Global Head of B2B Finance Operations & Order to Cash,
Manager of Fintech Products,
Thank you all for joining our session today, our session is going to be over understanding the necessity of integrated analytics within your Order-to-Cash space. So our session today is actually going to be a little bit interactive. Don’t get scared. It’s gonna be great. But it would be really helpful if we could have everyone move up just a little bit closer to the front so that we can pass the mic around more easily if we need to. So if everyone could just move up to these first few rows, so we’re all a little bit closer together. But just to introduce everyone that will be leading the session today. We have Steve Strong, he presently leads Uber’s B2B order-to-cash and business operations for Uber freight. We have Jay Madduru and he currently is the manager of FinTech products with Uber and we also have two HighRadians with us today. We have Ramana, he is the VP of Product Management. And we also have Avi, he is on our Solution Engineering team. So with that, I’ll go ahead and let Steve kick it off and explain a little bit more.
[1:11] Steve Strong:
Hi, everybody, just a level set. We’re not gonna have slides. So if you want slides and handouts, a lot of takeaways, probably not a decision for you. Just to be clear that one, Okay, there we go. So yeah, well we’re going to do is today just to give you the background, how the session came about, we work pretty closely with HighRadius, we started with the cash app cloud, then we move to the collections cloud, then we move to the credit cloud, then we had to relaunch the collections cloud because our team wasn’t really using it. And now we’re looking at deduction. So again, this isn’t that’s just the background. So this isn’t a pitch HighRadius. It was really to tell you, our next steps with that was… “Hey, we use HighRadius” But Jay, who’s our tech guy, and his team, were actually doing a lot of dashboards. So HighRadius had some dashboards, but we were still using Tableau. And the teams were still doing Excel and other processes outside. So one of the things we wanted to do today was, this isn’t about kind of Uber’s journey.
[2:20] Steve Strong:
It was really, to get feedback from other people, not just Uber, to see what we need as leaders for our org in their products. So from reporting from dashboards from not getting into the nuts and bolts of each one of the clouds, but overall, what do you need as a leader to lead your business. So that’s the intent of this. So, again, if that’s not what you’re looking for, it was kind of a smaller session, we won’t be offended if you want to go somewhere else, because this is really going to be interactive so we can make the products better for us selfishly and for you all. So with that being said, when we first arrived at the conference, one of the things and you saw in the keynote, so she went over this dashboard, a lot of the things we were pushing for were in this product.
So what we were going to do was just spend a few minutes again, this is not a HighRadius, push the products. This has them go over this and see what else we need as leaders. For our org, what do you need in the dashboard, because what I told them is, I don’t want to email my team, I have my credit collections, cash app managers here. I don’t want to email them for data for a dashboard for this, I wanted in one place. So we were kind of shocked when they showed this at the first session. And again, we want to deep dive so think about in the back of your mind. What stuff if you were CEO of HighRadius, what stuff would you have them do now for you? So what’s with that being said, I’ll turn it over.
[3:55] Speaker 2:
Good morning, everyone.
[3:59] Speaker 2:
How do I put it so I’ve been giving this same presentation, but some of you are already there in other keynotes? And also, while we were launching this particular tool like Steve was mentioning, it is more of an interactive session to understand whether this product is useful or how to build on top of it and all those things right, especially from a high-level exec point of view. At the same time, once I just give a high-level overview of this solution, I will also tell three or four points at which we are looking at going forward when our vision or the mature stage of the product, maybe we can discuss that as well.
[4:41] Speaker 2:
So this is something that we’re calling as dotOne performance, this particular solution where there are two important things to this one is how do you get a high-level view drilled down easily to figure out the problem within three clicks you can figure out where exactly is the problem in terms of which customer is not paying my money, for example, and from there you can analyze the entire customer, how he’s doing, how the customer is doing in various other solutions as well so that it becomes easy for you to take actions right. So, let me first get into the Global Performance tab here.
