As the Sales Performance Intelligence Manager at DocuSign, I’m responsible for providing our sales leaders the insights they need to effectively manage their businesses. I’m also responsible for providing our stakeholders in other areas of the business with a clean, easily digestible, and accurate sales forecast. Anaplan has enabled me to empower our sales leaders with the data they need (click to tweet) to truly understand their businesses, and leverage this information into a forecast that’s quicker, smarter, and more consistent than what we’ve used in the past.
Prior to Anaplan, our sales ops team had to combine various manual inputs through spreadsheets into a single view. However, with every team forecasting differently, this process was messy, unproductive, and error-prone, not to mention non-secure.
Sales Forecasting 1.0: Gaining real time, accurate visibility
We needed a scalable and effective forecasting system for all of our sales managers. Enter Anaplan and what we call “Sales Forecasting 1.0.”
DocuSign’s long-term goal is fully optimized forecasting.
To start, we use Salesforce data to give us a baseline number. We then have our managers use Anaplan to apply their forecasts on top of that number. Here’s how it works:
- In-quarter forecasting: This functionality coalesces opportunity-level detail into company-wide actuals and pipeline. It allows for sales manager and VPs to forecast, and provides easily accessible key metrics and in-quarter tracking and visibility.
- Refresh time: Our forecast is updated with live Salesforce and SuccessFactors data every three hours. Gaps and flaws in Salesforce are quickly highlighted, and we capture forecast snapshots at the end of every week, which provides us with great data on forecast trends and historic analysis.
- Access: We’ve been able to provide selective access for our employee hierarchy. Each manager can only view information that’s relevant to them, and sensitive information has been secured.
- Source of truth: The forecast to us is not just a sales number. Although we source information about sales from our CRM and sales team, we don’t just use only CRM data for our forecast. We augment our forecast with information from other systems, and we’ve also acquired companies that have different CRM processes. As such, we’ve been able to create a process to combine all of this data into a single view, which has become our source of truth and provides our true bookings number.
- Tailored views and dashboards: We are able to display different information to different audiences. We also have the ability to reflect different forecasting methodologies for different segments of our business.
Managers utilize their judgment on top of Salesforce data to provide a better-informed forecast than they could get solely from our CRM tool. Ranging from changing the value of an individual opportunity, to overriding the forecast that rolls up to their manager, our sales leaders leverage Anaplan to forecast at any level of granularity. There’s a consistent, easy flow that can be drilled down from the top level of the organization.
Sales Forecasting 2.0: Giving sales leaders more value
To help our sales managers be more accurate, effective, and data-driven, we introduced what we call a Datapack. The Datapack is a set of automated KPIs provided to sales leaders within Anaplan. Here are some of the key components:
Ten automated KPIs make up DocuSign’s Datapack.
The Datapack allows users to dynamically filter the opportunities that are relevant to them. Users can also view as many or as few teams at the same time as needed. In addition to seeing real-time data, our users can compare their metrics against where they stood at this point in previous quarters, or where they closed previous quarters. As we move from quarter to quarter, we add additional data points to help determine the health of the business. This is a significant value-add over Salesforce, which only offers live data.
We have also built in an ability to proceed deal by deal: You can select all deals expected to close, with easy filtering on—e.g., everything that’s committed in Salesforce, everything over a certain value, all deals past a certain stage, etc. The system automatically totals those opportunities, which offers a triangulation of numerous different data points. This is particularly useful for our enterprise business, where a handful of deals will make the difference between a good and bad quarter.
Advice if you’re considering Anaplan
Here’s what we learned:
- Focus on “go-live” and adoption. A great product with no users adds no value, so make sure you support your end users and enable them to use the tool.
- Don’t re-create processes—improve them! Use the system as a change agent to streamline and improve legacy processes that no longer suit the needs of the company. Don’t view Anaplan as solely a technological improvement; leverage it as a process improvement tool as well.
- Find secondary usages to drive additional value. Recognize and take advantage of additional capabilities that may not have been in the initial design.
