The evolution of compensation: Standardizing incentive compensation management

Watch Kevin Faranetta of New York Life and Arun Ramalingam of Deloitte as they share their experience implementing Anaplan for sales incentives and discuss the approach, lessons learned, and best practices for successful compensation management in banking and capital markets.

Arun Ramalingam 0:00:03.6:
Welcome to this breakout session, which is titled The Evolution of Compensation Leveraging Anaplan to solve complex incentive compensation management solutions. Like he mentioned, my name is Arun Ramalingam. I am a senior manager and solution architect at Deloitte Consulting. Over the last four-and-a-half, five years, I had the privilege of assisting New York Life in their connected planning journey using Anaplan. I have here with me, Kevin. Kevin, do you want to say a quick introduction?

Kevin Faranetta 0:00:37.2:
Hi everyone. I’m Kevin Faranetta. I work at New York Life Investments. I’ve been there for about 11 years, and I’m currently director of managing reporting and data strategy in our finance function. I started out in FPNA in the beginning of my career and have moved into business unit level support, and most recently responsible for management reporting, compensation, and data strategy for our US retail business.

Arun Ramalingam 0:01:02.6:
Thanks, Kevin. So, as the title suggests, we’ll be talking about the incentive compensation use case that we are delivering for New York Life. We have a three-point agenda for this talk. We’ll begin with briefly talking about Anaplan at New York Life and, how incentive compensation came into a use case and the business processes it’s trying to solve for us. From there, we will talk in detail about the approach that we took to identify scope and go about delivering this solution. From there, we’ll go to and talk about some of the key lessons that we learned, and leading practices so that you can use them if you are going down this journey in your organization. Eventually, we’ll have a Q and A session. With that, I will transition it to Kevin to talk about Anaplan at New York Life.

Kevin Faranetta 0:02:01.1:
Okay, so I’ll start by just giving a background on New York Life for those… Most of you probably know about the core insurance business that’s the foundation of New York Life, but there’s a portfolio of strategic businesses that support that, and with that, there’s an asset management division that manages over 700 billion of AUM. So, it’s a pretty sizable business. In 2020, New York Life decided to go with Anaplan as a corporate FPNA tool to consolidate all those businesses, and that was the ultimate goal. The first use cases were in that corporate finance area, so we’re talking about workforce planning, expense planning, allocations, things that had been a root cause for a lot of our FPNA. Then, in 2021, we saw those use cases come to life, and our US retail business decided that Anaplan was a tool that we wanted to bring in for business unit-level FPNA, and that’s a pretty common use case that a lot of people have seen. So, we built an FPNA model for US retail, and then as we saw that, we got to thinking that it might be worthwhile to investigate Anaplan as a compensation admin system. So, what I’m going to go through next is just some of the challenges we had with our previous compensation process and what we’re hoping to gain by working with Anaplan.

Kevin Faranetta 0:03:20.6:
So, the first two things go together, siloed workstreams and inadequate workflow. The way our process was set up previously is we had someone manage incentive compensation through Excel workbooks in a shared drive, and it was locked down like it should be, but then you have sales reporting in its own silo. That data would be downloaded from a system and then fed over to someone to put in their workbooks. You also had our distribution teams where we needed to get discretionary bonus inputs. We needed to get the compensation statements back out to them, and that was all done via email. Then the other silo is HR. Getting HR data is very difficult, as it should be, and getting the data back to payroll to get paid out. So, we were looking to solve for those things. The last one on here is the net new build. That’s where each year, we have 130-plus salespeople in our organization. Each one of them has an incentive comp plan. Building an incentive comp plan for them, while it has its challenges, that is not really the biggest lift. The biggest lift is building the Excel workbooks that have 130 tabs for every single individual and making sure that each one of those can be turned into a PDF that gets sent out. So, with Anaplan, we are hoping to get more of a collaborative work environment and that streamlined workflow. So now the sales reporting, hopefully, will be fed into the compensation workbooks altogether, and we don’t have to download a whole bunch of data on business day two and send it across.

Kevin Faranetta 0:04:48.7:
We’re hoping that our distribution team will be able to get their hands on this tool and they’ll be able to input their bonus recommendations right live in the tool. We won’t have to track it over email. Then, with HR data, we’ve already done workforce planning – a few years ago – so we can leverage that data and then, hopefully, connect it to something in payroll that we can send. So, we went through this whole thing, and we’re hoping to get a lot of that through discovery. Discovery is a key phase for us in learning, and Arun’s going to talk to you a little bit about that.

