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DueDash Capital Networks GmbH

Making Startups Investable

Germany
Market: Education, training, Financial services, Artificial Intelligence
Stage of the project: Operating business

Date of last change: 21.09.2020
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Idea

Startups often have difficulty getting funding on time. Investors have difficulties analyzing the associated risk. Statistically, 4 out of 10 startups fail because they don't have enough runway with financing process taking 6-9 months, even in seed stage. With the help of artificial intelligence and our private capital tool, we shorten the lengthy due diligence process by 40%. Founders receive automated, visual feedback through our CX-focused software, keeping their data up to date, resulting in greater transparency and trust in the investor.

Current Status

On Apr. 29th, we have launched our community platform to first build the top of our funnel and a greater captive audience for our investability platform. So far, we have fully grown organically to in total 1768 users from 82 countries across the world. Alongside we have partnered with more than 15 important ecosystem players across the world. Those include startup support programs like HubSpot or Freshworks for Startups, venture funds from the U.S., UAE or Singapore as well as the e-cells of universities such as IIT Bombay or IIT Guwahati. Further, through our city leader approach, we have already built 17 local communities in countries like India, Germany, Israel, UAE, Nigeria and others, in which DueDash city leader take care of building and leading the local ecosystem.

Our user demographics of the community platform consist of 70% startup founders, 5% investors and 25% of other relevant ecosystem players. Represented industries include FinTech, EdTech, Aerospace, AI / DataTech, VR/AR, e-commerce and many others. Regarding our platform usage, we have a usage rate of 52%, a retention rate of 39% and an engagement rate of 43%.

Furthermore, we have already provided the commercialization structure for the U.S. venture fund Quake Capital by having them fully run their 5G accelerator program together with Deutsche Telekom and RTL Media Group on our platform, through which 7 curated startups got majorly invested.

Market

With DueDash, we address the global startup ecosystem. Whereas with our community platform we target all ecosystem players that are needed for startups to flourish (startups, investors, accelerator/incubator, academia/universities, co-working, startup communities, corporations, service provider, government initiatives), we strongly focus on startups and investors for our investability platform.

On a global scale, there are currently around 300 million active startups and 13 million investors worldwide, all of which are our potential users. Taking our subscription model in mind, this results in a TAM of 512 billion US dollars. If we base the typical 10% survival rate of startups as a coverable market, we will receive a SAM of USD 51.2 billion. If we target only 0.1% of the total market, we still have an annual revenue potential of USD 512 million. In addition, 105 million new startups are being set up each year alone, of which almost all will one day seek funding and thus be potential users of our platform.

Especially in Europe, our platform can address a market that has hardly been covered up to now. So far, more than 50% of Europe's Series A funding has been allocated to hub cities, with the other half spread over the "long tail" of about 70 cities. This creates a huge opportunity to cover these cities, as it is becoming increasingly difficult for investors to view and finance relevant startups at an early stage with traditional approaches.

In addition, our product cannot only be applied to the financing process between startups and investors. The 5% annual growth in the secondary market and the 40,000 M&As worldwide can also benefit from our platform solution in the future and thus offer further expansion and market potential.

Problem or Opportunity

The financing process of startups by investors is globally inconsistent and inefficient. Both sides share three very similar problems.

First, the sighting of suitable partners, the so-called deal sourcing. Startups often don't know where and how to find the right investor. This is especially the case for first- and second-time founders. Investors around the world, on the other hand, are looking for highly potential startups that fit their thesis or portfolio.

Secondly, the preparation and implementation of the due diligence. This process is not standardized and characterized by strong information asymmetries and inconsistencies. Investors have clear ideas about which documents they need and in which form, but there is no uniformly recognized framework in which this process takes place. For the necessary documents, they depend on the voluntary provisions by the startups. In addition, they can view and analyze sources of third parties, e.g. from the internet, but this manual process, like the entire due diligence, is very time-consuming and money-intensive. This limits the number of potential investments. Analysts must manually work on multiple screens to detect complex inferences between sources and results. The consequences are limitations in the startup rating that resemble a black box. However, the due diligence also involves a great time investment and uncertainty for startups. Founders usually lack insights into what content they need to provide for the respective documents in order to be seen investable from the investor's point of view.

