Category Archives: Finance

The New 2017 Gartner Magic Quadrants for Cloud Strategic CPM (SCPM) and Cloud Financial CPM (FCPM) – How to Download; A Few Thoughts

For some odd reason, I always think of this scene — The New Phone Book’s Here – from an old Steve Martin comedy whenever Gartner rolls out their new Magic Quadrants (MQ) for corporate performance management (CPM). It’s probably because all of the excitement they generate.

Last year, Gartner researchers John Van Decker and Chris Iervolino kept that excitement up by making the provocative move of splitting the CPM quadrant in two — strategic CPM (SCPM) and financial CPM (FCPM). Never complacent, this year they stirred things up again by inserting the word “cloud” before the category name for each; we’ll discuss the ramifications of that in a minute.

Free Download of 2017 CPM Magic Quadrants

But first, let me provide some links where you can download the new FCPM and SCPM magic quadrants:

Significance of the New 2017 FPCM and SCPM Magic Quadrants

The biggest change this year is the insertion of the word “cloud” in the title of the magic quadrants.  This perhaps seemingly small change, like a butterfly effect, results in an entirely new world order where two of the three megavendors in the category (i.e., IBM, SAP) get displaced from market leadership due to the lack of the credibility and/or sophistication of their cloud offerings.

For example:

  • In the strategic CPM quadrant, IBM is relegated to the Visionary quadrant (bottom right) and SAP does not even make the cut.
  • In the financial CPM quadrant, IBM is relegated to the Challenger quadrant (top left) and SAP again does not even make the cut.

Well, I suppose one might then ask, well if IBM and SAP do poorly in the cloud financial and strategic CPM magic quadrants, then how do they do in the “regular” ones?

To which the answer is, there aren’t any “regular” ones; they only made cloud ones.  That’s the point.

So I view this as the mainstreaming of cloud in EPM [1].  Gartner is effectively saying a few things:

  • Who cares how much maintenance fees a vendor derives from legacy products?
  • The size of a vendor’s legacy base is independent of its position for the future.
  • The cloud is now the norm in CPM product selection, so it’s uninteresting to even produce a non-cloud MQ for CPM. The only CPM MQs are the cloud ones.

While I have plenty of beefs with Oracle as a prospective business partner — and nearly as many with their cloud EPM offerings — to their credit, they have been making an effort at cloud EPM while IBM and SAP seem to have somehow been caught off-guard, at least from an EPM perspective.

(Some of Oracle’s overall cloud revenue success is likely cloudwashing though they settled a related lawsuit with the whistleblower so we’ll never know the details.)

Unlikely Bedfellows:  Only Two Vendors are Leaders in Both FCPM and SCPM Magic Quadrants

This creates the rather odd situation where there are only two vendors in the Leaders section of both the financial and strategic CPM magic quadrants:  Host Analytics and Oracle.  That means only two vendors can provide the depth and breadth of products in the cloud to qualify for the Leaders quadrant in both the FCPM and SCPM MQ.

I know who I’d rather buy from.

In my view, Host Analytics has a more complete, mature, and proven product line – we’ve been at this a lot longer than they have — and, well, oligopolists aren’t really famous for their customer success and solutions orientation.  More infamous, in fact.  See the section of the FCPM report where it says Oracle ranks in the “bottom 25% of vendors in this MQ on ‘overall satisfaction with vendor.’”

Or how an Oracle alumni once defined “solution selling” for me:

Your problem is you are out of compliance with the license agreement and we’re going to shut down the system.  The solution is to give us money.


For more editorial, you can read John O’Rourke’s post on the Host Analytics corporate blog.

Download the 2017 FCPM and SCPM Magic Quadrants

Or you can download the new 2017 Gartner CPM MQs here.

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[1] Gartner refers to the category as corporate performance management (CPM).  I generally refer to it as enterprise performance management (EPM), reflecting the fact that EPM software is useful not only for corporations, but other forms of organization such as not-for-profit, partnerships, government, etc.  That difference aside, I generally view EPM and CPM as synonyms.