- Maintain end-user focus. Be willing to tailor the system to your stakeholders and sales organization. The success of the implementation and the tool is contingent upon end-user adoption. Make Anaplan something your end users want to use, not something they have to use!
Watch a full video discussion of DocuSign’s Anaplan implementation in their recent Hub session, “Merging Analytics With Functionality: DocuSign’s Transition to Sales Forecasting 2.0.”
Matt Schweiger: We’re going to walk through what our history has been with Anaplan, using it for a sales forecasting use case. And I’m going to talk through a lot of what we use it for [at DocuSign], some of the key tools, and the key things we do within our sales forecasting model.
But because I know it’s a use case that a lot of people are thinking about, I want to make sure along the way I’m stressing where we started, where we are now, where we’re going, and a lot of the lessons learned.
So, hopefully it’ll be a useful session for you guys who are considering picking up Anaplan for sales forecasting. And I’ll make sure I keep some time open at the end so we have time for questions.
So, as Rowan [Tonkin of Anaplan] mentioned, we’re going to talk about DocuSign’s use of sales forecasting and the journey, or maybe more appropriately, the adventure we’ve had along the way getting from the initial implementation to where we are now.
So, as a means of an introduction, my name’s Matt Schweiger. I’m a manager on the sales performance intelligence team at DocuSign, and what that means is basically sales strategy and analytics. A big piece of that is Anaplan.
And so, ranging from our forecasting motion, which is where we use this, as well as metrics and KPI [key performance indicator] reporting, that’s a lot of my role, ultimately. It kind of comes down to, if one of our sales leaders needs a number, that email usually finds its way into my inbox.
In terms of background, I actually spent a few years at Deloitte as a consultant. I was in the team there that was doing Anaplan implementation, so I’ve seen Anaplan from a variety of different perspectives, and I think that’s been helpful for me to inform the process we’ve used at DocuSign, as well as a lot of the lessons we’ve learned along the way.
So, this is what we call our sales forecasting journey, and as a quick “where are we now,” I think we’re in the Forecasting 2.0, that middle stage. I’m going to circle back to this at the end in terms of where we’re going, but I think it’s important to understand as we go through this process, where we started, where we want to be, and how we’ve gotten there.
So, I’m going to dive deeper into the simple forecasting, what that means, the analytics and KPI integration, Forecasting 2.0, our current state, and then talk about some of the goals, what we’re trying to do as we continue to move forward, as we continue to go along the spectrum.
So, as we talk about what we’re doing now, I think it’s really important to understand where we started, so we do a quick flashback to 2015.
Our sales forecasting model in some way, shape, or form has been live since about the middle of 2015. But if we’re talking about before that time, maybe January, February of that year, this is sort of what one of our global forecast calls would look like.
We’d have a 10:00 call with our CRO, running it with all of his direct reports. All of a sudden, this stuff would start popping up. It would take the sales ops team four, five hours. We’d be up at 6:00 in morning pulling together disparate spreadsheets.
And by the time the call came around at 10:00, everything was outdated, numbers changed, deals got pushed, and one of the managers has an emergency, they can’t make the call anymore, and all of a sudden, we have all these disparate processes that we don’t even know. We can’t even pull everything together.
And to dive a little deeper into what that looked like, this is the old way of doings. So, we have all these different inputs, and a lot of the challenges, our different teams all forecasted differently.
As much as you can try to force a consistent format, or a consistent style, or a consistent standard, at the end of the day, your manager and the GOEs might forecast differently than your manager of [financial services], and your commercial business is going to do things entirely differently than your enterprise business, and let’s not even start talking about the international team.
So, we’re getting these spreadsheets from all over the place, and it falls within the sales ops team to pull them all together to get this into a productive meeting where you’ve got all of your heads in the sales in the same room, got finance, you’re really trying to figure out what’s about to happen this quarter.
So, that was the first challenge, a dozen of forecasting processes. Slow manual assembly, I touched on that a little bit. There are no roll-ups for the 18-plus or so teams we had at that point, and once again, this is two years ago for us, as a means of comparison.
