Tom Bogan is CEO of Adaptive Insights, a Workday company whose business planning cloud automates previously manual planning tasks—for finance, sales, and workforce planning—and supplements those capabilities with enterprise-scale reporting and analytics. Tom joined Adaptive Insights when it was at $40 million in revenue. Under his leadership, in 2017 Adaptive Insights topped $100 million in annual recurring revenue (ARR), a watershed milestone that only 1% of cloud software companies ever reach. Then, in June of 2018, three days before the company was to go public, Bogan and his team struck a deal with Workday, a leader in cloud corporate performance management, to acquire Adaptive Insights for $1.55 billion.
Now a Workday company, Adaptive Insights continues to cook up ways to help businesses see planning as a competitive advantage. Most recently, Adaptive Insights commissioned Plan to Win: Achieving business agility in the age of urgency. The new book seemed like a good reason to sit down with Tom and get his thoughts on the origins of Plan to Win, the state of cloud software tools, and how businesses will plan years from now.
Let’s start with the book. What prompted it?
We’re always talking to customers. In doing so we learned that many felt there simply wasn’t much written about why modern planning was critical to the success of a business. They’d gone through the transformation themselves—from slow, manual, siloed static planning to automated, collaborative, and continuous active planning—so they knew what it had done for their business agility. They wanted something they could share with others, a book they could pass along to colleagues in their organization who may still be stuck doing static planning, or might be considering a change but not sure how to map their journey.
The book isn’t just a reflection of our world view on planning. That’s in there, but it’s also a researched exploration of how businesses have used planning over time, and how the digital age made those old planning processes obsolete. And it’s filled with lessons we’ve learned from our customers, including a few whose experiences are profiled in the book. It’s a helpful tool for both companies new to a cloud planning process and for our customers who continue to expand their vision for business planning in their organizations.
We’ve known for a while that static planning doesn’t work. But for many companies, budgeting remains a painful annual ritual. Why haven’t they been able to break out of it?
A big reason is the tools they’re using. Most organizations still use spreadsheets. And unless you’re a very small company, spreadsheets quickly hinder your ability to plan across a wide population in your company.
Business and market conditions don’t change just once a year—they change constantly. So if you plan annually, you’re creating a plan that’s obsolete almost as soon as it’s done. So to operate with agility, you need to be able to update plans, budgets, and forecasts continuously, not just once a year. Many of our customers update their plans quarterly and some do it monthly, or at least as often as conditions change.
And as businesses grow, they realize that spreadsheet planning is cumbersome because they make it hard to coordinate all resources across the organization. Cloud-based planning enables a more agile process because everybody is using the same cloud system—not dozens of individual spreadsheets traded by email. They all work from the same data. There’s a single source of truth.
Putting planning into the cloud, having a flexible modeling platform, and deploying capabilities that allow you to engage in both financial and operational planning—this is what is helping break people out of static planning.
It seems like we've seen a mass proliferation of data analytics tools, data visualization tools, and so on. Companies seem to have one of each. Does that help planning or does it actually make it more fragmented?
I think it helps. When companies have a good understanding of their operations and the operating data, it inspires them to do richer planning.
For example, when I started doing planning in companies, we'd look at a revenue forecast. It would be more trend-based: What percentage are we going to increase this year? Today when we do our sales planning, we're looking at the number of reps we have and the number of leads we're generating. What are those conversion rates from marketing leads to sales opportunities? How many sales opportunities can our reps execute against? What are the expected win rates? All that rich operating data is now part of the plan.
Now apply that to agility. If our actuals are either better or worse than the assumptions in the plan, we can change and update that plan. Those regular updates allow you to course-correct when needed. Visualization systems, the tendency to collect metrics and KPIs and the ability to see those through the various analytics tools—all this provides a strong foundation because the operating metrics become the drivers or the assumptions in the plan. We built the plan and the models on top of that analytical data. They fit together beautifully. So the proliferation of analytics tools, together with the planning model, is very synergistic.
