Category Archives: EPM

The New Gartner 2018 Magic Quadrants for Cloud Financial Planning & Analysis and Cloud Financial Close Solutions

If all you’re looking for is the free download link, let’s cut to the chase:  here’s where you can download the new 2018 Gartner Magic Quadrant for Financial Planning and Analysis Solutions and the new 2018 Gartner Magic Quadrant for Cloud Financial Close Solutions.  These MQs are written jointly by John Van Decker and Chris Iervolino (with Chris as primary author on the first and John as primary author on the second).  Both are deep experts in the category with decades of experience.

Overall, I can say that at Host Analytics, we are honored to a leader in both MQs again this year.  We are also honored to be the only cloud pure-play vendor to be a leader in both MQs and we believe that speaks volumes about the depth and breadth of EPM functionality that we bring to the cloud.

So, if all you wanted was the links, thanks for visiting.  If, however, you’re looking for some Kellblog editorial on these MQs, then please continue on.

Whither CPM?
The first thing the astute reader will notice is that the category name, which Gartner formerly referred to as corporate performance management (CPM), and which others often referred to as enterprise performance management (EPM), is entirely missing from these MQs.  That’s no accident.  Gartner decided last fall to move away from CPM as a uber category descriptor in favor of referring more directly to the two related, but pretty different, categories beneath it.  Thus, in the future you won’t be hearing “CPM” from Gartner anymore, though I know that some vendors — including Host Analytics — will continue to use EPM/CPM until we can find a more suitable capstone name for the category.

Personally, I’m in favor of this move for two simple reasons.

  • CPM was a forced, analyst-driven category in the first place, dating back to Howard Dresner’s predictions that financial planning/budgeting would converge with business intelligence.  While Howard published the research that launched a thousand ships in terms of BI and financial planning industry consolidation (e.g., Cognos/Adaytum, BusinessObjects/SRC/Cartesis, Hyperion/Brio), the actual software itself never converged.  CPM never became like CRM — a true convergence of sales force automation (SFA) and contact center.  In each case, the two companies could be put under one roof, but they sold fundamentally different value propositions to very different buyers and thus never came together as one.
  • In accordance with the prior point, few customers actually refer to the category by CPM/EPM.  They say things much more akin to “financial planning” and “consolidation and close management.”  Since I like referring to things in the words that customers use, I am again in favor of this change.

It does, however, create one problem — Gartner has basically punted on trying to name a capstone category to include vendors who sell both financial planning and financial consolidation software.  Since we at Host Analytics think that’s important, and since we believe there are key advantages to buying both from the same vendor, we’d prefer if there were a single, standard capstone term.  If it were easy, I suppose a name would have already emerged [1].

How Not To Use Magic Quadrants
While they are Gartner’s flagship deliverable, magic quadrants (MQs) can generate a lot of confusion.  MQs don’t tell you which vendor is “best” because there is no universal best in any category.  MQs don’t tell you which vendor to pick to solve your problem because different solutions are designed around meeting different requirements.  MQs don’t predict the future of vendors — last-year’s movement vectors rarely predict this year’s positions.  And the folks I know at Gartner generally strongly dislike vector analysis of MQs because they view vendor placement as relative to each other at any moment in time [2].

Many things that customers seem to want from Gartner MQs are actually delivered by Gartner’s Critical Capabilities reports which get less attention because they don’t produce a simple, dramatic 2×2 output, but which are far better suited for determine the suitability of different products to different use-cases.

How To Use A Gartner Magic Quadrant?
In my experience after 25+ in enterprise software, I would use MQs for their overall purpose:  to group vendors into 4 different bucketsleaders, challengers, visionaries, and niche players.  That’s it.  If you want to know who the leaders are in a category, look top right.  If you want to know who the visionaries are, look bottom right.  You want to know which big companies are putting resources into the category but who thus far are lacking strategy/vision, then look top-left at the challengers quadrant.

But should you, in my humble opinion, get particularly excited about millimeter differences on either axes?  No.  Why?  Because what drives those deltas may have little, none, or in fact a counter-correlation to your situation.  In my experience, the analysts pay a lot of attention to the quadrants in which vendors end up in [2] so quadrant-placement, I’d say, is quite closely watched by the analysts.  Dot-placement, while closely watched by vendors, save for dramatic differences, doesn’t change much in the real world.  After all, they are called the magic quadrants, not the magic dots.

All that said, let me wind up with some observations on the MQs themselves.

Quick Thoughts on the 2018 Cloud FP&A Solutions MQ
While the MQs were published at the end of July 2018, they were based on information about the vendors gathered in and largely about 2017.  While there is always some phase-lag between the end of data collection and the publication data, this year it was rather unusually long — meaning that a lot may have changed in the market in the first half of 2018 that customers should be aware of. For that reason, if you’re a Gartner customer and using either the MQs or critical capabilities reports that accompany them, you should probably setup an appointment to call the analysts to ensure you’re working off the latest data.

