The highlight of the meeting with Prof. Aswath Damodaran was about fifteen minutes after we had taken our seats. We were sitting in his office, in front of (probably) the most popular professor at NYU, when he was suddenly giggling: “Every company and investor has their Bar-Mitzvah moment,” he said. “The moment in which laws and principles are binding and twisting without notice. Then comes the moment in which a kid, measured by his future potential, turns to be an adult, measured by performance.”
It’s easy to misinterpret Prof. Damodaran. He is open-minded, lighthearted, fluent, but you know you can’t blink. By doing so you might lose an extremely important insight that comes out of his mouth. “The smart thing to do is not to realize this at your Bar-Mitzvah ceremony, but much before that,” he continues. “This feeling, that comes way ahead, is the principle that distinguishes Facebook from Twitter. The latter will stay 13 forever because it didn’t realize the sudden transformation to a market that measures each user by financial standards.”
Prof. Aswath Damodaran, NYU
Later on, we discussed other companies and markets’ Bar-Mitzvahs, figuring out precursors (“a migration of employees and customers”), seminal events during companies’ lifecycle (“Lawsuits in progress and other stuff swept under the rug”), and how to frame all of it into a general and a valuable model.
In Damodaran’s eyes, we saw the same curiosity that brought us to establish Zirra. And his voice made us realize that not only is the industry experiencing a Bar Mitzvah, but we are as well. This is a great feeling, despite the changing voice and the hair that suddenly appears on the face.
The Age of Transparency
The private tech industry is one of the most secretive and mysterious – yet it is rich and growing rapidly. It is one big black box for most of us, but not only to us; those who are working in the startup ecosystem themselves, entrepreneurs, venture capitalists, reporters, lawyers, and accountants, are suffering from serious disinformation regarding the economy that surrounds them.
The black box of the private tech business
On the face of it, the startup ecosystem is quite noisy. You hear daily about new financing rounds, sometimes four of five a day, and about an unending array of product launches. Each day tech growth companies raise dozen of millions of dollars, echoing their funding achievements throughout the press and on social networks. But you can rarely guess the price tag a company gets as a byproduct of the round. And you will never have a clue about how much the company is making or how much it is going to spend in the years to follow.
This is quite understandable from the point of view of the entrepreneur. It seems that no one wants to reveal his company’s losses or discuss the fact that its first revenue is going to be five years from now.
Also, exposing one company’s valuation will anchor the market to a number that might go down in the future if something goes wrong. Wishing to prevent such an embarrassment, entrepreneurs prefer not to talk about valuation at all unless the company they run is too big for the number to stay a secret (e.g. Uber, Airbnb, Snap). In that case, analysts and investors disclose that number willingly, although off-record.
Discussing the cap table is also a taboo. Investors and entrepreneurs like to keep the cards close to their chest. But, imagine a world in which the cap table is transparent, allowing everyone to tell the difference between abusive and fair investors.
The result of this panoply of secrets, taboos, and mysteries is enormous disinformation surrounding the startups market. The only information that is already outside is the PR: which company raised money and when did they launch their products.
That is not enough if you’d like to make your way in the industry. If you’d like to invest wisely as a VC investor, to know better your competitors as an entrepreneur, to choose wisely your next job, or even to produce a better coverage as a journalist or an analyst, it is simply not enough.
How to Valuate a Startup
Here at Zirra, we decided to do something about that. We have made it our mission to bring transparency to the private tech market.
First, we set out to valuate tech companies. But how do you assign a value to a startup company? As the valuation guru, Prof. Aswath Damodaran, would say you should take into account the company’s discounted cash flow, growth, and risk. This is intrinsic valuation. Then, you’re allowed to use a relative valuation based on other companies in the field, using standardized metrics and multiples.
But what happens when you don’t have the intrinsic data on the company? This is commonly the case. Entrepreneurs are not going to start disclosing revenue data or their discounted cash flow in the next few years.
Usually, a startup in its first year doesn’t make any revenue, and it invests most of its capital in future growth. This could mean hiring new engineers, building new products or growing their marketing team. The cash flow, then, is an imaginary number predicted by the management, and it stays that way until the company is significantly growing. And, there is nothing wrong with that, says Damodaran. The investors expect the company to lose money – even in areas that hadn’t been thought of.
That’s why, according to Damodaran, that in the new digital economy companies are usually evaluated by the number of users.
In the startup economy growth in users, engagement, or momentum might say a lot more about the company than its current cash flow. But it may also have some implications for the future of company’s revenue.
We at Zirra took the challenge to break the taboo and tell how much a private company is worth. We do that because we cherish the same factors cherished by investors: growth, product adoption, engagement, momentum, brand power, funding, and a healthy team. We do that because we also take into account external risks like fierce competition, a non-existing or too-early market, a lack of funding or an inexperienced team.
We assume we don’t know everything, but we built a good-enough model that helps us manage both intrinsic and relative valuations, based on public data. By doing so we try to challenge the valuation of the company as it appears in the financial books. We don’t claim that our estimated valuation is the ultimate true valuation of the company, but we do try to make it as close as possible.
Is the valuation of the company written in financial books locked deep inside an accountant’s safe the real valuation? Not necessarily. A few factors such as preferred stocks and a hot investment market can inflate the company’s valuation in an artificial way. And that’s exactly what made us develop our own algorithms: We’d like to put a solid mirror in front of those numbers and to pull away from the bubble effect.
