Finance notes from underground
Unit economics
There is a domino effect that starts at public markets and tips its way back, stage by stage, to the first round of venture capital for a startup. The initial tip is the exit or liquidity event for the privately funded enterprise, the valuation that gets set for comparable inventory items on the venture shelf.
In principle, this stands to reason, particularly for the late-stage venture-backed investments that may be on the cusp. It’s good to know what may await, in order to prepare, or celebrate, or try something different, and figure out how best to mark the portfolio to market. This, especially in the case of the second domino in line.
But as the tipping progresses, and the advance gets farther from the point of origin — the public exit reference point — the comparisons weaken on two fundamental counts: (1) there is a long and unpaved road to travel, with unexpected turns and obstacles along the way, before that exit reference point has any real significance, and (2) the time that this can take is such that public market tone, liquidity, and expectations are prone to change by then, as public market tone, liquidity, and expectations always do.
Nevertheless, markers are important, particularly on an unpaved road with unexpected turns and obstacles along the way. And in the early stages of an enterprise that’s likely to need further funding as it grows, it’s good to keep track of what these upcoming funding sources will expect, to plan ahead and work to optimize the probabilities.
Where the exercise can become very messy though, is in the apples-oranges comparisons and certain reference markers that may be rigidly and formulaically set, without the needed scrutiny of scope and nuance, the differences in circumstance between the case at hand and its supposed model, and the strategic implications of financial decision making that may ensue, perhaps correctly or perhaps not. The critical result — to not run out of cash before the next shipment has arrived — is always in the balance.
The current day example of such formulaic dominos that seem to be tipping, triggered by the class of last year’s IPOs and their performance, is unit economics.
The concepts of lifetime value (LTV) and customer acquisition cost (CAC) aren’t new, but it feels perhaps like now they’ve become elevated, universally, in stature and attention.
This is a good and healthy thing for markets and financial planning, as unit economics are about as fundamental as it gets, for both. By the same token, however, it can be dangerous where the concept is misapplied, misunderstood, and improperly compared, because unlike, say, a P/E ratio, which is truly formulaic — there’s a price, and there’s an earnings figure, and there’s a ratio of the two — LTV and CAC are entirely dependent on underlying assumptions.
The nature of the business or the product, its stage, the costs and revenues included or excluded, the timeframe in question, the growth or churn assumptions (and definitions), the scope of the analysis, these basic elements of unit economics are fundamentally subjective, and the results much more interpretive than, say, a high or low P/E multiple based on growth.
For the clearest and most comprehensive overviews of what makes up this fickle exercise, here are the classic summaries that you may want to bookmark and review as a reminder from time to time:
- The Dangerous Seduction of the Lifetime Value (LTV) Formula by Bill Gurley
- Why is Customer Acquisition Cost (CAC) like a Belly Button? by Tren Griffin
And from the ledger’s other side, the venture funding market, which is always top of mind and governed by unit economics of its own:
- The Venture Capital Math Problem by Fred Wilson, (as updated in February 2020)
These have withstood the test of time, and maybe even grown in relevance in the new decade and environment.
The question and the answer budget
Was thinking about the following things a little after reading Brad Feld’s post on budgets the other day…
“When you add up all the time spent on budgeting across all the organizations on the planet (including government), the human species wastes an enormous amount of time on a thing we don’t do very well. There must be a better way.” — FeldThoughts
I agree.
When merchandise was still purchased in stores and the news was published once a day on paper pages, the budgeting exercise was straightforward. Square footage, same-store sales, circulation, market size, these sorts of things were standard, and while some year-over-year fine-tuning was required, there was not usually a reconfiguration. There was no need.
Think of these, for lack of a better term, as answer budgets — created in relatively stable circumstances, in which the business perhaps grows or shrinks a little, but systems typically do not change and there are trusted ratios and formulas to fall back upon. What’s required is straightforward answers, for planning, reporting, the lenders, cash management, and as a point of reference the next time around.
With the emergence, scale and transformations of the digitized economy — in which even mature or maturing businesses have elements of the startup, and the startups can only model their business plans after incumbents to a limited extent, the previously straightforward exercise of budgets, plans, reports and forecasts, has become more volatile.
Pricing models, market sizing and dynamics, product timing and conversion, the impact of expense categories on revenues (when) or customer growth (how), or data growth (why); the sequence, pace and timing of these and other drivers that sometimes relate and sometimes don’t; even the fundamental value unit in many cases… these are often matters of experimentation, testing, frequent revisit and adjustment, within reasoned parameters — always with a view to cash, and the balancing objective of conservation and value formation.
