms, specifically mark off chain based upon. A human analyst can do this every single day. The fundamental premise being, uh, you know, if a, if a prospect interacted with five different channels, how do you assign credit to them?

Right. And we all do the dumb version, which is as clutch gets 25% of the credit last. Where did that? That's like 25% is just guesswork. It's not data-driven right. A good analyst, uh, can actually go and put in the right weights every day, based on the channel's track record of success, as opposed to its position.

And our customer's journey. Um, if they did that everyday manually, it would probably take them 12 hours of work to get ready for the next day and rinse and repeat. Right. So it's just not scalable by a human. What does Falcon do? We turn that into a Markov model and we basically do the exact process that I've described, but with machine learning, it's automated.

So every channel has a dynamic weight that is assigned and refreshed automatically based on its track record of success. So you end up getting significantly more accurate attribution than you would if you just did a rule-based approach. So that's one example of how we use AI. In fact, Um, I'll give you one more example, which is, you know, if, especially in product led growth companies, if you have, let's say a million, um, uh, users that are on your lowest tier, that can be free, or it can be a minimally paid skew.

How do you figure out from those million users, which 5,000 are real product qualified? Again, you could have, you know, 10 analysts do ad hoc analysis and tell you, okay, this is the list of these users based on certain usage, markers and whatnot, but doing that every single day programmatically and sending those product qualified leads to Marquette or HubSpot, and ideally putting them in automated sequence.

That's what AI can do. Right. Um, and other ways in which we use machine learning and AI in Falcon is finding product qualified leads that are accurate and automatically delivered from start to finish. 

[00:13:08] Rosalyn Santa Elena: Got it. Got it. That's a great way to describe it. It helps. I think it really frames up the understanding of what AI is and what it isn't right.

And how best to leverage it and how to think about it. I love that. Um, so let's talk a little bit about data, right? Because having the right data and the right insights at the right time is always so critical, right. It's sort of the, the north star. Everybody wants to get there, but it's certainly not, you know, it's not easy, right.

To have accurate, comprehensive, real time data available to the right people and keep it clean right. At a point in time. Yes, it's great. And then the next, you know, minute late. Changing, but Falcon helps with this, um, revenue analytics engine. Right. And I was looking at, you know, more about your business and on your website, you talk about, you know, your playbook for revenue growth is hiding in your data.

And I love that. Um, can you, can you share maybe more about this and maybe help our listeners understand why this is just so key, right. To everything that we all want. Right. Which is really that growing more revenue and growing more revenue faster. 

[00:14:08] Mona Akmal: Yup. Absolutely. Um, so I would say there are two distinct things here to consider.

One is on data hygiene, right? I tend to take the same approach to data that I do to my closet, which is if it has not been worn or used in six months, get rid of it. And it, 

[00:14:28] Rosalyn Santa Elena: I love that. Yes. 

[00:14:31] Mona Akmal: Yeah. This is where you start to realize people's real relationship with data. They don't collect data to use it.

They, they collect data almost as an insurance policy to hoard.

First of all, if you want good data hygiene, you have to challenge your mindset and get away from hoarding and move into usage. Right. If you're not using it. And I often ask people because we often get this objection, Hey, you say that, you know, uh, our success playbook for success is hiding in our data.

But what if our data is so messy that it isn't. And then my answer back is great. Then go ahead and do it. If you don't think it is useful enough to use, then why can't you get rid of it? Right. Um, because I think that really drives a, uh, the point I'm trying to make is data will always be messy. Um, if you don't use it, you are in this, uh, chicken and egg cycle, which will never break because when you don't use it, it's going to stay messy when you start using it.

And it's. Everyone has an incentive to, uh, fix it as an example, seek out as one of our customers. And you know, when we first started talking to them, they loved this automated report that we send, which is, um, whenever we see opportunities that are not being worked and are really low in the funnel, it clearly signals a data hygiene issue.

It's just a stale opportunity. It's practically dead. Um, no one's working on it and it says it's in procurement. What the hell? Of course it's not on procurement. Um, but if a manager's job is to go figure all this out and then nag people, we all know it's not going to happen. It'll be like a one-time data cleaning project.

And then, and then it'll be back to the same. But if you have a system which is nagging you and saying, Hey, E you have seven opportunities that are super low in the funnel, and they've seen no activity in the last, uh, you know, 30 days or 15 days. Um, it's an upper out thing, right? Like either close so that you actually get a more accurate representation of what's happening in your pipeline or do something about it.

