To me, thinking and bleeding “real time” is the core of the term “digitalization” (which I am so sick of hearing, but that is another topic). And it is mind-boggling how many businesses do not consider themselves “real-time”. Interestingly this applies to both established as well as new business and basically across all industries. Simply because you are selling something physical or not time critical does not mean you can ignore it.

Your (potential or actual) customer has her demand / interest/ engagement right now. NOW. Not in 5 minutes, not tomorrow. And you better be prepared for that moment having processed all the available and relevant information of the past and be able to fulfill the need as fast as possible. Some applies to internal stuff, why limit yourself to some snapshot of the past? The past just concluded and simplifying your approach by just processing the data of the last month or yesterday simply is not enough, you will never be able to react to the latest events (a value from a sensor, the click of a consumer, the change in your inventory system, …) which in many cases will make all the difference. …


AI (as in Artificial Intelligence) and ML (as in Machine Learning) are the latest buzz in the early stage startup and tech scene and as such there is a lot of startup stories and pitch decks build around these magical technologies.

Most of these pitches follow a common pattern / storyline:

  1. This is industry X, huge market.
  2. In industry X lots of data is available or could be generated. Seriously, lots of data. Big Data.
  3. Current players in industry X are clueless with regards to IT (especially latest trends such as AI and ML) and as such inefficient. …


An amazing aspect of working in the venture capital space is the continuous debate how this business makes the most sense as in can be most lucrative for a venture firm and its investors (Limited Partners) over a longer period of time (the longer period of time is important here to somewhat lessen the effect a single massive hit can have on performance). I constantly find myself in really interesting discussions about this topic and this is probably one of the most blogged about topics by VCs.

The discussions interestingly cover almost all aspects of the setup of venture firms, from sizes of funds (varying from 50m to >1bn), the investment approach (focussed large investments vs. lots of smaller ones) to the team setup (larger operating team supporting companies vs. small investment team with basically no support staff) and so on. Even which KPIs are most or actually relevant when evaluating VC funds which have not yet been fully returned is a continuous discussion. …


I have a certain fondness for companies build around developing and marketing open source software (OSS) and made multiple investments in this space over the last few years. Based on those years and experiences I have developed a certain set of analysis factors when looking at new opportunities which I’ll try to outline (very briefly) below:

Known vs New

The first and probably most dominating differentiator is the type of solution itself. The first type are solutions replicating known and successful products, often with a limited but more focused functionality. …


Seed and early stage venture investing somewhat is like jumping into puddles. It makes a lot of fun, but all to often the puddle is a lot deeper than you initially think.

But as an investor you have to keep on jumping, especially since sometimes the value of a company lies in its unexpected depth, despite all the problems that you all to often find yourself into right after jumping

And no, this post was not triggered by a most recent experience … just a general observation of jumping into puddles for a couple of years now.


Internet of Things and Industrie 4.0 (a German term for the industrialization of manufacturing environments) are hot investment topics these days. Collecting machine data, monitoring production cycles and taking steps towards predictive maintenance all fascinate me.

But there is a big obstacle that most startups face and — when talking to them — a surprising high amount have not even realized: The lack of a technical stack established at their target customers.

A (really) simplified stack looks somewhat like this:

  1. Sensors
  2. Data processing & transportation
  3. IoT cloud
  4. Software & algorithms

This lack of a technical stack forces startups to offer complete solutions to place their products. Even if your focus is on developing algorithms around predictive maintenance … well, you first need some data to give your algorithms something to work with and most likely your customers do not have the data today and do not want to do all the research which components to get to make your solution work. If your business is selling sensors, your customers most likely have no clue what to do with the data and it is your job to present a compelling use case and solution processing the data. …


With the current trend / resurgence of corporate investments into startups I hear a lot of people claiming that “being involved” and “learning from startups” is a core part of their goal and justification. I consider this flawed both for the corporates themselves as well as all other players in the ecosystem (entrepreneurs, fellow investors).

For most larger corporates even the best outcomes of their investment activities will never move the overall needle of enterprise value and profitability unless their commit to invest significant amounts of money. …


Investors in Venture Capital talk a lot about their value add and some make it sound like they are the ones who make companies successful. I am not a big believer of this marketing blabla.

It’s entrepreneurs that make companies successful. An investor might help here and there with some valuable input, an intro to a potential employee or customer or various other forms of support — but it is not those things that ultimately make a company successful. It’s the daily work of the entrepreneurs, management team and employees that is the foundation and driver of any success.

What I have however seen is investors making companies unsuccessful and ultimately fail. Actually this is rather easy for an investor to do, despite wanting achieve the opposite with their actions…


Liquidation Preferences (LPs) are an essential part of almost every VC investment. The basic concept is a preceding payout of the investors prior to all other shareholders in the event of an exit. (There is a large amount of detailed differences which I am not going to discuss here and others have written about this extensively.)

Liquidation preferences in Germany are usually staged, meaning that investors get their money back in the reverse order of the investments (LIFO). Lets take a hypothetical company X which received 3 rounds of investments:

  • Angel: 2 investors, 100k each
  • Seed: 1 investor, 500k
  • Series A: 1 investor…


I used to spend some of my time attending startup conferences in Europe, however not any more since I consider the overall development over the recent years just bad. The overall trend of almost all events has been a pretty dramatic shift towards mostly entertainment while gathering as many visitors as possible. The overall value for doing business (for me: getting in touch or catching up with technology-minded entrepreneurs) has almost completely vanished.

Seriously, what is the value of bigger and bigger entertainment shows with fancy projections and lightnings, star power on stage (some with the same speeches, often without any valuable content, again and again) and hefty ticket prices to finance it all? …

About

Jan Sessenhausen

Technology & Software Investments at Tengelmann Ventures, previously with HTGF, SapientNitro, Capgemini and Hewlett-Packard. IT background.

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