CAC Payback Period:  The Most Misunderstood SaaS Metric

The single most misunderstood software-as-a-service (SaaS) metric I’ve encountered is the CAC Payback Period (CPP), a compound metric that is generally defined as the months of contribution margin to pay back the cost of acquiring a customer.   Bessemer defines the CPP as:

bess cac

I quibble with some of the Bessemerisms in the definition.  For example, (1) most enterprise SaaS companies should use annual recurring revenue (ARR), not monthly recurring revenue (MRR), because most enterprise companies are doing annual, not monthly, contracts, (2) the “committed” MRR concept is an overreach because it includes “anticipated” churn which is basically impossible to measure and often unknown, and (3) I don’t know why they use the prior period for both S&M costs and new ARR – almost everybody else uses prior-period S&M divided by current-period ARR in customer acquisition cost (CAC) calculations on the theory that last quarter’s S&M generated this quarter’s new ARR.

Switching to ARR nomenclature, and with a quick sleight of mathematical hand for simplification, I define the CAC Payback Period (CPP) as follows:

kell cac

Let’s run some numbers.

  • If your company has a CAC ratio of 1.5 and subscription gross margins of 75%, then your CPP = 24 months.
  • If your company has a CAC ratio of 1.2 and subscription gross margins of 80%, then your CPP = 18 months.
  • If you company has a CAC ratio of 0.8 and subscription gross margins of 80%, then your CPP = 12 months.

All seems pretty simple, right?  Not so fast.  There are two things that constantly confound people when looking at CAC Payback Period (CPP).

  • They forget payback metrics are risk metrics, not return metrics
  • They fail to correctly interpret the impact of annual or multi-year contracts

Payback Metrics are for Risk, Not Return

Quick, basic MBA question:  you have two projects, both require an investment of 100 units, and you have only 100 units to invest.  Which do you pick?

  • Project A: which has a payback period of 12 months
  • Project B: which has a payback period of 6 months

Quick, which do you pick?  Well, project B.  Duh.  But wait — now I tell you this:

  • Project A has a net present value (NPV) of 500 units
  • Project B has an NPV of 110 units

Well, don’t you feel silly for picking project B?

Payback is all about how long your money is committed (so it can’t be used for other projects) and at risk (meaning you might not get it back).  Payback doesn’t tell you anything about return.  In capital budgeting, NPV tells you about return.  In a SaaS business, customer lifetime value (LTV) tells you about return.

There are situations where it makes a lot of sense to look at CPP.  For example, if you’re running a monthly SaaS service with a high churn rate then you need to look closely how long you’re putting your money at risk because there is a very real chance you won’t recoup your CAC investment, let alone get any return on it.  Consider a monthly SaaS company with a $3500 customer acquisition cost, subscription gross margin of 70%, a monthly fee of $150, and 3% monthly churn.  I’ll calculate the ratios and examine the CAC recovery of a 100 customer cohort.

saas fail

While the CPP formula outputs a long 33.3 month CAC Payback Period, reality is far, far worse.  One problem with the CPP formula is that it does not factor in churn and how exposed a cohort is to it — the more chances customers have to not renew during the payback period, the more you need to consider the possibility of non-renewal in your math [1].  In this example, when you properly account for churn, you still have $6 worth of CAC to recover after 30 years!  You literally never get back your CAC.

Soapbox:  this is another case where using a model is infinitely preferable to back-of-the-envelope (BOTE) analysis using SaaS metrics.  If you want to understand the financials of a SaaS company, then build a driver-based model and vary the drivers.  In this case and many others, BOTE analysis fails due to subtle complexity, whereas a well-built model will always produce correct answers, even if they are counter-intuitive.

Such cases aside, the real problem with being too focused on CAC Payback Period is that CPP is a risk metric that tells you nothing about returns.  Companies are in business to get returns, not simply to minimize risk, so to properly analyze a SaaS business we need to look at both.

The Impact of Annual and Multi-Year Prepaid Contracts on CAC Payback Period

The CPP formula outputs a payback period in months, but most enterprise SaaS businesses today run on an annual rhythm.  Despite pricing that is sometimes still stated per-user, per-month, SaaS companies realized years ago that enterprise customers preferred annual contracts and actually disliked monthly invoicing.  Just as MRR is a bit of a relic from the old SaaS days, so is a CAC Payback Period stated in months.