Arun Ramalingam 0:05:19.7:
Thanks, Kevin. So, when you think incentive compensation management, you are talking around total capabilities, right, and that’s what you are seeing in front of your screens right now. They can be grouped into five categories. The first category is the administration portion of it, administering your compensation plans. Looking at your PAEs and administering them. Assigning territories to those PAEs and target management for them, and then the plan document administration. So, this is all your admin capabilities. The second portion is data. Data is on its own a separate capability and it’s really important. We’ll talk about it in a bit. The third one is calculation. This is where you have features that include your metrics that are needed for paying out your incentive compensation to your PAEs. How do you go about adjusting those metrics and recalculating them? Then, obviously, the payroll processing side of it. Processing the payroll. The fourth category here is reporting – reporting and analytics – that needs to be there in the tool, and modeling and forecasting that needs to be done to be able to forecast your incentive comp. Then the last category here is where we deal about capabilities like inquiry and dispute management and any client-specific capabilities that you might have. Now, each and every capability here has a set of processes that enable those capabilities.

Arun Ramalingam 0:06:59.2:
What you’re seeing here in front of you is all the processes that go behind those capabilities. Now, when we do our design, our development… Sorry, our design and discovery phase, it’s very important to take a bottoms-up approach and talk about each and every capability and align on a future state, right? We have a series of workshops that we conducted with New York Life, wherein we talk about, number one, how that process is done right now in the organization. Then, going from there, what are the other peers, other competitors, how are they doing those processes? What are the leading practices around targeting those processes and then, towards the end, using this opportunity to standardize that process? How do you take this opportunity to get away from Excel and do it in a more sophisticated and streamlined manner? Coming out of it, we arrive at the MVP scope, and I will quickly touch on a few of these important capabilities. For example, the incentive plan set up. This is where sit and analyzed all the compensation plans that are currently existing. How do we make it easier for the compensation admins to administer them, compensation cycle over compensation cycle? Taking this opportunity to rationalize those comp plans, that’s something that we did, and then came up with the scope for that. This next one that I would talk about is probably territory and target management. This is where we sit with the users and talk about, how do you go about assigning territories to the PAEs?

Arun Ramalingam 0:08:42.9:
How do you timestamp them? How do you effectively date them? What happens when a PAE goes out of a comp plan middle of the month versus beginning of the month? How do you pay out those guys? Then, the next one I’ll briefly touch upon is metrics and components. Obviously, it’s important. All the metrics that needs to be calculated within the tool to be able to pay out your compensation. How do you handle things like large trades? How do you amortize your payment of your incentive? How do you do minimum guarantees? Is it how have you done it so far and what is the best way of doing it and aligning on the future state for that? The next topic – main topic – reporting and analytics. Very important. This is where we sit and do designing of future state dashboards that cater to the various personas of users. How are the dashboards going to look like for the incentive comp admins? How are the PAEs…? How are the compensation plans going to look like? How will they be able to drill down from their compensation to the sales that they have done, so that there is accountability for the PAE dashboards. This is where workflow, also, is very important. What are the dashboards that facilitate the workflow process? Data comes in. How do managers look at it? How do they add on discretionary bonus on top of it? How does it go from there to the payroll processing? All the details are on that.

Arun Ramalingam 0:10:19.6:
There are some capabilities here that were deemed out of scope. For example, even though from a management standpoint, it’s very important to have a forward-looking modeling of your incentive compensation, we decided that we need to do an iterative approach and have a solid foundation first. So, we said that we’ll do that as a later feature to add on to the top of it. The last one is data integration. Very important. Sitting with the IT team to discuss all the data that needs to come in, be it transaction, at what level do you want to get them into the tool, be it HR data as Kevin talked about, as already having an HR interface, reusing that? Then, all the things that we need to do a payout. Given from an outbound standpoint, how does Anaplan interact with payroll? Deciding on all that is what we did there. Overall, the story here is that it’s very important to make sure that you have a detailed discovery cycle to align on your scope. Make sure that you are not doing the same mistakes that you’re doing in your Excel and have a solution that is future-looking. That’s what we recommend, and we embarked on. So that, Kevin?

