As a former equity research analyst I built financial models on
numerous companies in several industries. I wrote on
telecommunications companies (RIMM) I wrote on Software Security
Companies (SYMNC) but mostly, I wrote on Internet companies.
The key to writing on companies is to understand the financials and
what drives them. That is best achieved by building a model of the
company and focusing most on the income statement. The income
statement, after all, is the source of the revenue and EPS
estimate, the things my clients cared the most about.
To get a better understanding of Facebook, I thought I would walk
you, the reader of Zacks.com through how I would do this if I were
a research analyst. This is a top-level discussion and all data is
from a recent S-1 filing with the SEC.
When I did this professionally, I had a 50 call rule. I had to make
at least 50 phone calls to customers of the target company. I had
to make at least another 50 phone calls to the target company to
talk to their sales department, the service department or anyone
else at the firm to give me insight that I would not get from SEC
filings or earnings calls.
I didn't do this for Facebook as I am no longer a research analyst,
but I didn't do it for
)either and I was ranked #1 for Earnings Estimate Accuracy by
Starmine… so take that with a grain of salt. For a small company,
the calls are invaluable but for a huge company, they are an
infinitely small sample from which I would base my assumptions.
Google, like Facebook, has hundreds of thousands of advertisers, so
my 50 calls would be meaningless.
In order to estimate the revenue in a credible fashion, you have to
rely on the metrics. At this point, the only metrics we have are
ones we have from the S-1. Use the most recent S-1 when looking at
a new company, or rely on 10K's for modeling public companies that
have been around for more than a few years.
The metrics will help you connect the dots on how to estimate the
total revenue. For Facebook, the company published MAU's (monthly
average users), DAU's (daily average users), Advertising Revenue,
Payment Revenue and ARPU (average revenue per user). We find these
metrics on page 48 of the S-1, they are embedded in the graphs so
get them into a useable format in a spreadsheet. I did it in excel,
but Google docs work just as well.
Facebook took the unprecedented step to break out the users and
revenue by geography, giving us a clear idea how this worldwide
social networking phenomena is not just a big deal in Palo Alto.
It's a big deal worldwide. I started making my model by focusing on
revenue. So I listed all the metrics and then added in a few lines
beneath each item to insert an equation for annual and quarterly
growth. If you do this, part of your spreadsheet would look like
For purposes of clarity, I color code things so I know without
checking what type of metric it is. Brown is MAU, Blue is DAU and
Green is Revenue (for the color of money). I do not color code ARPU
because it is a calculation more than a given data point (even
though it is a supplied data point). I also added a % of total
revenue for the revenue lines, this might help me down the road
when I model out
The idea is to work into a "proven" system that accurately shows
how the data points are manipulated into revenue. That is done by
dividing historical revenue by historical MAU's giving us a proven
ARPU. The ARPU that the company supplied us with for each geography
doesn't match up perfectly as there are times when the ARPU rate is
higher or lower than our "proven rate" due to the difficulty
Facebook has with coming to actual numbers of estimated users (they
guess based on IP address) and assuming a 5-6% duplication of extra
I believe it's best to do this one region at a time as opposed to
doing it over the blended average, and yes that is basically 4
times the work, but over time it will pay off. Down the road after
four more conference calls, we will pick up on trends by geography
and our estimates should get better. The more details, the more
data points the better.
Next I want to go ahead and work out what the proven ARPU rate is
and break that out in terms of both advertising and payments.
Basically I just divide the historical MAU's by the desired Revenue
line to get the ARPU I want. Also include the two lines to show
percentage growth for both annually and sequentially. I also want
to put in a graph right now that measures the different ARPU's and
their respective annual growth rates to get a good look how things
trended. It should look like this:
The more astute readers will notice that I have what appears to be
an estimate. I have made my estimates for 2Q12 ARPU for the Rest of
the World segment and put them in blue text. This helps me
understand what is an equation and what is an estimate. Why 3% for
both… well I am just showing how to do it, I am not really giving
The next step would be to do the same for MAU's and DAU's. Again I
am using a static 5% for both, but this time I am looking at
sequential growth instead of annual. I have found it best to use
the time frame that fits best with your knowledge of the situation
and the one that has the most apparent trend. A 5% sequential
growth rate gives MAU's a reasonable annual growth rate that shows
a deceleration, but still growing. Your model should now look like
Again, the astute reader will see that I have already stuck in a
revenue estimate for both advertising and payments. Others will
notice that the stated ARPU is here, as are a few extra lines that
help me adjust the stated ARPU with my proven one. At this point we
are just about done, if you can believe it. Just wash rinse and
repeat for North America, Europe, and Asia. Once you have completed
all four segments it is just a function of adding up the sums for
each to come to a global estimate.
Those of you who are already good with excel know that pasting and
dragging will save you a lot of time, and if you put that 3% and 5%
in for all quarters for all geographies, you will end up with the
wrong answer, but you will have modeled out Facebook revenue for
2012 as $4.9B or an increase of 32%. Being aware of seasonality and
looking at the trends in each geography for each revenue line will
lead you to creating the best estimate you can.
When you have your estimates completed, let us know in the comments
Brian Bolan is the Aggressive Growth Stock Strategist for
Zacks.com. He is also the Editor in charge of the
Run Investor service
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