When I joined DocuSign 18 months or so ago, I was the second person on our team. Now, we have eight of us, and that’s kind of similar with what we see in our sales organization. We’re a very rapidly growing company, and that’s why we needed a tool like this to help us scale.
There’s a manual distribution problem. Once again, emailed spreadsheets all over the place, it’s incredibly messy. When you’re pulling these together, there’s human error involved. And there’s no security either. Who knows where an attachment on an email goes to. Anyone can be seeing anything.
That’s a big problem for us as well as we’re bridging this gap between the quintessential Bay Area startup, and trying to become a more public-ready company. So, that was a huge problem for us at the time as well.
And some maintainability of it. If we had someone spending five hours pulling this stuff together before a forecast call, if they’re on PTO [personal time off] or something, if they can’t pull together where do these things go, where’s our source of truth, how do I find what my forecast was three weeks ago, get people digging through all these different folders on Box, it’s just a total mess of a process.
So, to our credit, we were able to identify that this isn’t something we can do moving forward, this isn’t a scalable process, we need to do something differently.
So, we found ourselves saying, “There’s got to be a better way. How do we enable a scalable and effective forecasting system for all of our sales managers and for the sales operations teams so that we can do our job effectively, and that our managers have a clean and easy way to forecast and get the insight into their businesses that they need?”
So, this is the first piece. This is what we call Sales Forecasting 1.0, and I’ll talk about what this means in a second, but before all this happens, just how our process works. We have a connection with Salesforce, so every three hours, we’re getting new information, all of our opportunity information from Salesforce for deals in this quarter or in the next quarter.
We also get a feed from our data hub where we have our employee hierarchy information. So, every three hours, we work through Dell Boomi, and that kicks off a process which is going to pull the latest employee hierarchy information, the latest Salesforce information, and using all of our logic to map the opportunities to their owners at the various levels of the hierarchy. It’s going to reallocate where the deals are going, that sort of thing.
So, once those are in there, how do our managers forecast, what are we giving them, what are they supposed to do? At the lowest level, it’s a weighted pipeline information. So, using the Salesforce information, we look at the stage, we look at the size of the deal, apply a weight to it, and that’s going to spit out our baseline number.
But now, if you’re a sales manager, that’s not a huge value add right there. It’s easy enough to export Salesforce information, multiply it by something, and there you go, a weighted forecast.
But what we want to do is give our managers an additional ability to forecast on top of that. So, the first thing they’re going to do, they go into their dashboard and add a plan. On the dashboard, they see all of their deals, and they can say, “Okay. I saw that company X, I think my rep’s sandbagging it a little bit. He’s got it into the 50K. I think it’s going to be 250.”
They click on that deal, they change the value, they override the deal forecast in Anaplan, and so, one-way integration. So, this isn’t going back to Salesforce, this is solely for our managers to play around with, help them inform their forecast a little bit.
They changed that deal from 50 to 250K, all of a sudden, that number rolls up. So, that initial weighted pipeline has now increased by 200K, times the weighting of the stage of that opportunity. So, they make a bunch of adjustments to opportunities. Next level is their subordinate adjustments. It’s really the direct report.
So, if you’re a direct report to our CRO, then your subordinates are going to be other managers. If you’re a first-line manager, it’s for your reps. But using the stage weighting and opportunity adjustments, we get this initial forecast.
Then, as the manager of these guys, you can say, “Well, I know my rep John, he sandbags every quarter. He always pulls a deal out of thin air with two days left, so the system’s telling me he should be coming in at right around this plan this year, but because he always says, ‘I know better. I’m not going to fall for this again,’ let me bump up his calculated forecast by 50%.”
You can do that for as many or as few of your direct reports as you want. Also, you get a roll-up of them, which is going to be the calculated system-generated forecast for you.
And then the last piece is your forecast. So, it’s the manager judgement. Using all the information available to you, you can see what your reps are at, what their forecast is, what your plan is, your coverage or pipeline, that sort of stuff.
And that’s where you’re going to go in, check a box, type a number, and that’s you submitting your forecast. It’s going to stay there until you change it, and your manager is going to see it, his manager is going to see it, and there’s a very consistent, easy flow that can be drilled down to you from the top level of the organization.