Kevin Faranetta 0:11:50.3:
So, just to add to that on discovery, I agree about the detailed element that we did. The whole discovery phase, we looked at every single download we have. Every single Excel workbook, and make sure we can replicate this in Anaplan. That goes to the few things on this slide. So, the first one is having a centralized source of truth. After going through discovery, we realized Anaplan can be that for us. We won’t have to download 15 queries on business day two and send them into Excel workbooks anymore. Now, it’s all integrated, but if you… You’ve probably heard that six times today from different presents that data integration is important. I’ve heard it all day, too, and I agree, and Anaplan is good at that. I think what impressed me the most was the flexibility of that data integration that we had. The way we look at our data at Anaplan now is the way that we looked at it before. We’re using our same structures of tables. Everything matches what we’re used to reporting on, and that’s a nice thing that we’re able… We didn’t have to fit it into a box. They fit our box. The second one is really where it changed things for me. So, we’ve looked at compensation systems before, and they can all do some version of data integration. Where we’ve hit a roadblock in the past is unique elements of our comp plan, being able to build those into a system. Arun mentioned one of the examples – large trades – where, historically, we paid basis points time sales equals compensation for people.

Kevin Faranetta 0:13:16.0:
As the business starts to grow, we get a little more complex and learn, okay, well, if there’s a large sale that comes in, can we protect our company? Do we pay those out a little bit differently? So, now we need a system that can identify those large trades with the same lens that we put on it and give us the same oversight that we had in Excel. That’s where other systems had fallen short before. Anaplan really met that, as well as a lot of other of our supplemental processes, and that’s what pushed us forward. The last is accuracy and auditability [sic]. Obviously, it’s important, and with Excel, we always were tracing back audit questions from one Excel workbook to the next, VLOOKUP to the next VLOOKUP. Now, it’s easy to drill in and see the calculations right within Anaplan and it’s a lot more controlled environment. So, that was after discovery, and this is really where we are today. So, going live, you can see all the different statistics, 12 inbound data sources, 24 product lines, 54 compensation plans, 51 dashboards. There’s a lot that was built into this tool, and that doesn’t even show the amount of workbooks that were decommissioned. The amount of Excel or SQL queries that we were able to streamline and put right into here, which is freeing up capacity for resources to work on the things that matter. So, now we have more time for analytics. We have more workflow with our sales partners where are…

Kevin Faranetta 0:14:44.4:
We don’t have to trace emails and track down bonus recommendations from everybody. It’s in the system. We also don’t have to email 100 PDFs to 100 different people every single month. They have their own access to the tool. They can see only their own compensation or the compensation they’re supposed to see, and that gives us the ability to continue to grow with our sales partners. This business is only going to get more complex and trying to find ways to make our margins, and that means compensation plans make it more complex. So, having a tool will allow us to get there.

Arun Ramalingam 0:15:22.7:
Right, let’s talk about a few of the things that we learned along the way when we implemented the solution. I’ll begin with the emphasis on having a scalable solution. To give you an example, you don’t want to have an Anaplan tool which is giving more power to the compensation admins. You don’t want a situation where, every year, you need an Anaplan team to build your new comp plans for you. Try to build a tool that allows the comp admins to build comp plans on their own and administer them. Having that frame of mind and having that design thought is very important. The next one I would talk about is embrace transition. As we talked about a lot of this work – and a lot of our clients are doing this in Excel – and when you go from Excel to a tool like Anaplan, it is very important for them to embrace that transition and look at it from a system standpoint. What is it trying to solve? From an implementation standpoint, it’s very important that you do a lift and shift of your Excel. A lot of other clients that we have seen are lifting and shifting Excel into Anaplan. They are basically transforming their Excel mess into Anaplan mess. Use this opportunity to make sure that you have a tool that is scalable, and you are not falling back to old practices that you had. The third one is data. I think I’m going to beat this to death, but data is not an afterthought. I cannot emphasize it more. In fact, I would go to say it’s 50 per cent of your battle, especially in use cases like incentive compensation management.

