<aside> 📒

This is an actual application that has been anonymized with the permission of the applicant. We’ve written some comments on the side on our thoughts & expectations.

</aside>

Application title

Personal Grant – DAP

Description of initial startup idea / industry

While working at Slush I tried understanding what critical problems founders were facing while building their companies, so that we could help them with Slush.

For most of the founder problems, we created event-style solutions. As examples: fundraising was difficult – we further improved founder-investor meetings at Slush. Founders felt lonely – we built mini-events just for founder peer support networking. Founders didn’t know what to do next in their company– we introduced founder to founder mentoring. But some founder problems could not be solved with event-style solutions at Slush.

And as I want to keep helping founders and startups, due to how much impact there is, I’ve turned my attention to software solutions that solve the most critical problems that startups face. The idea is to solve the SaaS “data silo hell” that each startup is facing by building a centralised data tool, coined as the SaaS data-aggregation-platform (DAP).

The DAP would be linked to all of the SaaS tools, their separate databases and analytics, and provide a centralised touchpoint for company-wide data and analytics needs.

To help startups even more, the DAP would be prescriptive, giving the company recommendations with what actions or decisions to take.

Use case example #1: a marketing lead wants to improve user growth, but isn’t sure what the most effective method would be. This person would ask the DAP (like using GPT4) and the DAP would respond by sharing the relevant data gathered from different tools and recommend a plan of action, in this case a certain type of marketing campaign.

Use case example #2: the DAP itself sees that the CAC (Customer Acquisition Cost) for a startup is much lower than average - by combining expense data from marketing tools and new user data from customer metric tools - and it would notify and suggest actions for how the startup could increase the CAC in order to get new customers.

From an industry perspective, it gets even more interesting. The DAP not only helps startups at all stages, but it also helps the largest enterprises, as almost every tech-enabled enterprise uses several SaaS tools. As such, the DAP solution being built would be in the B2B Data industry, helping startups, enterprises, and in fact, any other entity utilising numerous SaaS products.

The scalability of the DAP tool is ensured by being built as a software-as-a-service product. An increase in customers would not sacrifice the quality of the product either. As we scale the product by integrating new SaaS tools iteratively, we’ll also consequently increase the customer base causing continuous increases in growth.

Here’s a link to a very simple revenue blueprint on how this company could do 100 million euros in ARR in less than ten years – Revenue Blueprint Sheet.

Using the “Triple, Triple, Double, Double, Double” growth rate of SaaS startups (achieved by Salesforce and Zendesk in the past) we can see that DAP can reach 100 million in ARR in the sixth year with a tiered pricing strategy for the preliminary segments. The ARPUs (average revenue per user) are realistic estimates based on averages within each segment tier.

The ambition of this company is to be amongst the fastest growing startups in the world, which justifies using the T2D3 metric as a benchmark.

Why did you start working around this problem?