[5:22] Speaker 2:
So if you see this particular dashboard, here you can see there are four different regions. And you can also see, if you scroll down, you can see various metrics across which you can analyze the data points. In this particular metric, which is the past year amount, you can see 18.48 million is a past amount in North America. However, in Europe, the past year amount is about 26.75. If you see this, interestingly, there’s a target mentioned here which says 12.5 million and Europe is at 15.8 million. Meaning that they’ve already crossed their target I mean they have to maintain it 12.5% past year but they are at close to 16% which is they breached the target. So you can see that there is some problem in Europe because they breach the target you can click on this and kind of have a deeper view in terms of different countries. Again if you see Poland is something that the highest amount of past few is present. If you check the open, Poland is again the highest open amount and Portugal is the highest open amount present right.
[6:34] Speaker 2:
So going back to past you, you can see here Poland, you can click again. And you can use this chart to see how the trend has changed over the last one year. And if you see Korean Larsen, which is a fictitious name, it has the highest amount of pasty. Right now within three clicks, you’ve understood that “Okay, Europe is where the target has been breached.” And in Europe, Poland is the country with the highest amount of mastery. And over the Corbin Larson is that particular account where the highest amount of past-due is spending. So this is a drill-down effect. Now once you identify the customer, which is Korean Larsen, in this case, you can go back to the home and you see the customer performance.
[7:26] Steve Strong:
Now, let me pause for one second, how many people deal with customers globally? So, alright, let me ask you a question now that we couldn’t ask her in the main thing. So this is fine, global view polling customer. What if that same customer was let’s just throw out a name Walmart or something. And they weren’t the top in one country, but combined in aggregate they were the top exposure. Can I find that out here is important? That’s important to us. I don’t know how important is it for everyone else? So it’s good that you went down to that level. But what if I want to know global?
[8:04] Speaker 2:
Yes, sure, depending upon the way you want to do it in terms of “Okay if Walmart is located in one place, but they’re doing business across the globe”, if you want that kind of you can have a parent-child relationship, or a subsidiary relationship, all those things needed to populate which exact region.
[8:22] Steve Strong:
Walmart is not paying. Can you go back to the larger again, I told you guys we interact, can you go back for one second? So from this dashboard, if I wanted to see who’s my top global customers from AR and past due, how would I see that?
[8:48] Speaker 2:
Okay, so these are your top customers at a global level.
All right, past due.
[8:54] Speaker 2:
And if the moment you drill down to Europe, you have all the customers with the highest possible in Europe itself, right?
[9:02] Jay Mudduru:
Let me ask the group if this product was in front of you all, what are you really wanting to get out of this? But this view, which is more of a CFO kind of view, be helpful for the different levels of the groups that are here. So if collections had or if your deductions had or if you were cash apps head what kind of views and this would help, please.
[9:38] Steve Strong:
Come around with the mic a little bit.
[9:44] Audience 1:
So I think at a broader level, if I think about a tool like visibility tool like this is, I think there should be two dimensions of this one is what the product already has, which is give me visibility helped me problem solve figuring out my operating challenges so I can take action based on where up where I am today. I think that’s something that the tool is doing and the product will improve and get better at the dimension which I think the other dimension which is equally important is it’s not just about visibility and problem solving, how do I drive a step-change in my performance based on this data?
[10:23] Audience 1:
I think that you cannot keep looking internally. So a second dimension that comes to my mind for a tool like this is, on one hand, you should be able to see your company performance and drill down click down to the last level take actions, you should have an external view to say from, in collections from an APQC or CRF or other benchmarks. How am I doing for my industry? Visa-Versus others and there are many clients HighRadius runs in the same industry. If there is a possibility of how other customers perform instead of getting visibility of how I solve a problem that will allow me to actually think ahead that if I look externally if I look at benchmark data, I’m doing very well. This is my best month of the year so far on my performance. But how do I take it to the best in class? So that’s the feature functionality, which I think will make it?
[11:23] Jay Mudduru:
Absolutely. I think if I just have to summarize what I don’t know, for you, Rajan okay, great. a) Actionability: How am I driving into actionability from here? b) Where are the benchmarks from industry standards, and c) if there are other customers that are on the HighRadius platform? And if you have seen other customers doing better, can you compare our processes versus theirs and see if we can get any productivity increase? And the tool is suggesting as the productivity increase candidate, did I say that right? Okay. Got that.
Great, thank you. That was helpful. Any other? Oh, sorry. Go ahead.
