For pre-seed and seed stage startups, investor updates are a challenge. Often, founders try to make them too ornate and end up getting behind. Similarly, investors don’t always have the time to fully digest a finely crafted narrative and lose track of what’s happening.
At RSCM, our portfolio of pre-seed and seed-stage investments is at about 400 today, so we have lots of experience with updates. Not only do we read them all, we write a 3-7 line internal summary and each one goes into our CRM system so we have a complete history at our fingertips.
In my opinion, useful investor updates have three requirements: they must get done, they must be easy to produce, and they must be easy to consume.
Anatomy of an Update
You can deliver on all three requirements by breaking updates into modules and putting the most important modules first. That way, you need only produce the modules you have time for and we need only consume the modules we have time for. Everybody wins.
Here are the modules and order I recommend:
[Company Name] Investor Update for Month Ending [Last Day of Month]
- Highlights (Optional)
- Asks (Optional)
- Thank Yous (Optional)
- Commentary (Optional)
Notice that the only required module is “Metrics”. This should be easy to produce because, at any given moment, you should have a handful of Key Performance Indicators (KPIs) you track anyway. This should be easy to consume because most investors have lots of experience absorbing tabular business data. This should be easy to get done because, in our modern software-driven world, KPIs are at your fingertips. Most importantly, if they are the metrics you are actually tracking to run your business, then they will be reasonably informative to investors. Requirements satisfied!
More detail on metrics in a minute, but first some quick notes on Highlights, Asks, and Thank Yous. If you opt to include these modules, do them as bullets. Easier to produce and easier to consume. But, as with PowerPoint slides, no more than 7 bullets per section! Even then, only go to 7 on rare occasions. No more than 5 most of the time. It’s easy for people to get saturated and when they get saturated, they flush the entire list from their attention. If you’ve got more to say, put it in the Commentary.
Everything after Metrics really is optional. Better to get the update out the door quickly than wait until you come up with points for every section. If you ever find yourself thinking something like, “I’ll crank out the Asks later,” stop! Just hit send. Then if you do think of important items later, put them in a notes file and include them in the next update. Or send out a specific Asks email.
Now for some depth on metrics. There are really two types: (1) those that are universal to all pre-seed/seed startups and (2) those that are particular to your business. Investors need both. The first type gives us a general sense of how things are going for you relative to the typical startup lifecycle. Kind of like the vital signs that all doctors want to know regardless of patient or condition. They help us triage our attention. So start with them:
- Revenues: [revenues | date when you plan to start selling] (+/- ?% MoM)
- Total Expenses: [expenses] (+/- ?% MoM)
- Net Burn: [total revenues – total expenses] (+/- ?% MoM)
- Fundraising Status: [not raising | planning to raise | raising | raised]
- Fundraising Details: [how much, what structure, valuation/cap, who]
- Ending Cash: [last month’s Ending Cash – this month’s Net Burn + this month’s Amount Raised] (+/- ?% MoM)
- Full Time Employees: [FTEs, including founders] (+/- # MoM)
Note 1: we strongly encourage a monthly update cycle. Anything longer means we get data that’s too stale. Anything shorter, and the financial metrics don’t really make sense. Though if you’re part of an accelerator that encourages weekly updates, we’d love to see them. Just make sure we also get the monthly metrics!
Note 2: always put the percentage or absolute month-over-month changes in parentheses next to each entry. It turns out that highlighting the deltas make updates dramatically easier for us to absorb by drawing immediate attention to the most volatile areas.
A couple of quick explanations. Always have a Revenues line. If your product isn’t finished or you aren’t actively trying to generate revenues, just put the target date for when you do plan to start selling. Either piece of information is enormously helpful to us. Also, provide an FTE number that logically reflects the labor resources at your disposal. A full time contractor is a unit of full time labor that you can call on. Two half-time employees are also one unit. An intern may or may not be a unit or fraction of a unit depending on how much time he/she is putting in and whether the output is roughly equivalent to what a regular employee would produce. Don’t exclude people based on technicalities, but don’t pad your numbers either.