So my point being DDA is, and will always be messy. There is no such thing as absolutely accurate. Um, if you need automated workflows where you shine a light on where the mess and the data is. And go right to the people that are empowered to fix it in an automated way on a regular basis. Hygiene goes up dramatically and quickly.

Right. Um, the second question you asked, which is the, you know, the playbook for successes hiding in your data. I'll give you an example. One of the reports that we automatically publish, uh, to our customers is a data-driven deal breaker. Um, so, you know, any customer, when they win a very large account, everyone wants to know how did we Bennett trait this account and how did we win it?

Like, we want to figure that out so that we can rinse and repeat as much as by. You can read all the blog posts out there. That'll tell you the ideal touch frequency is blah. And the ideal response rate is blah. None of that actually works in our experience. Businesses have so much variance in, uh, who they're selling to how long their deal cycle is going to be.

What the best way to reach out to prospects is. And so. That instead of looking for insights in third-party data and general wisdom, if you start to do things like let's do a data-driven deal breakdown for every deal above a certain price point that we went and every deal above a certain price point that we lose, you just have to do three of these to start seeing what the patterns are.

And the patterns actually are pretty obvious when you do it. Um, that's just one of, uh, success hiding in your data. I love that. 

[00:18:53] Rosalyn Santa Elena: I like the shining, the light piece of that. I think, um, it's, it's hiding there, right? It is there, but you just need to be able to, um, focus in on the right pieces of the data.

Right. And what, what actually matters. And I think that's one of the challenges that we have, um, You know, being, let's maybe shift gears a tiny bit, you know, being someone with just an incredible background in product, right. You probably have a very unique perspective on product differentiation, you know, in your own platform.

I think you've talked, touched on it a little bit already in some of your examples, but how has this product differentiation really come into play in helping to drive, you know, revenue growth and retention specifically at Falcon? 

[00:19:38] Mona Akmal: Yeah. So. I'll give you a little bit of a background on our team. Uh, we are predominantly engineers and scientists, um, which basically means.

We are compelled to build things in a very generalizable and flexible way, uh, sometimes to a fault, honestly. Um, but what that's created for us is differentiation in that we can reason over any data, right? So. Um, Falcon is not a tool just for sales teams. It is not a tool just for marketing teams. It is not a tool just for customer success teams or sales development teams.

It's a tool for all these teams, because for us being in Salesforce, data is actually as easy as bringing in amplitude or Mixpanel or Bendo data to get product usage signals out of the data. Right. Um, so often what we see is, um, with. A lot of our competitors, they are very focused on one organization within the revenue team.

Uh, we take a contrarian view there, which is in order for companies to hit their revenue goals. They increasingly need to have sales, marketing, and product work off of the same data and the same single source of truth. Yep. Um, Salesforce is a wonderful data store for that, uh, because they have so many integrations, but Salesforce is not a great tool for insight generation and reporting it.

Wasn't built for that. So, All these teams have one unified layer that is reasoning over all the data and finding you your most important opportunities, your most important tasks based on what your data's telling you. Across marketing sales and product. You end up with a collaborative organization.

That's going to win together. I think that's one of our key differentiator. Uh, and you know, I've been given grief by our investors over the years. Pick one, just focus on one. But my, I want these two teams to function like, you know, three fingers on a hand, not separate. And, um, a little bit of consolidation actually goes a very long way to bring these teams together.

And then the second thing is I would say. Again, given our background, we care a lot about putting their own site where the user is, as opposed to insisting that they have to log into. So for instance, a lot of our reports and insights, we don't want you to come into the Falcon UI at all. Like a salesperson, not be spending their time, learning Falcon.

They should be spending their time going chasing revenue. We bring the insight to that email slack, uh, is very important for us and writing data back to outreach, to gong, to Marquetto to HubSpot Salesforce. Um, is really important. So that's, I think another way in which we, uh, we differentiate. Yeah, 

[00:22:48] Rosalyn Santa Elena: yeah. I left that kind of meeting the user where they are, because, you know, to your point that not only the value aspect of it, but also just adoption and usability, right.

The user experience. Great. Thank you. So, you know, you have a chance to work with a number of different companies, you know, what are you seeing in terms of organizations, you know, doing right. And maybe some of those things that are, you know, doing wrong as well when it comes to data and analytics. 

[00:23:15] Mona Akmal: Yeah. So you think a few things that I'm very happy about nicely, our customer customers doing this, though.

I have the saving B. There is a selection bias here, right? I love how data driven a lot of sales development leaders are. And, uh, they are not just looking at results. They're also looking at efficiency of their teams. And, uh, so that I think is wonderful. I would love to see sales leaders pushing for data-driven.