In contrast with the earlier exercise described, these budgets are more like question budgets. In the extreme case of a pure startup with no historical basis in financial projections, the exercise is to test hypotheses. It should be interpreted as such. The goal is to arrive at improved assumption sets next time around, which should be soon, and regularly repeated.
Most of the world these days exists somewhere between the bookends, between the question and the answer, with elements of one or the other depending on the budget category, and sometimes both in certain aspects of the business. The distinction, though, is critical, as questions should not be confused with answers (just as inputs aren’t outputs), and the different risks and degrees of variance inherent in the different types should be considered differently.
With all the moving pieces and dynamic market patterns, the future can’t possibly be predicted, but its functions can be managed, and the exercise can be valuable, interesting, and important on all levels, above and below the financial.
The story of the long-term forecast
Continuing from where the prior section ends, this one is about the long-term forecast. The model isn’t used to manage cash and operations or to track line-item performance. Its purpose is to tell a story.
Like all linear tales, it has a beginning, a middle, and an end. Its characters are the value drivers of the business. They evolve as the story unfolds, they grow or sometimes surge, or, if the plot calls for paring down a certain product or deemphasizing a direction, that character trails off with a diminished presence. The one that leads is the one that dominates the current vision.
In the beginning, the baseline year, this may be a young adult already, or possibly much younger. With time’s passage (i.e., business execution), the character will grow to early maturation — but probably no further. The promise of adulthood is more valuable than old age, and this story is about character development.
There may be a whole cast of characters, some more critical to the plot than others, some who lead and some who follow, some take charge and some support, but all are interesting and real, developed to the story’s end, which is the last year of the forecast. There is sufficient detail to make them come alive, but not so as to slow the story’s progress.
This calls for reality and familiarity in the tale, although it is a fantasy, without which the story falls apart and the audience loses interest. That final year, the end, should leave them wanting more, like all good stories peak our curiosity and lead to further speculation.
The key is to know your story well before you tell it, to know the plot points and the thrills, the dangers and the conquests, as though it has already happened. For as much as the audience may be the usual constituencies to whom such forecasts would be shown — investors, lenders, partners, prospects — the audience is also you, and it’s a story that you must believe and love in order to tell properly.
This isn’t an operating tool, like a budget, it’s an expression of your vision.
The formula that works
In a best and ideal case, the business has grown, or is about to, at some point, like this.
But the smoothness of the curve, its slope, its symmetry and balance, its components underneath, have been, or will be, much more likely, thus.
That is, assuming that the business grows, which it may not, or may not have… and even if it has, or will, note that the values on the axes of these charts, the legends, are all blank.
This is because all businesses are, have been, and will be, different… with variables that make each one a constellation of a different type… even if from sufficiently far away, or from within, it may not seem so.
From just the proper distance, from up close but not so close as to be microscopic, the variables that come into play are more along these lines, though even this is an idealized depiction…
… because the force and aim and perfectly offsetting angles are, in actuality, all different, and liable to change at different points in time and circumstance…
… so, the machine, in actuality, is rarely just like in the movies.
And even if it were, or has been, by miracle of circumstance, note once again that the velocity of movement in the picture isn’t shown.
Because it’s always different, and, what’s more, uneven. Sometimes it slows, sometimes it picks up, and sometimes, by happenstance, it’s just exactly perfect. But that doesn’t last.
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These arguments are raised because today there is a great deal of advice that flows about, for business builders or investors, really for everyone in different ways, predicated on a set of rigid formulas or definitions or how-to lists.
Some serve a purpose, though others on the other hand may just confuse the situation. But either way, and best case all around, the ideal purpose is to inspire thought, to question and to analyze, because the outcome is never formulaic. Though, hopefully, approximate enough.
The most important part of the analysis, regardless of the context, is to understand assumptions, differences and similarities, where the analogy might well apply and where it won’t, and why… the nuances are key and make all the difference.
In the end, the object is the same, for anyone, and it’s more fundamental than to grow. Before all that, it’s to survive (and maybe growth can be a side-effect, or precondition).
The formula that always works, no matter what and how and when, is this:
First, do no harm.
Within reason.
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The preceding were originally published separately in my daily journal of snippets about markets, other networks, technology, finance, books, and assorted other subjects. The selections seem to fit together, and collectively comprise a longer post for the benefit of those who like their reading longer.
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