In a one-hundred-percent annual prepaid contract world, the CPP formula should output in multiples of 12, rounding up for all values greater than 12.  For example, if a company’s CAC Payback Period is notionally 13 months, in reality it is 24 months because the leftover 1/13 of the cost isn’t collected until the a customer’s second payment at month 24.  (And that’s only if the customer chooses to renew — see above discussion of churn.)

In an annual prepaid world, if your CAC Payback Period is less than or equal to 12 months, then it should be rounded down to one day because you are invoicing the entire year up-front and at-once.  Even if the formula says the CPP is notionally 12.0 months, in an annual prepaid world your CAC investment money is at risk for just one day.

So, wait a minute.  What is the actual CAC Payback Period in this case?  12.0 months or 1 day?  It’s 1 day.

Anyone who argues 12.0 months is forgetting the point of the metric.  Payback periods are risk metrics and measured by the amount of time it takes to get your investment back [2].  If you want to look at S&M efficiency, look at the CAC ratio.  If you want to know about the efficiency of running the SaaS service, look at subscription gross margins.  If you want to talk about lifetime value, then look at LTV/CAC.  CAC Payback Period is a risk metric that measures how long your CAC investment is “on the table” before getting paid back.  In this instance the 12 months generated by the standard formula is incorrect because the formula misses the prepayment and the correct answer is 1 day.

A lot of very smart people get stuck here.  They say, “yes, sure, it’s 1 day – but really, it’s not.  It’s 12 months.”  No.  It’s 1 day.

If you want to look at something other than payback, then pick another metric.  But the CPP is 1 day.  You asked how long it takes for the company to recoup the money it spends to acquire a customer.  For CPPs less than or equal to 12 in a one-hundred percent annual prepaid world, the answer is one day.

It gets harder.  Imagine a company that sells in a sticky category (e.g., where typical lifetimes may be 10 years) and thus is a high-consideration purchase where prospective customers do deep evaluations before making a decision (e.g., ERP).  As a result of all that homework, customers are happy to sign long contracts and thus the company does only 3-year prepaid contracts.  Now, let’s look at CAC Payback Period.  Adapting our rules above, any output from the formula greater than 36 months should be rounded up in multiples of 36 months and, similarly, any output less than or equal to 36 months should be rounded down to 1 day.

Here we go again.  Say the CAC Payback Period formula outputs 33 months.  Is the real CPP 33 months or 1 day?  Same argument.  It’s 1 day.  But the formula outputs 33 months.  Yes, but the CAC recovery time is 1 day.  If you want to look at something else, then pick another metric.

It gets even harder.  Now imagine a company that does half 1-year deals and half 3-year deals (on an ARR-weighted basis).  Let’s assume it has a CAC ratio of 1.5, 75% subscription gross margins, and thus a notional CAC Payback Period of 24 months.  Let’s see what really happens using a model:


Using this model, you can see that the actual CAC Payback Period is 1 day. Why?  We need to recoup $1.5M in CAC.  On day 1 we invoice $2.0M, resulting in $1.5M in contribution margin, and thus leaving $0 in CAC that needs to be recovered.

While I have not yet devised general rounding rules for this situation, the model again demonstrates the key point – that the mix of 1-year and 3-year payment structure confounds the CPP formula resulting in a notional CPP of 24 months, when in reality it is again 1 day.  If you want to make rounding rules beware the temptation to treat the average contract duration (ACD) as a rounding multiple because it’s incorrect — while the ACD is 2 years in the above example, not a single customer is paying you at two-year intervals:  half are paying you every year while half are paying you every three.  That complexity, combined with the reality that the mix is pretty unlikely to be 50/50, suggests it’s just easier to use a model than devise a generalized rounding formula.

But pulling back up, let’s make sure we drive the key point home.  The CAC Payback Period is the single most often misunderstood SaaS metric because people forget that payback metrics are about risk, not return, and because the basic formulas – like those for many SaaS metrics – assume a monthly model that simply does not apply in today’s enterprise SaaS world, and fail to handle common cases like annual or multi-year prepaid contracts.

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[1] This is a huge omission for a metric that was defined in terms of MRR and which thus assumes a monthly business model.  As the example shows, the formula (which fails to account for churn) outputs a CAC payback of 33 months, but in reality it’s never.  Quite a difference!

