Category Archives: Decision Making

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.

If Marc Benioff Carried a Rabbit’s Foot, Would You?

In business we have a sad tendency to copy success blindly.

I remember the first time I read about this I didn’t even understand what I was reading:

“Nothing in business is so remarkable as the conflicting variety of success formulas offered by its numerous practitioners and professors.  And if, in the case of practitioners they’re not exactly “formulas,” they are explanations of “how we did it” implying with firm control over any fleeting tendencies toward modesty that “that’s how you ought to do it.”  Practitioners filled with pride and money turn themselves into prescriptive philosophers, filled mostly with hot air.”

Through blind luck, I’d had the good fortune that Theodore Levitt’s The Marketing Imagination (1983) was the very first book I read on marketing.  That paragraph — the opening paragraph of the book — stuck with me in some odd way, but it would be years before I truly appreciated what it said.

I was business-educated in the In Search of Excellence (1982) era and, while I suppose the same approach had been happening for years, In Search of Excellence was about as unscientific as they come.  The authors, Tom Peters and Bob Waterman, started out with a list of 62 companies identified by asking their McKinsey partners and friends “who’s doing cool work,” cut the list rather arbitrarily to 43 (excluding, for example, GE — but retaining Wang, Atari, and Xerox), and then “derived” eight themes which they thought were responsible for their success.

That was the mentality of the time.  Arbitrarily identify a set of companies you deem “cool” and then arbitrarily come up with things they have in common.  (And that’s not to mention the allegations of “faked data.”)

So I was happy when Jim Collins came along in 2001 arguing that he was bringing a more scientific approach in Good to Great.  Arguing that seeking only common traits could you lead to discoveries such as “all great companies have buildings,” Collins strove to differentiate good companies from great ones.  Starting with 1,435 companies and examining their performance over 40 years, Collins’ team identified 11 companies that became great along with 11 comparison companies in the same markets that did not.

While Collins’ thinking may have been clearer than Peters’, his luck was no better. Seven years after the book was published, several “great” companies like Circuit City were in deep trouble, Fannie Mae required a Federal bailout, and only only one of the eleven companies, Nucor, had dramatically outperformed the stock market.  Amazingly, despite the poor to lackluster performance of the “great” companies, it remains a best-seller to this day, ranking #5 on Amazon in management at last check.

Even when trying to avoid it, fake science and, in particular, survivor bias had struck again.  Thank goodness Phil Rosenzweig came along in 2009 with The Halo Effect, describing it and eight other business delusions from which managers suffer.  Here’s a nice excerpt:

On the way up to a stock market value of half a trillion dollars, everything about Cisco seemed perfect. It had a perfect CEO. It could close its books in a day and make perfect financial forecasts. It was an acquisition machine, ingesting companies and their technologies with great aplomb. It was the leader of the new economy, selling gear to new-world telecom companies that would use it to supplant old-world carriers and make their old-world suppliers irrelevant. Over the past year, every one of those characterizations has proved to be false.

As I often said about running analyst relations at Business Objects: “when the stock was going up everything I said was genius, when we missed a quarter, everything I said was suspect.”  This is, in my estimation, the real reason why some bad-egg companies such as bubble-era MicroStrategyFast Search & Transfer, or Autonomy (not yet settled) are tempted to inflate results.  I think it’s less about inflating valuation, and more about inflating the company’s perception of success in order to “validate” their strategy going forward.

But, to Levitt’s point at the start of this post, we are swimming in advice from successful practitioners.  

We have advice from Sequoia billionaire Mike Moritz who says the best advice he ever received was to “follow his instincts” which, as it turns out, works swimmingly well if you happen to have his instincts.  (And perhaps less so well, if you don’t.)

We have advice from billionaire Peter Thiel, who sounds vaguely like Timothy Leary with the drop-out part of turn on, tune in, drop out.

We have advice from Steve Blank, one of the more reasonable and thoughtful sources out there, and someone, in my opinion, to be admired for his commitment to giving back intellectually to Silicon Valley.

We have a plethora of advice from Marc Benioff, for example, the 111 “plays” in Beyond the Cloud, including “make your own metaphors” and “cultivate select journalists.” 

Who knows, maybe “beware of billionaires bearing business advice” may become the new “beware of Greeks bearing gifts.”

Finally, we also have advice from, dare I say, Kellblog who, while not a billionaire (yet), has opinions as tempered by experience and as firmly held as any of the above — and often as unscientific.

Given this sea of advice, how do I recommend processing it?  In the end, as Rosenzweig reminds us, in the absence of real silver bullets and magic formulae, we need to think for ourselves.  So every time I hear a successful businessperson bearing business advice I remind myself of one key fact — the plural of anecdote is not data — and ask myself two key questions:

  • Do I believe that he/she was successful because of, in spite of, or completely independent of this advice?
  • If Marc Benioff carried a rabbit’s foot, would I?

