If your customer onboarding process is inefficient, you risk losing new customers or onboarding those with a poor credit history. Join Paul Watters, Director Worldwide Credit & Treasury, MercuryMarine, to learn how they addressed this issue with the help of HighRadius Credit Cloud Solution and provided a better onboarding experience for new customers while also boosting the company’s efficiency.

On Demand Webinar

Shared Services in a Multi-ERP Landscape

Session Summary

If your customer onboarding process is inefficient, you risk losing new customers or onboarding those with a poor credit history. Join Paul Watters, Director Worldwide Credit & Treasury, MercuryMarine, to learn how they addressed this issue with the help of HighRadius Credit Cloud Solution and provided a better onboarding experience for new customers while also boosting the company’s efficiency.


Key Takeaways

Impacts of an ineffective customer onboarding process
  • The longer it takes to onboard a customer, the longer it takes to generate revenue
  • Inconsistent credit review processes between business units result in loss of revenue.
  • Customers struggle to get real-time updates on their credit application status
Leveraging technology to automate the onboarding and credit review process for new customers.
  • Integrating multiple ERP instances into a single source of truth
  • Adapting a centralized interface for all new customer data and records
  • Retrieving credit reports from various sources and populating them into a single repository
  • Automating credit reviews and customer onboarding helped the credit team move towards strategic tasks
MercuryMarine’s approach to change management post solution implementation
  • Appointing a senior credit team member to track end-user adoption rate
  • Working with end-users during testing helped them address potential solution related roadblocks
  • Training teams on how to use the solution post implementation
Improvements in MercuryMarine’s credit process after implementing HighRadius Credit Cloud
  • 67% reduction in new customer onboarding time
  • 1800 new customers onboarded every year
  • Team sizes were optimized to quickly handle new accounts &  periodic reviews.

Paul Watters 0:10:
As Melanie mentioned, my name is Paul Watters and I’m the director of Global Credit and Treasury for MercuryMarine. And what I want to talk to you about today is our experience in implementing the credit cloud product. And some of the things that I think you need to look out for. And one of the key things I want you to remember throughout this presentation is that this business land and sea which reports under the Mercury group is, I would describe their products as commodity products. They are available from many, many different distributors.
And so, you know, our role as providing a service to that business is to ensure that we can get those products out to customers as soon as we possibly can. And then when we came to an onboarding situation for new customers, we were facing challenges that we couldn’t address through our existing processes.
So if we have a look at the summary of where we were before and after, we had an inconsistent credit approach between our business units. You know, I think most you will be aware of if you’ve been involved in credit application processing, manual processes, emails, fax, thankfully not as much of that anymore, chasing around approval from salespeople, pulling information from various sources. So that was the challenge we were facing with a number of applications each year. In fact, close to 2000 each year across the US and Canada.

Paul Watters 1:38:
And we also had difficulty in tracking the application status. You know, regularly, if you’re involved again in that process, you’ll find that if you’re sending information via email, as I say, you’ll be waiting on approval. Customers will typically be calling you to find out what the status of the application is. And having that volume of applications, as I mentioned, around 150 to 200 a month or going around at the same time was a very difficult issue for us to manage and was also costing us revenue as well. The longer it takes us to onboard a customer, then the longer it takes us to start earning revenue.
And as I mentioned, in a commodity business like ours, if they’re not buying from us, then they’re buying from someone else. So after the implementation, we achieved a 67 percent reduction in onboarding ton and we also obviously had considerable productivity improvements as well. So just looking at the agenda, in brief, I’ll talk a little bit about the nature of the business that we’re dealing with here. Some of the challenges we faced, the solutions, some of the processes we went through to determine what the right solution was for us.
Project implementation and some of the lessons we learned and we made mistakes and everyone does, I think, in these types of implementations. And then the results and you know what we think the future looks like with AI. Because this is certainly a changing space for sure. And I think you’ll see a lot of things. A lot of the talks here at the conference are around.