[12:01] Steve Strong:
Yeah, no, just open it up for anyone else. Anything else top of mind before we go on, we’ll keep stopping. But is there something else that hey, I don’t again, this is not a sales pitch this is to say what we all know.
[12:12] Jay Mudduru:
We want to benchmark, it’s something that we have already agreed on to say yes, we wanted. I’m glad you said it. Thank you. Okay. And anybody else on the other side? Okay.
[12:27] Speaker 2:
One of the questions that we were kind of pondering around, specifically with respect to benchmarking is, there are two ways to benchmark one is the company level metrics we can say, DSO is let’s say one example, saying my company let’s say DSO is 40 however, the market benchmark is 30. So I want to reach 30 from 40 that is a company-level metric. Others are analyst or manager level metrics. For example, there are a hundred different collectors. And how is the top 10 percentile of my collectors performing and I want to benchmark the rest of the 90 with that 10% the or across all the customers in HighRadius make of the same industry? How is my collector performing across all different collectors in the US or within HighRadius? So these are the two kinds of benchmarking that we were thinking. But that’s a great point.
[13:23] Jay Mudduru:
Cool. So from a roadmap standpoint, are you guys thinking about benchmarking?
[13:27] Speaker 2:
[13:28] Speaker 2:
And those were surface back into this. Yeah, that’s the idea. So let me ask you another question from an existing customer’s point of view. And please add if you guys have similar problems, when Steve comes in to me and says, “Okay, tell me how much of collections interaction with cash apps, and how much is the cash app’s problems that are surfacing back into collections?” And how is this whole, cross-functional, if you will, or cross-module interaction working? Ask me that question today, if I have to go in, I get what cash apps give me, I get what collections give me, I don’t know how these two are working with each other. So we have to pull all this data out, feed into our analytic systems outside. And we also have to pull logs from the back end to figure out who these people are that are doing these different changes. And then I might be able to give him something. But I don’t even know if it’s accurate enough. I have no way to say if it’s accurate enough because it’s all log data that we are pulling. We don’t have metrics around productivity in short.
[14:32] Speaker 2:
[14:33] Jay Mudduru:
Is that a problem that you also have today? If you’re using all of these products individually, they work for their functions, where do they work with each other?
Yeah, I think one of the live examples that I give Jay again, he’s my tech guy. So I have cash apps and then I have collections. I want to see how many teams are touching an account. Like do I have an account sitting with Cashouts are two days to figure out, is it a short payment? Why is the deductions team touching it is a collector sitting and waiting on it. So those are some of the real business problems that we are trying to solve. How much time in addition to just the metrics, how much time are people spending on the account? Is the data in collections the same as the data in the cash apps do? Is there a timing lag? Is that so? How is the data I guess, again, to summarize, Jay’s thing? Is the data all pulling from the same sources, which is ours? And the second part is productivity. I think it gets into that, how do we get into things like a collector or cash apps or credit productivity?
[15:44] Speaker 2:
So the way we structure, at least in the current roadmap is, first of all, we want to define what is productivity, the business core, right? That definition is what we’re saying as business value metrics, saying DSO is one, productivity is one, number of days taken to resolve a deduction could be another one. So, this is a different definition of business metrics right. Next, the way we want to come to solve this business metrics is first the first layer is the product usage. So like you said, how many people are actually doing using the product in itself? That is one of the major challenges like not all people use the product the way it needs to be used.
[16:28] Speaker 2:
Maybe it can be called it can be correspondences, it can be Exception Handling within the cash app tool, all those things. So once that is measured, the next step is the analyst performance, and that’s where the correlation of different products come into. So who was actually touching which account how many times is it required to touch in the first place? Do we need multiple touchpoints if something is a second layer of, you know the product evolution right?
[16:55] Speaker 2:
Product usage. Next is the analyst’s performance, and then we want to tie it back to our KPIs of the business value. So that’s how we are trying to envision the product as of now.
