This article will be dedicated to elaboration of a startup idea. Let’s imagine we want to create a website for startups, where a special algorithm will analyze the information provided by founders and estimate an investment potential of the given startup. Probably, one part of the market for such a platform will be early stage startups. One more target audience is investors. Anyway, let’s figure out! We will perform basic desk research for this startup idea.
There are a lot of platforms where startups can publish their innovative project and reach potential investors, participate in startup competition, or simply be listed. Among the global ones are Crunchbase, ProductHunt, FundraiseFromHome, Gust etc. Surely, there are also big startup communities on Reddit, Facebook, Linkedin where founders, mentors, experts, investors meet each other and discuss new promising startups. After all, there are plenty of startup accelerators, business hubs, clubs, where demo-days and startup conferences take place. With all the mentioned activities, startups can find investors and get funding after startup analysis is executed.
Startup idea: Creation of a platform for startups with digital investment attractiveness assessment
Let’s describe our startup idea proposal briefly: A website where startups can be listed, where a special software algorithm performs in-depth analysis of the startup and scores the given startup with a 0-100 mark, where 100 means that the startup is worth funding, and 0 means that investing in this startup is a waste of time. And money, of course.
How should this website work
Now in more detail. Let’s describe the use case for better understanding of this platform capabilities. Here are the steps:
- Create a listing. Startup founder submits the listing. Enters such information as Title, Description, Address, Pain, Team, Management Board, What has been done and what is planned to be done, Market, Target Audience, Traction, Revenue streams, Financial flow, Sales plan, Investment needed etc.
- In-depth analysis starts. An internal intelligent algorithm starts working. It performs analysis of the information provided as well as studies the relevant information available on the internet.
- Update of the startup’s listing. The internal intelligent algorithm completes the analysis and adds the result to the startups “card”. This information includes integrated investment potential score, as well as breakdown with relevant information, conclusions, made by the algorithm.
- Investors browse the analytics and select startups. This information becomes public. Investors, mentors, experts can browse it and make data-driven decisions to work with this given startup or not.
What should be analyzed?
Let’s talk about step 3 in detail. So, we see that the system somehow analyzes submitted startups. We wrote “somehow” intentionally, because the specification for this analytics algorithm development is a subject for a separate research. For now, let’s assume that the task of these algorithms is to examine a startup and say whether this startup is worth funding or not, calculating an investment potential. What aspects should the developed intelligent algorithm take into consideration?
Firstly, some basic things:
- Data provided by a startup representative.
- The idea lying behind the startup.
- Pain(s) addressed.
- The market.
- Business model.
- Sales records.
- Team members, their expertise and capabilities, their relevance to project needs.
- Progress achieved.
- Founders’ personality, his/her/their capabilities.
- Startup’s technology status, be it a startup’s own innovative technology – ready, in development, patented; startup uses existing technology or no specific technology required.
Then, some more global aspects:
- Current online presence, startup’s online reputation.
- Startup’s industry status.
- Records/history/statistics of similar past startups’ development.
- Records of investments in similar startups and/or startups in the given industry.
And, finally, on the basis of everything mentioned, the system should predict the startup’s success chance.
Here it is fair to say that the main value of the proposed website development is exactly this internal intelligent algorithm. Just in case, this is what Business Model Canvas names “Key Resources”, and Guy Kawasaki names “Underlying magic” in his startup pitch deck template. The algorithm that performs in-depth analysis of big amounts of data quickly is of great value: even a team of analysts will need some weeks to analyze the startup according to the aspects from the 2 abovementioned lists. So, development of an intelligent algorithm for in-depth startup analysis – making the process digital – is expected to save a lot of time.
Summing up the section. We have come to a conclusion, or it is better to say, the hypothesis, that it may be a good decision to offer comprehensive analytics about early-stage startups to investors. Supposably, investors do some research on their own or ask third-party teams for this before making a decision to invest in this or that startup. Therefore, we could try to offer such a product to investors, explaining that using it they will save time. This is a hypothesis because it has to be tested and then confirmed or rejected, which is not the subject of the article, today we are limited with desk research for the startup idea only )
Thus, let’s do some more desk research.
What is good with this startup idea?
Successful development of a website with a powerful analytics module will let investors and experts get comprehensive information about startups that look for investments. The strong point of this analytics is that it is prepared by a soulless, but impartial, smart software. Existence of such information will make the investors’ decisions data-driven, that is, based on comprehensive, properly organized and processed data about growth and funding prospects. Unlike how it takes place today, when investors read reports prepared by a team of professional auditors – there is too much human factor in it and the process takes time.
By the way, for those who detected similarity to the due-diligence process a couple of paragraphs ago, we would like to underline that due-dil takes place when an investor is ready to invest in the given startup. Our proposal aims at a step before due-dil. That is, due-diligence focuses on the startup assessment in the sense of an already made investor’s decision to invest, and the startup idea we propose focuses on helping an investor to decide what startup to invest in.
Then, such a website and possibility to get comprehensive analytics might be interesting for startups itself. No doubts that founders would take such an analysis of their startup positively, because such “data-centric feedback from the machine” can be used for further product development, its improvement, maybe even change of business model or even overall strategy and so on. Most likely that exactly early-stage startups will be interested in taking part to get this useful analytics as a benchmark for product development. Or simply in additional promotion. And it still remains possible that big companies originated as startups will participate, create listings and request our “magical” algorithm to perform an analysis to get, for instance, useful industry insights.
What is bad with this startup idea?
Firstly, there is an extremely ambitious task to design and develop the intelligent algorithm that will analyze submitted startups. And here is the first big risk.
Obviously, this software algorithm should be something like a self-learning AI-based solution. To become operational, that is, to become capable of analyzing and making comprehensive predictions for incoming startups, the software should collect a critical mass of knowledge. Developers should “feed” the algorithm with data about existing startups and their development results. But prior to this step, the algorithm itself, its principles, methodology, criteria of assessment, should be developed.
One more negative aspect of this startup idea is its marketplace nature. In fact, we are talking about the task to create a marketplace, where founders and investors will meet, instead of sellers and buyers. The problem is that marketplace development is considered to be a risky sphere by default: to launch it you need sellers/founders so that buyers/investors come and start browsing goods/startups available. That is, startup founders are not interested in a platform with no investors. This is, for sure, the second significant risk. To mitigate this risk, we should focus our efforts on promotion among startups in the first wave, offering, for instance, a free listing for better online presence and free of charge assessment performed by the software algorithm. If we succeed at this step, most likely investors will find us with low additional efforts from our side, because as startups look for investments, investors also look for promising startups to invest in. Though, again, this is just a hypothesis to test.
All these 4 aspects should be taken into account at the stage of business model development for this startup idea development.
That’s all for today. Let’s pause the discussion and continue in the 2nd part which will be published later. Link will be here when part 2 becomes available. For now, we see a potentially interesting startup idea to create a platform where investors and founders will meet, and the platform offers in-depth analysis of registered startups and their economic environment. Taking into account that investors are always interested in information about startups, they probably will find such a platform useful. In the next part we will continue desk research of this startup idea and describe its business model, frameworks of MVP development and make a conclusion. Stay tuned! And let us know if you have questions, comments, corrections to any aspect explained – we will be happy to chat!