Part 1 - Who the players are.
Part 2 - The process of fundraising.
Part 3 - What metrics matter, and why.
Part 4 - Additional thoughts.
One of the greatest lessons I've ever learned was from Jonathan Jackson who simply said "Everything is a story, and the best story wins." It's easy to read that quote and say "Facts!" but it goes even deeper than that. When lawyers are in court arguing in front of a jury, the story that is told the best using the evidence, is at times the story that the jury decides to make its verdict on. When there's a public relations issue that hits the web, it is typically the best story that wins. Even in extremely important elections, the candidate that tells the best story about themselves and the vision for the role they're campaigning for, is the candidate that wins. Your business is no different. Here's this week's edition of The Dime💰.
When pitching your startup, you're telling a story. It usually goes something like this:
"(insert product name) is a product that will revolutionize (insert industry) by providing a novel service that (insert current competitors) haven't figured out yet. If you just invest x dollars into (insert product name) then you will have the chance to bring (insert product name) to market and be along for the ride as we shift (insert industry) into a new age. You will also make a ton of money in the process."
Of course that's not the entirety of a pitch but it usually should be short, sweet, and to the point. Especially when you're in an elevator with someone who is an investor or are connected to potential investors. This is why many accelerators will tell you to craft what they call an "Elevator Pitch."
Over time you will have to hone that pitch and also expand on that pitch. Depending on the audience you will have to insert data points and additional information about your customer to make your business, your story, the best story ever. That's what part III of this series is about. This piece will be focused on the metrics that Venture Capitalists (VC's) look at to determine if your business is worth investing in. You need to know some (or all) of these metrics. Preferably by heart. If you asked about some of these metrics and you don't know what they are, most VC's will assume you're just wearing the costume of a founder and not an actual founder for real. It's no different than speaking with a person who says they are a math wiz and are unable to tell you what the quadratic formula is. There are just some things in this world you simply should just know.
Knowing these metrics aren't simply for vanity. These metrics also tell the story of your business. They're simply a story told with numbers, instead of being told by words. The better you know the numbers and how to explain the numbers, the better story you're able to tell. The better story you're able to tell, the more money you can raise. The more money you can raise, the better chance you have a running a successful business. Successful business either leads to an Initial Public Offering or an acquisition which means an exit for all of the investors. In simple terms, the best story leads to money for everyone. So let's give you the ABC's to telling the best story using metrics for your startup. I'm pretty sure that I will not cover every single metric known to man here, so please do not enter the comments saying "You missed x metric, you don't know anything." If anything, if you're a founder, I'd love it if you added metrics that you use, or metrics that you created, as a comment below this article. As my assumption is that other founders will use this document to guide them through their journey. If you choose to do so, please also add why you use your metrics and tell us what story it tells about your business.
Why Metrics Matter
The game hasn't been the same ever since.
Metrics tells you how your business is doing. The more metrics you use, the more details you have about your business' performance. Instead of digging into business to find value though, I'll use another sports story.
Baseball is a very old sport. If I'm correct, you can trace baseball back to the 1700's (or even earlier). During that time, all you cared about was how many times a person hit the ball and how many times they scored. There wasn't much to it until 1858 when a guy by the name of Henry Chadwick invented this thing called the "box score." The box score changed everything about baseball because it was the first time that you can quantify multiple actions about one player and place them in a chart. It was the very first time that we could measure a players performance. By measuring a players performance you can measure the impact that they have on a team. By measuring a teams performance, you can not only determine how good they are, but also the chances that they will beat another team that they're playing at a particular time.
People who loved math and sports, fell in love with the box score. They would use the metrics from the box score to bet on games, bet on players, and also discuss what new metrics should be measured to better predict outcomes. In 1964, Earnshaw Cook wrote a book called Percentage Baseball which was the first book of its kind. It didn't just discuss numbers, Cook decided to formulate the game of baseball around numbers and numbers only. Here is some of what he covered in that book:
1. Quantitative Analysis
Cook’s approach applied statistical and mathematical models to evaluate baseball strategies and player performance systematically. This was at a time when decisions in baseball were mostly made based on experience and intuition rather than hard data. Cook compiled extensive data on various aspects of the game, including batting averages, on-base percentages, and other performance metrics. He used this data to perform statistical tests and create models that could predict outcomes based on historical performance. His work was a precursor to the use of data analytics in baseball, showcasing how statistical analysis could provide insights that traditional methods might overlook.