Just to dive a little bit deeper into some of the specific features, the in-quarter forecasting, like I mentioned, it’s the opportunity level forecasting for in-quarter deals. We have next quarter on there as well, so if you want to pull in a deal, that sort of thing. But it goes from the bottom all the way up.
Refresh time. As I mentioned, stuff coming in from Salesforce every three hours, so we’re always accurate to Salesforce within three hours. We can deal with opportunities being deleted, being moved out of quarter, all that sort of stuff.
Access is a huge piece. I know I mentioned this before when it came down to our old process of Excel and sending these spreadsheets all over the place, but right now, we have selective access for our employee hierarchy, so if you’re a midlevel manager, you’re only going to see your own information and that of the people who report directly to you, and you’re only going to see the deals you own.
So, everyone’s going in there thinking this thing is tailored directly to them, but it’s the same experience for every single manager, and they’re only seeing the information that’s relevant to them.
Source of truth. As I mentioned, it’s accurate within three hours of Salesforce. Another big piece for us was that we acquired a few companies that aren’t fully integrated into our Salesforce.
So, because that Salesforce can’t be our source of truth, we built in some functionality. It’s not a full automation, but we calculate the bookings and the actuals offline from our acquisitions, we put them into Anaplan so Anaplan becomes our source of truth, and it’s the only place we have that’s going to take all these numbers and put them together, and give us a true bookings number for the quarter.
And tailored views and dashboards. We have the ability to have different forecasting methodologies be reflected. We have generally a commercial dashboard and an enterprise dashboard, two very different businesses that are run very differently.
If enterprise does one thing one way, we have the flexibility to tailor their view to them. Same thing on the commercial side, and we also have tailored views for our key stakeholders and our leaders. So, that’s great, right? We have an ability to forecast.
But one thing we’re super conscious of is, we don’t want to be just a means to an end. As much as we have this opportunity-level adjustment and team-level adjustment and all this great stuff, we don’t want our managers to just be going in here checking off a box, submitting their forecast, exiting out of the window, and not thinking about it again for four weeks.
So, as we’re moving forward, a thought for us was, how do we make this somewhere our managers want to go? How do we give them extra value that they’re not already getting? And that was the move from that simple forecasting to what we’re calling 2.0 KPIs.
So, we introduced something we call the Data Pack, and this is our automated KPIs within Anaplan. The usability is a big piece in this. The metrics are, I have some of them listed here. That’s just an example. There’s all sorts of the standard stuff, and some of the things we look at specifically.
The usability is a big piece. So, I was able, in a week of build for the most part, to get an automated version up and running using a new hierarchy that doesn’t exist anywhere in Salesforce, and it allows us to filter dynamically, once again, only the opportunities that are relevant to you. But also, it’s filtered on, you can view as many or as few teams at the same time as you want.
We have week-over-week metric visibility, so you can say, “Okay, it’s week six of this quarter. My new customer win rate is 70%. Great. I don’t know what that means. What does that mean relative to what I’ve been doing in the past?”
So, I can look at, “Okay, I’m at 70% six weeks into this quarter. Where did I end last quarter? Where was I at six weeks in the last quarter?” And we get that going back and back, and that’s a huge piece of information that is a big value add over Salesforce, because one of the issues we always have with Salesforce is it’s only live data, and we don’t have this historic tracking readily available.
The different levels of granularity, so I can look at our enterprise business as a whole, or I can narrow down on just the East Healthcare team. And then we have, like I mentioned, the historic comparisons at the end of the quarter, and the current snapshot, and tailored metrics based on the role similar to some of the other stuff I’ve mentioned.
If you’re the head of all sales, you’re going to have a different view than a first-level manager, but we have a few different instances of this type of data, and it gives us the ability to tailor that to our audience.
So, where are we now? There’s one other piece I didn’t specifically dive into, which is bottoms-up forecasting. That’s the third piece here. That initial simple forecasting is really just that manager discretion in the bottom right corner.
But specifically, within our enterprise business these guys just want to say, “I have 15 huge deals this quarter. If I close 10 of them, I’m great. If I close five of them, I’m in a lot of trouble.”
