We spent the better part of 2015 working with fast-growing tech companies on their internal HR & recruiting analytics. Effective recruiting can be incredibly challenging: First Round Capital’s State of Startups 2016 Report assessed that talent acquisition is founders’ biggest concern for the second year in a row. Recruiting metrics help to identify problems and optimize the talent acquisition process: if you can’t measure it, you can’t improve it. Below we share some of the most common metrics we observed across startups that serve as the basis for understanding and improving your recruiting process. The list below isn’t comprehensive, nor applicable for everyone, but it’s a good place to start.
The Mattermark Daily is an excellent daily newsletter that curates first-hand perspectives on entrepreneurship, investing, sales, hiring, and more, as it emerges everyday. As a weekend hack, we deep-indexed every article featured in the Daily over the past few years, and built StartupDB: a simple search interface so you can find the best, most relevant startup content from the Daily when you need it. We’ve been using it internally to field questions from readers and pull together some of the best startup resources — for example, this post here on 10 excellent resources for enterprise sales.
Here are some excellent resources & guides to better understand enterprise sales. These resources span all sorts of concepts appropriate for early-stage founders and anyone generally interested in the subject: pricing, metrics, management, business development, sales tools to use, and more.
Moreover, because the goal of Requests for Startups is to support entrepreneurs, we’ll begin to more formally solicit ideas for interesting topics we should write about in the future. Please submit your ideas here, and as always, we would love your feedback on this mini-series, and the newsletter more generally, via email.
Why/when to IPO
Going public via an IPO or being acquired are two mechanisms by which startups seek liquidity. Timing of an IPO depends on industry. Typically, a company chooses to go public when:
The company has made significant progress often in the form of sustainable profitability, solid revenue growth, or other material milestones. There has been substantial analysis done that demonstrates what companies look like at the time of their IPO by reviewing their S-1 filings — here are many excellent SaaS benchmarks.
Existing investors, founders, and employees are seeking liquidity and would prefer additional financings via the public markets, which may allow for higher valuations that mean less dilution for existing shareholders. As expected, public excitement and high demand for the stock can drive up its valuation. Various studies (like this one) seek to explain the valuation premium that IPOs engender over acquisitions in other ways — one such study suggests a 22% markup, and others (like this one) indicate there is no such premium in certain scenarios.
In some cases, a company may require significant growth capital, but if the company already has a high valuation, private market investors may not find the opportunity to invest as appealing as earlier stage companies that have greater potential upside, thus making an IPO an effective fundraising strategy. The IPO market, however, is volatile — IPO volume varies from year to year. This year has been a slow IPO year, meanwhile we’ve seen companies raise private capital at very high private valuations, like Uber and Airbnb. Pitchbook reports that we’ll see the fewest number of IPOs in 2016 since 2009 after the market crash. Companies exploring both an IPO and a potential acquisition engage in a “dual-track” process typically led by investment bankers.
A potential acquirer may take interest in a company around the time of an IPO, as an IPO implies that the acquirer may need to pay a significant markup on the company’s valuation after the IPO. In this sense, the IPO market and the private M&A market are coupled: if there are lots of IPOs happening, acquirers are more keen to make acquisitions, as they may lose the chance to pay favorable prices if the seller is no longer privately held.
Advantages of going public:
Potential to raise money at a higher valuation than on the private market, as described above.
A public company can effectively use its stock to make acquisitions. Using stock can be more effective for a public company because their stock is liquid, while as a private company it is not, and thus may be less palatable to a potential seller.
Public visibility can allow for additional, favorable fundraising later on the public market. This is great should the company need to raise additional growth capital. This also gives the company additional credibility with potential customers and employees.
Liquidity for founders, investors, and employees — the ability to sell shares on the public market.
Disadvantages of going public:
Significant legal & disclosure obligations and information provided to shareholders. This also applies to the company’s officers and directors, who can be heavily scrutinized.
Required disclosure of specific types of transactions, including stock option practices and executive compensation.
The process is both expensive and time consuming for the management team. We’ll discuss the process in a later post.
Restrictions on stock sales. There is typically a 6-month lock up after the IPO to protect the stock against price volatility as it’s new to the public market. This means shareholders can’t sell their stock in this time window. There is also much regulatory scrutiny around stock sales for fear of insider trading. The SEC’s Rule 144 restricts the amount of stock that can be sold by major shareholders in any 3-month period to prevent major price fluctuations or impact on liquidity. It also may look bad to the public market if a significant stockholder is dumping a lot of stock.