[2] If I wanted to be even more rigorous, I would argue that you should not include subscription gross margin in the calculation of CAC Payback Period.  If your CAC ratio is 1.0 and you do annual prepaid contracts, then you immediately recoup 100% of your CAC investment on day 1.  Yes, a new customer comes with a future liability attached (you need to bear the costs of running the service for them for one year), but if you’re looking at a payback metric that shouldn’t matter.  You got your money back.  Yes, going forward, you need to spend about 30% (a typical subscription COGS figure) of that money over the next year to pay for operating the service, but you got your money back in one day.  Payback is 1 day, not 1/0.7 = 17 months as the formula calculates.

Finance Transformation Themes from the IE FP&A Conference in Boston

After attending our amazing, sold-out Future of Finance Tour session in Minneapolis earlier this week, I swung out to Boston attend a chunk of the IE Groups’s FP&A Summit.

I had some great conversations with delegates and attended several interesting sessions.  Transforming finance was, as expected, a big theme.  Here are some of things I heard:

  • Old-school finance used to say, like a TV anchorperson, “we don’t make the news, we just tell you about it.”  This finance-as-spectator role is passé.
  • We need to transform FP&A from the engine room for report production — “here are the variances, don’t ask us about them” — to an active role in working with the business.
  • FP&A needs to no longer be the data crunchers, but insight providers who can tell the story in the data.
  • Finance needs to engage with the business.  Interact with them.  Sit with them.  Ask them.  Iterate with them.  Financial processes (e.g., forecasting) are inherently iterative and require finance to interact with subject matter experts (SMEs).
  • FP&A needs to challenge the conventional wisdom or common knowledge about the business.  It’s amazing how often common rules of thumb (e.g., which products are profitable and which aren’t) simply aren’t true when you dive into the data.
  • Finance should be more focused on having a seat at the table than knowing what the table cost and how far it is into its depreciation cycle.
  • “I spent years leveraging my ‘CPA’ doing copy / paste / attach from Excel spreadsheets into board books and presentations.”
  • While overuse of Microsoft Excel is definitely part of the problem, Excel is also definitely part of the solution.   “There are over 1,000 person-years of Excel experience in this (not very big) room.  You can’t throw that out.”
  • “I fell out of bed knowing how to do that in Excel.”
  • In leveraging technology for FP&A the cloud is now a given — five years ago that was not the case.

And the old classic, which I really believe in:  finance needs to focus on becoming a business partner to the CEO and the business.

It was a great conference and I’m glad I stopped by.

Why Modeling Cloud Matters in EPM and Operational Planning

Two weeks ago, Host Analytics launched an amazing new product called Modeling Cloud.  Built by an elite team of some our most experienced developers, Modeling Cloud represents a breakthrough in cloud enterprise performance management (EPM).

In this post, I’ll discuss why Modeling Cloud matters to customers, to the market, and to the company.

Why Modeling Cloud Matters for Customers

  • Ability to build non-financial models. Planning and budgeting tools are built for planning and budgeting.  As such, you want them tied to the general ledger (GL) so, for example, you can easily get actual vs. plan for periodic reporting.  But that requires a level of financial intelligence that can become cumbersome; in a typical planning system every line needs to tie to an account in the GL, be a debit/credit account type, be associated with a legal entity, and have an associated currency.  That intelligence, which is so wonderful when making budgets, becomes baggage when you just want to build a model — for example, of bookings capacity given productivity and ramping assumptions, or new sales model given advertising spend, conversion, trial, and purchase rates. That’s why most models today are built in Excel and completely disconnected from the financial planning system.
  • Ability to integrate non-financial models. The problem with departmental Excel-based modeling is that everything ends up disconnected from the central financial planning.  Consulting can tell you what happens to billings if you hire 5 more consultants in the East and sales can tell you what happens to bookings if you hire 6 more salesreps in the East, but you need to start mailing spreadsheets around if you want to see the financial outcomes (e.g., revenue, EPS) of such changes.
  • Enterprise-wide scenario analysis. The beauty of connecting departmental modeling to the corporate financial plan is that you can perform enterprise-wide sensitivity analysis.  Say we’re thinking of making a big Eastern region push next year.  When the models all tie to the financial plan, we can see the financial outcomes for the company associated with such a push, and what it means to setting expectations with board and Wall Street.  This captures the real spirit of what is often called driver-based planning.
  • The bookings-to-revenue bridge. Models can help the finance team better forecast revenue because sales tends to be bookings-oriented whereas finance is revenue-oriented.   Everyone knows that given a pipeline of 100 opportunities there can be scores of combinations where sales hits the bookings target, but each one produces different revenue depending on the composition of the orders.   This is also, more subtly, true of sales expense because any given combination will consist of a given set of deals, for a given set of products, by a given set of saleseps, and each product may have different incentives on it, and each salesrep may be in a different stage of acceleration in their compensation plan.  By modeling bookings and doing scenario analysis of various combinations of orders, finance can better predict revenue, expense, and ultimately EPS.  In a world where a minuscule EPS miss can knock off 20% of a company’s valuation in a heartbeat, this is a critical capability.