Paul Watters 3:13:
So if you look at MercuryMarine, where we make airport engines for boats, where I am by the Brunswick Corporation, one of the oldest publicly listed companies in the US, and within that business, we have distribution parts and distribution business that is the market leader with around 25 percent share called land and sea. So as I mentioned, this business is providing tens of thousands of SKUs to dealers all over the country. And your distribution price and service are where we live and die. So we need to be treating our customers right.
And we also need to be bringing them on board as soon as we possibly can. So a large customer base, around 18000 customers across us, Canada and Latin America, and high volume, low-value transactions. Typically, our average transaction is in the order of about $200. And we do between four and five hundred million dollars a year in that business. So it’s high volume. Lots of credit cards, reasonably tight margins. And accordingly, we need to be taking cost out and making every opportunity to where to create revenue for the business.
The technology landscape point of view, as I mentioned, we have a number of businesses under MercuryMarine land and sea is one of them, which is a distribution business. We also have other businesses. Atwood Marine, which is based in Michigan. We’ve recently acquired another electrical business in August of last year called Power Products, and we have a range of disparity pieces across those businesses.
So we had to operate on versions of Oracle. We also have an ERP that I think most you will have never heard of called kopeks that were written in the mid-70s. So I think you’re getting an idea for our technology landscape. And also within Garlick, we also have a safe manufacturing business. We have another product, another program in as well called Momentum Price.
So a lot of different programs across the business. In addition to that land and sea, we have an IS-400 version. So if you’re familiar with the green screen, then you’ll be familiar with the IS-400. So before we get going, just a poll question here and put your hands up, answer via your app and so on. Have a think about some of the top reasons why your customer applications are delayed. It may be manager approval that you’re waiting on, perhaps back and forth for customers for missing information.
This was a key area for us where we weren’t getting sales tax exemption certificates. We might have been getting tired, the reference information we needed, etc. Downloading credit reports. It can be another pain point and also entering data in spreadsheets and calculating credit scores. So the typical process for a new application in our world was multiple points of data entry. We’d have a customer who would complete an application for us. We would then enter that data into a spreadsheet.

Paul Watters 06:14:
We’d go into it again into Experian for our credit report, and then we would go into the data again into ERP. This process leads to potential errors being made within the application process. So we wanted to take a lot of that workout and essentially put that work onto our customer where they’re completing the actual application and everything else flows through and aggregates data where it needs to. So some of the challenges were mentioned in customer onboarding and also in our periodic reviews.
You can imagine trying to periodically review 18000 customers each year. That was a challenge for us. And to be honest, we weren’t compliant with what we needed to be. So we needed a better way of doing this where we could score those 18000 customers without employing an army of people to do it. So if you have a look, we’ll have a look. Firstly, their new application process and the slide looks busy because the process was disjointed and difficult for us.

Paul Watters 07:13:
So on the left here, manual entry of data collecting from emails, paper sources, we would have people who would mail in their application to us. You know, maybe it came in on the fax machine. Sometimes it got lost, sometimes it didn’t. So multiple sources coming from the original customer there. Then we would receive the information. We would obviously aggregate the information together with credit reports, trade references, and financials. If the case required potentially personal guarantees, again, depending on the exposure and we would try and bring that altogether within the one place which obviously we had difficulty doing, given we didn’t have a system to do that.
Then after we had that through or I guess in the case of the sales manager before we commenced, we needed to get approval for that appointment. Does our salesperson know who this customer is? Have we validated that they have a shopfront and they are a bona fide business because we don’t sell to consumers? So our credit analyst would obviously need to consider all of the information before them as well. And then in there, depending on the situations, a credit manager would also need to sign off on that application, too. So that is just getting us to the point of approval for the application. After that or during those processes, we would update spreadsheets along the way with statuses.
As I mentioned, we would break down multiple times and then assuming that the application was ultimately approved, we would then go and reallocate that data into the IS-400 system. So you know, so far as I’m concerned, duplicating all of that data entry is one of the most low-value activities. So I think we performed as credit people. So we wanted to get away from doing that. And also to have a product or an application that more befitted the size and market-leading position of our business. So long to onboard new customers. As I mentioned earlier, about 150 applications a month.
We are a seasonal business based in Florida and in 11 other locations around the country. And so we need to be processing those applications promptly. We had errors due to manual data entry and multiple manual data entry. And also, you know, a lot of you’ll be familiar with the back and forth that goes on, as I mentioned before, about trying to get information from customers have either forgotten to include the information or don’t want to provide it to you. So we had around four and a half days plus for new customers.
We had many that went out to two and three weeks. You know, I mentioned earlier our average transaction varies around $200. So for most customers, our credit lines were sitting under $5000. So we don’t have thousands of customers. A whole lot of work at that front end because with a broad spread of risk on thousands of low-value accounts, it just doesn’t make sense for us. So now looking at periodic credit reviews I mentioned before, we have around 18000 customers, probably 14000 of those customers are active within any given year. And we had similar issues to what we do with the new application process.