Now, some detail about fundraising status. This topic turns out to be pretty important to existing investors. First, it lets us know that you’re on top of your working capital needs. Second, some investors like to participate in future rounds and even the ones that don’t are a great source of warm leads. Third, it makes us feel good to know that other people have or will be validating our previous investment. Here are a couple of example fundraising bullets:
- Fundraising Status: planning to raise in 4Q2015
- Fundraising Details: $750K – $1M Series Seed at a $5M-$6M pre-money from a small fund and/or local angels
- Fundraising Status: raising
- Fundraising Details: $300K – $500K on a convertible note at a $2.75M cap with $175K soft committed from [prominent angel name] and other local angels
- Fundraising Status: raised and raising
- Fundraising Details: $400K closed of a $600K convertible note at a $4M cap from [small fund name], [AngelList syndicate name], and local angels.
At any point in time, there should be a handful of top-level KPIs that you monitor to help run your particular startup. Of course, they vary across lifecycle stage, technology area, and business model. Just pick the most important 2-6 and give them to us. Feel free to change them as you pivot and mature.
Here’s an example for a pre-product enterprise SaaS company:
- Projected Alpha Delivery Date: 11/30/2015 (+15 days)
- Alpha Access Wait-list: 47 Companies (+8)
And one for an enterprise SaaS company that recently shipped private beta
- Max Queries/Minute: 1,201 (+29% MoM))
- Outstanding Critical Bugs: 3 (-2)
- Inbound Inquiries: 481 (-17% MoM)
- Qualified Prospects: 19 (+2)
- Paid Pilots: 3 (New Metric!)
And finally one for a consumer Web company in full operation
- Max Concurrent Users: 1,006 (+30% MoM)
- Registered Users: 23,657 (+13% MoM)
- Monthly Actives: 3,546 (+4.5% MoM)
- Users Making Purchases: 560 (+21% MoM)
- Total Purchase Value:$17,993 (+28% MoM)
- CAC: $12.55 (-7% MoM)
That’s it. We estimate that, if you keep your accounting system up to date and use MailChimp, producing an update with metrics and a few extra bullets should take about 15 minutes (with some practice). And you’d be heroes in our book. Well, all entrepreneurs are already heroes. So you’d be superheroes!
Regular readers know that I’ve been trying to bust the “Seed Bubble” myth for years. In my latest analysis, I show that total seed funding in 2014 was nearly identical to that in 2008, not even adjusting for inflation or economic growth. Over the last year, I’ve encountered several other persistent VC myths that similarly conflict with the data. Given that the seed bubble meme seems to be subsiding (though I dispute talk of a nonexistent bubble “popping”), I thought I’d tilt at some of these other windmills.
Myth 1: Series A Crunch
The easiest target is the myth of the “Series A Crunch”. As far as I can tell, the first mention of this hypothesis was in a November 2011 blog post by Elad Gil. After a year of bouncing around the echo chamber, this November 2012 Pando Daily article by Sarah Lacy was pretty typical: “Everyone — to a person — says it’s a real phenomenon…That means we’re getting a very different ‘nuclear winter’ as a result of industry excesses this time around.”
OK, so let’s look at the data on Early Stage VC dollars and deals from the NVCA.
Hmm. I’m having trouble seeing much of a “crunch”. Pretty much up and to the right since 2009. Perhaps a slight valuation correction from 2011 to 2012, but deal volume was still going up. Certainly not a “nuclear winter”. Basic version of the myth.. busted.
Myth 2: Seed – Series A Imbalance
But there’s a variant of the Series A Crunch argument that the real problem is a Seed – Series A Imbalance. It’s not the absolute amount of Series A activity, rather there’s an overhang of increased Seed activity that is/will be causing a shortage of Series A down the line. Both the aforementioned Gil and Lacy pieces presage this twist. But this March 2015 Fortune article says it all in the title, “Free-flowing seed capital is giving startup founders a false sense of confidence,” and subtitle, “And it’s causing chaos ahead of the Series A round.”
Now, if you’ve read my Seed Bubble posts, you know that seed capital has not been “free flowing” over the past few years. But even I was shocked at the stark reality when I overlay VC Early and All Seed funding on the same graph.