Here are some of my quick thoughts the Cloud FP&A Solutions magic quadrant:

  • Gartner says the FP&A market is accelerating its shift from on-premises cloud.  I agree.
  • Gartner allows three types of “cloud” vendors into this (and the other) MQ:  cloud-only vendors, on-premise vendors with new built-for-the-cloud solutions, and on-premises vendors who allow their software to be run hosted on a third-party cloud platform.  While I understand their need to be inclusive, I think this is pretty broad — the total cost of ownership, cash flows, and incentives are quite different between pure cloud vendors and hosted on-premises solutions.  Caveat emptor.
  • To qualify for the MQ vendors must support at least two of the four following components of FP&A:  planning/budgeting, integrated financial planning, forecasting/modeling, management/performance reporting.  Thus the MQ is not terribly homogeneous in terms of vendor profile and use-cases.
  • For the second year in a row, (1) Host is a leader in this MQ and (2) is the only cloud pure-play vendor who is a leader in both.  We think this says a lot about the breadth and depth of our product line.
  • Customer references for Host cited ease of use, price, and solution flexibility as top three purchasing criteria.  We think this very much represents our philosophy of complex EPM made easy.

Quick Thoughts on the 2018 Cloud Financial Close Solutions MQ
Here are some of my quick thoughts on the Cloud Financial Close Solutions magic quadrant:

  • Gartner says that in the past two years the financial close market has shifted from mature on-premises to cloud solutions.  I agree.
  • While Gartner again allowed all three types of cloud vendors in this MQ, I believe some of the vendors in this MQ do just-enough, just-cloud-enough business to clear the bar, but are fundamentally still offering on-premise wolves in cloud sheep’s clothing.  Customers should look to things like total cost of ownership, upgrade frequency, and upgrade phase lags in order to flesh out real vs. fake cloud offerings.
  • This MQ is more of a mixed bag than the FP&A MQ or, for that matter, most Gartner MQs.  In general, MQs plot substitutes against each other — each dot on an MQ usually represents a vendor who does basically the same thing.  This is not true for the Cloud Financial Close (CFC) MQ — e.g., Workiva is a disclosure management vendor (and a partner of Host Analytics).  However, they do not offer financial consolidation software, as does say Host Analytics or Oracle.
  • Because the scope of this MQ is broad and both general and specialist vendors are included, customers should either call the Gartner for help (if they are Gartner customers) or just be mindful of the mixing and segmentation — e.g., Floqast (in SMB and MM) and Blackline (in enterprise) both do account reconciliation, but they are naturally segmented by customer size (and both are partners of Host, which does financial consolidation but not account reconciliation).
  • Net:  while I love that the analysts are willing to put different types of close-related, office-of-the-CFO-oriented vendors on the same MQ, it does require more than the usual amount of mindfulness in interpreting it.

Conclusion
Finally, if you want to analyze the source documents yourself, you can use the following link to download both the 2018 Gartner Magic Quadrant for Financial Planning and Analysis and Consolidation and Close Management.

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Notes

[1] For Gartner, this is likely more than a semantic issue.  They are pretty strong believers in a “post-modern” ERP vision which eschews the idea of a monolithic application that includes all services, in favor of using and integrating a series of cloud-based services.  Since we are also huge believers in integrating best-of-breed cloud services, it’s hard for us to take too much issue with that.  So we’ll simply have to clearly articulate the advantages of using Host Planning and Host Consolidations together — from our viewpoint, two best-of-breed cloud services that happen to come from a single vendor.

[2] And not something done against absolute scales where you can track movement over time.  See, for example, the two explicit disclaimers in the FP&A MQ:

Capture

[3] I’m also a believer in a slightly more esoteric theory which says:  given that the Gartner dot-placement algorithm seems to try very hard to layout dots in a 45-degree-tilted football shaped pattern, it is always interesting to examine who, how, and why someone ends up outside that football.

The Use of Ramped Rep Equivalents (RREs) in Sales Analytics and Modeling

[Editor’s note:  revised 7/18, 6:00 PM to fix spreadsheet error and change numbers to make example easier to follow, if less realistic in terms of hiring patterns.]

How many times have you heard this conversation?

VC:  how many sales reps do you have? 

CEO:  Uh, 25.  But not really.

VC:  What do you mean, not really?

CEO:  Well, some of them are new and not fully productive yet.

VC:  How long does it take for them to fully ramp?

CEO:  Well, to full productivity, four quarters.

VC:  So how many fully-ramped reps do you have?

