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The traditional theoretical price demand curve is often drawn like this. The chart makes two points: there is some relationship between price and demand / revenue opportunity, and customer segments underpin that relationship. Each segment demands different products to satisfy different needs and presents a different revenue/profit opportunity.

Even if the details are very hazy, price demand curves are useful tools to inform product strategy and prioritization. To make PD curves useful requires marketing research. It means identifying customer segments, estimating the the demand of each segment, quantifying the investment required to build the right product, and uncovering the costs of acquiring and servicing customers within that segment. After all that, there’s also a process of testing various price points to get a better sense of price elasticity. All of these are useful in the process of establishing product market fit.

Raising money for a startup is expensive. The typical legal fees for a Series A are about 1% of the total money raised: roughly $40k on $4M. Of course, this doesn’t factor in the time for the process and the dilution of the investment.

But if your startup is considering an IPO be prepared to pay eight times as much in fees. Across 360 venture backed technology IPOs in the last 10+ years which on average raised $107M, 8.8% of the dollars the startup raises in the initial public offering is paid to investment banks, accountants and attorneys.

When startups are acquired, there are many considerations in accepting an offer. Does the vision of the acquirer fit the startup? Will the startup operate independently or be integrated? What is the price and structure of the transaction?

Most of these questions have to be answered through extensive conversations with suitors. As for the structure of the acquisition, there’s data that can be used for benchmarking. I’ve assembled about 2400 M&A events of venture-backed technology companies since 2000 to compare the fraction of the total consideration which is stock and cash.

Financial statements are a Rosetta Stone for startups. They reveal the strategies and the tactics of how to bring a product to market. These are the ten metrics I look at when sifting through a startup’s operational model, whether when considering an investment or in a board meeting.

1. Revenue growth indicates how quickly a company can grow under the current way of doing business. The top line shows whether the market affords steady growth (SaaS) or lumpy revenue growth created by the long sales cycles of big customers (Telecom) and whether the company must sell one product or a collection of complementary products. The revenue growth projections indicate the potential of the business.
2. The Net Income, aka bottom line or burn rate if negative, is the revenue minus all the costs incurred. Net Income dictates the minimum amount a startup needs to raise to become profitable. By comparing Cash, Net Income and Revenue, I can calculate when a startup will need to raise its next round, what its financial profile might be when it does go to market and get a sense of follow-on financing risk.
3. Gross margin is a measure how expensive it is make the product. It’s calculated by taking the revenue and subtracting all the COGS (costs of goods sold), which in software businesses includes serving and hosting costs, software licenses used, and revenue share agreements in the case of ad networks. Most software businesses have gross margins of 80% or more, which make them very attractive. On the other hand, grocery stores operate at 1–3% gross margin. Gross margin is the glass ceiling of profitability because the net margin can never exceed the gross margin.
4. Contribution margin measures profit per unit, without considering fixed costs. To calculate contribution, take the total revenue generated by selling one unit and subtract the variable costs to sell that unit. Selling and marketing costs tend to form the bulk of contribution margin costs in software companies. The greater the contribution margin, the more profitable the business on a unit basis, the more sales and marketing dollars can be spent to acquire customers and fuel growth. Contribution margins range from 5% to 25% depending on the sector. Of course, contribution margin has to be put into context. The contribution margin of producing a kilowatt of energy from a fusion reactor might be nearly 99%, but the fixed costs are measured in the billions. Contrast that with a 15% contribution margin ecommerce business which requires only $25M to become profitable. Which is more attractive?
5. Customer acquisition payback period or sales efficiency is a gauge for how aggressive a company can be marketing and selling its services. The longer the payback period, the greater the risk that a customer churns and the marketing dollars paid to acquire the customer are lost, and vice versa. The most efficient businesses I’ve seen recover their customer acquisition cost in 6 months. A 12 month recovery window is more typical. In SaaS startups, the payback period tends to be the driver for moving from monthly pricing to annualized contracts because it eliminates the profitability risk of a customer.
6. Churn quantifies the revenue potential of each customer. A 3% monthly churn rate, which is typical of public SaaS companies, implies most customers will only hang around for 2 years. The greater the churn, the more challenging revenue growth becomes over time. This often means a company will stimulate demand using paid acquisition, decreasing contribution margin and impacting profitability.
7. The single biggest expense for most startups is salary. By looking at salaries across functional areas, I can get a sense for how a startup pays its employees relative to market rates. Low salaries could spell employee retention questions in the future. Excessive salaries reduce the company’s runway. I compare salaries to a set of benchmarks across venture backed companies as a litmus test.
8. Sales quotas provide indications of how easily the product is sold and how well run the sales team is. Inside sales quotas vary from $150k to $600k per year and enterprise sales quotas vary from $1M to $2M, depending on the product. At the early stages of a startup, I tend to value consistency: smaller deal sizes but more predictable deal velocity (number of customers closed per week).
9. Non-personel marketing spend is the most significant controllable expense in a business. It typically includes ad spending and event spending. This expense bucket can be turned on and off from month to month unlike salaries or rent. It’s important to me because it provides an indicator of how well understood the marketing process has become. For most startups I’ve seen, demand generation budgets range from 5 to 20% of total expenses. The optimal ratio depends on the payback period.
10. Revenue per employee. The beauty of software businesses is their leverage. Google’s market cap is 40% larger than Walmart but it has only 2% the size of Walmart’s employee count. Revenue per employee is a measure of how efficient a business is in using technology to bring their product to market. Some sectors and products intrinsically need more people to be sold.