Paul Watters 10:32:
There’s an argument that perhaps it wasn’t quite as complex, but we had a need to aggregate data from our 400 pull credit reports. Depending on the history, we might check trade references and also depending on exposure as well as how much they were spending with us. We would update spreadsheets again. We would try and put that through to the appropriate approval hierarchy. But often, as I said earlier in the piece, we were non-compliant with our need to assess those customers. Are there every year for customers over a certain credit line? Very. Or every other year for customers.
The majority of our customers are under a certain credit limit. So that was an issue for us. Again, trying to pool all that information together. And I think one of the key benefits of the product that we use is the ability to pull information together. That’s one of the key areas of benefit that we saw in a high volume application and review environment. So we’re unable to cover all of those reviews. We credit other portals manually, as I mentioned, manual paper-based processes. And quite often our credit analyst says, “I don’t have the time to do my reviews at the moment, not with the number of customers I have”. So we needed to find a way to address that challenge.
So we needed to find a way to address that challenge. So as we looked at solutions, we had a look at a number of providers, including BestTrained, I think GetPaid, has a product in this space, HighRadius has a product in this space as well. And I think we looked at a couple of others as well, and they ranged in quality from very poor to very good as we were looking through for the solution we wanted. We were also conscious as well of what other modules we might want to look to in a shared service or centralized environment in the future.
You saw earlier we have dated antiquated legacy systems within our business and it’s our strategy to try and bring all that information together into one place, but also to have a system that will talk to each other. This is one of our challenges at the moment across these four businesses that we operate in the US. We have many common customers, but we have single people delivering with HP within each of those businesses. So we might have a customer who is past you in three different businesses and we have three different people calling that customer, that same customer. So we were conscious.
After we go through this periodic review process and assuming we move to collections and other software in the future, how can we get systems that talk to each other? Because that’s one of our major challenges today. So ultimately, we obviously pick the HighRadius product. If you take a look at the right-hand side, first you can think about this is the detail we get through really for our new applications and data providers. We mentioned, as I mentioned, pull Equifax here. We pull Experian and other parts of the way we do business. And we need to aggregate that information in.

Paul Watters 13:49:
We also need to produce a credit score for ourselves as well. We have a system whereby if it’s above a certain amount, the system will automatically approve the application. If it’s below a certain amount, it’s going to automatically decline. And that’s probably 90 percent of the applications we receive. Anything else that sits in the middle, we take another look at from a credit scoring point of view. So we pull in information like, how long have you been in business? What credit score do you have? Financial information potentially in trade reference information as well as to produce that score at the end of the day.
So we’re trying to aggregate that information that’s coming in from other data providers, from our customer, from trade references, from financial, from bank references where the case might require as well. We then want to pull information into one place to score it if we have it in a spreadsheet. We can still score, but we used to have approximately 25 columns across that spreadsheet to be doing at making our decision process. And we also have a workflow that we need to do as well. So, you know, where we have applications, where a director of credit or whether a manager within the business needs to sign off or potentially someone within our corporate office needs to sign off. We were chasing around e-mails and so on to try and get that to happen in a timely manner. So it was causing frustration to our salespeople, our customers, our credit people as well.
So in terms of the left here of our existing applications as well, when we’re doing periodic reviews. We needed to pull together information from external sources again from our ERP. We wanted to pull in information about how long they’d been trading with us. We wanted to point out information about their data. So what they peak exposure was during the year. Again, we need to aggregate all that data in one place so that we can do a periodic credit review efficiently and be able to meet the expectations in terms of compliance around that process.
So one of the challenges we had and with the new application process was we had a paper application that hadn’t really changed in 20 years. Nobody had really reviewed the application. For what information is that? I really need to make a decision on an account that is typically lower than a $5000 credit limit. So our application and feel free to have a look at this if you want to see it, later on, you can get it through land and say dot net. You just click on our website and it’ll bring up an application that is not dissimilar to the one you see here. So please don’t submit it.