Workday's focus is primarily finance and HR. Does that constrain what you get from customers and does that kind of make it competitively disadvantaged for you because you don't have access to too much operational data?
That’s a great question. In the Adaptive Insights Business Planning Cloud, we certainly integrate with Workday solutions, but we also integrate with all other systems as well: General ledger systems from Oracle, SAP, NetSuite, and others; CRM systems like Salesforce; marketing automation platforms like Marketo, HubSpot or Eloqua—we integrate with all of these systems so that data is feeding our assumptions.
We describe the Business Planning Cloud as “best in class, better in suite.” This means if you’re using Adaptive Insights on its own or with a non-Workday ERP system, you’re still getting the best, most highly-rated planning solution in the industry. But it gets even better if you choose to implement solutions from Workday. We have not only HR and financial data from Workday business systems, but we can add information from other GL systems, from sub-ledgers, and from operating analytical data. We can either bring that natively into Adaptive Insights through our interfaces or we can bring it in through Workday’s Prism Analytics.
SAP has something called Integrated Business Planning, and they go into all kinds of manufacturing and transportation and distribution planning. Does that put you at a disadvantage?
Not at all, because we place no limitations on the amount of information or the number of dimensions you can build into a model. And it’s important to remember that Adaptive Insights is a broad-based modeling platform. You can model any business scenario in Adaptive Insights, just as you could in a spreadsheet.
For operational planning, we pre-build some functionality. For example, in sales planning, we know that most organizations will want to plan around the number of reps they have, by market segments, and possibly by vertical industry. They'll want to do territory planning and to measure key aspects or dimensions. We provide the framework that allows them to do that. Our customers use that framework to build models and solutions that are specific to their business needs.
One customer, an airline, uses Adaptive Insights to run route profitability analysis for their routes all over the world so they know which are the most profitable. Another is P.F. Chang’s, who is featured in the book. They model the operations at a restaurant level. Again, customers and users model any aspect of their business on the platform.
You mentioned how cloud has evolved planning; made it much more collaborative, much more available to a broader audience, etc. What is machine learning going to do to planning?
Great question. It's going to change everything. We think that planning is one of the areas that will be more impacted by ML than almost any other space. In the short term, we'll be able to leverage machine learning to do things like anomaly detection and to determine when information in a plan looks aberrant relative to what we'd expect based on analysis of history and trends. We'll be able to flag exceptions. The user will be able to decide whether they're appropriate exceptions or whether it's something that has to be looked at further.
Then we'll learn from that. We'll be able to better anticipate and predict what the data should look like. That's the short term.
Going forward, I think we'll be able to leverage machine learning to create additional planned scenarios automatically. We’ll identify the most critical assumptions or drivers in the plan. We’ll take the heavy lifting out of scenario planning.
We want to shift where people are spending their time, from creating the plan and crunching numbers to running analyses and really understanding what’s in the plan. What are the critical assumptions? What kind of scenarios can I run? Then I believe we'll also be able to assess confidence. A CFO can go to the board that this is a 75% probability plan or maybe it's a 25% probability plan. They can know where they stand before going in.
Improvement will be gradual, and then it will accelerate once it hits critical mass. I’m sure years from now, we’ll do planning in a completely different way. But I’m hopeful that five years from now, we’ll do planning in a completely different way.
Five to 10 years sounds realistic. But many people make it sound like it will happen tomorrow.
It depends on what we're talking about. Anomaly detection will happen sooner but that’s only the tip of the iceberg for machine learning. I think about the world in which we can automatically create plans and do a first pass. That’s a little further out.
Do you think planners are preparing for that? I talk to some companies where they have literally hundreds of planners, category planners, assortment planners in the CPG industry and so on. It just seems like there is a lot of labor in planning. Are you helping planners look forward to the day where machines will be their assistants?
Yes. Whenever you talk about automation, everybody assumes we’re going to eliminate jobs. That’s not the goal. It’s to reduce the number of labor hours on preparing a plan so you have more time for strategic analysis. That’s part of the return companies and organizations will experience.