Arun Ramalingam 0:17:15.6:
Make sure that you are bringing on the IT folks in the beginning of your project. Clearly talk to them about the format of the language you need that in Anaplan. Talk about things like refresh strategies. Are you going to do a clear and load, or are you going to do an incremental load? Data validation dashboards are really important. It’s all about confidence in data. Make sure that you have all those data validation steps in place and then the governance around it when it comes to go live. Because multiple teams are coming together to do that validation, setting that is very important to begin with. On the right-hand side, change management and end user training is really important. I think you’ll see a similar theme in a lot of these topics. The trick is to make sure that you identify resources from business who are… You need to have a good population of them. When I say good population, it’s not necessarily that you get everyone on board, but make sure that you have a sizable amount of client champions who are sitting with you in the discovery process that we talked about. They have their say on the dashboards that we are designing. When they have that say, and when you do those demos, they will see their thoughts getting translated into Anaplan, and they will feel confident about it during the development cycle, and the same when you build that confidence when it comes to SID and UAT. They will be your champions and they will make sure that Anaplan is adopted in the end product.

Arun Ramalingam 0:18:52.4:
So, change management and making sure that you get your users along is really important. The last one probably that I will touch upon here is the iterative approach. Especially incentive comp, it’s basically affecting people’s pay. It is very important for them to make sure that they are confident about the data. They have dashboards that are telling them not only their compensation, being able to explain that compensation; make sure that you have put all that in place. One more thing that you need to make sure is that, when you are planning for this implementation, make sure that you have enough time for iterative parallel runs. You are going to find issues when you do a first parallel run. You will need a few cycles before the tool is perfect. So, make sure that you bake those timings in, and then set up on your implementation. Overall, making sure you take the users along, documentation, reviews, and touchpoints, and adhering to leading practices is going to get you to the end product, and you’re going to have a good implementation of your incentive compensation solution. With that, I think we have come to the end of what we wanted to share. Any questions that you guys want to ask; we’d be happy to answer.

Audience 0:20:36.5:
Hi, [unclear name 0:20:36.5]. In incentive compensation, there are a lot of transactional processes. Could you list the whole technology stack that you are using for the end-to-end process? For example, what tool is being used to distribute comp letters, etc.?

Arun Ramalingam 0:20:52.9:
Yes. Kevin, do you want to take that.

Kevin Faranetta 0:20:54.5:
Yes, I can take that. So, New York Life actually went through a data transformation 2018, so that’s six years ago, and our sales reporting data got moved into an on-premises data hub within New York Life, and that’s where our sales are sitting. We’re int the process of migrating to the cloud for the data hub, but it’s one database that has all of the transactions in it. That database, what we’re doing is we’re taking the monthly trades, and we’re pushing them into Anaplan. So, it’s just a one-stop jump to there. Then, when Anaplan’s done, we send the compensation memo to HR and payroll for them to distribute the comp.

Audience: 0:21:43.1:
Can you tell us a little more about your journey? How long was the discovery? How long was implementation and parallel run?

Kevin Faranetta 0:21:49.7:
Yes, discovery, so we started with data at the beginning.

Arun Ramalingam 0:21:56.1:

Kevin Faranetta 0:21:56.1:
That was probably two or three months to talk about how we were going to get data from our data hub into Anaplan. That brought us to the summer. We probably then did another three months of discovery on the incentive comp plans, more or less.

Arun Ramalingam 0:22:11.8:
So, yes. I think like Kevin mentioned, we wanted to prioritize data, and there was a very specific exercise done on data to make sure that it’s ready. Once we had that, we did a six-week discovery session where we aligned on all the scope and what the MVP score is. We developed this from a development standpoint it was a three-month exercise. I think it was two-and-a-half, but two-and-a-half to three months is where we did our development cycle, and we had a four-week UAT. Obviously, like I mentioned, after UAT, we had to do multiple parallel runs, and we are in the process of doing multiple parallel runs to make sure that we are comfortable with the tool. So, that’s, from a timeline standpoint, what we had done.

Kevin Faranetta 0:23:01.6:
Yes, and we baked a decent amount of time for parallel runs because our testing really only happens once a month. So, we get one shot at it in January. We find what tweaks we need to make, we work on those, and then we get another shot in February. So, we extended the timeline intentionally to keep mind for that.

Audience 0:23:22.2:
Hi. What was your biggest challenge during the implementation, and in hindsight, the things you may want to do it differently? What was your learning from this entire exercise of implementation?