Paul Watters 16:41:
So we wanted to try and pull out some of the information from that form that we really don’t use. And one example of that was bank references. We’re asking customers for a bank reference for a credit line of five or ten thousand dollars. Most of those customers didn’t complete it. And we wanted to focus on the information that we really need to make a credit decision. So I guess the point is you can see along the bottom here that there are five tabs. When we started this process, we started by pulling out the information from each of the subheadings on our existing paper application. What we ended up with is 14 tabs in the application. Now, anybody who’s applying for an account of the very few that we’re offering is going to run the other way when they say 14 tabs.
So what we had to do was we had to pull that information together and to rethink the layout because it doesn’t work the same way on a piece of paper as it does on an online form. And again, as I mentioned, we needed to pull out some of the information that was really not necessary for making a decision in a credit application. You can see up on the top left, we have branded as well. We brand the application for our businesses.
We have plans with Marine Group in Canada. We have land and sea across the US, Latin America, and Canada as well. So those businesses in Canada are EastWest locations and focus. So we wanted to brand it. You’ll also notice here as well that we have a region dropdown box. The second lesson we learned, if we did this again, which we can at some point in the future, that region drop-down box asks the customer to complete whether they’re from the US, Canada or other. Now there are other sections on the application below this right here where there are different rules for sales tax.

Paul Watters 18:34:
And in the case of Canada, I think it’s British Columbia tax that is driven by this box here. So it works okay. Our customers haven’t complained about it, but if I had my time again, we would do one for the US, one for Canada and one for Latin America. Because on this page that you see here, there’s only one ability to drive everything else that comes down afterward. And that’s that region box. And we would have liked greater flexibility in that. So we would have a single application for each region was one of the lessons we learned from our implementation.
New credit applications in the system. So you can see it there. This is the work table that you’re looking at in the system. You can see periodic reviews here. You can see new applications in another tab. And this is the work table that our credit analyst uses to process those eighteen hundred to 2000 new applications a year. It’s a similar worktable to what they used to conduct periodic credit reviews as well.
I mentioned earlier integration with multiple credit agencies. You can see along the bottom here. Experian US, if we wanted to pull a consumer report with the consent of our consumer, we have the ability to do that as well. And then we have an Equifax as well. So what we do is we trigger off a work-flow that starts. So we have multiple work-flows. We can have a work-flow to score customers with financial. We have another work-flow for a customer without finances. We can have a different work-flow for a customer within Canada. And what that does is it drives not only the scoring, but it also drives where the credit reports are pulled from. So we can also assign for review and check the approval status in a single view.
So I mentioned to you a little earlier with our new applications, we had customers calling into us. Nobody knew where the application was. People are searching through their email. Did I send it to Bob Ward and John to sign it off? Nobody can remember in an environment where you’re processing applications at the numbers we do.
So this facility here not only provides us with a constant reminder of the periodic credit reviews we need to do, but we’re able to give answers to people, both internal and external customers in real-time about where their application is. We’re then able to follow that up through email and correspondence as well to those two, to internal customers, in particular, to chase them down for the information that we need to complete the application and to decide the application. So there’s another poll question coming up here.
The reason I wanted to include this was I think this really encapsulates the benefits of products not only like HighRadius but other people who are offering this product in the market. What benefits that it really provides. So I want you to all wake up for a minute and to put your hands up for a moment. I’m not even going to ask you to stand up. So I want you to start with your hands up. Then as we go through each the poll questions, I want you to keep your hand up unless the answer is no.
So do you get a customer or a credit application when you open a new account? Do you get (this can sometimes be old times), do you get financing? Do you get tired and bank references? Do you get credit reports? Does your system aggregate the information together in one place? Does it automatically score and decide the application and does it apply completed account information to your ERP? That’s part of the process that we implemented, all of the information we put in here comes to every update. So if you look around you now, there’s not one person and most people’s hands were down after trade and bank references in this room who has the ability there to put all of this together.
Now, in this instance, this relies on a high volume of applications. If I was opening 10 accounts a month, this system doesn’t make any sense. If I have high numbers of existing customers or I have high numbers of new applications, the ability to pull all this information together, to decide it, to score it and to send it to my ERP overnight for setup is something that we just couldn’t do with our existing process. And that’s one of the key benefits of this type of software. So in terms of project implementation, I’ve already talked a little earlier about a couple of the lessons that we’ve learned and also about vendor selection as well. Also, we wanted something that was customizable to our requirements.