Why Modeling Cloud Matters to the Market

  • Cloud penetration.  EPM is under penetrated by the cloud, with cloud-penetration of less than 5% today.  That means that 95% of all EPM systems sold in 2014 (between $3-4B worth) were on-premises.  By comparison, sales force automation (SFA) is about 50% cloud-penetrated.  While cloud-based planning and budgeting tools have existed for over 5 years, most cloud vendors are still working on completing their suites, with a handful introducing consolidation only in the past one to two years, and just two vendors offering a modeling engine in the cloud.  While it’s not the only factor hindering cloud penetration, rounding out cloud EPM suites will definitely help accelerate moving EPM to the cloud.
  • Market penetration.  Cloud aside, EPM is an under-penetrated market, overall. A recent survey by Grant Thornton, 40% of companies reported that they weren’t using any EPM system, relying only spreadsheets for FP&A work.  This implies the $3-4B EPM market could nearly double simply by better penetrating target customers.  And the best way to penetrate these companies is not by attacking Excel, but instead to bring an intelligent Excel strategy that makes it easy to import and build both budgets and models that are connected to the financial planning system.


  • Customer penetration.  EPM is under-penetrated within EPM-consumer companies.   Many EPM customers start with a dream of true enterprise-wide planning, but fallback to EPM deployment only within finance and rely on emailed spreadsheets for the “last mile.”  That’s too bad because mailing spreadsheets is both insecure and error-prone.  This situation develops often in on-premises EPM because the hassle of deploying the software across all potential users is simply too high and because the software itself is built for finance not end users.  Cloud EPM — with cloud modeling — will help with improving customer penetration not only because it introduces new reporting and slicer/dicer options, but also because — in the case of our Modeling Cloud product — it introduces the new ability to build and manipulate sub-models which give end users the data they want — and only the data they want — without having to rely on IT for configuration.

Why Modeling Cloud Matters to Host Analytics

  • Unique position.  With Modeling Cloud in the product line, Host Analytics now has the most comprehensive EPM suite in the cloud.  If you look at our primary cloud competitors, one does low-end planning and budgeting, one does visualization and mobile, and the other does cloud modeling but has only both new and functionally thin applications for core finance.
  • The finance choice.  Host Analytics has always been the finance department’s choice when it comes to core EPM (planning, budgeting, consolidation).  That’s because experienced finance people understand the depth and breadth that we bring to the cloud and aren’t interested in buying either unproven solutions or solutions that they will outgrow.
  • The operations choice.  With Modeling Cloud, Host Analytics is now also the operations choice.  Be it sales ops, marketing ops, or services ops, Host Analytics allows ops departments to do the planning and modeling that they require — and to do so in a way that easily integrates with the core financial planning system.  This gives them the best of both worlds — the ability to build any model they could build in Excel, using Excel formulas (and even using an Excel front-end if they so desire) and to do so in a way that automatically integrates with the core financial plan.
  • The best architecture.  Only Host Analytics offers a true multi-dimensional (i.e., OLAP) backend and an architecture built atop cloud-native, dynamic, elastic, NoSQL technology where we deliver phenomenal multi-dimensional analysis and leverage modern/standard components for managing physical storage, sharding, and parallelism.  This provides us with a huge advantage going forward both in terms of productivity and scaleability.

