Some of the most common founder questions revolve around the timing and size of funding rounds. These two components are interconnected. After all, if you raise a large round, you likely won’t need to raise again for a while.

I find that the best way to structure the size and timing of a startup’s financing is to “fund the answer”. What the “question” is, depends heavily on the industry, stage, type of technology and market, and the startup’s needs itself. The founder has an investment hypothesis, or a question, about the future of the startup, and the investor finances the answer to that question. For example, a very early stage R&D-heavy startup developing something revolutionary will have the question “Does it actually work”? or: “Can we even build it?” In this situation, the founders and the investors partner up to answer that question. If answered affirmatively, you have a Proof of Concept. The funding required to test the hypothesis of “We can build it”, may be small or quite large. Again, it depends on many variables, namely the technology and how revolutionary it is (how much further advanced it is from the current state of the art).

As another example, consider an eCommerce company. Such a new startup doesn’t have at its core a question such as “Can we build it” (the required tools have been standardized for over a decade) but rather evolves from marketing-centric questions: “Will people purchase off our webshop?”, then “Can we acquire customers cheaply enough to make a profit per transaction”, then finally “Can we scale above $10mm in revenue?”. Each of these questions represents different phases of the eCommerce startup’s growth, and require different funding amounts (and likely different investors) for each of the periods.

So the right amount of funding is how much is required to answer the question, and the right timing is how much time you’ll need to do that.

Many times pitches sounds like the company is looking for financing without a clear idea of what question they are addressing. In this cases, it sounds like the financing itself will be used to buy time for the company to figure out what the question should be. This implies a lack of strategic focus. This becomes clear when the company is showing promise, but the management is not clear about what the next steps should be. Founders should always be considering the next step and can firmly articulate their strategy showing exactly what the financing should accomplish (according to their hypothesis). Fund the answer, not the question.

This way of thinking has the added bonus of helping to identify the right investors. If you address investors that don’t care about the answer or the question (because it’s not their area of focus or stage of focus), these investors won’t call back. Try to identify investors who want the answers to the same questions you do. These are the right partners.

Image credits:
Flickr//User: ed_needs_a_bicycle

Finding the Investment amount if exercising Pro Rata Investment Rights.

As discussed in an upcoming post, the following demonstrates that execution of Pro Rata Rights and the resulting new investment amount is independent of the company’s valuation. The portions containing the pre-money valuation cancel themselves out and in the end only the new investor’s investment amount determines how much an existing shareholder must re-invest in order to not be diluted.

This makes sense conceptually if you think about it. For example, if the valuation is low, you are strongly diluted but can purchase new shares cheaply to compensate. On the other hand, with a high valuation the shares are expensive but you are not diluted by the new round as much.

Especially in the beginning, B2B startups often find themselves without the capacity or network to hit up potential customers on their own. A common practice of having an external consultant or expert in the market help with introductions and sales pitches can be very helpful, provided the contracts with him are done correctly. The typical agreement is that the consultant will help with lead generation, follow up calls, introductions, and material preparation and in return receives some percent of the revenue that results from a successful deal.

The problem with this solution is that most of the time the consultant is owed his percentage or flat fee at the time of closing with the customer, while the customer pays the startup based on some other schedule. For example, at Day 0 the startup and the customer sign a contract for 100T€ over 12 months, payable by month. If the consultant has a 10% success fee (or flat fee), then depending on how is agreement is worded, the startup owes him 12T€ immediately at Day 1, before the customer has paid anything! At Day 30, the customer finally pays the 1^st month’s payment of 8T€ to the startup, but the startup is still had a negative cash-flow for the month. Now imagine it was a 500T€ contract payable by quarter over 3 years. In this case, the startup has to pay the consultant 50T€ even though he will see his first payment 3 months later! For a startup with very little working capital this can be deadly. It becomes even worse for the startup if the 1^st XT€ of income go to the consultant up to his percentage amount or if he has a flat fee per customer.