Paul Watters 23:44:
We want to be able to brand that application for our customers. We wanted the ability to score it. We’re in a high volume environment. We cannot we can’t have people looking to make subjective decisions based on the data that they’re getting. Automating credit agency data extraction, simplifying the tracking of the application status. I mentioned we were regularly chasing around to both external customers for information and also internal customers for information on decisions. And then also to increase our adherence to periodic review policy as well. So we shortlisted our vendors and ultimately we picked HighRadius after looking across the broader scope of software for about 12 months.
So resource and risk management, resource allocation, with this project took us around nine months. There are a few reasons for that in terms of the resources that we are allowed to. We had a credit manager from MercuryMarine/ Land and Sea. I was involved in some of the processes as well. And we also had credit analysts involved in testing phases for, say, are phase three. So that was kind of what we needed to do in terms of resource allocation. Risk management for us was somewhat simple because we could go back to the manual process we had before. If this didn’t work, it did work. But if it didn’t, that was kind of our fallback. And another piece around.
One of the key risks that we identified in implementing a system like this is resistance to change. So your ability to change, manage and also manage the people within that process and ensure that they have a voice is really, really important to them using this application or using any new system or process going forward.
Training and on-boarding. We had people involved at the coalface from the moment we were designing the system right through to when we were testing it as well from a challenge as a point of view. We had some issues integrating the online application and mapping that to our IS-400. We could have done a better job of that. It worked when we launched it, but it took us a lot of time because there were different scenarios for different fields within our ERP. You know, somebody might call or field this. Somebody else called it that. That caused us a lot of confusion and and and caused these issues with them with implementing get it done in a timely fashion.
It took us nine months to produce the application in the end. The issue is we wanted to make sure we did it right. So I’d rather take a bit longer to do it and do it right because it’s a very sensitive area of our business subject. As I mentioned a little earlier in the executive summary, at the outset, we had about 100 percent productivity improvement. We had two people in this space before we started. We now have one person who does this work for us. And the second person who used to do that is now serving in other parts of the business. We also had a reduction in our customer on-boarding time at its crudest level.
All of our jobs in private businesses maximize profit and maximize revenue. And this system helps us to do that by bringing on hundreds of thousands of customers a year in a shorter time. That increases our revenue and increases our earnings. So we reduce credit risk as well through periodic credit reviews because we were now adhering to the process that we needed to adhere to in terms of review of our customers. Members of those 14000 customers and material credit risk actually reside in about 300 of them. The others, the other 13 plus thousand that are active in a given year. If 10 of those fall over, it really doesn’t make a difference to us because there is insignificant exposure to those customers.
So looking forward in terms of I think most you understand it’s the ability of systems and computers to think that is going ahead of her at a rapid pace at the moment. When we ask Siri or our Google assistant, we’re really talking about narrow intelligence. We ask a simple question and it comes back with an answer to us. In the future, these systems will be able to think, as I think Sashi mentioned in his keynote address. They’ll be able to think many millions of times faster than you and I can. And that is exciting and somewhat scary as well.
So in a more basic sense, what it means for us in terms of blocked orders, which this system will also handle, means that we’re going to be able to use this system to release orders in a more sensible way based on customer risk. I’ve been involved in credit in a number of businesses over my life. And as someone who used to release credit orders myself, I think most of you will probably appreciate that 90 percent of the orders we see blocked is ultimate, or at least probably most of those in the same day. And that is because of the inflexibility of some of the systems, particularly the systems that I’ve worked with, to be able to do anything different other than to apply a vanilla approach.
One customer, one record as well, that’s here now. There’s not a whole lot of AI to be involved in that, to be honest. But as a business that deals with many common customers across our four businesses in the US and Canada, we need a better way to measure our exposure to those customers and having them in disparity. Our piece without information being pulled together is not the ideal situation to be in. So that was really our experience. Some of the lessons we learned around implementing an online credit application and also a periodic review process. And with that said, I think we’re up to questions. So does anybody have any questions about how bad our experience with the online application or reviews is? Yes.