One-on-ones more, uh, we see less of that with the account executive. Um, so I guess the things I see going well, our sales development becoming a lot more data driven and sales development managers really focusing on data-driven coaching, uh, with account executives, I think there's room to improve, um, in other, uh, a few other things I.

And very happy that there are now good standards for how people implement Salesforce and they're not doing crazy things for the most part. Right. Um, so that's, that's great because there's so much that gets lit up when you try to stick within the frameworks that are innately supported in Salesforce, versus when you decide you're going to build everything as custom objects.

Uh, you're like in a world of being down the lines, I think. Wonderful. Um, I would say I'm seeing a lot more engagement across the aisle where marketing wants more visibility in, know what happens to the leads and opportunities that they're creating after they've been created with the sales team. And I see the same thing happening back, uh, where sales wants more visibility and know what's happening top of funnel what's happening within the marketing organization.

Uh, that's a wonderful trend. I still see more animosity there then, then I'd like to see personally, like a little bit less collaboration. Uh, and that's often because they're working off of different data sets. Um, the marketing team is saying we've generated X high quality leads. And the sales team has a completely different view and data actually can help solve that.

He said, he said, or they said, they said, um, but I don't see that happening as much. The last thing that I see am hopeful about, but I think more needs to happen. Yes. All of us had this view, that product led growth was like this very specific thing, which was, you know, you have a free product and it's a bottoms up motion and so on.

And I think the, um, generally the, our customers understanding of product led growth is a lot more evolved than that, which. Every company, regardless of whether you have a free bland or not, regardless of whether you have a bottoms up motion or not is a product led company because the product led companies, they customer led company.

Right. It's all about the city. Um, so I'm seeing that shift, um, but it remains very. Uh, manual and instinct driven, right? How people are identifying product qualified leads, how people are identifying expansion opportunities still seems predominantly human leg, which doesn't scale. And so I would love to see, um, more automation and a higher level of maturity on product led growth, uh, motions with.

It's about the right message at the right time to the right contact and the right. And cannot happen manually. That has to be automated. 

[00:27:08] Rosalyn Santa Elena: Yeah. Yeah. I love that. I love that. Um, okay. So let's, um, let's shift gears a little bit again, you know, as a, as a female leader in tech, you know, in technology, especially.

A woman of color. I'm often asked questions about, you know, breaking barriers being heard, right. And about accelerating career growth, you know, and I think for you as a C-level executive and a founder, you know, what advice do you have maybe for other women who are looking to grow their career and sort of move up that proverbial corporate 

[00:27:39] Mona Akmal: ladder?

Yes. Um, Yeah. So, you know, I would say first and foremost, and this makes me very sad because so many of my beers, uh, that are women have, uh, chosen to bow. It is a difficult journey. You just have to, first of all, I think you just have to accept, and it's very hard to accept what I'm about. As you're going to have to be 10 times better than the average man to get the same opportunity.

It just is. Right. Step one is succeeding is accepting reality for what it is and not how you want it to be. Right. Um, so just accept that. That's true. It's not fair. I wish it was different. But if you're going to continue to get disappointed by that and be surprised by that over and over and over again, you are spending more time, uh, anxious and frustrated and less time making progress.

Right. So acceptance is very important. I just have to be 10 times better period. Right? The next part of that is how can I be 10 times? I have to be more invested in my success than anyone else. Nobody's opinion matters along the way. Other than mine, just get up, put in the work, you know, even on my darkest days, I, I will make a list and I will not stop until that list is done.

And then I feel good that I can at least clean. Success for the day. And honestly, a successful career is not a thing. It's just a sequence of success, right? One day at a time, every day, that's the mantra I live by it. 

[00:29:34] Rosalyn Santa Elena: Oh, I love that. I love that. That's actually, you know, incredible advice because I think people, you know, I can definitely see where people can get frustrated.

Right with the path and then focus all of their energy there versus just accept and move on. Let's let's go ahead and move on and dig deep. I love that. Um, so, you know, as I think about, you know, really the revenue engine in this podcast, you know, I'm always helping others will be able to take some tips and learn how to accelerate revenue growth, right.

Power, that revenue engine. So maybe from your perspective, you know, what are the top couple of things, maybe two or three things that you think all revenue leaders should really be thinking about today? To drive revenue. 

[00:30:14] Mona Akmal: Got it. Uh, yeah. So I would say a few things. One, uh, if you're a revenue leader that believes that you can get away with looking at two dashboards a week, um, you're wrong challenge that you need to know your numbers and look at your data every single day.