CEO:  9 fully ramped, but we have 15 in various stages of ramping, and 1 who’s brand new …

There’s a better way to have this conversion, to perform your sales analytics, and to build your bookings capacity waterfall model.  That better way involves creating a new metric called ramped rep equivalents (RREs). Let’s build up to talking about RREs by first looking at a classical sales bookings waterfall model.

ramped rep equivalents, picture 1, revised

I love building these models and they’re a lot of fun to play with, doing what-if analysis, varying the drivers (which are in the orange cells) and looking at the results.  This is a simplified version of what most sales VPs look at when trying to decide next year’s hiring, next year’s quotas [1], and next year’s targets.  This model assumes one type of salesrep [2]; a distribution of existing reps by tenure as 1 first-quarter, 3 second-quarter, 5 third-quarter, 7 fourth-quarter, and 9 steady-state reps; a hiring pattern of 1, 2, 4, 6 reps across the four quarters of 2019; and a salesrep productivity ramp whereby reps are expected to sell 0% of steady-state productivity in their first quarter with the company, and then 25%, 50%, 75% in quarters 2 through 4 and then become fully productive at quarter 5, selling at the steady-state productivity level of $1,000K in new ARR per year [3].

Using this model, a typical sales VP — provided they believed the productivity assumptions [4] and that they could realistically set quotas about 20% above the target productivity — would typically sign up for around a $22M new ARR bookings target for the coming year.

While these models work just fine, I have always felt like the second block (bookings capacity by tenure), while needed for intermediate calculations, is not terribly meaningful by itself.  The lost opportunity here is that we’re not creating any concept to more easily think about, discuss, and analyze the productivity we get from reps as they ramp.

Enter the Ramped Rep Equivalent (RRE)
Rather than thinking about the partial productivity of whole reps, we can think about partial reps against whole productivity — and build the model that way, instead.  This has the by-product of creating a very useful number, the RRE.  Then, to get bookings capacity just multiply the number of RREs times the steady-state productivity.  Let’s see an example below:

ramped rep equivalents, picture 2, revised

This provides a far more intuitive way of thinking about salesrep ramping.  In 1Q19, the company has 25 reps, only 9 of whom are fully ramped, and rest combine to give the productivity of 8.5 additional reps, resulting in an RRE total of 17.5.

“We have 25 reps on board, but thanks to ramping, we only have the capacity equivalent to 17.5 fully-ramped reps at this time.”

This also spits out three interesting metrics:

  • RRE/QCR ratio:  an effective vs. nominal capacity ratio — in 1Q19, nominally we have 25 reps, but we have only the effective capacity of 17.5 reps.  17.5/25 = 70%.
  • Capacity lost to ramping (dollars):  to make the prior figure more visceral, think of the sales capacity lost due to ramping (i.e., the delta between your nominal and effective capacity) expressed in dollars.  In this case, in 1Q19 we’re losing $1,875K of our bookings capacity due to ramping.
  • Capacity lost to ramping (percent):  the same concept as the prior metric, simply expressed in percentage terms.  In this case, in 1Q19 we’re losing 30% of our bookings capacity due to ramping.

Impacts and Cautions
If you want to move to an RRE mindset, here are a few tips:

  • RREs are useful for analytics, like sales productivity.  When looking at actuals you can measure sales productivity not just by starting-period or average-period reps, but by RRE.  It will provide a much more meaningful metric.
  • You can use RREs to measure sales effectiveness.  At the start of each quarter recalculate your theoretical capacity based on your actual staffing.  Then divide your actuals by that start-of-quarter theoretical capacity and you will get a measure of how well you are performing, i.e., the utilization of the quarterly starting capacity in your sales force.  When you’re missing sales targets it is typically for one of two reasons:  you don’t have enough capacity or you’re not making use of the capacity you have.  This helps you determine which.
  • Beware that if you have multiple types of reps (e.g., corporate and field), you be tempted to blend them in the same way you do whole reps today –i.e., when asked “how many reps do you have?” most people say “15” and not “9 enterprise plus 6 corporate.”  You have the same problem with RREs.  While it’s OK to present a blended RRE figure, just remember that it’s blended and if you want to calculate capacity from it, you should calculate RREs by rep type and then get capacity by multiplying the RRE for each rep type by their respective steady-state productivity.

I recommend moving to an RRE mindset for modeling and analyzing sales capacity.  If you want to play with the spreadsheet I made for this post, you can find it here.

Thanks to my friend Paul Albright for being the first person to introduce me to this idea.

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Notes
[1] This is actually a productivity model, based on actual sales productivity — how much people have historically sold (and ergo should require little/no cushion before sales signs up for it).  Most people I know work with a productivity model and then uplift the desired productivity by 15 to 25% to set quotas.

[2] Most companies have two or three types (e.g., corporate vs. field), so you typically need to build a waterfall for each type of rep.

[3] To build this model, you also need to know the aging of your existing salesreps — i.e., how many second-, third-, fourth-, and steady-state-quarter reps you have at the start of the year.

[4] The glaring omission from this model is sales turnover.  In order to keep it simple, it’s not factored in here. While some people try to factor in sales turnover by using reduced sales productivity figures, I greatly prefer to model realistic sales productivity and explicitly model sales turnover in creating a sales bookings capacity model.

