Why Bloomberg, CapIQ and Co miss out on Start-Ups

Working for institutional asset management and VC before, I know my way around data service providers such as Bloomberg, FactSet, Reuters (Eikon, Datascope), CapitalIQ, Pitchbook and Co.
What these companies really miss out on is capitalizing on start-ups and young companies that just cannot afford these massive sales tickets.
Why is this relevant?
Let’s look at some really relevant use cases for start-ups that make or break the scaling process and take away power from founders and shuffle it to hopefully benevolant investors.

  1. Market Sizing and Segmentation

How do you cluster a market into brackets from small companies with up 50 million market cap to those few giants with 500+ billion in market cap? Yes, you can use Yahoo Finance. But that is always a „best bet“ analysis. There is no way you are replicating the 120 trillion in equity market cap in yahoo finance to come up with the number of companies in each bracket that you need to hunt with different go-to-market strategies. Period. Why is this data not publicly available or made available to start-ups? It’s easy and defensible to build profiles of potential IT budget, spending on a particular kind of SaaS software or even calculate revenue or cost per employee just with top of the head market cap, multiples and headcount data that can go a long mile to build model to show business impact of a solution on key KPIs around headcounts.
2. Talent Management
Almost any company can get a feeling for average salaries in key industry hubs around the globe. From Paris to Milan to Berlin to London to Stockholm to Sao Paolo to Seoul to Beijing, Shanghain, Tokyo, Shenzen, New York and greater Boston Area to Utah, etc. etc.
But that is not enough. Knowing CEE, India, China and APAC, Central Europe and Nordics and the LatAm and US space is a start. But you really need market capitalization of key industries in these areas to understand the powder that is in these ecosystems and the competitiveness and potential educational catering of human resources in these areas. Nobody wants to hire a top A.I. engineer form an industrial region and plotting the 120 trillion in global capital on a map would really help you sift out regional comparative advantages and making smart estimates on key human resource profiles flying around in these areas. Which makes using ranking statistics on top engineering and sales schools in these regions a bit easier to understand, by understanding how these educational systems cater to the local and regional corporate environments.
3. Sale organizations
The same is true for structuring sales organizations and their footprints. It really makes no sense to set up a sales office in Houston, if you are targeting the US defense industry, which you find in the upper east coast. Nothing is easier to identifying and analysing budget authorities in different geographic regions based on market capitalization by industry and region by plotting companies in these regions.
The only way to retrieve such data right now is to hook up LinkedIn sales Navigator and write in-browser scraping engines to get statistics on geographical distribution of potential budget authorities within organizations.
Only if you manage to tie your organizational development plans to the reality of the global capital markets you will ever be able to make B2B work in a sensible fashion.
So all those data vendors completely suck at this. Dating services such as okCupid at least manage to publish statistical infographics on their cohorts. But those huge aggregators of financial and company data don’t get it. The same is true for LinkedIn, which offers so many solutions around matching connections between individuals, but fails to capitalize on the massive informational advantage that human capital information may provide to investors and entrepreneurs.
It’s kind of sad. And yes, likely a lot of service vendors can use this to build their partnership channels to these vendors to eventually capitalize on it. Reasonable. But the price points of the entire ecosystem make them completely irrelevant for the start-up community. And the corporate silos typically are too siloed and untrained to capitalize on these massive data pools.
Again, you can say this is all good, because it advantages investors with sufficient fund sizes and decent understanding of capital markets of using all this data to their advantage. But the reality is that most early stage investors don’t get the data part of their business. And on top of that, the assymmetry adds to the non-gaussian results we see in the VC world and why spray and pray – or a monkey shooting darts to select portfolios – outperforms. There are too many bad apples that invest into more apples. Overall leading to nonsensical allocation of capital to failing investments. Losing investor money, shrinking the total volume of capital flowing into the tech ecosystems and thereby slowing down innovation. The cost of assymmetry doesn’t pay on the global level and hurts the entire ecosystem more than it makes it benefit. (that is debatable, but in this line of argument, it appears reasonable).
But when you ignore the interests of all those data companies, you are really ignoring the fact that there is a massive amount of value simply destroyed that otherwise could benefit society. So why?


Schreibe einen Kommentar

Deine E-Mail-Adresse wird nicht veröffentlicht. Erforderliche Felder sind mit * markiert.

Next Post

The New CFO - 02 Validating the OpModel

So Jul 8 , 2018
Part two of „The New CFO“ series on my blog. After introducing the new CFO as the master of capital allocation or internal investor of the company into spending portfolios, we want to look at what it means to validate a segment – or to establish product market fit in […]