As bad as this can be, it can even be worse if the output of the consultant’s contract is not perfectly aligned with the startup’s input needs. In the example above, there was at least an alignment in the consultants output (a closed revenue-generating deal) and the startup’s needs (a closed revenue-generating deal). But all too often the consultant’s deliverables are not directly tied to revenue (or whatever other metric the startup needs). For example, if a consultant is promising to open up his network, it would be very easy to agree to a flat fee of 2T€ per introduction in the market vertical that the startup is trying to penetrate. But what happens after 10 introductions and no follow up? Now the startups pays 20T€ and received zero economic benefit for the consultant’s work. Or think on the situation where an app startup agrees to pay a marketing company based on the number of clicks on its website or the number of downloads of its app. If the revenue model of the startup is based on usage of the app, then this could be a very bad deal. At the extreme, the startup pays the partner for downloads but the app is never used.

So bottom line is to make certain that the contractual incentives for the consultant are exactly aligned with what the startup needs.

(By the way, the amount of effort that goes into partnering with such a consultant should be the same as any hiring decision, make sure you know what you are paying for. A founder cannot say “well, we only pay him for success, so what’s the worst that can happen?” but rather should establish his experience and contacts in your target market. Keep in mind, this consultant is going to be representing you and your company, make sure he does it well. )

Network Effects are defined as a network becoming more useful the larger it grows. The classical example is the telephone: how useful is it if you are the only one that has it? And how useful if everyone does?

Networking effects are great for startups.  Virality can be achieved by making existing users want to invite their friends, since they receive more benefit if their friends join as well.  Social networks are the modern example of this – they become more useful as more friends join.

But all networks have to first reach a point where it is worth it for someone new to join, some form of critical mass that incentivize users outside of the early-adopters to join. This value is defined as the point where the cost of joining is less than the value of the network. It’s important to include the user’s time as a cost here as well.  The startup can lower the number of users/devices to reach the critical mass point in two ways:

1. Keep the “cost” per ‘N’ (download) as low as possible, in order to lower the critical mass point

In the simplest form this means to make the “transaction” (going to a website, registering for a service, downloading an app, etc) as frictionless as possible.

2. Maximize the value of the network (the N^x curve) as early and quickly as possible:

Improving this metric can be much more difficult, especially over the short term.  Value can be added to the user by both the features (independent of number of users) and by the network effect (fully dependent on the number of users).  Both are necessary: without features you won’t get the early-adopters and without the large number of users you won’t get the mass public involved, regardless of how good the features are (and it becomes a niche product).  So the trick is to setup the product so that there is at least a bare minimum in value to the early adopters and then try to grow to a critical mass as quickly as possible.

The critical mass point itself can be hacked a bit, by making the total population of available users smaller.  Let’s say your app has a great use for students.  There are currently ca. 120 million university students in the world.  If the critical mass crossover were to be at roughly half of that, you would need somehow to convince (at great cost) 60 million early adopters to get involved with your product.  But by bringing the total population down through some targeting (only ivy league university students, only Texas students, only Florida students, etc) you lower the crossover threshold dramatically. It may still be 50% (or whatever it turns out to be in your market), but that no longer is 60 million early adopters but only 200,000 or so.

Interestingly, this “total universe of users” can have an upper limit per user.   According to the Dunbar number, the maximum “useable” relationships for humans is about 150.  The number of close relationships is less and the number of loose relationships can be more, but this Dunbar Number throughout history has consistently been the maximum number of realistically useful relationships a human can hold.  The implication is that for most social networks (which are based on human relationships – that’s the “social” part) have decreasing marginal benefit after N > 150.  Beyond that, the features become important again as the network finds ways to be beneficial for even looser connections.

Image Credit: IEEE Spectrum // Brian Metcalfe’s original slide

It seems that more and more early stage investors are using convertible debt as an investment form (convertible note, subordinated loan agreement, etc) so it’s worth taking a look one of the least discussed portions of the loan: the interest.