Kevin Faranetta 0:23:33.4:
Yes, I’ll give mine, and then you can…

Arun Ramalingam 0:23:35.7:

Kevin Faranetta 0:23:38.1:
So, from my perspective, our biggest challenge was making sure that our technology teams within New York Life provided the right data when we needed it and where we needed it. I wish at the beginning we would have connected with those partners earlier and gotten them to get us started because the actual Anaplan build itself was dependent on that. It’s hard for us to test that the comp plans were working correctly if the data wasn’t right, and even if we’re missing a hundred transactions out of two million, that could impact a hundred people, and we have to figure out where those differences are. So, making sure the data was right was probably a key for me.

Arun Ramalingam 0:24:21.2:
Yes, and from an implementation standpoint – and I’m going to be the geek here – making sure that, like I was mentioning, designing a solution in such a way that year over year, the incentive comp admins are not dependent on Anaplan folks to do the development, right? So, we had to spend a lot of time with Kevin and team to identify, what are you doing year over year? What changes are you doing? How do we make sure that we build those dashboards in such a way that you’re not depending on Anaplan? So, that’s probably from a technology standpoint my biggest issue was that we solved for. Another thing is that I think we were able to do, or we had some issues on, is to explain numbers. Making sure that you are seeing your final compensation number and trying to put it in a format that is easier to explain for the users and, get the values from transactions right, and make sure that it is very user friendly is another thing that we had a little challenge on. Obviously, data point, Kevin talked about it. How do we make sure that it’s not a moving target for compensation plan admins? How do we give them the ability to pull in transactions at a particular time and have that not change? Even from a comp payroll processing standpoint, if you run a payroll twice in a particular month due to some reason, you want to make sure that… Delta is what is sent to the payroll processing. You don’t want the person to get benefits twice. How do I come up with a delta, and only the delta portion goes into the comp statement? It’s also something that we had to be creative when designing and implementing the solution.

Kevin Faranetta 0:26:19.5:
Actually, one other thing I’ll add to that that I think is important for implementation was then you think about the go live for a system like this – and Arun talked about it before – this is compensation, so you want to be 100 per cent right. If you set yourself up to need to be 100 per cent accurate to go live, you’ll be testing forever. We had to build in a mechanism that said, okay, we’re 90 per cent there. There are a few salespeople – and we know the reason why – but their comp isn’t perfect. We needed a way to get us there quickly, and that way we always knew, well, if we went live and only one person’s comp was wrong, we could fix it right away and then we can move forward. Otherwise, we would have been going parallel for much longer than anybody wants to.

Audience 0:27:04.7:
I’m at a pretty small shop where the where the [unclear word 0:27:06.7] are intimately involved in comp decisions. Thank you. [Microphone provided] They like to add columns. They like to add rows. It’s chaos for me trying to keep track of all of that, I guess. What was your experience with the adoption and getting them into like, hey, you can touch this cell or is that not really relevant?

Kevin Faranetta 0:27:28.3:
I think my level of comfort is we don’t give them that ability! [laughter] Yes, that’s a challenge. For us, it’s really how much do the field goalposts move, which I guess is similar to what you’re talking about. So, Anaplan, we were trying to build with that in mind. Arun said it before, if next year they want to add another tier to the products, we don’t have to go back and say, ‘Well, how do we add a tier? Can you rebuild Anaplan to give us another product here or there?’ So, we set it up in a way that we can make the changes that we anticipated to have to make, knowing that if they came in and said, we want to just forget sales, we want to start paying on AUM, well, we’ll have to go back to the drawing board. We tried to look out for those things when we were building it and build as many as we can in.

Arun Ramalingam 0:28:14.9:
Yes, and Anaplan is an iterative tool, right? All the information is there is in the background. As long as you’re building it in a way that you’re capturing all that information, it’s pretty easy to go back to the model and expose another column, for that matter. That’s where training of users also comes into picture. You can provide some level of training to the compensation admins to be able to expose those back-end calculations, if need be, so that can also be done. All right. Any other questions? All right.

Unknown speaker 0:29:00.2:
Well, thank you to Kevin and Arun. [applause]


Kevin Faranetta, Director, Finance, New York Life Investments

Arun Ramalingam, Senior Manager, Deloitte Consulting LLP