Friday, November 23, 2018
Recent conversations on going public have reminded me that some assume taking a company public is inherently, completely good, necessary to being Important in the Industry. Here’s a few reasons why that is not always true, noting that I am not a financial professional.
Posit that the natural course of a successful company is to achieve a monopoly. Oligopoly will do in a pinch, but the ideal scenario for a company is to take all the cookies. This is generally viewed as a bad thing, so societies might pass laws or enact breakups to prevent it.
That needs a government actor, and current theory holds that government is bad. One should create market forces that enable good outcomes via greed and invisible fairy hands. To the degree that theory admits monopolies are bad, public markets seem to be the anti-monopoly agent.
Public markets love growth. Vastly oversimplified, there are two types of investments: safety and growth. Bonds and stocks. Monopoly provides safety, whether through bonds or dividends, but it has no growth. A startup provides growth opportunities, but it is not safe.
As an individual investor or fund, this is all fine. Select the balance of safety and risk that makes sense for your goals, and all will be well. As long as there are opportunities. But, if companies achieve their goals, there will just be a few safe monopolies and no growth.
Now let’s play Sim Captain of Wall Street and manage the balance of safety and growth opportunities. The first lever you might try is merger and acquisition. Encourage the monopolies to buy each other and form massive conglomerates with a few basic shared functions.
The outcome is socially fascinating, in that it appears to have encouraged the growth of functional careers like project management. Abstracting a role across the units of Buy-N-Large is good prep for considering that role as an abstract function for any organization.
However, it’s tough to argue that the resulting conglomerates have become growth investments. Jamming a bunch of unrelated businesses into a holding entity doesn’t increase productivity.
A more cynical lever exists in the tech industry: encourage the monopolies to self-disrupt. If a company jumps in a new direction, one of two things will happen: succeed and produce new growth for themselves, or fail and produce new growth opportunities for other companies.
Once initial investments are recovered, there’s almost no way to lose in encouraging a mature, successful company to try crazy risks.
Looking at this as Sim Company Leader, I don’t see how farming the market to increase growth helps me get monopoly. It’s great to get windfall money and lower interest loans, but I don’t want to lose control. I may not have a choice though: early investors expect their paydays.
What if I could table flip the market though? It would be distracting to the attain monopoly game... but after going public, I might be in a mood to gamble on an acquisition or a new product architecture.
It’s all fun and games until someone loses their job, but this cycle, if it’s real, creates higher opportunity jobs by creating duplicative roles across many smaller companies. Not so many gold watch careers though.
Sunday, November 18, 2018
www.honeycomb.io has great stickers
Monitoring and metrics! Theoretically any system that a human cares about could be monitored with these four patterns:
I’m hardly the first to notice there’s overlap... https://medium.com/devopslinks/how-to-monitor-the-sre-golden-signals-1391cadc7524 is a good starting point to read from. I haven’t seen these compressed to a single metric set yet, probably from not looking hard enough. Or because “DURSLEy” is too dumb for real pros.
- Duration: How long are things taking to complete?
- Utilization: How many resources are used?
- Rate: How many things are happening now?
- Saturation: How many resources are left?
- Latency: How long do things wait to start?
- Errors: Are there known problems?
- Yes: We’re done
These are popular metrics to monitor because they can be easily built up from existing sensors. They provide functional details of a service, in data that is fairly easy to derive information from.
In an ideal world, those metrics are measuring “things” and “resources” that are directly applicable to the business need. Sales made. Units produced.
In a less ideal world, machine readable metrics are often used as a proxy to value, because they are easier to measure. CPU load consumed. Amount of traffic routed.
In the best of all possible worlds, the report writer is working directly with business objectives. CAPS is a metric set that uses business level input to provide success indicators of a service, producing knowledge and wisdom from data and information.
- Capacity: How much can we do for customers now?
- Availability: Can customers use the system now?
- Performance: Are customers getting a good experience now?
- Scalability: How many more customers could we handle now?
These metrics present the highest value to the organization, particularly when they can be tied to insight about root cause and remediation. That is notably not easy to do, but far more valuable than yet another CPU metric.
Report writers can build meaningful KPIs and SLOs from CAPS metrics. KPIs and SLOs built from DURSLEy metrics are also useful, but they have to be used as abstractions of the organization’s actual mission.
Examples: the number of tents deployed to a disaster area is a CAPS metric, but any measure of resources consumed by deploying those tents is a DURSLEy metric. Synthetic transactions showing ordering is possible: CAPS. Load metrics showing all components are idle: DURSLEy.
Saturday, November 10, 2018
License Models Suck got a lot of interesting conversations started, time to revisit from the customer’s perspective. Let’s also be clear, this is enterprise sales with account reps and engineers: self-service models are for another day.
As a vendor, the options I describe seem clearly different; but as a customer I just want to buy the thing I need at a price that works. “Works” here means “fits in the budget for that function” and “costs less than building it myself or buying it elsewhere”.
A price model has to work when growth or decline happen. As a customer I build a spreadsheet model to find if the deal would quit working under some reasonably likely future scenarios. If it passes that analysis, fine. I don’t care if the model is good or bad for the vendor.
So, the obvious question: why doesn’t flat rate pricing rule the world? It’s certainly the easiest thing to model and describe! Answer: organizations are internally subdivided.
The customer may work at BigCo, and BigCo may use some of the vendor’s products, but the customer doesn’t need to buy for all of BigCo. They need to solve the problem in front of them. Charging them a flat BigCo price for that problem doesn’t work.
What’s more, the customer can’t do anything to make it work. Maybe they can help the sales team pivot this into a top-down BigCo-wide deal, but that’s going to take a long time and require all sorts of political capital and organizational skill that not every customer has.
This is easy to solve, right? Per-unit pricing is the answer! Only, we’re talking enterprise sales and products that require hand-holding. The vendor has a spreadsheet model too, and that model doesn’t work if a sales team isn’t producing enough revenue per transaction.
If the customer’s project isn’t big enough, then the deal won’t work with per-unit pricing. In response, the vendor will drop deals that are too small, set minimum deal size floors for their products, or make product bundles that force larger purchases.
If the customer has no control over the number of units, a per unit price might as well be a flat rate. There’s no natural price elasticity, and the only way to construct a deal is through discounting.
Why not get unnatural then? Just scale the price into bands! You want 10 of these? That’s $10,000 each. You want 10,000 of these? That’s $10 each. Why not sell the customer what they want?
Because the cost to execute a deal and support a customer is variable and difficult to model, and the more complex a pricing model is, the less clarity your have into whether your business is profitable and healthy.
The knock on effects from that non-clarity are profound, because they affect anything that involves planning for the future. It’s more difficult to raise capital or get loans, negotiate partnerships, hire and retain talent.
And so we mostly see fairly simple pricing systems in mid-sized enterprise software vendors. I’m most familiar with “platform with a unit price, less expensive add-ons locked to the same unit quantity.”
This pricing works for the middle of the bell curve, but small customers are underserved while large customers negotiate massive discounts or all-you-can-eat agreements that can hurt the vendor.