Twelve Questions Executives Can Ask To Improve Decision Making

I first became interested in decision making more than a decade ago, back when I was running marketing at Business Objects.  My interest was prompted by the evolution of taglines among BI vendors.  In the early days, taglines were descriptive like First in Enterprise Decision Support or The Enterprise Data Mart Company.

Over time, pressure mounted on marketing to pitch benefits — the message shouldn’t just be about getting people information, but the benefit of having it.  Slogans evolved accordingly:  Now You Know, The Power To Know, and Business Intelligence:  If You Have It, You Know.

But was knowing enough of a benefit?  You could certainly take it up a level, and Cognos did:  Better Decisions Every Day.  For a marketing slogan it was good enough, but was it true?   Did providing better access to corporate information  invariably improve decision making?  It seemed like a leap so I decided to research it.

I’ll never forget when Cornell professor Jay Russo told me, “the primary use of new information is selective filtering to justify previously established conclusions.”  So, despite the commonsense appeal of the Cognos tagline, you most certainly could not draw a straight line from “more information” to “better decisions.”

I studied how individuals and groups  made decisions.  I read interesting books like Russo’s Decision Traps (later positively reframed into Winning Decisions) and Smart Choices.  Years later I became interested in mass decision making  in The Wisdom of Crowds and behavioral economics in Predictably Irrational and Why Smart People Make Big Money Mistakes.

I remember asking Russo why decision making wasn’t more of a focus in business schools.  His answer came down to two things:

  • If you can’t measure it, you can’t manage it.  Until corporations want to start measuring decision making, you can’t focus on improving it.  (I remember once suggesting a BI product that tracked votes on strategic decisions, evaluated their success years later, and calculated batting averages for team members.  The idea was shot down as my colleagues imagined executives fleeing like cockroaches under an illuminated light.)
  • Executives perceive their jobs as decision-making and themselves as experts.  Think:  Why would I need a class in decision making?  I make decisions for a living and my success in rising up this organization is proof that I am good at it.

But if quenching thirst is the ultimate benefit of Coke, improved decision making really is the ultimate benefit sought by BI consumers.  The problem was  — and is — that BI software can’t deliver it.

So if you want to improve your decision making, then you’re going to have to read up a bit, either through the books I’ve referenced above or via a recent article in Harvard Business Review entitled Before You Make That Big Decision, which provides 12 questions that senior executives can ask about decisions and decision-making processes to avoid the most common errors.

Here are those 12 questions and the biases that they are trying to detect:

  1. Is there any reason to suspect motivated errors, or errors driven by the self-interest of the recommending team?  (self-interest bias)
  2. Have the people making the recommendation fallen in love with it?  (affect heuristic)
  3. Were there dissenting opinions within the recommending team?  (groupthink)
  4. Could the diagnosis of the situation be overly influenced by salient analogies?  (saliency bias)
  5. Have credible alternatives been considered?  (confirmation bias)
  6. If you had to make this decision again in a year, what information would you want and can you get more of it now?  (availability bias)
  7. Do you know where the numbers came from?  (anchoring bias)
  8. Can you see a halo effect? (halo effect)
  9. Are the people making the recommendation overly attached to past decisions?  (sunk-cost fallacy, endowment effect)
  10. Is the base case overly optimistic?  (overconfidence)
  11. Is the worst case bad enough?  (disaster neglect)
  12. Is the recommending team overly cautious?  (loss aversion)
The full article is here.

The Blissful Ignorance Effect

I’ve always been a fan of studying how we, as people, make decisions whether they’re big ones (e.g., Smart Choices, Decision Traps) or day-to-day consumer ones (e.g., Why We Buy). On the latter, I’d always felt that the Internet was a marketer’s boon because Why We Buy shows the pains that merchandisers must endure in the physical world (using cameras and observers) that get replaced by a nice friendly clickstream in the virtual one. For example, market basket analysis can tell you what customers purchased in a supermarket and in which combinations; but only human observation can tell you each product they considered, which they looked at, which they picked up, why they tried to bend over to reach but stopped, and which they read the label on and then hastily put back.

All this is a long way of introducing an interesting article in today’s New York Times, entitled Some Blissful Ignorance Can Cure Chronic Buyer’s Remorse. The conclusion, named “The Blissful Ignorance Effect,” is that people who have more ambiguous information about a product expect to be happier with their purchases than those who have bought with more specific details.

Excerpts:

… there is a shift in buyers’ goals before and after purchasing something. Professor Nayakankuppam says these are called “accuracy goals” versus “directional goals.”