Thirdly, the added value through a successful financing process. Investment decisions are usually based on gut feeling. However, due to the complexity of the process, investors are often subject to cognitive biases, which statistically leads to disadvantages for minorities and women in particular. Similarly, funding requires mutual trust based on the accuracy of information, the quality of interaction, and time. The lengthy financing process, on the other hand, creates potential tensions. These circumstances therefore take away time for the fundamentally important evaluation of the founding team.

These described inefficiencies and information asymmetries can also be seen between other parties in the startup ecosystem (e.g. accelerators, universities) as well as in M&As and investments in secondary markets.

Solution (product or service)

AI & ML algorithms can analyze large, unstructured amounts of data and decipher patterns and relationships that are difficult for humans to recognize and distinguish. The latter subjectively analyze data, capturing subtle signals that lead to cognitive bias. With our AI-powered software and visual data presentation, we help investors save money and up to 40% of their time. This, in turn, can be used to assess valuable human aspects such as team chemistry or weaknesses, resulting in lower failure rates and cognitive bias.

Our tool improves the investors' access to higher-quality deals and, through the individual investment strategy, can automatically make recommendations for startups that have not previously been noticed or taken into account. At the same time, it provides a faster assessment and decision base by reducing information asymmetries and inconsistencies through transparent and up-to-date data sets. Intrinsically motivated, the founder keeps the data up-to-date as he or she receives automated feedback on their investability and rooms of improvements with each update. This interactive, structured and transparent approach creates mutual trust and focus for startups. In this way, we reduce friction points, improve collaboration and eliminate tasks without added value.

DueDash supports the full value chain from deal sourcing to portfolio support from AI to due diligence. At the same time, we improve the user experience through intuitive user interfaces, AI, and gamification. Our proprietary ML algorithm contextualizes and visualizes qualitative data provided by founders in files and images to give investors a better basis for decision-making. In addition, we triangulate data from our community platform and relevant internet sources to capture the usefulness and signaling impact of the startup’s actions, thereby providing relevant insights and feedback to both parties. Our AI produces thematic correlations and generates automatic, visual analysis reports from the startup. The provided feedback primarily is focused on the key 4T areas –technology, traction, timing, team - and thus provides the startup with incentives for recurring interactions with the software and its community.

Competitors

When we look at our global competition, we are not the first who want to make the financing process between startups and investors more effective. There are some companies that offer solutions for matchmaking, investor relations, virtual data rooms or due diligence as such.

Below is the list of our biggest competitors:

Matchmaking: Gust (www.gust.com), PrivCo (www.privco.com), Valuer (www.valuer.ai)

Investor Relations: Visible (www.visible.vc), Investory.io (www.investory.io), Foundersuite (www.foundersuite.com)

Deal Sourcing: Crunchbase (www.crunchbase.com), Owler (www.owler.com), AngelList (www.angel.co), Zirra (www.zirra.com)

Due Diligence: CB Insights (www.cbinsights.com), Tracxn (www.tracxn.com), Sourcinno (www.sourcinno.com), PitchBook (www.pitchbook.com)

Virtual data space: Shoobx (www.shoobx.com), DocuSign (www.docusign.com)

KI for Due Diligence: niologic (www.niologic.com), Luminance (www.luminance.com)

Advantages or differentiators

With DueDash, we are the only ones covering the entire value chain. All other competitors in the market omit at least one sub-process, which causes the loss of great potential. This can be illustrated by two examples.

Competitors such as Crunchbase, AngelList, Zirra or Owler allow investors to see internationally interesting startups. However, the underlying data is often outdated because it is automatically crawled from the internet and not proactively updated by the founders due to a lack of incentives. After the sighting, startups are guided away from the platform through their own time-consuming due diligence. Since investors then use their manual and non-standardized processes, they are again subject to information asymmetry with incoherent data.

Other competitors such as shoobx, DocuSend, or Luminance offer startups either a virtual data room or an AI-led due diligence but have no deal sourcing options. Thus, they convert startups that provide the investor a lower return on investment on average.