I think the point you're making applies just about everywhere. Automation takes over the more mundane, routine tasks, and humans move up to a higher value in the workstream. What else did you learn from preparing this book?
Two things. One is that there was strong affirmation of the need for active planning—planning that’s continuous, with monthly or at least quarterly updates to the plan, and with the best practice of rolling forecasts, where you’re not bound by this artificial annual time boundary. The book confirms that this is an important trend for companies.
The other is the importance of machine learning. The leading thinkers, and some are featured in the book, really believe there's a huge opportunity to leverage machine learning in the planning process, to make it more agile. We agree and believe that the tomorrow’s winners will be the most agile.
Bruce Cleveland - Part 2 - thoughts on current enterprise market
In Part 1, Bruce Cleveland talked about the framework in his new book, Traversing the Traction Gap. Here we talk about partnering and white spaces in the market.
Most enterprise vendors now offer their own platforms. Could they use your framework to develop a vibrant network of startups on their platforms?
At Siebel, we used partners to dominate – I know the word is overused— the industry. We grew from initial product release to $2 billion in revenue in 4.5 years. In 1999, Deloitte named Siebel the “fastest growing company in the US” with a staggering CAGR of more than 750,000%. We only accomplished this because we (by “we” I mean Tom Siebel) put significant resources (personnel and capital) into building a technical platform and a robust ecosystem to support it.
Tom knew he had to “remove the constraints on the growth of the business.” We needed systems integrators who were trained and certified on Siebel implementations. We needed to our applications tuned to run optimally on various hardware systems. And, we needed numerous third-party applications that could interface and interoperate with our application suite.
Consequently, Tom charged me with building the Siebel Alliance Program. Tom had created our first systems integration partnership with Andersen (now Accenture). He also had created our first hardware relationship with Compaq and our first software partnership with Microsoft.
I simply took Tom’s initial ideas and those partnerships and developed a scalable partner program from them. We began the Siebel Alliance Program with Accenture, Compaq and Microsoft and over the course of three years we accumulated a total of 750 partners. These partners generated about a $B of additional annual license revenue for Siebel and helped us ensure that those licenses were implemented successfully. By partners, I don't mean they were reselling Siebel applications. I mean we partnered with them to market, sell, and implement our joint solutions. We were compensated on our products/services and they were compensated on their products/services.
So, as you might imagine, I am a huge fan of partnerships. We won the Forbes award for the most innovative partnering program in the technology industry at the time and every technology company knew how powerful it was. It wasn’t a secret. I find it interesting – with all the success we had with the Siebel Alliance Program - that none of the incumbent technology companies have replicated its form or function.
I believe the current technology platform leaders have a tremendous opportunity to develop a similar program. Sure, they all have partner programs, but those programs don’t really put a lot of skin in the game. By that, I mean, they don’t obligate the leaders to do much or to invest in their partners beyond providing basic access to some technology and lightweight market awareness.
For our program, we actively recruited and hired hundreds of MBAs from the top universities as well as industry professionals and chartered them to create joint business plans with our partners, advocate for our partners with our product and sales organizations, and held the Siebel Alliance organization accountable for helping our partners achieve their revenue and market share objectives. We built specific marketing programs – brand and demand gen – that partners could sponsor to generate leads for themselves. We put skin in the game.
The program was so successful that Harvard Business School wrote a business case study about it. So, I'm pretty proud of it.
Today, though, for the most part, the large technology platform companies offer what I call “paper partnerships”. Those partnership don’t obligate either party to much of anything. They're not “skin in the game” partnerships so they are anemic, at best.
Most startup teams have little experience partnering with these large technology providers. And, even if they do, they have found that those companies offer little that their startup can benefit from. There's little provided by the large company that is likely to generate significant market awareness or revenue for the startup. For example, startups need to know who, from the partner’s organization, wakes up every morning worried about helping the startup to succeed; where are the demand gen programs they can co-invest in to generate awareness and interest in the startup’s products? Which teams inside the large company are held accountable for achieving the market share and revenue objectives of the startups in their partner community? The answer? None. Those partner programs are essentially worthless – at least as far as the startup community is concerned.