For a long time, seed funding was much greater than VC Early funding–twice the size or more. Then VC Early started to creep up. During the period of the supposed “Series A Crunch”, VC Early funding was actually shooting up from about 10% less to 60% more than All Seed. In fact, the ratio of VC Early to Seed tied the all time high in 2011 when the crunch supposedly began, then nearly doubled that record by 2014. If there’s an imbalance by historical standards, it’s the opposite direction!
Myth 3: “I’m Allocated to Seed”
Obviously, if people managing investment portfolios believe the Series A Crunch or Seed – Series A Imbalance myths, they won’t allocate dollars across startup stages correctly. This trap is compounded by a misimpression of the stages themselves. Many investors believe they are allocated to “seed” when in fact they have only fractional exposure to “something VCs call seed but is vastly different from the rest of the seed market.” From an allocation perspective, misidentifying your asset classes is a huge danger.
In my opinion, what VCs call seed is not the same as the rest of the market. Just look at the average size of a VC deal vs the average size of an angel deal. (Note that the angel number is the average size of at all stages so the graph actually understates the difference at the seed stage; unfortunately, CVR tracks angel deal volume, but not deal size, by stage.)
The ratio rose from about 4X in 2002 to 11.5X in 2014, with a peak of 14.5X in 2009. There’s clearly a categorical difference between what VCs and angels call seed.
Moreover, if you invest in an early stage VC fund, at best a modest fraction of each dollar actually goes to seed. Most early stage VCs make only a small subset of their initial investments at the round they call seed (unsurprisingly, most of the money goes into the round they call “early”). Moreover, even funds who always make their initial investments at what they call seed generally reserve at least $1 of follow-on for each initial $1. So at absolute best, investors who think they have seed exposure through VCs are getting half their dollars exposed to only the tiny slice of the seed market that accounts for the largest deals.
In my opinion, the combination of all these factors means most investors in funds are dramatically under-allocated to over 90% of the seed stage technology startup market–a market that’s roughly the same size as the Early Stage VC market.
Does this difference matter? Well, if you’re worried about portfolio allocation to illiquid assets, you should be pretty concerned about the future liquidity options for such assets. From that perspective, here’s a sobering statistic from CB Insights (secondary source because primary requires registration):
In 2014, 73% of technology companies acquired never took traditional VC.
So if you believe that that VC Seed gets you exposure to the entire seed stage startup asset class, your portfolio will lack exposure to 3/4 of the liquidity options. By the way, this statistic is up from 2/3 in 2013. And with record amounts of cash on the large technology company balance sheets that make these acquisitions, I could easily see this bias growing further. Imagine a portfolio that lacks exposure to 80% or 90% of the liquidity events in that asset class! This third party data dovetails nicely with my previous calculation of a massive difference in the small M&A vs IPO and large M&A market.
I will stipulate that the VC-backed exits are almost certainly each bigger. But the goal of portfolio allocation is to balance out risks within and across asset classes. The data makes it clear that relying on VC Seed leaves a portfolio exposed to idiosyncratic risks within a particularly narrow exit market. So finding some way to target the other 90% of the seed stage technology startup market seems like prudent portfolio construction.
Of course, there may be different data out there or I may have botched the crunching somehow. So as always, feel free to check the work on my spreadsheet.
According to my usual data sources, 2014 was not nearly as good a year in seed funding as I would have expected from reading news reports. I was fully prepared to see “bubbly” data. However:
- From angels, seed stage funding plunged nearly 50%, from $11.2B to $6.0B.
- From VCs, seed stage funding dropped about 25%, from $940M to $740M.
(While VC early stage jumped over 60% and expansion stage spiked over 110%.)
- But median seed valuations increased 20%, from $2.5M to $3.0M.
The run up of a bubble does not typically include sharp volume drops, but the price rise may indicate something interesting is going on.
See here, here, here, here, here, here, and here for previous posts in this thread. My data sources are the Center for Venture Research for angel data, the NVCA for VC data, and my personal tracking spreadsheet for “super angel” funds not part of the NVCA. I use the HALO report for pricing data.