2. Offensive Strategy
In his analysis of offensive strategies, Cook challenged the conventional wisdom surrounding how effective of certain traditional plays like the sacrifice bunt and stolen bases. By analyzing historical game data, Cook argued that these strategies often resulted in a net negative impact on a team’s ability to score runs. For example, he calculated the run expectancy (the average number of runs a team can expect to score from a given on-base situation) before and after bunts and steals, often finding that these plays did not contribute as positively to team scoring as traditionally believed. This made managers and players to reconsider when and how they used these tactics.
3. Optimal Lineup Construction
Cook argued that the order in which players appeared at the plate could significantly affect the number of runs a team scores over the course of a game. He suggested that lineup construction should be based on empirical data rather than tradition or gut feeling. For instance, he advocated for placing players with high on-base percentages (OBP) in the most crucial batting positions to maximize the chances of scoring. His analysis suggested that the conventional lineup arrangement underutilized players' abilities and that strategic adjustments could lead to more efficient scoring opportunities.
4. Run Expectancy
Cook developed the concept of run expectancy, a statistical measure that estimates the number of runs a team is likely to score from a specific on-base situation during an inning. This model considers the current base occupancy and number of outs to calculate the expected runs. This concept allowed teams to make more informed decisions regarding when to attempt certain plays (like stealing bases or executing bunts) based on the potential change in run expectancy rather than just intuition. This methodology has since become a foundational tool in sabermetrics, helping to shape modern strategies in baseball analytics.
5. Pitching Analysis
While Cook's work on offensive strategies and lineup construction had a huge impact, his analysis of pitching was less revolutionary but still provided insight. He examined the effectiveness of different types of pitchers and analyzed how different pitching strategies affected game outcomes. Cook looked into how pitchers' styles (like power pitchers vs. control pitchers) and their game management impacted their effectiveness and team success. Though this part of his analysis did not gain as much traction as his offensive critiques and strategies, it still contributed to the broader understanding of how to evaluate pitchers using statistical methods.
Earnshaw Cook became the father of statistical analysis for sports. In the 1970's a man named Bill James started releasing regular issues of what he called Baseball Abstracts which added onto Cook's work and he founded an organization called the Society for Baseball Research. The numbers and analysis that came out of it was called SABRmetrics. Fast forward to the late 1990's and the Oakland Athletics General Manager, Billy Beane, hired a statistician by the name of Paul DePodesta who both went on to completely change baseball by finding undervalued players to create a team that won 20 games in a row (only 5 teams in baseball history ever accomplished 20 wins or more in a row). This style of building teams became known as Moneyball which was popularized by the author Michael Lewis and became a movie starring Brad Pitt and Jonah Hill. If you haven't seen it, you should.
This is the style of approach you should take when looking into your business because this is the type of analysis Venture Associates are using to determine if your business is something worth investing in, how much should be invested, and at what price.
What Metrics Matter
Every metric doesn't mean the same for every business. Some metrics will matter for your business and other businesses, other metrics won't mean a damn thing and you're basically measuring nothing. You always want to measure metrics that are impactful. So here are some of the metrics I've seen other startups use along with some examples of how popular technology companies use them.
Customer Acquisition Metrics
Customer Acquisition Cost (CAC): The first metric in our tale is CAC, a pivotal figure representing the cost incurred to acquire a new customer. It's calculated by dividing the total marketing and sales expenses by the number of new customers gained in that period. For example, a digital marketing startup like AdQuick, which helps brands manage their ad campaigns, would closely monitor CAC to ensure the profitability of each campaign and adjust their marketing strategies accordingly.
CAC=Total Marketing and Sales Expenses/Number of New Customers Acquired
Lifetime Value (LTV): Counterbalancing CAC is LTV, which estimates the total revenue a business expects from a single customer over the course of their relationship. The higher the LTV relative to CAC, the more profitable each customer is. Startups like Dollar Shave Club, which rely on subscription models, use LTV to predict long-term revenue and adjust retention strategies to maximize profit.
LTV=Average Revenue Per User (ARPU)×Customer Lifetime
Performance Metrics
Monthly Recurring Revenue (MRR): MRR is crucial for any subscription-based startup like SaaS platforms. It's the predictable revenue expected every month, calculated by multiplying the total number of paying users by the average revenue per user. MRR is a heartbeat metric for startups like Zoom, where consistent growth in MRR can attract further investments.