So, what we built is an ability to go deal by deal, check a box. If you checked it, that means you’re forecasting that deal to close. Click everything that’s been committed in Salesforce, everything over a deal value of X, everything at a certain stage, and it’s going to automatically total up those opportunities.
So, whereas at first we were just saying, “Here’s all your Salesforce information. Here’s how it’s rolling up your hierarchy, here’s what you can do with it,” now, we’re giving you additional pieces of information.
We’re giving you the KPIs so you can look at win rates, for example, and say, “Okay, my win rate’s actually a little bit lower than it was the last few quarters, and it’s been trending down a couple quarters in a row,” or ideally, the other way.
So, you can say, “Well, I came in right around the plan last quarter at the same pipeline, but my win rates are down. Maybe I’m not exactly where I need to be,” and it gives you that additional piece of information and that knowledge to reconsider the forecast, as opposed to just the rollup of your deals or what your reps are telling you. And the bottoms-up piece is the third point on that as well.
So, it’s really a triangulation of a bunch of different data points, and ideally, we’re giving our managers the information that they need to come up with a more informed decision, and that’s really the goal here is to have a more effective and more efficient forecasting process.
So, what do we gain? I like to think of it in three buckets. It’s faster, and smarter, and it’s more consistent. So, the faster is obviously the automated metrics calculation. I have spent so many hours of my life deep in Excel doing all sorts of ridiculous functions I don’t even want to think about at this particular moment in time. Having this automated has been so helpful and so huge for us.
And it saves our team time as well. We don’t need our stakeholders when the sales organization comes to us every time they have a problem. They have the tools now available to them to go in there and figure it out themselves.
We have the live roll-ups and drill downs. So, back to our initial concept of running our global sales forecasting call at Anaplan. We’ve had an increased focus on going down to increasing levels of granularity. And maybe two years ago, our forecast calls were, “How’s our enterprise business doing? How’s our commercial business doing?”
Now, because we have Anaplan available to us, when we’re running our forecast call, we can say not just, how’s enterprise doing, “How’s enterprise North America doing on the GO business?” It’s one extra click, and all of a sudden, we’re getting this team-level granularity, which has been a huge value add for us.
Quick and easy development of new dashboards and modules. Tailored views are super easy. We’ve done all sorts of stuff. We’re building those out on an as-needed basis at this point, but it’s very quick and easy.
And the simultaneous usage, back to the initial spreadsheet sending all over the place, it had to have a very well-defined process of how you incorporate these things together. Now, we’ve got a bunch of people in there at the same time. We have multiple forecasts running out of Anaplan at the same time, and runs with no hiccups. So, that’s been huge for us.
Smarter. Huge savings in man hours, I think I’ve talked about that point a lot. It highlights errors and aberrations in our Salesforce, our CRM data. If you have opportunities that should be rolling up to you and aren’t, we highlight those.
If there is a deal that has significantly higher value than you had expected to, it’s going to throw off your numbers, it’s going to stick out like a sore thumb. So, there are all these things that may have gone undetected in Salesforce for an additional period of time that we catch earlier because of how we use that data.
So, the data-driven forecast is a big piece. It’s not total guesswork, we’re giving these guys the tools they need to have a more informed forecast, and it accommodates the non-CRM bookings, as I’ve touched on that before, but it allows us to deal with our acquired companies and incorporate them into one source of truth.
More consistent. All of our teams have the same feel, with the exception of minor things we’ve done to tailor them. It’s a similar feel and look, and everyone’s on the same page with that.
One source of truth, tailored access. And we drive the narrative that we’re trying to move towards a more data-driven company, and when we’re giving our managers ready access to this data, it really hammers home that point that, “Hey, guys, focus on the data, don’t just guess. Use the information available to you, and let’s make that move.”
So, what did we learn? And this is a combination of what we’ve learned along the way with a little bit of informed opinion back from my consulting days as well, but focus on go live and adoption. Change management and go live support isn’t always as sexy as the initial build, but it’s just as, if not more important.