Before the purchase, “people want clear, objective information — they want to make sure they get it right,” he said. “After a purchase, they want to reach a particular decision — they have a directional goal.”

[…]

Elliot Aronson, … co-author most recently of “Mistakes Were Made (but Not by Me)” [dk: great title!] … is not surprised by these findings. He has spent a lifetime looking at how people justify their decisions. “If you have just a little information and then you’re a little disappointed, you can convince yourself that it wasn’t your fault, or that the item may be better than you thought for other reasons,” he said.

In general, he says, people are “cognitive misers” — they do not want to do a lot of thinking and research. That is one reason that brands and slogans are attractive; they are a shortcut to information.

[…]

Max Kalehoff, vice president for marketing for Clickable, […] says “A product that really requires a lot of detail or work to understand creates implicit barriers,” he said. “Marketers are throwing things at us with increasing complexity. It would be stupid to assume they should hide information from consumers, but they need to focus — from a product or service standpoint — on prioritizing the most important information and presenting it with the greatest efficiency.”

The article ends with some advice:

Research big decisions thoroughly, but don’t worry so much about the small ones. Don’t overanalyze why you like things — and in the end, you can probably convince yourself that whatever you have done is the right thing.

Forget BI: Go With Your Gut

Newsweek recently published an article that should be sending shock waves through the business intelligence (BI) market, populated with vendors like Business Objects, Cognos, and MicroStrategy.

BI tools help business people get access to information in corporate databases and data warehouses, so they can make better business decisions. In fact, if you looked at the tag lines for these vendors over the years, they consistently played off the theme of knowing more and therefore making better decisions:

  • Better decisions every day
  • Now you now
  • The power to know
  • Business intelligence: if you have it you know
  • [Mumble, mumble] something about insight [with lots of black] (poking fun at BOBJ’s “margeketing”)

My interest in this implicit premise led me to research how people made decisions, enjoying books like Decision Traps by J. Edward Russo, its newer sequel Winning Decisions, and Smart Choices by John Hammond. After all, in the BI world, if we were in the business of providing better information for making better decisions, maybe we should learn something — and perhaps try to help improve — the next step down the line.

But what if the premise were flawed? What if more information didn’t help improve decision quality? I remember asking J. Edward Russo (who is both a psychology and business professor) what people would most likely do with increased access to information? His answer: selectively filter the information to justify already made decisions. Hum.

In the end I concluded two things:

  • Selling “better decisions” wouldn’t work because most people — particularly executives — don’t think they have a problem. “I’m a great decision maker; look how far I’ve gotten in my career.”
  • If that weren’t enough, given my reading, I felt that the first thing companies could do to improve organizational decision making would be to systematically record votes on major decisions and periodically review the decisions and who voted which way. When I proposed we do precisely that at Business Objects, executives scattered faster than cockroaches with the lights turned on.

Clearly, while there was a big market for “more information,” demand for “better decisions” seemed lacking.

So what does Newsweek have to say? Almost in the Blink school of thought, there’s a new book out called Gut Feelings that argues our subconscious can do a pretty good job filtering and processing information.

Excerpts:

Hunches, gut feelings, intuition—these are all colloquial English for what Gigerenzer and his colleagues call “heuristics,” fast and efficient cognitive shortcuts that (according to the emerging theory) can help us negotiate life, if we let them.

[…]

Gigerenzer calls such decision making “satisficing,” as in “satisfying” enough to “suffice.” Satisficers don’t feel the need to know everything, in contrast to “maximizers,” who do want to weigh every detail imaginable in making even minor life decisions. Interestingly, studies have found that satisficers are more optimistic about life, have higher self-esteem, and are generally happier than maximizers.

The whole story reminds me a humorous moment in my marketing career. We were running the BI Summit, a top-end executive event in the UK. We had Michael Heseltine, a member of parliament, secretary, prominent UK politician and businessman as our keynote speaker. We were donig Q&A in an interview format and the interviewer — on ear-bud prompt by our UK marketing director — kept asking increasingly leading questions about the power of information in making decisions.

And then he pressed once too far. It was many years ago, but as I recall it went something like this:

Interviewer [building in hyperbole]: Well, then, would you say that some of the best decisions you ever made in your life were based on data and analysis?

Heseltine: Well, in fact, no. No, I wouldn’t. I remember when we decided to start [magazine X] just having a flash of intuitive brilliance in looking at a newsstand and realizing there was no publication in the [X] space. In fact, well, I think I’d say that some of the best decisions I’ve ever made have been based on pure instinct and intuition. No data at all, really.

There’s a lesson on decision-making in there. And one on over-reaching as well.