With DueDash, we can achieve high-quality input from startups through global deal sourcing, effectively guide them through the due diligence, thereby ensuring an on average higher return on investment with lower risk for investors. This is particularly relevant for early stage startups as they are barely visible and have insufficient data online, which makes deal sourcing difficult for investors and puts even more emphasis on the trust aspect. Our private capital tool therefore enables these startups to gain the such important access to early-stage investors. In addition, our UX-focused software provides incentives for founders to keep their data up to date at all times, as it provides constant feedback on their ability to invest, which none of our competitors provide.

Finance

Generally speaking, we are building our startup on an array of different revenue streams and services, which all come with their own costs as well.

For our community platform, we have annual license costs which include major customizations for our need. However, even though 90% of our platform is free of use, it can still sustain itself through several monetization opportunities. These include ticket sales for local and global DueDash events, content promotions or founder stories, and sponsorships. Further, content providers, universities or accelerators can use our group or academy infrastructure for a fee to virtually target their content and programs, build an embedded community, and monetize them. Event organizers & scouts, accelerators & incubators as well as corporate innovators serve not only to generate revenue but also to create multiplier and scale effects.

The investability platform obviously comes with costs for development as well as maintenance and hosting costs once fully developed. On top, we have to continuously teach our AI engine based on our improving cognitive models, which will also occur costs. For the investability platform, we are focusing on our two primary target groups: investors and startups. Both parties will be charged an annual subscription fee. Startups, like investors, can choose from different packages, each with different services and prices. The proposed, average pricing will be around 120€ for startups and 12000€ for investors. However, prices still need to be fixed. On top of the annual subscription fee, our business model of the investability platform also provides in-app purchases such as pre-due diligence, data room validation or comparison analysis.

Business model

In regard to our business model, see the different revenue streams listed above. The subscription-based model for the investability platform will be our primary revenue source with additional in-app purchases coming on top. The community platform will allows us to keep increasing our top of the funnel, while sustaining itself through the different revenue streams. The greater our audience becomes, the greater the value we can also offer to partners and sponsors.

The way that we have built our sales funnel is by raising great awareness through different digital and physical channels, which also include our social media channels and events, that is converted into the community platform. While monetizing the same along our marketing activities on social media and in our newsletters, we further pipe our users through the funnel into the investability platform once looking to initialize the fundraising process.

Our most important metrices will be the amount of users acquiring a subscription on our investability platform alongside the number of recurring users as this will highly affect the actuality of the data provided. On the community platform, we mostly focus on the new members rate, engagement rate and retention rate. Only an engaged community is valuable to help startups become investable.

Money will be spent on

At DueDash, we follow the motto: Practice what you preach. That's why we follow the same path as the startups we support. In other words, increasing traction allows for further financing.

The required funding will enable us to initiate a self-reinforcing cycle that will greatly accelerate our market and product development. With our digital innovation solution and the use of AI in the due diligence process of startups, we have an ambitious project that depends on its dynamic network and scale effects. By securing initial product development, we can focus on traction on our current community platform, which acts as a lead generation and already solves the chicken-egg problem.

Specifically, we want to deploy nearly 50% of the funds on operating expenses, which include the necessary digital and physical infrastructure as well as first salaries being paid to the founders. Next around 35% will be used for the software development of the data- und built-intensive investability platform. The other 15% will be spend on marketing to accelerate the reach and spread of our digital services along our content marketing strategy.

Offer for investor

Currently, we are only fundraising through SAFE notes or equivalent.

Risks

As with any startup entering and disrupting a market, there are always some associated risks.
Firstly, it may be that founders will not be incentivized enough to voluntarily disclose their information and thereby keep them up to date at all times. Secondly, as the performance of VCs would now become even more evident to their LPs, we might see a slow adoption by VCs for the investability platform. Thirdly, even though we have a clear strategy on how to overcome the typical pitfalls of machine learning, we still need to prove that we can derive a high-quality data output, which will be dependent on the sources and volume of data input for the machine learning to function properly. This will greatly affect the value of our provided investability quotient. Lastly, it will also be a matter of time until we can increase the accuracy of the data output through machine teaching and artificial intelligence for a more precise determination of a startup’s investability.

Incubation/Acceleration programs accomplishment

Participation in the Cologne Startup Boost internationalization program by WeWork Labs and KölnBusiness along with 9 other local startups with global ambitions.

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Photo 1 - Making Startups Investable

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Idea
Current Status
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Finance
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