[5] This is one reason it’s so expensive to build an enterprise software sales force.  For several quarters you often get 100% of the cost and 50% of the sales capacity.

[6] Which should be an weighted average productivity by type of rep weighted by number of reps of each type.

My Appearance on DisrupTV Episode 100

Last week I sat down with interviewers Doug Henschen, Vala Afshar, and a bit of Ray Wang (live from a 777 taxiing en route to Tokyo) to participate in Episode 100 of DisrupTV along with fellow guests DataStax CEO Billy Bosworth and big data / science recruiter Virginia Backaitis.

We covered a full gamut of topics, including:

  • The impact of artificial intelligence (AI) and machine learning (ML) on the enterprise performance management (EPM) market.
  • Why I joined Host Analytics some 5 years ago.
  • What it’s like competing with Oracle … for basically your entire career.
  • What it’s like selling enterprise software both upwind and downwind.
  • How I ended up on the board of Alation and what I like about data catalogs.
  • What I learned working at Salesforce (hint:  shoshin)
  • Other lessons from BusinessObjects, MarkLogic, and even Ingres.

DisrupTV Episode 100, Featuring Dave Kellogg, Billy Bosworth, Virginia Backaitis from Constellation Research on Vimeo.

 

EPM, Project Orion, and the Beginner’s Mind

I’ll always be thankful for my time at Salesforce both because I met so many amazing people and because I learned so much.  I learned about the importance of Trust in a SaaS company (and was drilled in the mantra, “nothing is more important than the Trust of our customers.”)  And I learned about shoshin, the Zen concept of the Beginner’s Mind.

The Beginner’s Mind
It’s not unusual when working at Salesforce to hear about Zen concepts or get an email reply from Marc containing only a Zen proverb.  But of all the concepts I learned about, the most powerful and elusive was shoshin, a concept that Benioff says he adopted from Steve Jobs.  Per Wikipedia:

Shoshin (初心) is a word from Zen Buddhism meaning “Beginner’s Mind.” It refers to having an attitude of openness, eagerness, and lack of preconceptions when studying a subject, even when studying at an advanced level, just as a beginner would.

Shoshin is powerful because it enables you to take a fresh look at an old problem.  Shoshin is elusive, however, because it requires you to step outside your paradigm — the filters through which you see the world — which perhaps sounds easy, but can be incredibly difficult.  In fact, in what I all the paradox of knowledge, the more you know about something the more difficult it is to break out of your paradigm, to get outside the metaphorical box.

As an example of this, our head of products, Sanjay Vyas, recently went to a silent, ten-day vipassana meditation retreat.  Vipassana means “to see things as they really are”  and is a technique that has been passed down from the Buddha by an unbroken chain of teachers to the present day.  At the retreat, the first phase is three days spent simply trying to calm the noise in your mind.  Only then, after three days of silent meditation, are you ready to start to attempt to see things as they really are.  Such is the difficulty in breaking free from a paradigm.

The Problem We Approached With a Beginner’s Mind
What problem did we try to see with a Beginner’s Mind at Host Analytics?  End-user planning, budgeting, and forecasting (three key pieces of enterprise performance management, also known as EPM).  Why did we do it?  Because despite decades of great success within finance organizations, we believe that EPM has under-penetrated the overall market.

Far too many people rely solely on Excel for planning/budgeting and far too many EPM end-users build budgets in Excel and mail them to finance as opposed to using the EPM system.  The same is true for reporting, where far too often users drop out of the EPM system and into Excel to make reports and charts.  (This is less true of Host users due to our strong reporting, but the trend remains true at an industry level.)

While as EPMers, we take great pride in our category and, at Host, in our ability to move enterprise-class EPM to the cloud, we must recognize that at some level EPM has failed to deliver against its broad vision of accountability and empowerment.  To get to the bottom of this, as Clayton Christensen has often observed, you can’t just talk to your customers to understand your market, you need to understand non-consumers as well.  All those Excel-only or primarily-Excel users are Christensen’s non-consumers, so we decided to talk to them.  Here’s an example of what we heard.

“I hate budgeting.  They made me attend the meeting to look at these tools.  I don’t want to use any of them.”  — Chief Legal Officer

We heard this over and over.  The average business user would seemingly prefer a root canal to working on the budget.  Yet we knew these same business users were passionate about metrics, empowerment, accountability, and performance.  So where had the whole category gone wrong?  Thus was born Project Orion.

By Finance For Finance
We realized that for forty years EPM has been designed by finance for finance (or even more specifically, by FP&A for FP&A).  EPM vendors did a great job of listening to EPM customers.  And EPM customers, particularly EPM buyers, often had job titles like Vice President of Financial Planning & Analysis (FP&A).  These were the people who selected the tools.  These were the people who bought the tools.  But, these weren’t always the people who used the tools.  An important part of EPM is to roll it out broadly across an organization, meaning to put the tool into the hands of business end-users, budget owners, in all the various departments.