That's super important. Right? Um, second do not mistake operational rigor for forecasting. They are very different than. Uh, focus on for you operationalizing your data every day, such that every AAE, every marketer, every sales development rep is having the best possible day everyday and everyday comes.

Right. Um, I see these monthly business reviews and monthly retrospectives. If you look at your information once a month, you only have 12 opportunities to course correct. If you look at your data every day, you have 365 opportunities to course. Correct. Right. Um, so make MTD account and operationalize your data for your AEs, your marketers, and your sales development reps to, um, have the best possible, the most productive day they can have every single day.

And then the third. Your product usage data is a gem. It's like, it drives me insane how much money people pay for 60% of the time anyway, right? The real gems of what your customers love and will buy and will pay you money for is a hiring and their usage of the product. Use that data to drive. Not. What are my expansion ops?

What's my value prop. Who do I reach out to? You know, we all come up with these ICPCs in a vacuum. We talk to five customers and we figure out our ideal customer profile. Your ideal customer profile actually is in your most engaged users. Use that information. Um, they come up with everything from pricing to who you are targeting to, how you are going to target them, what the appropriate outreach to them looks like.

And lastly, focus on automation, uh, wherever you are doing things manually that are done often do them automatically because it drives a lot of efficiency, drives a culture of operational rigor and efficiency. Those would be my. My my wishlist, uh, for, uh, revenue leaders, the bare attention. 

[00:32:55] Rosalyn Santa Elena: That's great.

That's great. Thank you. I think that's super helpful. Um, are there things that maybe, you know, as you look back on your career that you wish maybe you knew earlier, or maybe that you might do differently, if you could, you know, do it all over again? Yeah. I 

[00:33:10] Mona Akmal: think my biggest sort of, I wish I could go back in time and tell myself this is, um, Every person's paths in life.

Is it a successful life? First of all is not necessarily a happy life just to be clear. Um, and you have to figure out very early whether, uh, you want to live a. Uh, happy life or you want to live a successful life. And by success, I don't necessarily mean professional or monetary success. Define what success means to you and then pursue that relentlessly.

And second is. Um, like I was saying, you know, accepting reality is the starting point of winning any game. Uh, you cannot play to win if you disagree with the rules of the game. Um, so you just have to really, really get it's very hard to do in breakfast. Get grounded in your reality, for instance, like applying this to data, right?

Yes. Your data is messy, except that reality. What are you going to do tomorrow about. If you keep rejecting that reality and you keep wishing you were in a different place, guess what? The reality is not changing and you're not making any progress. Um, so I guess that's what I would tell myself. Um, Except reality and start building from that point, you know?

[00:34:40] Rosalyn Santa Elena: Yeah. I love that. I love that. Well, thank you so much for joining me. I'm Mona, but as we wrap up and before I let you go, I always love to ask two things. One, you know, what is that one thing about, about you that others would be surprised? And to what is that one thing that you want everyone to know 

[00:34:59] Mona Akmal: about you?

Ah, interesting. So I would say one thing that people would probably be surprised by is that I'm very, um, I tend to be pretty serious and intense, um, in conversations like this. Uh, um, but. Eh, you know, I pretty much spend most of my time laughing and not taking things too seriously. Like I, there are very few things in life that I think are deal breakers one way or the other.

It's just that I have such conviction when I speak about any topic that. That it, it comes across as a lot more serious than maybe my, my intention is I've been told that as being surprising about me. Uh, and then what is one thing that I would want people to know about me? Um, I think it's the, um, to me.

At thinking analytically and thinking creatively, um, are symbiotic. You know, I hear people saying I'm a creative, I don't understand what that means. Everyone's creative. Um, everyone's analytical and these are like different muscles that, that help you solve problems. So the thing I would like people to know about me is I think my superpower is to be able to.

Creatively think about a space and apply a very analytical perspective to it, to try and solve the problem in the most elegant way possible, whether that's, uh, you know, um, what is the best way to do attribution? What is the best way to make an AI productive or, uh, what's the best way to grow potatoes this summer?

Right? Same fundamentals of life. Yeah. 

[00:36:50] Rosalyn Santa Elena: Got it. Love it. Love it. Well, thank you so much, Mona, for joining me. I really appreciate just all of your insights and your time and just sharing your story. I think there's some incredible information here and I can't wait to just go back and listen to the recording and just learn from you.

So thank you again for being a guest on the podcast. 

[00:37:08] Mona Akmal: Thank you so much, Rosalyn. You were such a. Warm and wonderful person they make it very easy. I appreciate it. 

[00:37:16] Rosalyn Santa Elena: Thank you so much. Thank you.

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