Salesforce built an App Store and prospective customers can certainly search for and find add-on applications and products for the Salesforce ecosystem there. However, that's a pretty lightweight endorsement for startups. Who is held accountable within the four walls of Salesforce to wake up every morning, ensuring that a particular partner has the leads it needs, generating a robust pipeline, and helping the startup to convert that pipeline into revenue?
I don’t mean to call out Salesforce. At least they developed an innovative approach and put a lot of energy behind it. And, they have done some advanced partnering with Vlocity. Vlocity was founded by some of my former Siebel colleagues. What's Vlocity doing? They're building vertical applications on top of the force.com platform.
Salesforce saw a need to have much deeper vertical versions of their platform. That's what we did at Siebel (create vertical solutions of Siebel) and David Schmaier led that initiative as the executive VP. David is the founder and CEO of Vlocity. David and his team are replicating what we did at Siebel for Salesforce and it's going very well.
I'm an investor in Vlocity, and, to your point about partnering, it's an example of what can – and I think should - be done between technology platform providers and the startup ecosystem. As this example shows, it's not just about partnering with systems integrators.
If you want these partnerships to amount to anything there needs to be alignment and shared objectives. For example, if you want to garner the attention of the partner’s sales organization, there is nothing like making partner revenue a part of the sales rep’s quota.
The truth is that partner programs as conceived and implemented by the current technology platform leaders just don’t do much for startups – in terms of market share and revenue - and therefore it shouldn’t be surprising that those programs aren’t valued by startups.
Apple has done well in its ecosystem – with the App Store - but the consumer world doesn’t require face-to-face interactions. In the B2B world, people's work reputations are on the line when they make a buy decision. And, in almost all cases, there are multiple decision makers who must be convinced. For B2B, startups need brand credibility from the platform provider. This happens when they implicitly endorse the startup by working closely with them.
In the consumer world, a startup can “borrow” the brand of Apple because if their application is admitted into the Apple App Store, it tells consumers, "Look, we vetted this product. It adheres to our rules."
Conversely, I don’t think that Salesforce’s App Store does a lot for startups. It’s essentially a digital catalog with some ratings/rankings. That’s not enough for most businesses to make a buy decision. If you speak with startups about the Salesforce App Store and ask them if it is a significant source of revenue for them, I think you will find that for most, it isn’t.
Startups lack credibility. They lack access to prospective customers. And, they lack sufficient marketing dollars. This is where the enterprise vendors could really help startups.
I had hoped that the technology leaders would have adopted at least some of the best practices we had created with the Siebel Alliance Program. Tom Siebel and his new company, C3, are developing a similar program to what we had at Siebel Systems. So, I believe the principles are as valid today as they were back then.
When you look at the cloud apps market there are so many white spaces - major geographies, industry verticals etc - and they do not seem to be targeted by established vendors or even by startups. How are investors like you guiding them through these white spaces?
Cloud computing with its subscription business model - and delivery and usage model -has democratized business application software. Prior to cloud computing, it was prohibitively expensive for smaller companies to acquire and implement enterprise-class applications.
But, by dramatically dropping the cost of these solutions, I suspect it has constrained the application providers’ ability to support smaller geographies and industry verticals. Going after smaller markets that require significant translation and support infrastructure at much lower price points isn’t practical.
Even with traditional (re: higher priced) enterprise software, it was always challenging to create localized versions of products for a specific market. Most US companies started out with the English speaking markets and then typically moved to countries such as Germany, Spain, and France with relatively large populations. But, when they were asked to support a Dutch or Finnish version, it was seldom worth the upfront and ongoing cost.
For a large company to continue to grow at a significant rate, it needs to acquire or build new solutions that add significant revenue streams – quickly. In that context, let's talk about SAP and Salesforce. One is a classic enterprise technology company with primarily a perpetual license business model. The other is the "modern" cloud computing company with a subscription business model.