Audience 30:04:
Ganadeep from Duracell. We don’t have this big of a size, but we are very interested in looking at the work-flow process in the credit application stream, the features that you get from high wages. Does it require you to log in to HighRadius just to approve or can like the director or even higher level of VP can just approve on their email and it records their status? So we were exploring the idea. We wanted to see what’s your experience in the work-flow features?

Paul Watters 30:43:
The answer to that question is no, it doesn’t. And the second answer is yes, it should. Because people at more senior levels are not interested in signing into another application. That’s the last thing they want, you know, and even an antiquated Oracle system that we’re on has the ability to send back approval. So I’m sure that’s the feedback that they’ll be receiving.
So if no one else from there, then we should proceed, Yes?

Audience 31:18:
Hey, good morning, this is Karen from WESCO Distribution. So I have a situation where we have multiple ERPs and I can either try to fix all those or maybe get something to lay on top. So how was the I mean, how many ERPs did you actually then interface to and how, you know, like if you have to extend to additional ERP? How hard is that? Like just that integration once you have it, like when you start to roll to multiple ERPs?

Paul Watters 31:48:
The key thing is it’s not difficult. We integrated this with two, one in the US, and a slightly different version in Canada. The key thing about it coming from different ERPs is this system having the ability to distinguish what’s different about them. So if we have a field that says this is coming from ERP 1, and another system, another field in another system that says this is ERP 2, we need a way of distinguishing it. I can’t guess. But that is a relatively simple process. And it gives you the other. The other piece about this as well is it gives you the ability to harmonize your processes as well through a single platform. So, yep!

Audience 32:41:
We’re deploying this credit cloud solution in April. And so I have a few questions if you don’t mind, more than once, but they’re quick. So the first one you talk about the credit scoring models that you have and you said that like 90 percent you can discard because it falls into the certain criteria, will those approve automatically?

Paul Watters 33:03:

Audience 33:05:
Okay, so you know that 20, 10 percent that you actually have to go through.

Paul Watters 33:07:

Audience 33:08:
My other question is regarding-

Paul Watters 33:09:
Sorry. The second thing on that piece, too, I think, is you can develop and customize your own scoring model here. HighRadius will give you a selection of sets, as you’ve probably discovered. I think in our instance, what we did is we wanted to backtest our decisions before we went to that next stage of all our approval. So we’d pull a subset of applications and we’ll go through and look at them and say, yes, I would have made that decision. And we tested to make sure that there was nothing wrong with the underlying scoring we did before we went to the automated stage. So my suggestion would be you should do that too, to make sure that your logic in your scoring is correct.

Audience 33:47:
Yes. Okay. Makes sense. And when you get the, uh, the credit limit is approved, does that get updated into the ERP? So do you approve of the cloud and then it gets updated in the ERP?

Paul Watters 34:00:
Yes, it does. So that the system we set up, provides a recommended credit limit. 95 percent of the time we stick with that recommended credit limit and that feeds back into the ERP for both new applications and for periodic reviews as well.

Audience 34:15:
Okay. And the credit blocks that you mentioned, you already have them deployed? Or is, uh, the credit block-

Paul Watters 34:24:
Sorry, no, we don’t have that deployed at the moment. It is something that the system can do. And to that end, the systems I’ve found will typically block credit that will give a tolerance over its credit line or alternately, it’s driven by the buckets, the aging buckets. So it’ll stop on day 1 or day 31 or day 61. If you want anything in between, or if you want to assist customers by their risk and determine which orders should be held and those that aren’t, you know, none of the employees that we have at least. And to be fair, we’re in an antiquated landscape. We’ll do that sooner.

Audience 34:59:
Okay. Thank you.

Paul Watters 35:00:

Paul Watters

Global Director - Credit

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