MRR=Total Number of Paying Users×Average Revenue Per User (ARPU)
Gross Margin: This metric reflects the efficiency of a startup in terms of production and service delivery by showing the percentage of revenue that exceeds the cost of goods sold. High gross margins are particularly crucial for tech startups like Tesla in its early days, where capital-intensive production can otherwise consume capital resources rapidly.
Gross Margin=(Revenue−Cost of Goods Sold/Revenue)×100%
Growth Metrics
Burn Rate: Often discussed in hushed tones, the burn rate tells the story of how quickly a startup is using up its cash reserves before becoming profitable. It's calculated by subtracting the monthly expenses from the monthly income. A startup like Snap, in its early stages, watched this metric closely to time their next funding round before the coffers ran dry.
Burn Rate=Monthly Cash Outflow−Monthly Cash Inflow
Virality Coefficient: The measure of a product’s organic growth rate from existing users referring new users. For social platforms like Twitter in its nascent stage, a high virality coefficient meant rapid scale-up without proportional increases in marketing spend.
Virality Coefficient=Number of New Users from Referrals/Number of Current Users
Usage Metrics
Daily Active Users (DAUs) / Monthly Active Users (MAUs): For consumer tech startups like Spotify, DAUs and MAUs provide a snapshot of engagement and scale, crucial for both attracting ad revenue and demonstrating growth to investors.
DAUs or MAUs=Number of Unique Users per Day or Month
Churn Rate: This reveals the percentage of customers who stop using the startup’s product over a certain period. It’s particularly vital for service-based startups like Netflix, where minimizing churn is key to maintaining a stable revenue base.
Churn Rate=(Number of Customers Lost during the Period/Number of Customers at the Start of the Period)×100%
Net Promoter Score (NPS): NPS measures customer satisfaction and loyalty by asking customers how likely they are to recommend a company's product or service. A high NPS indicates strong customer satisfaction, which is pivotal for companies like Apple and Amazon.
NPS=(% of Promoters−% of Detractors)×100
Customer Retention Rate: This metric is vital for subscription-based businesses like Netflix or software services, as it measures the percentage of customers who remain subscribed over a specific period.
Retention Rate=(Number of Customers at End of Period - Number of New Customers during Period/Number of Customers at Start of Period)×100%
Activation Rate: This metric tracks the percentage of new users who take a key action within a certain timeframe after signing up, indicating successful onboarding. For example, a social media platform like Instagram might track the percentage of users who make their first post within the first week.
Unique Company-Specific Metrics (AKA Metrics companies created for themselves)
Amazon - Unit Economics Model: Amazon tracks its "Unit Economics," which break down the profitability and cost structure on a per-unit basis across its various segments. This metric is crucial for understanding the profitability of Amazon's diverse range of products and services, from AWS to its e-commerce operations. Amazon communicates these insights during quarterly earnings calls to showcase efficiency improvements or growth in high-margin sectors.
Tesla - Direct Order Efficiency: Tesla developed a metric called "Direct Order Efficiency," which measures the cost-effectiveness of customer acquisitions via their website versus traditional sales channels. This metric is critical for Tesla's direct-to-consumer sales model, reducing reliance on dealerships. Tesla discusses this metric in shareholder letters and earnings calls, highlighting the streamlined efficiency of its sales model.
Facebook (Meta) - User Engagement Score: Facebook (now Meta) developed its own "User Engagement Score," which combines daily active users, time spent on the platform, and interaction rates across posts. This proprietary metric offers a comprehensive view of user engagement, which is critical for selling ads. Meta reports this score during earnings releases to link platform engagement directly with advertising revenue growth.
Spotify - Playlist Engagement Index: Spotify tracks a "Playlist Engagement Index" that measures user interaction with curated playlists, including plays, shares, and follows. This metric helps Spotify demonstrate the value of its discovery features to artists and advertisers, communicated through its annual reports and investor presentations to underline the strength of its platform in retaining user attention.
Conclusion
It's time for you to lock in on the metrics for your business. Not only to tell you what exactly is going on, but also so that you can communicate that to current and potential investors. A lack of metrics tells an investor you may not know what it is that you're doing and instead are acquiring customers and revenue because of vibes. People don't invest in vibes. People invest in businesses.
That's it for this week's edition of The Dime💰. Don't be stingy with the 🏀. Pass it to a friend.
See y'all next week for part IV.
CJB