Out of concern that one possible explanation for individual angel investments dropping is a shift to angel funds, I reconstructed my angel fund tracking spreadsheet from scratch. I was worried that my list of funds was too haphazard. So I pulled a longer, hopefully more complete, list of micro VC firms from CB Insights. I then removed ones that are members of the NVCA, whose investments should already be included in that data. I also went through fund CrunchBase listings and Web sites, filtering out those who invest primarily outside the US or not at the seed stage. I noted the reason for any such exclusions in my latest spreadsheet. This updated source contains 74 funds totaling $3.2B, while my 2013 source contained 29 funds totaling $1.2B—a substantial increase in coverage. There were also 23 funds on the new list for whom I could not find dollar amounts. However, I assume each fund with dollar amounts is completely deployed in the current year so the total should still be a gracious estimate given that even very fast funds actually deploy over two or three years.
Despite the addition of $2B in covered super angel investments, the 2014 graphs were still sobering with a 25% total volume drop year-over-year:
The big question is how do you get a pronounced volume drop and a pronounced price increase? First, one of the data sources could have a problem. The obvious candidate here is the CVR angel data because it accounts for most of the total volume I track, the methods aren’t documented, and this is its biggest one-year drop ever. According to the latest report, total angel investment volume was only down 2.8%. But the proportion of seed and early investment plummeted from 45% to 25%. I can think of several reasons for a potential measurement inconsistencies here. Note that the 2012 measurement was 35% so it has a lot volatility.
Similarly, the CB Insights pricing data could be the result of an anomaly, as it also experienced its biggest one-year move. In contrast to CB Insights’ 20% jump in valuations, the CVR valuation data showed a slight decrease across all stages (but the CVR valuation data is reported inconsistently, so I have avoided using it in the past). Of course, the CB Insights and CVR data collection methods could somehow result in systematically different samples that explain the conflicting data.
There’s another explanation that I find tantalizing, if only because it would confirm my hypothesis that founder opportunity cost drives the earliest company valuations. Think of seed stage companies as “supplying” investments and investors “consuming” them. Econ 101 says that a simultaneous decrease in volume and increase in price implies that the supply curve has shifted left. It’s like a freeze wiping out a significant fraction of the orange crop. People buy less at higher prices because there’s a shortage. But why would this happen now in the seed stage startup market?
I have a guess: the macroeconomy recovered. Unemployment eased from 7.5% in June 2013 to 6.1% in June 2014. Moreover, according to Indeed.com, software engineering salaries jumped 20% in 4Q2013. All of a sudden, the opportunity cost of founding a seed stage startup went up dramatically. That could definitely have an effect on formation rates, which would show up first in the seed stage funding data. Another “supply side” explanation would be that founders simply need less money to advance their ideas past the seed stage. The total number of angel-funded deals actually went up 3.8% according to the CVR report.
At RSCM, we’ve certainly seen no shortage of quality opportunities at low valuations. If anything, we are deluged. Of course, we purposely focus on smaller deals so we wouldn’t expect to see any shortage if lower capital requirements were the underlying cause. Our experience is also consistent with a data collection anomaly. I’d love to get my hands on a good dataset for accelerator program application volume. That might allow us to distinguish between declines in formation rates and capital requirements.
Bottom line: I’m still very skeptical that there is a seed stage bubble.
So there are finally some signs if life in seed-stage technology funding:
- In nominal terms, we roughly equaled the global peak from 2005.
- Seed stage valuations have remained flat since 2011.
- Adjusting for the size of the economy and our wealth, the level is still down.
Kind of hard to call this situation a “bubble”. But I can live with calling it a “recovery”.
Once again, see here, here, here, here, here, and here for previous posts in this thread. My data sources are the Center for Venture Research for angel data, the NVCA for VC data, and my personal tracking spreadsheet for “super angel” funds not part of the NVCA. For super angel investment, I worry most about detecting new chunks of money, not necessarily measuring the “true” level. I use the HALO report for pricing data, which goes back to 2011.
Here are the graphs (spreadsheet here):
It looks like all the components are recovering, though traditional VC somewhat more slowly and super angel somewhat more quickly. The question is still, “Bubble or no bubble?”