The Perils of “Configuration” to Dumb Down the Interface
The universal answer to the end-user question was dumb it down.  Configure it.  Take the product that was built for a heavily analytical, highly skilled, finance professional — and FP&A people are whip smart — and dumb down the interface for a business end-user.  Hide some menu items.  Remove some toolbar buttons.  Take away some tabs.

That was the conventional wisdom.  Take a product built for one person and configure it for use by another.  Now some EPM vendors were better than others at this bluff, some had slicker interfaces that would be relatively more appealing than others.  But amazingly, nobody ever said,  “wait a minute, what if we designed the product for people who actually used it?

Thank to shoshin, that’s exactly what we did with Project Orion at Host Analytics.

Task-Oriented Design
Instead of starting with what we had, a template-oriented product built for finance people, and a desire to twist/configure into something else, we started with a blank sheet.  We asked business end-users what they wanted to do with an EPM product.  Those end-users gave us a three-part answer:

  • We want to be able to quickly figure out where we stand relative to the plan.
  • We want help in determining where we are going to land on the current quarter — and to optimize that result.  (Not an easy problem, mind you.)
  • We want to get the next period planned in line with objectives and targets.

And we want to do all of the above quickly and easily because, much as we love this stuff (and we don’t), we’ve got a business to run.  This idea, what we came to call the stand / land / planned message, became the center of Orion design.

How We Knew We Were Onto Something
We noticed quickly that people had strong reactions to Orion, which typically fell into one of two types:

  • Reaction 1:  “Holy Cow, why didn’t I think of that?  It’s kind of obvious in 20/20 hindsight.”
  • Reaction 2:  “That’s not needed.  You just need to configure your way out of the problem.”

In the early days, we got a lot of reaction 2 — particularly from our internal EPM experts, who were somewhat blinded by the paradox of knowledge.  The internal resistance was, at times, intense.  But that resistance told me that we were onto something.  We were challenging the conventional wisdom in a way that could lead to a major breakthrough.  And the more we asked people outside Host, and the more we showed Orion to business end-users, the more convinced we were that we had made such a breakthrough.

The same chief legal officer who said “I hate budgeting” above, said this:

“When I look at Project Orion, it’s clear that you are the only folks thinking about me.  I could and would use this tool.” — Chief Legal Officer after seeing Orion.

Tips on Adopting a Beginner’s Mind
We’re launching Project Orion today and proud both of the software we built and how we came to build it.  We believe Orion is a breakthrough product that is going to change the EPM market.  All because we looked at an age-old problem in EPM with a Beginner’s Mind.

I’ll finish the post with some tips on how to take a shoshin approach that we learned along our journey — and which happily don’t involve 10 days of silent meditation.

  • Put a mix of veterans and neophytes on the project.  This will reduce the paradox of knowledge and naturally bring in some fresh eyes.
  • Confront tough facts.  The data says lots of people still use only or primarily Excel despite 40 years of EPM.  That’s a fact.  The question is why?
  • Challenge the team to document hidden assumptions.  Configuration as the solution to the end-user problem was one such huge assumption.  You can only go outside the box when you know its edges.
  •  Talk to non-consumers.  Talking to customers is great, but it can create an echo chamber.  Talk to non-consumers, too, particularly when fishing for breakthroughs, and ask them why they have not purchased in the product category.
  • Embrace resistance.  View resistance as a good sign, as a sign that you’re changing something big, and not just as a yellow flag.
  • Test early and often.  Go back to the non-consumers you interviewed and ask if your prototype would change their mind.  Iterate in response.

 

 

Kellblog Predictions for 2018

In continuing my tradition of offering predictions every year, let’s start with a review of my hits and misses on my 2017 predictions.

  1. The United States will see a level of divisiveness and social discord not seen since the 1960s.  HIT.
  2. Social media companies finally step up and do something about fake news. MISS, but ethical issues are starting to catch up with them.
  3. Gut feel makes a comeback. HIT, while I didn’t articulate it as such, I see this as the war on facts and expertise (e.g., it’s cold today ergo global warming isn’t real despite what “experts” say).
  4. Under a volatile leader, we can expect sharp reactions and knee-jerk decisions that rattle markets, drive a high rate of staff turnover in the Executive branch, and fuel an ongoing war with the media.  HIT.
  5. With the new administration’s promises of $1T in infrastructure spending, you can expect interest rates to raise and inflation to accelerate. MISS, turns out this program was never classical government investment in infrastructure, but a massive privatization plan that never happened.
  6. Huge emphasis on security and privacy. PARTIAL HIT, security remained a hot topic and despite numerous major breaches it’s still not really hit center stage.
  7. In 2017, we will see more bots for both good uses (e.g., customer service) and bad (e.g., trolling social media).  HIT.
  8. Artificial intelligence hits the peak of inflated expectations. HIT.
  9. The IPO market comes back. MISS, though according to some it “sucked less.”
  10. Megavendors mix up EPM and ERP or BI. PARTIAL HIT.  This prediction was really about Workday and was correct to the extent that they’ve seemingly not made much progress in EPM.