A fantastic product with no adoption and no users doesn’t do anything. You just wasted a bunch of money on something that’s going to sit on a shelf. So, really hammer home that point. Make sure you have your support and your end users. Make sure you’re giving them what they want and you’re not force-feeding them something. So, work with them to get them a product that they need and that they want, and really focus so much on adoption.
Don’t recreate processes, improve them. If you’re implementing Anaplan for sales forecasting, you’re already undergoing a pretty major change in how you do your sales forecasting.
So, use that as an opportunity, as a change agent to not just do the same process in a different tool or a different medium, but any of the things that along the way have kind of been legacied in from previous stages of the company.
We had tons of those. Use that as an opportunity to streamline them, to change them. You’re already doing so much work to improve your process. Don’t just do it from a technology perspective, do it from a systematic and a process perspective as well.
Find secondary usages. So, for us, I’ve mentioned this briefly, but we have a week-to-week snapshotting, which is how we structure our data at Anaplan. One of our biggest problems in reporting on the Salesforce is it’s all live.
Now that we have these snapshots of where our data was at different points in time, we can go back and see how certain opportunities changed over the course of the quarter.
That’s not why we have this model. That’s not why we designed it, but now that we have it, we’re like, “Oh, wow. This is another capability that it’s allowing us to do,” and it makes it a more appealing model and a more useful model if you can find these secondary uses that maybe weren’t in the initial design, or weren’t maybe in the initial value proposition.
And maintain end user focus. I mentioned that at the beginning, but at the end of the day, the point of this is to obviously help, for us, it was to help us as a sales ops group, but it was also, we wanted to give our stakeholders, the sales organization, something that they don’t already have. So, that was a huge piece for us.
Be willing to tailor stuff to them. Be willing to work with them and not force things down their throat. It’s been hugely helpful for us to look at it from that perspective.
So, coming back to here, like I said, I promised we’d go full circle, so I just want to talk for a second, and then I’ll leave some room for questions. I’m sure you guys are hungry and have sat through a bunch of these things at this point, so I’ll try to get you to lunch on time.
But for us, we want to integrate quota. We want to integrate attainment into our models, that we could have live leaderboards, President’s Club qualification, that sort of thing, within our sales forecasting model.
And then the dream, the end goal is the fully optimized forecasting. So, all the things I’ve been talking about where I’m saying, “Oh, managers can go bottoms up, or they can look at their KPIs and use that to inform an opinion,” we want to actually give them that opinion at some point. And obviously, not tell them what their forecast needs to be, but using this historic information we’re accumulating over time, we ought to be able to use that to our advantage.
And even from an operations perspective, we can say, “Hey, guys, forecast whatever you want, but this is going to be our internal forecast. And we might differ on a few things, but this is given the data we’ve accumulated in the last six to eight quarters or so, and this is what the model is telling us we’re going to do.” And that’s it.
Audience: Hey, Matt. Just a quick question. So, all the sales forecasting, the topline revenue forecasting is done in the sales organization? Or what role does finance FP&A have at—?
Matt: So, as an SaaS company, when I say sales forecasting, it’s all bookings. So, it’s the deals we’ve booked in that quarter. I know we have some of our FP&A guys in here who could talk to the revenue piece better than I could, but this is just closed deals. Our revenue recognition is an entirely different process. Yep.
Audience: With the three-hour thing, do you guys struggle with that?
Matt: No. So, another piece I didn’t mention is we have a one-off manual process we can run as well. So, if I ever want to go in there and do it myself, I can click a button and it’ll run. That happens a lot towards the end of our quarter because we’re pretty back-end loaded, and all sorts of stuff is happening in the last couple days.
But the three hours is pretty solid. And we’ve done a lot of work. Our initial time of that load, as the size of the sales or the number of opportunities coming in grew and grew, it was taking eight, nine minutes to run, and that became an issue.
So, we did a lot of work on that, and now it’s running in the background. It’s a little slow, but it’s only a three or four-minute data load, and that’s been hugely helpful for us and not all that disruptive if there are people in the system at that time.
All right. Well, thank you guys, and go get some food.