Bitcoin has captivated the imaginations of many with its quasi-anonymous, hyper cost-efficient payment network. The potential for Bitcoin to change foreign exchange is hard to overstate. In the same vein, the technologies that enable the internet of things (IoT) like Bluetooth Low Energy and Apple’s Beacons and Electric Imp’s infrastructure will transform the way we interact with the physical world to something akin to the mall in Minority Report.

The startups that bring Bitcoin and IoT to the mass-market won’t declare themselves Bitcoin companies or connected devices companies. I’ve never seen a computer marketed as a TCP/IP machine or a bank that promotes the ACH compatibility of its checking accounts or a social network trumpet its H.264 codec support. I doubt I ever will because supporting a protocol isn’t a value proposition that attracts end users.

I was lucky enough to spend some time with Monica Adractas, a former McKinsey partner who is now Churn Czar at Box. She and I chatted about the challenges in managing churn and her view on how to handle it. I thought she had some terrific insights and a clear understanding of the methods to reduce churn from her experiences. These are my notes from that conversation:

Managing churn is a complicated problem because everyone in the company can impact it. The product and engineering teams must build a product that meets customer needs. Sales must find and close the right kinds of customers. Support must educate, inform and cross-sell customers properly, and marketing must price and message the product effectively. If any one of these teams makes a misstep, churn rises.

Sam Altman argues in How to Grow Huge that the only way for a startup to grow really large is to create products that people love and promote. As the user base grows, users attract ever larger numbers of users to the product, producing compounding growth.

The point is a terrific one and I think it can be generalized. To grow really large, startups have to create proprietary distribution channels. The one Sam champions, word of mouth marketing intrinsic to a strong brand, is one example of a proprietary distribution channel.

A key component in a startup’s formula for success is educating customers about the product and driving sales. The sales and marketing teams of a startup are responsible for this. There are many ways to structure sales and marketing teams.

The diagram above outlines a sales and marketing team structure that I’ve observed across many startups. It is consistent with the organizational design Salesforce used to drive revenue from $0 to $100M, described Aaron Ross’s book, Predictable Revenue. Jon Miller, CMO of Marketo, has also written an important blog post on the subject, The Definitive Guide to Sales Development.

For retailers of any size from startup to Fortune 50, the tablet application has become more important than the mobile application. IBM’s annual Black Friday ecommerce report, which tracks 800 internet retailers proves the point.

Tablet users buy at a conversion rate which is 300% greater than mobile phones and despite generating only 60% of smart phone traffic, drive 65% more transactions at 14% larger basket sizes. Ultimately, the average tablet user is worth 3.5x the average mobile phone user. This is true even though the download ranks of the top 4 retail applications in the iTunes store are nearly identical.

I first learned about Sankey diagrams in my thermodynamics class and they’ve since become one of my favorite data visualizations and analysis tools. Sankey diagrams, like the one above of visitors to this blog, show the flow of things. Originally created for measuring the flow of energy through powerplants, they are incredibly useful for content marketing analysis, visitor analysis or any other kind of funnel analysis.

The best Sankey diagram ever created is Charles Joseph Minard’s depiction of the Napoleonic War, which was made famous in Edward Tufte’s Book, The Visual Display of Quantitative Information. It’s amazing because there are at least five different dimensions of data displayed in an intelligible way. Try that with any other chart.