Kellblog’s Predictions for 2018

1.  We will again continue to see a level of divisiveness and social discord not seen since the 1960s. We have evolved from a state of having different opinions about policies based on common facts to a dangerous state based on different facts, even on easily disprovable claims, e.g., the White House nativity scene.  The media is advancing, not reducing, this divide.

2.  The war on facts and expertise will continue to escalate. Read The Death of Expertise for more.   This will extend to a war on college. While an attempted opening salvo on graduate student tuition waivers didn’t fire, in an environment where the President’s son says, “we’ll take $200,000 of your money; in exchange we’ll train your children to hate our country,” you can expect ongoing attacks on post-secondary education.  This spells trouble for Silicon Valley, where a large number of founders and entrepreneurs are former grad students as well as immigrants (which is a whole different area of potential trouble).

3.  Leading technology and social media companies finally step up to face ethical challenges. This means paying more attention to their own culture (e.g., sexual harassment, brogrammers).  This means taking responsibility for policing trolls, spreading fake news, building addictive content, and enabling foreign intelligence operations.  Thus far, they have tended to argue they are simply keepers of the town square, and not responsible for the content shared there.  This abdication of responsibility should start to stop in 2018, if only because people start to tune-out the services.  This leads to one of my favorite tweets of the year:

Capture

4.  AI will move from hype to action, meaning bigger budgets, more projects, and some high visibility failures. It will also mean more emphasis on voice and more conversational chatbots.  For finance departments, this means more of what Ventana’s Rob Kugel calls the age of robotic finance, which unites AI and machine learning, robotic process automation (RPA), natural language bots, and blockchain-based distributed ledgers.

5. AI will continue to generate lots of controversy about job displacement. While some remain optimistic, the consensus viewpoint seems to be that AI will suppress employment, most likely widening the wealth inequality gap.  A collapsing educational system combined with AI-driven pressure on low-skilled work seems a recipe for trouble.

6.  The bitcoin bubble bursts. As a reminder, at one point during the peak of tulip mania, the Dutch East India Company was worth more, on an inflated-adjusted basis, than twenty of today’s technology giants combined.

tulips

7.  The Internet of Things (IoT) will continue to build momentum.  IoT won’t hit in a massive horizontal way, instead B2B adoption will be lead by certain verticals such as healthcare, retail, and supply chain.

8.  The freelance / gig economy continues to gain momentum with freelance workers poised to pass traditional employees by 2027. While the gig economy brings advantages to high-skilled knowledge workers (e.g., freedom of location, freedom of work projects), this same trend threatens low-skilled workers via the continual decomposition of full-time jobs in a series of temp shifts.  This means someone working 60 hours a week across three 20-hour shifts wouldn’t be considered to be a full-time employee and thus not eligible for full-time benefits, further increasing wealth inequality.

freelancers

9.  M&A heats up due to repatriation of overseas cash. Apple alone, for example, has $252B in overseas cash.  With the new tax rate dropping from 35% to 15.5%, it will now be ~$50B less expensive for Apple to repatriate that cash.  Overall, US companies hold trillions of dollars overseas and making it cheaper for them to repatriate that cash suggests that they will be flush with dollars to invest in many areas, including M&A

10.  2018 will be a good year for cloud EPM vendors. The dynamic macro environment, the opportunities posed by cash repatriation, and the strong fundamentals in the economy will increase demand for EPM software that helps companies explore how to best exploit the right set of opportunities facing them.  Oracle will fail in pushing PBCS into the NetSuite base, creating a nice third-party opportunity.  SAP, Microsoft, and IBM will continue to put resources into other strategic investment areas (e.g., IBM and Watson, SAP and Hana) leaving fallow the EPM market adjacent to ERP.  And the greenfield opportunity to replace Excel for financial planning, budgeting, and even consolidations will continue drive strong growth.

Let me wish everyone, particularly the customers, partners, and employees of Host Analytics, a Happy New Year in 2018.

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Disclaimer:  these predictions are offered in the spirit of fun.  See my FAQ for more on this and other usage terms.

Putting the A Back in FP&A with Automated, Integrated Planning

I was reading this blog post on Continuous Planning by Rob Kugel of Ventana Research the other day and it reminded me of one of my (and Rob’s) favorite sayings:

We need to put the A back in FP&A

This means that the financial planning and analysis (FP&A) team at many companies is so busy doing other things that it doesn’t have time to focus on what it does best and where it can add the most value:  analysis.