It’s been about 2.5 years since I joined Host Analytics and I’m quite proud of the work done by our entire R&D team in industrializing the core products, introducing a new layer of solutions, and now rolling out the industry’s most innovative cloud-based modeling engine.

Free Download of the Gartner 2015 Magic Quadrant for Corporate Performance Management Suites

Just a quick post to let you know that my company, Host Analytics, is offering a free copy / free download of the Gartner 2015 Magic Quadrant (MQ) for Corporate Performance Management (CPM) Suites.

You need to give about six fields of basic contact information to get the report, which can be downloaded here.  CPM is also known as enterprise performance management (EPM) and financial performance management (FPM) and includes corporate financial planning, scenario planning, budgeting, consolidations, financial reporting, profit modeling and optimization, and analytics.

Summary of the 4Q14 Fenwick & West VC Survey

Because I was reading it and had a minute, I thought I’d do a quick post summarizing the 4Q14 Fenwick & West Silicon Valley Venture Capital Survey (PDF).  As the name indicates, this is an ongoing quarterly survey  on the state of venture capital that pulls from many sources, integrating lots of data into a single picture.

Some highlights (glossary here):

  • Up rounds exceeded down rounds 79% to 6%, with 15% of rounds flat.
  • Average price up 115% in 4Q14, compared to 79% in 3Q14, and the highest value they’ve recorded since they started measuring this in 2005.  (Yes Virginia, prices are good.)
  • 50% of deals were in software companies
  • $14 B was invested in US VC-backed companies in 4Q14, the highest post-bubble amount yet.  (However, remember that during Bubble 1.0, the peak ran around $25B+/quarter.)
  • $49B was invested on the year.  (And you wonder why traffic so bad on 101.)
  • There were 21 VC-backed IPOs in 4Q14 which raised $3B, and 105 on the year.
  • There were 102 acquisitions of VC-backed companies for a total price of $32B in 4Q14 and 531 such deals on the year.
  • $33B was raised by VC funds in 2014, hitting 2005-2007 levels, but not coming close to the $106B raised in 2000.
  • China passed Europe in terms of VC funding raised, tripling from less than $5B in 2013 to more than $15B in 2014.  India more than doubled going from $2B to $5B.
  • Corporate venture capitalists invested $5B in 2014, the highest amount since 2000 (where it was $15B).
  • There are currently 225 accelerators worldwide which have assisted 4264 companies.  AngelList reported over $100M was raised in 2014 across 243 startups.  (This all contributes to a system imbalance where it’s relatively too easy to get angel money, resulting in a fairly large die-off rate between angel round and series A.)
  • When classifying VC deals by the university the CEO attended and then grouping by athletic conferences, the rankings go:  Pac 12, Ivy League, Big Ten, ACC, Big Tweleve, and SEC.  (I did my part for the Pac 12 in 4Q14 — Go Bears!)
  • The Silicon Valley Venture Capitalist Confidence Index published by USF reported confidence of 3.93 in 4Q14, up from 3.89 in 3Q14, and above the (eleven-year) average of 3.72.  Full report here.
  • 19% of rounds had a senior liquidation preference (to existing preferred, not just the common).  Reminder:  glossary here.
  • Only 5% of rounds had senior multiple liquidation preference.
  • 20% of rounds had participation in liquidation, down from a recent high of 34% in 2Q13.  53% of those that had participation, had it uncapped.
  • 5% of rounds had pay-to-play provisions.
  • 13% had redemption rights.

Why, as CEO, I Love Driver-Based Planning

While driver-based planning is a bit of an old buzzword (the first two Google hits date to 2009 and 2011 respectively), I am nevertheless a huge fan of driver-based planning not because the concept was sexy back in the day, but because it’s incredibly useful.  In this post, I’ll explain why.

When I talk to finance people, I tend to see two different definitions of driver-based planning:

  • Heavy in detail, one where you build a pretty complete bottom-up budget for an organization and play around with certain drivers, typically with a strong bias towards what they have historically been.  I would call this driver-based budgeting.
  • Light in detail where you struggle to find the minimum set of key drivers around which you can pretty accurately model the business and where drivers tend to be figures you can benchmark in the industry.  I call this driver-based modeling.