Interest on loans has historically been intended as compensation to the loaner for the cost of capital of the loan. This makes sense in banking and financial services where the interest is the primary source of revenue and loaning is the main business activity of the bank. Keeping interest in the loan for angel and venture capital investing also makes some sense for the simplest of convertible loans, in particular if it is not clear if there will be a further financing in the future (but instead an exit or insolvency). In these two cases the conversion event never takes place, so the loaner never has the opportunity to benefit in the upside of a higher valuation through the increase in value of his shares (for insolvency that is a good thing, his loan now has a higher priority of payment than the shareholders). To compensate for the risk of there being an exit before the next financing or insolvency before the next financing (particularly common with bridge loans), the interest becomes an important component incentivizing the loaner.

However, convertible debt has become so common that many additional clauses have snuck in over time, namely Forced Conversion, Discount, and Caps. I’ll be talking primarily about the Discount clause and its effect on Interest in this post.

The “Discount” in Discounted Convertible Note is meant to compensate the investor for the massive risk he is undertaking by investing without setting a fixed valuation on his investment. Instead, the investor receives a discount to the next financing. (For example, if the next financing round has a $ 10 million pre-money valuation and he has a 20% discount, then he invests at a $8 million pre-money valuation).

Where it gets interesting is if the convertible loan contains both a discount as well as interest. In this case, the interest accumulates over the life of the loan (until the next financing) and also converts. So if a $ 1 million loan with 10% interest (for simplicity let’s say it is non-accumulating interest –i.e., “bullet loan”), then after a year $1.1 million is converted. Remember: interest is also discounted. So in the example above with 20%, the $1.1 million is converted not at the round’s $ 10 million pre-money valuation but rather at the discounted $8 million pre-money valuation.

So let’s compare:

(Assuming a $ 10 million pre-money Series A, in which $ 1 million was invested as a loan 1 year before.)

(The loan without interest or discount might as well be thought of as an equity investment now – it doesn’t get any special economic effects so it is the same as $ 1 million investing in this new round).

Obviously the presence of the discount and the interest are beneficial to the investor, but what most founders forget is that they are compounding. Even with numbers as low as these and with only 1 year interest, we see enough change for it to be a discussion point. The final results of the negotiation depend on many factors of course, but a founder should recognize that the “interest plus discount” combination can lead to more dilution than he was expecting.

Cheat Sheet for Consumer-Web Startup Metrics and links for explanations:

Runway - “How long until the company flies or dies?”

CAC-CLV (Customer Acquisition Cost minus Customer Lifetime Value) – “How close am I to a viable Product/Market fit?”

Burn Rate – “Is my Runway getting shorter or longer even as I accelerate to the end of it?”

Number of Downloads – “How many potential real users do I have?”

Monthly / Weekly / Daily Active Users – “How many real users do I have?”

MRR (Monthly Reoccurring Revenue) – “How much am I extending my runway each month?”

Finally, for an overall guide to SAAS metrics, I find David Skok’s article to be be an  excellent primer.

Image credits:
Flickr//User: ronploof
Flickr//User: peacenik1
Onstartups.com: Dharmesh Shah
Memegenerator.net
TableSoftware: “Tale of 100 Entrepeneurs, user:amorrison5122, Ellie Fields”, free download available here.

Slides to my talk at Pioneers Festival 2013 on “Why Hardware Startups are the Future”. I outline my thoughts on why now is the most exciting time to get involved both as a founder and investors with hardware startups.

With all the excitement of building the product, getting the code ready, and launching the product, it’s very easy to leave your go-to-market strategy until after launch. It happens all the time; founders do a Press Release and then wonder why the phone doesn’t ring. Ideally, founders would have been in constant with prospective customers during the Beta phases so that the launch itself is a seamless progression of a newer version of a product, rather than a milestone where the founders then say “Ok, now let’s concentrate on the sales thing”. This is of course especially true for B2B products (true also for B2C software products, but at least then distribution is less of a problem). From the many enterprise B2B startups I’ve seen, the wrong process generally goes something like this:

1. Develop (maybe get some feedback during this process, maybe not)
2. Launch (make a press release, try to get some mentions in press)
3. Phone doesn’t ring, no callers. Realization that B2B products must be actively sold.
4. Call up the beta testers, partners, and customers you originally got feedback from (hopefully such a list exists)
5. Offer them a free trial. (For enterprise software this also means you probably do an installation on-site, at your own expense!).
6. Search for other potential customers and offer them the same, thinking, “If only we could get a reference customer we would have credibility”.