This begs the question:  where did the A go?  What are the other things that are taking up so much time?  The answer:  data prep and spreadsheet jockeying.  These functions suck time away and the soul from the FP&A function.

dataprep

Data-related tasks — such as finding, integrating, and preparing data — take up more than 2/3rds of FP&A’s time.  Put differently, FP&A spends twice as much time getting ready to analyze data than it does analyzing it.  It might even be worse, depending on whether periodic and ad hoc reporting is included in data-related task or further carved out of the 28% of time remaining for analytics, as I suspect it is.

spreadsheetsrule

It’s not just finance who loves spreadsheets.  The business does do:  salesops, marketingops, supply chain planners, professional services ops, and customer support all love spreadsheets, too.  When I worked at Salesforce, we had one of the most sophisticated sales strategy and planning teams I’ve ever seen.  Their tool of choice?  Excel.

This comes back to haunt finance in three ways:

  • Warring models, for example, when the salesops new bookings model doesn’t foot to the finance one because they make different ramping and turnover assumptions.  These waste time with potential endless fights.
  • Non-integrated models.  Say sales and finance finally agree on a bookings target and to hire 5 more salespeople to support it.  Now we need to call marketing to update their leadgen model to ensure there’s enough budget to support them, customer service to ensure we’re staffed to handle the incremental customers they sign, professional services to ensure we’re have adequate consulting resources, and on and on.  Forget any of these steps and you’ll start the year out of balance, with unattainable targets somewhere.
  • Excel inundation.   FP&A develops battle fatigue dealing with and integrating some many different versions of so many spreadsheets, often late and night and under deadline pressure.  Mistakes gets made.

So how can prevent FP&A from being run over by these forces?  The answer is to automate, automate, and integrate.

  • Automate data integration and preparation.  Let’s free up time by use software that lets you “set and forget” data refreshes.  You should be able to setup a connector to a data source one, and then have it automatically run at periodic intervals going forward.  No more mailing spreadsheets around.
  • Automate periodic FP&A tasks.  Use software where you can invest in building the perfect monthly board pack, monthly management reports, quarterly ops review decks, and quarterly board reports once, and then automatically refresh it every period through these templates.  This not only free up time and reduces drudgery; it eliminates plenty of mistakes as well.
  • Integrate planning across the organization.  Move to a cloud-based enterprise performance platform (like Host Analytics) that not only accomplishes the prior two goals, but also offers a modeling platform that can be used across the organization to put finance, salesops, marketingops, professional services, supply chain, HR, and everyone else across the organization on a common footing.

Since the obligatory groundwork in FP&A is always heavy, you’re not going to succeed in putting the A back in FP&A simply by working harder and later.  The only way to put the A back in FP&A is to create time.  And you can do that with two doses of automation and one of integration.

Win Rates, Close Rates and Milestone vs. Flow Analysis

Hey, what’s your win rate?

It’s another seemingly simple question.  But, like most SaaS metrics, when you dig deeper you find it’s not.  In this post we’ll take a look at how to calculate win rates and use win rates to introduce the broader concept of milestone vs. flow analysis that applies to conversion rates across the entire sales funnel.

Let’s start with some assumptions.  Once an opportunity is accepted by sales (known as a sales-accepted opportunity, or SAL), it eventually will end up in one of three terminal states:

  • Won
  • Lost
  • Other (derailed, no decision)

Some people don’t like “other” and insist that opportunities should be exclusively either won or lost and that other is an unnecessary form of lost which should be tracked with a lost reason code as opposed to its own state.  I prefer to keep other, and call it derailed, because a competitive loss is conceptually different from a project cancellation, major delay, loss of sponsor, or a company acquisition that halts the project.  Whether you want to call it other, no decision, or derailed, I think having a third terminal state is warranted from first principles.  However, it can make things complicated.

For example, you’ll need to calculate win rates two ways:

  • Win rate, narrow = wins / (wins + losses)
  • Win rate, broad = wins / (wins + losses + derails)

Your narrow win rate tells you how good you are at beating the competition.  Your broad rates tells you how good you are at closing deals (that come to a terminal state).

Narrow win rate alone can be misleading.  If I told you a company had a 66% win rate, you might be tempted to say “time to add more salespeople and scale this thing up.”  If I told you they got the 66% win rate by derailing 94 out of every 100 opportunities it generated, won 4, and lost the other 2, then you’d say “not so fast.”  This, of course, would show up in the broad win rate of 4%.

This brings up the important question of timing.  Both these win rate calculations ignore deals that push out of a quarter.  So another degenerate case is a situation where you win 4, lose 2, derail 4, and push 90 opportunities.  In this case, narrow win rate = 66% and broad win rate = 40%.  Neither is shining a light on the problem (which, if it happens continuously, I call a rolling hairball problem.)

The issue here is thus far we’ve been performing what I call a milestone analysis.  In effect, we put observers by the side of the road at various milestones (created, won, lost, derailed) and ask them to count the number opportunities that pass by each quarter.  The issue, especially with companies that have long sales cycles, is that you have no idea of progression.  You don’t know if the opportunities that passed “win” this quarter came from the opportunities that passed “created” this quarter, or if they came from last quarter, the quarter before that, or even earlier.