Let’s look at prices, as I did in my 1H2013 post. According to the full year 2013 HALO report, the median seed valuation is still $2.5M… just like 2012… and 2011. The 75th percentile valuation is up slightly in 2013, from $3.7M to $4.2M. But the 25th percentile valuation is down a hair from $1.5M to $1.4M. According to the methodology described in the report, this data includes angel group deals before Series A. So what I think is happening is that some companies that might have gone for a VC round in the past are doing a larger angel round instead. If you check out my spreadsheet, you can see that check sizes for what the NVCA calls “seed” have taken another swing up, probably pushing some early startups out of that market. So no obvious pricing pressure.
Moreover, I think the following graphs make a bubble quite unlikely. I’ve been waiting for years to pull these out. The first one “deflates” the seed investment levels by adjusting for GDP. Thus it measures how seed investment has changed relative to total economic output. The second one deflates seed investment levels by adjusting for the level of the S&P500 index (on July 1 of the given year). Thus it measures how seed investment has changed relative to the total stock of wealth.
Compared to our economic output and total wealth, seed-stage investment seems like it still has a significant amount of headroom. I’m actually pretty sure I could build a darned accurate forecasting model based mostly on the S&P. Given that the index is up roughly 25% from July 2013 to July 2014, my eyeball estimate is that 1H2014’s numbers will show us somewhere around a $16B annual rate.
Well, it’s been almost three years since I started watching for quantitative evidence of a “bubble” in seed-stage technology funding. I feel like a broken record saying there’s still no sign. Here are the highlights:
- 1H2013 volume is 30% below the 2005 global maximum
- 1H2013 volume is 10% below the 2011 local maximum
- Seed stage valuations have been flat since 2011
You simply don’t have a bubble when volume is down and prices are flat!
To review the history of my seed bubble watch, see here, here, here, here, and here. Recall that I use the Center for Venture Research’s angel data, the NVCA’s VC data, and my personal list of “super angel” funds not part of the NVCA. The volume calculation methodology is not designed to produce the most accurate estimate of the true number of seed-stage dollars. Rather, I want it maximally sensitive to sudden influxes in new seed money. I use the HALO report for pricing data, which started coming out in 2011.
That said, here are the graphs (spreadsheet here):
The story continues to be that traditional VCs have become increasingly irrelevant as their seed dollars have dropped 60% from 2009 to 1H2013 and their share of all seed dollars has plunged from 22% to 7.5%.
Angel’s position has gradually eroded from 2011 to 2013, with their share decreasing from 88% to 77%. Super angels and seed funds have gained in share during that time, jumping from 3.0% to 15%. My guess is that trend will continue unless the individual angel pool increases via new platforms like AngelList. In any case, the new breed of funds is not growing fast enough yet to make up for decreases from other sources.
[Edit 8pm: Somehow this paragraph got deleted from my draft.] There also appears to be no pricing pressure at the seed stage. According to the 2012 and 2Q2013 HALO reports, the median seed-stage pre-money valuation has remained $2.5M since 2011. Moreover, the 25th and 75th percentile valuations have actually decreased, making it hard to argue that there is some hidden dynamic masking a buildup in prices.
Interestingly, the HALO report shows a continued drop in California’s share of angel group activity. From 21.0% in 2011, to 18.1% in 2012, to 17.3% in 1H2013. I’ll take this as continued confirmation that RSCM is right that some of the best values are outside the Bay Area.
It will be interesting to see what the data shows for 2H2013 and 1H2014. With the S&P reaching new highs throughout 4Q2013, institutions should increase their allocations to alternative investment funds and angels should feel like they have more wealth to invest in startups. Assuming the public markets don’t experience a sudden drop in the beginning of 2014, of course.
While my goal is to eventually apply the Market Space model to large enterprises, I’m going to begin with startups. Obviously, my work at RSCM makes startup close to my heart. And most large enterprises were new entrants at some point, so analyzing the birth of firms seems like it should lay some crucial groundwork. (For previous posts in this series, see here: one, two, three, four.)
Looking at the search for profitable products as a Multi-Armed Bandit (MAB) problem illuminates the general complexity of the firm’s challenge (see previous posts in this series: one, two, three). But in terms of analyzing specific firm behaviors, I think it’s important to acknowledge that we don’t have a pure MAB here. It seems pretty clear there’s more causal structure in Market Space.