[17:05] Jay Mudduru:
So Balto problem at the group here, right? So if I were to think about ‘X’ number of things that we have to do to meet certain KPIs, these are the operational side of the metrics, so each function does its own thing. And then they have this cross-functional stuff that they’re doing. So if I take the operational metrics, driving the operational metrics in conjunction with the metrics that we have on the top, which is like a pass-through percentage, for example, right, so how’s my productivity? Or the people that are that we need to work, make work on this platform? Would it be helpful? I think it will be helpful to have a view in the main view, to say you have these three different kinds of data centers or if you will see, “Oh, he’s working.” And they are working with these different groups and here’s how they are: Work is improving productivity because when we look at the performance of our CEO or the VP’s, we want to look at what value they are bringing back to the table. I think that is significant to improve the process downstream. Now, this is not a view that a CFO would want to see, this is not a view that even at one level above, Steve, in our group wants to see, but Steve definitely wants to see that, I thought.
[18:32] Speaker 3:
Does that make sense for anybody here? The other thing that I wanted to ask is if you know how cash apps does do its thing. And then if you are anomalies. Is there a plan to move all the anomalies and any process deficiency triggers to surface those back as an action?
[19:07] Steve Strong:
Again, let me ask the audience something on that just to give. So can you raise your hand how many people are using HighRadius, but also have a process wherein that same cloud via credit collections, cash apps, you have a process where you go into your ERP to complete something? So are you using HighRadius? 100%? Like for cash apps or 100% for credit, or 100% of collections? Can you raise your hand if you’re using it? 100% in your workflow? All right, can you raise your hand if you’re using 75% of your workflow? 50%? How many people are using HighRadius? That’s probably a lot of people are using.
[19:53] Speaker 2:
[19:55] Steve Strong:
So these are things to look out for implementation. Yeah, like we were a year or so ago. Go back to me. So I think what do I need to know? When I’m implementing it? Yeah, let me just give everyone that’s new and looking at it kind of the context, because we dove right into this isn’t working. This isn’t? Yeah, a lot of it does work. Just kind of the philosophy that we gave HighRadius, I’ll say for cash apps, there were some ways and I can get into technical details, but just simply applying cash. They were like, “Yeah, it does this, but then you have to go to your ERP to do these things.” Our approaches. No, we’re not going to our ERP to do anything, you all need to solve it. And we’ve had that more with cash apps, which we’ve also had the most success with just to be balanced on it. Collections is a workflow tool so that they don’t tell you to go to your ERP a lot. But I think for cash apps, and credit we’re kind of going back and forth in the tool.
[20:56] Jay Mudduru:
Outside collections write-offs go through a manual process. Yeah.
[21:00] Steve Strong:
Some people do write-offs in cash apps.
So yeah, so just for the newer people that are evaluating it when you’re going through your blueprinting phase, you definitely want to make sure like map out your process kind of the lessons learned for us to be really pushed to see how much and try to minimize as much as possible. The exceptions are the work you have to do in SAP or Oracle, how many people are SAP? [21:30] And how many people Oracle? [21:33] Yeah, so, probably more how many people are something else other than SAP an Oracle?
[21:39] Jay Mudduru:
Nice and you can you guys tell us what’s going on? Other than SAP and Oracle? Can we find out? Yeah.
[21:56] Audience 1:
Hi, we are on Yardi. It’s a property management company. And we’re looking at.
[22:02] Speaker 2:
You’re not audible ma’am.
[22:05] Audience 1:
Oh like that? Okay, so we are on Yardi. We’re a property management company, and we’re looking at the collections module.
[22:15] Jay Mudduru:
[22:17] Audience 1:
So for the summary, as you mentioned in here, I would agree to havehaving it at the parent level, because we have a tendency to keep the mic go again. Because we have tenants in multiple models, and when we’re looking at the collection, we want to be able to see at the parent level, but how much the total exposure is for the aging?
[22:38] Jay Mudduru:
So how’s integration working today? So you have your ERP system, and then you feed everything into HighRadius.
[22:45] Audience 1:
We don’t have HighRadius yet.
[22:46] Jay Mudduru:
Or they’re looking okay. Okay, so how does it happen today? I mean, where do you get your-
[22:53] Audience 1:
Today for that Cash one. Yardi works well because he has the workflow with the scanning of the checks. right into the system for the collection part, I develop or I got IT to develop a custom table in Yardie. And that’s where we track when you called the tenant the last time you called and what was discussed. And then there’s a report that I got created that has the aging, and then the information from the collections. Got it. And that’s what we use to track our collection. So yardie is efficient on the collection side as to how to track it.