Advantages of M&A:
If the deal is all in cash, the company can get immediate liquidity instead of relying on the public markets. However, in a stock deal, SEC Rule 145 is similar to Rule 144 and applies to the company that is acquired if they will be receiving stock in the acquiring public company. In other words, the acquired company has similar restrictions on stock sales as they would have in the event that their company went public.
Potentially less market risk. In a cash deal, the seller knows what they’re getting. In a stock deal, the company that is getting acquired still bears risk via volatility in the public market price of their parent company’s stock, but this is usually less volatile than a brand new post-IPO company.
None of the regulatory and administrative burdens of running a public company: Disclosures, forecasts, analyst calls, shareholders, etc.
Disadvantages of M&A:
The company could command a higher valuation in the public markets.
Their upside is fixed, based on the purchase price, or is no longer in their hands, if they’re receiving stock in the parent company. If a company IPOs, they control their future upside via control & management of the company going forward.
Liquidation preferences could mean less upside for the founders than for early investors. Preferred stockholders will make most of the proceeds if the liquidation preferences exceed the fair market value of the company. At an IPO, preferred stock is usually converted to common stock so the liquidation preferences are nullified, which transfers upside to the founders.
Less control, affinity, and/or agency. Employees may be required to stick around after the acquisition but they no longer run their own company. The parent company may choose to shutdown or repurpose the acquired company’s product — Microsoft shutdown Sunrise, Dropbox shutdown Mailbox, and Twitter recently announced it is sunsetting Vine, for example.
We welcome any questions or feedback via email. In our next post, we’ll discuss factors that make a company ready for an IPO as well as the IPO process.
Continuing our series of deep learning updates, we pulled together some of the awesome resources that have emerged since our last post on September 20th. In case you missed it, here are our past updates: September part 1, August part 2,August part 1, July part 2, July part 1, June, and the original set of 20+ resources we outlined in April. As always, this list is not comprehensive, so let us know if there’s something we should add, or if you’re interested in discussing this area further.
Open Sourcing 223GB of Driving Data by Oliver Cameron of Udacity. 223GB of image frames and log data from 70 minutes of driving in Mountain View on two separate days. Log data includes latitude, longitude, gear, brake, throttle, steering angles and speed. GitHub repo here.
Youtube-8M Dataset by Google. 8 million video IDs and associated labels from over 4800 visual entities (e.g. vehicle, concert, music video, etc.), making it possible to advance research & applications of video understanding. Blog post here.
Deep3D: Automatic 2D-to-3D Video Conversion with CNNs by Eric Junyuan Xie. 3D videos are typically produced in one of two ways: shooting with a special 3D camera or shooting in 2D and manually convert to 3D — both are hard. This project demonstrates automatic 2D-to-3D conversion, so you could potentially take a 3D selfie with an ordinary smartphone.
Anticipating Visual Representations from Unlabeled Video by MIT. Anticipating actions and objects via computer vision is hard (e.g. if someone is gesturing forward to shake hands). Humans do this through extensive experiential knowledge and inference — it’s much harder for a machine. This implementation trains deep neural networks to predict the visual representation of images in the future. Forbes article here.
TensorFlow in a Nutshell by Camron Godbout. A three part series that explains Google’s deep learning framework TensorFlow. The guides cover the basics, hybrid learning, and an overview of supported models. Part 1, part 2, and now, part 3.
The Neural Network Zoo by Fjodor Van Veen. A cheat sheet that covers many of the popular neural network architectures. Great way to keep track various architectures and their underlying structures and relations. The cheat sheet has descriptions of each architecture and links to their original academic papers.
Torch Video Tutorials by Alfredo Canziani. A video collection of intro tutorials on leveraging Torch, providing an overview of Lua, Torch, neural networks, CNNs, and relevant Torch packages. RNNs coming soon.
The Alexa Prize by Amazon. A new annual competition for university students to advance the field of conversational AI. Participants develop a bot that converses coherently with humans for 20 minutes. The application process closes October 28, 2016 — apply here.
Bay Area Deep Learning School held at Stanford in late September and organized by Pieter Abbeel, Samy Bengio, and Andrew Ng. Speakers included Yoshua Bengio, Hugo Larochelle, Russ Salakhutdinov, and many others. All slide decks here and live stream videos from day 1 and day 2 are available.
By Isaac Madan. Isaac is an investor at Venrock (email). If you’re interested in deep learning or working on something in this area, we’d love to hear from you.
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