Stryber’s approach // #3 AI applied on Ideation (Clustering of Signals)

How our AI-powered signal-based ideation can help your organization to identify the best ventures to build.
December 14, 2023
4 minutes
Ivan Chandra Suriady
Venture Architect

Stryber’s Venture Building Framework: How our signal-based approach can complement traditional human centric design / research-based ideation

The rapidly evolving world of startups is a testament to the innovative spirit of entrepreneurs. However, the traditional methods of ideation, predominantly human-centric and research-based, often fall short in identifying and solving unobvious problems. This is where signal-based ideation, when complemented with human-centric design, can be a game-changer for startups - as outlined in our first blog post series.

Human-centric design has proven to be effective in identifying problems that need solutions. It involves a deep understanding of the target audience, their needs, desires, and challenges. However, it may not necessarily lead to the most effective solutions. Many potential solutions remain unobvious and unsolved due to the limitation of research ideation. This is where signal-based ideation can fill the gap.

Signal-based ideation goes a step further by not only identifying problems but also the solutions to those problems. This approach uses a robust dataset to unearth both unobvious problems and their solutions. These signals can be leveraged to build solutions that truly address people’s problems - one of Stryber’s key differentiators during the ideation phase of our venture building framework, as outlined in our second blog post series

However, the challenge lies in traversing a vast dataset. With a large amount of data available, valuable insights can be locked behind unsupportive data formats. To unlock these insights and choose the best ones, startups need to have a strong data capability.

This is where Stryber’s AI-powered ideation comes into play. It combs through signals and extracts valuable insights, such as business models, from them. This process was demonstrated effectively in a case study involving 350,000 startups from the fintech industry.

Case Study: AI-supported identification of ideal open banking business model

The objective was to find the best business model in open banking for a client. Our venture architect on this project utilized our tried-and-tested signal-based ideation method augmented with AI, and set out to find open banking startups across different regions, which show good traction. A quick query into Stryber Analytics gave us 350,000 startups in the Financial Services sector and 30,000 in Banking, from which we needed to find the signals relevant to open banking. Without AI, it would be hardly possible to analyze the relevant startups to understand the latest business models and trends around open banking at this speed. . 

Before we were able to include the appliance of AI to our signal-based ideation approach, we would typically limit the filter results further using Stryber scores and keyword matching. This approach is effective in finding relevant signals, but it has limitations:

  • First, a good number of signals which are relevant, yet do not have the exact keywords, would be filtered out. These are for example startups which enable open banking to happen, such as KYC or data infrastructure
  • Second, the market map and value chain analysis that is built might still be subject to the evaluation of the venture architect even though we are working data-driven as much as we can
  • Third, the higher signal-to-noise ratio from the dataset results in extra work for the venture architect on the project in “separating the weed from the chaff”

This is where AI comes into play. Stryber’s AI builds on top of our Stryber Analytics by categorizing startups based on their value propositions and business models. The ‘market maps’ produced by our AI help us to precisely choose those groups of startups signals relevant to open banking for our client. We can then directly zoom into those AI groups and find the relevant startup signals very quickly.

In this case study, Stryber’s AI enabled our venture architect to efficiently get a lay of the land. Instead of having to sort through tens of thousands of irrelevant startups in financial planning and tax preparation, he was able to focus on the one thousand categorized into open banking and neighboring plays. Very quickly, we identified all the possible business models in this space and developed a value chain out of the market map (the following is an illustrative, non-exhaustive subset of the value chain):

  • Consumer-facing: Neobank Brands, White-label Banking-as-a-Service Platform, etc
  • Infrastructure: Banking API Integration, Banking Data Aggregation, etc
  • Enablers: KYC, AML, Document Verification, Cybersecurity, etc
  • Etc

With the relevant signals in hand, we focused our efforts on evaluating the attractiveness of each part of the value chain. We developed a set of strategic filters with the client, and meticulously applied them to the list of relevant signals. At the end of this process, we came up with a shortlist of 10 of the most promising signals for the client in the open banking space, and successfully concluded the project.

With AI, Stryber can truly stand on the shoulders of giants and move much faster than traditional research-based ideation. If we were to ask people about their problems in open banking, many might just shrug and not know. As Steve Jobs famously said, "it’s not the customers’ job to know what they want." In conclusion, complementing human-centric design with signal-based ideation can take ventures to new heights. By leveraging AI, we can not only identify problems and find effective solutions around specific target groups, but be even more efficient in doing so - thus, combining risk-managed exploration of new business with increased speed which can add a competitive advantage to our clients’ new businesses.

Ivan Chandra Suriady
Venture Architect
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