The problem with this strategy is that even if a few customers are won, it’s hard initially to tell if they are real customers. By “real”, I mean they internally made the decision that the worth of your product is great enough for them to pay for it. Breaking that down:

• “made the decision” – If a free trial is available by simply downloading the software or filling out a form, then you don’t have confirmation that a decision-maker in the company approves of the product. Until the decision maker actually obligates the company in some way for a payment, you don’t know how serious he is as a customer. There is a world of difference between the CEO that says “ok, we need this” and the lowly employee that wants to try it out risk free, since he doesn’t obligate the company in any way (meaning he doesn’t get approval beforehand). Direct sales are very time and manpower intensive and you as a founder should be concentrating on the most promising leads, the ones that are serious about the product. Making a product available for a free period moves the question of seriousness further into the future and makes it hard for you to determine which customers to prioritize.
• “worth of your product” – Getting the product to a point where the market approves of it and recognizes that it solves a pain point (the Product / Market Fit) is a big challenge, often requiring many iterations of the product (or the market). Hitting this point is a big deal, but it’s only truly hit when you see customers verifying your hypothesis on what they need. A customer “trying something out” doesn’t do that.
• “pay for it” – This point is easy. Cash in the hand is the only true verifier that a product is appreciated. This part has also the added bonus that it verifies your pricing model (or not), making cash and financial planning easier.

Dharmesh Shah made a great point on this topic several years ago, that instead of offering a free trial, you should offer a money back guarantee (with a longer period). The great thing about the money back guarantee is that it addresses all three points above. The money is paid, meaning that internally the decision maker agreed to it, and the worth and pricing of the product is mostly validated. This filters your lead-list into customers that are serious enough to make that decision, while still allowing you to offer to them what they psychologically perceive to be low risk.

It dramatically changes the dynamic of the customer relationship as well, since they will see themselves as customers during the money-back period, as opposed to prospective customers in the trial period. While there will always be some customers that want their money back, the good news is that unless the customers feel tricked, keeping this churn rate low should be doable.

As an added bonus this method also improves the cash situation of the company, since you are being paid earlier – always a good thing for a startup.

In October 2013 I held a series of lectures, together with www.armeniatomorrow.com , on startups and an outsider’s perspective on their prospects in Armenia.  This was a thrilling event for me, not just because of the enthusiasm of the students, but also because it’s the first talk I’ve given which was live translated (in this case into Russian).  The primary lecture was at the State Engineering University of Armenia, but earlier in the week I had also spoken at the Yerevan State University.

(Email me for the full presentation).

“I had that idea, if only I had…” Is regrettably a common lament among founders.  But it’s important to remember that the timing of the execution can be as important as the execution itself.  (Update: this Onion article is a pretty good tongue-in-cheek perspective!)

Granted, the evolution of the market is almost completely outside of the founder’s hands, but that doesn’t mean he shouldn’t have a strong grasp of what it means for his company and product. For example, a coder could have developed the coolest high-res, high-quality video streaming site on the internet back in 1998, but have ignored that the vast majority of the potential users were still on dial-up connection speeds.

The point is, you absolutely must have a strong grasp of the drivers for your product’s adoption. What is the most important KPI for your growth or revenue generation? Now: what is the most important external driver of that number? You won’t be able to influence that driver itself, but what can you do to influence its impact on your product. This is critical for product planing.

For example, at hfield the biggest external threat to our long-range WiFi product was the evolution of faster cell connections. Cell providers were rolling out faster and faster connections, but mostly in urban areas. Through better marketing we were able to target the product on poor cell signal areas and we discovered to our surprise that our most lucrative market was campers and mobile-home owners!

Another example is Shazam, the music ID service. Founded in 1999, it was really first in 2008 that their growth exploded with the launch of their iOS App. The premium SMS service was nice, but nothing compared to the App. Here it was important to realize the potential of a new market driver (smartphones) for the service and plan for it. As the most important driver to growth, the key insight here was the maturity of the driver (market penetration of smartphones).