While driver-based budgeting can be an important step in building an operating plan, I am actually bigger fan of driver-based modeling.  Budgets are very important, no doubt.  We need them to run plan our business, align our team, hold ourselves accountable for spending, drive compensation, and make our targets for the year.  Yes, a good CEO cares about that as a sine qua non.

But a great CEO is really all about two things:

  • Financial outcomes (and how they create shareholder value)
  • The future (and not just next year, but the next few)

The ultimate purpose of driver-based models is to be able answer questions like what happens to key financial outcomes like revenue growth, operating margins, and cashflow given set of driver values.

I believe some CEOs are disappointed with driver-based planning because their finance team have been showing them driver-based budgets when they should have been showing them driver-based models.

The fun part of driver-based modeling is trying to figure out the minimum set of drivers you need to successfully build a complete P&L for a business.  As a concrete example I can build a complete, useful model of a SaaS software company off the following minimum set of drivers

  • Number and type of salesreps
  • Quota/productivity for each type
  • Hiring plans for each type
  • Deal bookings mix for each (e.g., duration, prepayments, services)
  • Intra-quarter bookings linearity
  • Services margins
  • Subscription margins
  • Sales employee types and ratios (e.g., 1 SE per 2 salesreps)
  • Marketing as % of sales or via a set of funnel conversion assumptions (e.g., responses, MQLs, oppties, win rate, ASP)
  • R&D as % of sales
  • G&A as % of sales
  • Renewal rate
  • AR and AP terms

With just those drivers, I believe I can model almost any SaaS company.  In fact, without the more detailed assumptions (rep types, marketing funnel), I can pretty accurately model most.

Finance types sometimes forget that the point of driver-based modeling is not to build a budget, so it doesn’t have to be perfect.  In fact, the more perfect you make it, the heavier and more complex it gets.  For example, intra-quarter bookings linearity (i.e., % of quarterly bookings by month) makes a model more accurate in terms of cash collections and monthly cash balances, but it also makes it heavier and more complex.

Like each link in Marley’s chains, each driver adds to the weight of the model, making it less suited to its ultimate purpose.  Thus, with the additional of each driver, you need to ask yourself — for the purposes of this model, does it add value?  If not, throw it out.

One of the most useful models I ever built assumed that all orders came in on the last day of quarter.  That made building the model much simpler and any sales before the last day of the quarter — of which we hope there are many — become upside to the conservative model.

Often you don’t know in advance how much impact a given driver will make.  For example, sticking with intra-quarter bookings linearity, it doesn’t actually change much when you’re looking at quarter granularity a few years out.  However, if your company has a low cash balance and you need to model months, then you should probably keep it in.  If not, throw it out.

This process makes model-building highly iterative.  Because the quest is not to build the most accurate model but the simplest, you should start out with a broad set of drivers, build the model, and then play with it.  If the financial outcomes with which you’re concerned (and it’s always a good idea to check with the CEO on which these are — you can be surprised) are relatively insensitive to a given driver, throw it out.

Finance people often hate this both because they tend to have “precision DNA” which runs counter to simplicity, and because they have to first write and then discard pieces of their model, which feels wasteful.  But if you remember the point — to find the minimum set of drivers that matter and to build the simplest possible model to show how those key drivers affect financial outcomes — then you should discard pieces of the model with joy, not regret.

The best driver-based models end up with drivers that are easily benchmarked in the industry.  Thus, the exercise becomes:  if we can converge to a value of X on industry benchmark Y over the next 3 years, what will it do to growth and margins?  And then you need to think about how realistic converging to X is — what about your specific business means you should converge to a value above or below the benchmark?

At Host Analytics we do a lot of driver-based modeling and planning internally.  I can say it helps me enormously as CEO think about industry benchmarks, future scenarios, and how we create value for the shareholders.  In fact, all my models don’t stop at P&L, they go onto implied valuation given growth/profit and ultimately calculate a range of share prices on the bottom line.

The other reason I love driver-based planning is more subtle.  Much as number theory helps you understand the guts of numbers in mathematics, so does driver-based modeling help you understand the guts of your business — which levers really matter, and how much.

And that knowledge is invaluable.