The challenge with both of those organizations is that to grow meaningfully they have to build or acquire something of meaningful size. Moving into a small geography requires the same resources or at least the same FTE costs that might be invested in something else and could generate a much larger impact. So, it should be little surprise why we are seeing these large companies look to make large acquisitions and avoid niche industries and geographical markets.
To your point, one might think, “Hey, if these market leaders are not seeing a lot of companies growing fast enough to make a measurable impact on their top line why aren't they helping smaller companies to grow so that they can eventually acquire them?”
Technology leaders may want to look at making investments in smaller technology companies where they see strategic value. These leaders can protect their interests by exchanging an equity investment and access to their channels with a Right of First Refusal (ROFR) that allows the leader to acquire the smaller company when it reaches a certain revenue threshold, at some predetermined price based upon a number of factors such as the acquirer’s stock multiple at the time.
With respect to investing in and expanding the number of verticals supported, there may be a dearth of deep domain expertise inside some of the larger technology companies to do that well. I don't know whether these companies have initiatives to try to hire subject matter experts from industry to help them drive their product management. They may and I'm just not privy to it.
I do know that Salesforce took a run at creating vertical solutions and it wasn't as effective as they had hoped, at least not at the time. This is one of the reasons that led to their investment in Vlocity. I think this makes the case that the technology leaders, at least those in the B2B markets and not normally inclined to partnering, could really benefit by embracing partnerships.
Instead, it looks like the majority of them sit back and wait to see which startups “make it”, without their help, and then acquire the few that do. This seems rather silly because by doing that they have to then compete in the open market – and pay a premium - to keep them out of the hands of competitors.
What do you find interesting in today's enterprise space?
Roughly every ten years, new technology is introduced that dramatically changes our work and personal lives. In the 60s it was mainframe computing. In the 70s it was minicomputers. In the 80s it was PCs. In the 90s it was client/server computing and in the 2000s, it was largely cloud and mobile computing which spawned “digital transformation”, beginning in the consumer markets with companies such as Amazon, Apple, Facebook, NetFlix, Google, etc. Digital transformation has now moved full speed into the enterprise markets.
I believe digital transformation - digital disruption - is such a big transition it will require multiple decades. I think it has a chance to undo a lot of the current market leaders in every industry. To your point, maybe vertical providers will emerge and dominate the markets due to digital transformation issues.
The emerging opportunity in the enterprise is in systems of intelligence. This is where I think vertical solutions can dominate. Systems of intelligence use the data generated and captured by systems of record and systems of engagement to enable companies to derive better business insights, faster and far more accurately than ever before. I mean real-time insights and real-time responses, without humans involved, that can be triggered via key business events and moments of value.
Here is an example in the retail space. Let’s say I'm walking through PetSmart and I'm a repeat PetSmart customer. I’m a member of the PetSmart loyalty program. If PetSmart has implemented a system of intelligence and placed beacons throughout the store, I have PetSmart’s loyalty application on my phone, and I provide my permission to send me offers, PetSmart can enable brands to generate contextually-relevant real-time offers to me as I walk through the store. An offer could be from a brand I don’t normally buy for my dog. The system of intelligence has access to POS data from my prior store purchases. PetSmart makes $ by serving up the competitive offer and if/when I decide to redeem the offer in a coupon that could be set to expire if I walk out of the store without making the purchase. Powering this type of application requires a different type of computer and systems architecture.
The biggest opportunity may come from vertical systems of intelligence built on top of the older systems of record and systems of engagement. In my PetSmart example, it means that you've got to be able to interface with an existing point of sale system, not replace it. This is where I would advise B2B application startups to focus. Trying to replace the existing systems of record and engagement is tough to do.
Trying to rip and replace technology is hard to do. Building next-generation systems of intelligence that utilize the existing systems of record and engagement is far easier. That's what a lot of the startups we work with are doing.
April 03, 2019 in Analytics, Big Data, Industry Commentary, Venture Capital/Private Equity, Vertical Markets (Banking, Retail etc) | Permalink | Comments (0)