[23:30] Jay Mudduru:
Yeah, I think when you move from that phase to have ideas kind of a system, I think that it’s going to transform your processes a lot. So right now you’re probably tracking all of these like basic trackings.
[23:47] Audience 1:
It is so when you the collection part, like I said, it’s a custom table that we had to develop within Yardi. So then what at the end of each collection day tracks is so user-friendly, manual. They put the collection information in there and populates the table. And then when we have that report, it has the aging information, the collections, who the contact is and phone number, because I don’t want the staff to go to a different location to get the information who to call.
[24:14] Jay Mudduru:
Great. Thank you. Anybody else wants to explain what they are going through. If they’re not part of the standardized or SAP or Oracle side. I guess I saw some hands from here. Or you understand what you guys have gone through?
[24:35] Audience 2:
We’re still in the process of implementing but I think one of one of the areas of concern I have is, we get to the end of the day and we’ve got unresolved transactions. The thought right now is that those get transmitted over to the ERP but then they’re going to be handled in the ERP and not in HighRadius. So that’s not a point of interest for me. How do you keep them in HighRadius but still account for them? From a financial perspective?
[25:10] Jay Mudduru:
So when we go into the implementation tours, we could give you some tidbits on how we designed this. To Steve’s point, our goal was to keep the workflow of anybody that’s working in collections, cash apps, deductions, or credit to stay in HighRadius, and the ERP will just catch up to whatever the work is that is done from an operational standpoint inside the cloud. There’s a reason why we wanted to do that is that we don’t want this sync issue where somebody is doing some work over here, Europe does its own separate thing. These two are out of sync.
The work that these people are doing at the top is getting overridden either by what happened in ERP. And now you have workflows or two different teams that have to collaborate outside of HighRadius or any other system. We went to all of this work between the two systems. It’s crazy to think like that. So once you start thinking about the full workflow inside hire ideas is when you will reach to the point where our problems are problems. If you don’t have that, as a version, I don’t think you will see the problems that we are talking about here. But some of the other metrics that we want to drive, we can’t drive because all of our workflow is inside HighRadius.
[26:29] Jay Mudduru:
So from a product standpoint, what it needs to do for us has gotten from the rudimentary phases or the first initial phases to where we are today. That helps give you an indication of when I think the process and how the implementation happens and how the customers are adopting high res and how much of the process is going to be in HighRadius like Steve’s question 70%, 60%, and 80% will change a lot on what they want to see here. So the sync issues are bigger issues because of 50% processes here. And 50% process, it’s there, the requirements are going to be completely different from what our systems and our requirements are where we are completely over almost 90%. On the HighRadius side. I mean, not even nine, I should say 99%. And that’s it.
[27:13] Steve Strong:
So, we have two minutes. Just want to dive for 30 seconds deeper, which cloud? Are you looking at it? Cash. Okay, for a cash app, and, okay, we looked at that just one thing, I’ll say there’s a workload and as you said, we’ll take it offline. The month end close is always a big thing we had to solve for. Throughout the day, I didn’t mind if work was in process. And it had to go over during the month, that doesn’t matter. But when you get the month in, close, quarter and close, that’s where it matters where things have to sync up. So we’ll chat about that offline on some suggestions that we have like another minute to go. Any questions from the floor.
Again, this was Kind of selfish for us to see what we want to build not just in this product, but reporting for all the clouds. Anything from the new people that, hey, you don’t have a cloud yet, but this is what you want. And it’s actually the perfect time. Because it’s exactly what we did like a year and a half, two years ago. We sat here, we knew what we wanted. And and we were on the other side. Yeah. So anything else? Any dashboards any, like, again, if you are if they were listening to you, if you had sashi zero right now, and you needed one thing and in your cloud or in your implementation, kind of what would it be? Do a quick round.
[28:40] Audience 3:
So my name is Renee, and I’m with Curtis, right? I’m currently evaluating the various tools. So I love the high level view and I look forward to intermediary views, and that’s great. What I would be looking for from a CFO perspective would be “Listen, is this an ABCD condition or not?” I don’t know. No, sometimes it’s a little bit of too much information. So I just want to know, who are my C’s, D’s and F’s. If my A’s are good, and my B’s are okay, then they’re passing, right? But I really am worried about who’s about to fail, and who’s in jeopardy of failure, and highlight those in a very specific way down to drilling down to the reasons and things like that, that we might have some actionable material from.