Zirra’s Estimation Process
Zirra has developed AI and machine learning technology to effectively analyze the private tech market. It provides insights on startup companies, including estimated valuations, competitor lists, estimated time to exit, risk and success factors, as well as ratings of the team, product, momentum, and execution. Based on its technology, Zirra serves investors, entrepreneurs, and job-seekers with spotlight reports on companies that help them learn more about the companies they are engaging or competing with.
The Zirra valuation process involves both intrinsic and relative valuation algorithms. The intrinsic data includes revenue and expense estimations, traffic trajectories, investment history, and velocity, based on aggregated sources.
In the relative analysis, data is compared and benchmarked with a database of 1,200 companies with correlation to stage, space, size, and trajectory. This produces a preliminary set of company ratings and valuation metrics. For example, the algorithm concluded that teams of up to 3 co-founders, each specializing in his/her field of business, technology or marketing leads to growth, whereas teams of 4 or more co-founders carry a higher degree of risk. The machine learning algorithms track the growth of companies, arrive at conclusions independently, and dynamically adapt for future analysis.
We then produce a map of competitors based on the data set, rated in accordance with the degree of direct competition, its size, threat, and proximity of the shared customer and partner base. Results are sent to relevant experts in our expert community, which is already over 450 experts strong. Experts comment on both quantitative analytics (scores and metrics) and qualitative analytics (risks, opportunities, competitors).
Where do we get the data from? The data is extracted from 85 different data sources, and is updated regularly (daily to weekly). Sources include both open and licensed directories such as the company’s website, Bloomberg, LinkedIn, SimilarWeb, and Adwords. It can be “derived data” such as Glassdoor reviews, consumer reviews, sentiment analysis from open web articles; or other data points such as academic researches, stock indexes, and macro-economic parameters.
Zirra’s Full Valuation Process
A. Data Collection – The data fetched from 85 different data sources such as:
- Information derived from open and licensed directories (e.g. CrunchBase, Bloomberg), LinkedIn, the Company Website, SimilarWeb, Google Adwords research.
- Sentiment analyzed from open web articles, consumer reviews, business reviews or Glassdoor.
- Other data points such as academic researchers, macro economic parameter or stock indexes.
- Relative Analysis – The data is compared and benchmarked with a database of 1,200 companies with correlation to stage, space, size, and trajectory. This produces a preliminary set of company ratings (comparative scores) and valuation metrics.
- Intrinsic Analysis – Revenue and expense estimations, traffic trajectories, investment history, and velocity. The intrinsic valuation fine tunes the relative valuation based on intrinsic (DCF) and averaging algorithms.
C. Expert curation – results are sent to relevant experts from our community of over 450 experts. Experts comment on both quantitative analytics (scores and metrics) and qualitative analytics (risks, opportunities, competitors).
Valuation – Estimated company valuation assuming it was traded today in the open market.
Exit Valuation – Estimated company valuation at time of M&A or IPO.
Chances of Exit – Chances of successful M&A or IPO, at a value higher than current valuation.
Time to Exit – Expected average time between today and a successful exit.
- Qualitative Sets:
Risk & Opportunity Analysis – The data is scanned with 300 business rules which produce risks and opportunities the company is facing based on the data set.
Competition Analysis – Competitors are collated based on the data set and rated in accordance with the level of direct competition, the size of the threat, and proximity to the shared customer and partner base.
Product Rating– Product rating is a factor of customer reviews, analysis of product sentiment on the media, current hirings, Google trends analysis of product searches, IP search, usage metrics (downloads, Similar Web), and others.
Team Rating– Team rating is a factor of the LinkedIn recommendations, record evaluation, social sentiment analysis, success and seniority track record, social media feedback, success as previous entrepreneurs, managers, investors, and more.
Momentum Rating– Momentum rating will rate the company’s progress, pace, and acceleration of translating their vision with execution.
Momentum will be rated by Alexa and Similar Web trajectories, funding history, frequency, and sentiment of media & news analysis, usage history of the product, and others.
Opportunity Rating– Opportunity Rating will encompass the potential opportunity the company is facing should it execute successfully. This will include the potential market size and addressable market audience, the competitive situation – direct and indirect, and how they all translate to ROI potential.
Opportunity will be rated based on Google Trends analysis of the sector to validate growth, general sentiment analysis towards the sector,and a competition or a lack of a competition within a sector.
Vision Rating– Vision rating will rate the company’s ability to effectively align, arrange, and strategize. This will include their clear understanding of the market, translating that to a marketing and sales strategy, business model, required innovation, end-to-end solution providing and setting the stage for market leadership years down the road.
Vision will be rated based on social media and media sentiment analysis, a rating of the visionary capabilities of the founders, by a combination of the product ranking as well as team ranking and more.
Ability to Execute Rating– Ability to execute rating will rate the company’s ability to deliver on its vision effectively. Can the company translate their vision to independent operations, intellectual property, regulatory and compliance issues, eco system support, and required financial depth.
Ability to execute will be rated based on the combination of team ranking, product ranking, ratio of existing funding to remaining funding needs, as well as external factors such as the state of regulatory clearances and other external dependencies that can impact the company’s ability to execute.