AnalyticsAnalyzing the Investment Market Using External Data

Analyzing the Investment Market Using External Data

The connection between the data and your wallet is not always as straightforward as it could be. Let’s take a look at a real-life example and ask ourselves: what can we do with a proper piece of data if we apply some reasonable analysis behind it?

Where is the wisdom?
Lost in the knowledge.
Where is the knowledge?
Lost in the information.
– T. S. Eliot*

Ever heard the phrase “knowledge will let you to stay on top of things”? Many of us say it, and now it’s time to be frank: “staying on top of things” doesn’t increase your wealth, but rather escalates feelings of overall importance.

However, the connection between the data and your wallet isn’t always as straightforward as it could be. You would think that I would want you to compensate me, wouldn’t you?

Let’s take a look at a real-life example and ask ourselves: what can we do with a proper piece of data if we apply some reasonable analysis behind it?

We all enjoy a venture or two, and startups are an extremely popular trend right now with a good deal of money to be obtained. However, one problem lingers – too many of these startups are doomed to fail.

We’re led to question the best method of choosing an appropriate and successful startup to invest in. Some individuals are quite well-versed in choosing efficacious investments.

In order to fuel our gluttony, how can we steal a piece of their faultless wisdom? In an effort to obtain these supposed trade secrets, we now commence an assailment into the mentalities of our big-name investor comrades.

Where is the information?
Lost in the data.
Where is the data?
Lost in the f#@%&! database!
– Joe Celko*

Where is the Data? Not Lost in the F#@%&! Database!

The general idea is rather straightforward, as we will essentially utilize external data in order to locate commonalities in both flourishing and botched startups. From there, we will complete a calculation so as to determine exactly which investor is successful (and therefore should be followed) as well as which is unproductive in the end.

The first step of an analysis never fluctuates, and merely consists of running an inventory of our data in order to determine what we have to manipulate.

Our startup database is courtesy of CrunchBase and is rather considerable. In conjunction with CrunchBase, we will utilize external tools in order to estimate the startups and we are going to begin with SEMrush (disclosure: I’m COO of SEMRush).

The market value of LinkedIn is approximately $10.5 billion in conjunction with Facebook’s estimated worth of $65 billion. The chart below indicates a similar finding:

facebook-vs-linkedin

Although it isn’t exactly the same, there is a correlation.

Could it be the result of a mere coincidence? To find out, let’s attempt something unique.

Pinterest is an innovative and popular new startup, while Groupon is… well, stable, as the chart below displays:

groupon-vs-pinterest

Our graph is basically speaking masses to us, showing that individuals are searching for Pinterest. It appears within search results, hence causing our search results to skyrocket and price to increase.

On the other hand, what about social projects whose startup may not be open for the Internet?

As an example, we’re going to look at a Facebook store engine entitled Payvment. They have substantial activity, but solely within Facebook’s partitions. SEMrush claims that Payvment is negligible which is accurate only in terms of search traffic, and the same approach can’t be applied. In order to examine social activity around the Payvment project, we are in need an additional tool such as a Facebook graph:

payvment-facebook

Look at that! Payvment is perfectly alive and well, however living in a distinct environment.

Let’s verify the source for a second time by analyzing our original comparison of Pinterest versus Groupon. Pinterest possesses – 25K+ talking, 2 million likes. Groupon, on the other hand, retains – 10K talking, 0.5 million likes. This social graph is articulating something as well.

To attempt to make what could be a very long story a bit shorter, there are many APIs that are similar. Alexa provides a great deal of data as well as Topsy, MixRank, and so on. With all of this data, a table such as the one below will transpire:

search-traffic-general-social-finance-data

Where is the Information? Not Lost in the Data!

Even deprived of complicated calculations we can perceive that Pinterest is 2-3 times below Overstock in terms of search, however 3 times larger with regards to social media attention. Factually, Pinterest isn’t a store but rather is comparable to LinkedIn and Facebook.

Overall across various types of data, Pinterest appears to be three times smaller than LinkedIn and 15-20 times lesser than Facebook. If we base this finding on the established market prices for Facebook and LinkedIn, we can generalize that the cost of Pinterest would be approximately $3 billion.

Nevertheless, estimation such as this is a bit barbarian. The complete table is comprised of virtually 100,000 lines (the amount of companies within CrunchBase), and the actual estimation is meant to incorporate heavy mathematic artillery. As we are conserving the actual number crunching for an additional article, we’re going to jump ahead in a valiant effort to embezzle the knowledge and wisdom of the renowned stakeholders.

According to CrunchBase, an assemblage of individuals invested $138 million in Pinterest. While we aren’t aware of the magnitude of their share, we can expect that it is around 30-70 percent, centered on common knowledge of course. Since Pinterest is set at a $3 billion evaluation, this seems like a good arrangement.

Investors such as Jack Abraham and Michael Birch define their accomplishments as $138 million to $3 billion. Despite this, we are oblivious to exact shares and conditions, but we can manipulate average market numbers to fulfill supplementary calculations. These averages may be incorrect for a particular company, but when expanded to all 94,000 CrunchBase listed companies, we can aspire that the average aggregated numbers will be relatively accurate.

In contrast to these investment success stories, there are also those that are not quite so prosperous.

Let’s investigate the failed business of HiGear. $1.3 million was initially invested into this endeavor by BVCapital, 500Status, and Battery Ventures. In this particular case, BVCapital, 500Status, and Battery Ventures were at approximately $430,000 each and have now repositioned from the $1.3 million investment to zero dollars.

However, these investors have partaken in more effective cases as well, so therefore if we combine all of these endeavors we will seemingly receive an investment rating that defines who is and isn’t worthy.

Let’s delve into another example: Investor M continuously invests on the first round and promotes the company. Next, he passes the business on to the next round of investors and promptly unloads his share. Subsequently, all of these companies are touched by catastrophe and experience the dreaded reality of failure.

These observations proclaim that we would benefit from joining Investor M in his investment undertakings during the initial financial round, but that we should under no circumstance purchase from him.

In order to keep this article to a relatively reasonable size, it has been reduced into two separate pieces, and this is only the beginning (I promise). In the succeeding article, the mathematics of the estimation will be clarified and investor rankings will be finalized.

In the meantime, you’re strongly encouraged to utilize the information within this article to make a valid attempt at crunching the numbers on your own prior to the posting of the next article. Click here to download the table, use your own method of estimation, and inform us of your deductions within the comments. To be continued!

* I wonder which link could possibly be more relative?

Resources

The 2023 B2B Superpowers Index

whitepaper | Analytics The 2023 B2B Superpowers Index

8m
Data Analytics in Marketing

whitepaper | Analytics Data Analytics in Marketing

10m
The Third-Party Data Deprecation Playbook

whitepaper | Digital Marketing The Third-Party Data Deprecation Playbook

1y
Utilizing Email To Stop Fraud-eCommerce Client Fraud Case Study

whitepaper | Digital Marketing Utilizing Email To Stop Fraud-eCommerce Client Fraud Case Study

1y