Milestone analysis has two key advantages

  • It’s easy — you just need to count opportunities passing milestones
  • It’s instant — you don’t have to wait to see how things play out to generate answers

The big disadvantage is it can be misleading, because the opportunities hitting a terminal state this quarter were generated in many different time periods.  For a company with an average 9 month sales cycle, the opportunities hitting a terminal state in quarter N, were generated primarily in quarter N-3, but with some coming in quarters N-2 and N-1 and some coming in quarters N-4 and N-5.  Across that period very little was constant, for example, marketing programs and messages changed.  So a marketing effectiveness analysis would be very difficult when approached this way.

For those sorts of questions, I think it’s far better to do a cohort-based analysis, which I call a flow analysis.  Instead of looking at all the opportunities that hit a terminal state in a given time period, you go back in time, grab a cohort of opportunities (e.g., all those generated in 4Q16) and then see how they play out over time.  You go with the flow.

For marketing programs effectiveness, this is the only way to do it.  Instead of a time-based cohort, you’d take a programs-based cohort (e.g., all the opportunities generated by marketing program X), see how they play out, and then compare various programs in terms of effectiveness.

The big downside of flow analysis is you end up analyzing ancient history.  For example, if you have a 9 month average sales cycle with a wide distribution around the mean, you may need to wait 15-18 months before the vast majority of the opportunities hit a terminal state.  If you analyze too early, too many opportunities are still open.  But if you put off analysis then you may get important information, but too late.

You can compress the time window by analyzing programs effectiveness not to sales outcomes but to important steps along the funnel.  That way you could compare two programs on the basis of their ability to generate MQLs or SALs, but you still wouldn’t know whether and at what relative rate they generate actual customers.  So you could end up doubling down on a program that generates a lot of interest, but not a lot of deals.

Back to our original topic, the same concept comes up in analyzing win rates.  Regardless of which win rate you’re calculating, at most companies you’re calculating it on a milestone basis.  I find milestone-based win rates more volatile and less accurate that a flow-based SAL-to-close rate.  For example, if I were building a marketing funnel to determine how many deals I need to hit next year’s number, I’d want to use a SAL-to-close rate, not a win rate, to do so.  Why?  SAL-to-close rates:

  • Are less volatile because they’re damped by using long periods of time.
  • Are more accurate because they actually tracking what you care about — if I get 100 opportunities, how many close within a given time period.
  • Automatically factor in derails and slips (the former are ignored in the narrow win rate and the latter ignored in both the narrow and broad win rates).

Let’s look at an example.  Here’s a chart that tracks 20 opportunities, 10 generated in 1Q17 and 10 generated in 2Q17, through their entire lifetime to a terminal stage.

oppty tracking

In reality things are a lot more complicated than this picture because you have opportunities still being generated in 3Q17 through 4Q18 and you’ll have opportunities that are still in play generated in numerous quarters before 1Q17.  But to keep things simple, let’s just analyze this little slice of the world.  Let’s do a milestone-based win/loss analysis.

win-loss

First, you can see the milestone-based win/loss rates bounce around a lot.  Here it’s due in part due to law of small numbers, but I do see similar volatility in real life — in my experience win rates bounce within a fairly broad zone — so I think it’s a real issue.  Regardless of that, what’s indisputable is that in this example, this is how things will look to the milestone-based win/loss analyzer.  Not a very clear picture — and a lot to panic about in 4Q17.

Let’s look at what a flow-based cohort analysis produces.

cohort1

In this case, we analyze the cohort of opportunities generated in the year-ago quarter.  Since we only generate opportunities in two quarters, 1Q17 and 2Q17, we only have two cohorts to analyze, and we get only two sets of numbers.  The thin blue box shows in opportunity tracking chart shows the data summarized in the 1Q18 column and the thin orange box shows the data for the 2Q18 column.  Both boxes depict how 3 opportunities in each cohort are still open at the end of the analysis period (imagine you did the 1Q18 analysis in 1Q18) and haven’t come to final resolution.  The cohorts both produce a 50% narrow win rate, a 43% vs. 29% broad win rate, and a 30% vs. 20% close rate.  How good are these numbers?

Well, in our example, we have the luxury of finding the true rates by letting the six open opportunities close out over time.  By doing a flow-based analysis in 4Q18 of the 1H17 cohort, we can see that our true narrow win rate is 57%, our true broad win rate is 40%, and our close rate is also 40% (which, once everything has arrived at a terminal state, is definitionally identical to the broad win rate).

cohort7

Hopefully this post has helped you think about your funnel differently by introducing the concept of milestone- vs. flow-based analysis and by demonstrating how the same business situation results in a very different rates depending on both the choice of win rate and analysis type.

Please note that the math in this example backed me into a 40% close rate which is about double what I believe is the benchmark in enterprise software — I think 20 to 25% is a more normal range.