[29:24] Steve Strong:
Okay, and if I could summarize some of that, too, I don’t want to put words in your mouth. But is it also like a credit risk or some kind of risk? On top of that, or? All right, we’ll talk because that’s one of my selfish things. So again, the reason we’ve been asking this question, they’re taking notes. So that’s why we’re kind of looking at them. And we’re gonna circle back from the feedback from this. So if you have something else for us, I just thought about this after this session, email us because we’re going to be on them pretty heavily for this tool, and for some of the other clouds. There was one more on this side and then we’ll wrap up We’ll be here a few minutes after we just don’t want to keep you too long after.
I think one of the things I’d be interested in as-is from a metrics standpoint is kind of the average days to pay, it’d be interesting to see it kind of in this format. Also, potentially, kind of a cash forecasting. What expectations should we have?
[30:23] Steve Strong:
That’s what I beat this guy up on all the time. And he’s beating them. Yeah. So let’s, let’s wrap, but I gotta touch on that one day to pay one of these because that’s one of the dashboard metrics.
[30:33] Speaker 2:
Yes. So this in this particular dashboard, it is not there, but that is definitely you know, you can just have one of those metrics here.
[30:42] Steve Strong:
Okay, so this just to be clear, these are the metrics like in your version, but we could have days to pay you can have whatever key metric you want there. Okay, these are some of the metrics that we have shown here. But again, these need not be there; you can have any metric that you want to have here. So that it makes more sense and easy to use for you.
Alright, what we’re out of time we’ll take it up one, we’ll have to do one enemy.
[31:08] Steve Strong:
You just got to talk people out to get the other session.
[31:13] Steve Strong:
Let me thank everyone who will be here afterward. Again, this session made you all kind of work more than us. Again, this is a self-serving session for us, but also for you, especially the people evaluating the ones that have the tool. We’re available here afterward. Jay is used to me clipping him because he’s passionate about it. We can come up and have a conversation if you want. We’ll be over here for a few minutes. So thank you. Thank you.
[31:38] Steve Strong:
Last minute. This is good stuff. We will be wanted now. Please. Thank you. Let’s get to work. Thanks, guys.
[0:00] Anchor: Thank you all for joining our session today, our session is going to be over understanding the necessity of integrated analytics within your Order-to-Cash space. So our session today is actually going to be a little bit interactive. Don't get scared. It's gonna be great. But it would be really helpful if we could have everyone move up just a little bit closer to the front so that we can pass the mic around more easily if we need to. So if everyone could just move up to these first few rows, so we're all a little bit closer together. But just to introduce everyone that will be leading the session today. We have Steve Strong, he presently leads Uber's B2B order-to-cash and business operations for Uber freight. We have Jay Madduru and he currently is the manager of FinTech products with Uber and we also have two HighRadians with us today. We have Ramana, he is the VP of Product Management. And we also have Avi, he is on our Solution Engineering team. So with that, I'll go ahead and let Steve kick it off and explain a little bit more. [1:11] Steve Strong: Hi, everybody, just…
Attend this brainstorming workshop facilitated by Uber to come up with several use cases and instances for integrated analytics in O2C.
Problem Statement: How to make the cloud solutions more interconnected, what role can integrated analytics play in the concept of integrated receivables?
HighRadius Integrated Receivables Software Platform is the world's only end-to-end accounts receivable software platform to lower DSO and bad-debt, automate cash posting, speed-up collections, and dispute resolution, and improve team productivity. It leverages RivanaTM Artificial Intelligence for Accounts Receivable to convert receivables faster and more effectively by using machine learning for accurate decision making across both credit and receivable processes and also enables suppliers to digitally connect with buyers via the radiusOneTM network, closing the loop from the supplier accounts receivable process to the buyer accounts payable process. Integrated Receivables have been divided into 6 distinct applications: Credit Software, EIPP Software, Cash Application Software, Deductions Software, Collections Software, and ERP Payment Gateway - covering the entire gamut of credit-to-cash.