5 Steps to Innovating Profitable Connections with TechCrunch

by Brandon Klein

Collaboration Ai and TechCrunch Matching

 

Connectivity and relationships are critical components of successful innovation, but understanding how to create these intelligent opportunities to ensure the unbiased connection of right people, at the right time, continues to challenge. Below are the 5 steps of analysis we’ve found useful in creating high performance connections.

This process highlights how TechCrunch is innovating in the conference space by employing these tactics to drive innovative, ultimately profitable, connections.

Practical Application of Integrating Data and Design at TechCrunch

On the heels of Crunch Match NY (2016), TechCrunch was seeking to take their Disrupt attendee’s connections to the next level. We worked with them to implement our innovative data and design approach to increase their odds of success.  Upon engaging with TechCrunch, we were able to utilize the registration data they had collected to perform the following 5 steps of analysis:

  1. Individual’s Comprehensive Profiles: We analyzed each individual and their company. We believe that an individual has much more to offer than their listed skill set, so we provided a comprehensive analysis that allows us to better examine the whole individual, including:
    1. Social details – audit of all social network presences
    2. Companies worked for – determine seniority, relevant experience, market understanding
    3. Skills, interests, habits, etc. – learn from all traits both listed and determined by public sharing, joining, etc
    4. Personality – harnessing IBM Watson, our inputs usually predict each individual’s personality with ~90% accuracy
  2. Data Source Comparison: Each individual however is only part of the story when attempting to get a start-up to succeed. So we also examined each start-up to understand:
    1. Competitors – are there existing start-ups in the same space? Big competitors pivoting into the space? What does the landscape look like?
    2. Potential funders/capital sources – Who is funding in their field? Who has already funded a competitor? What individuals within each capital source may have direct interest and connections in the field?
    3. Start-up Genome Report and Start-up Metrics – previous research and history can weigh very heavily when all details are available
  3. Network Science: We profiled their importance or connectivity using social network analysis, social media analysis, and social network mapping with colorful network graphs. Founders with stronger personal networks are more likely to succeed, but those that are ‘over-connected’ as in they have so many connections, that they can’t distinguish value through the noise or are too distracted by their network and can’t focus on building a business. We used existing open source network science models plus our proprietary ‘Power Score’ and different biodiversity measurements to achieve this.
  4. Machine Learning/Ai: We use existing ‘big player’ Ai engines paired with our own machine learning work, in this instance, to teach which groups/pairings are likely to get funded as well as which are unlikely to lead to success, based on:
    1. Past funding data  
    2. Past profiles of individuals and how they work together. We compared everything to our machine learning algorithms that have been trained with hundreds of thousands of people – from world leaders at the World Economic Forum (see how WEF approached this similar challenge as detailed in Fast Company) to 50,000+ person corporations to University student bodies
  5. TechCrunch Weighting Scales: Although our software can achieve 100% optimized matches, most customers like to ensure their inside knowledge is reflected in the mix as well. In other words, TechCrunch, like most clients, likes to ‘weight’ certain preferences such as:
    1. #s: No single Start-up could meet more than 5-10 (depending on the conference) funders. This is a very fair criteria since some Start-ups were much better positioned than others with dozens of potential matches
    2. Competition:. If a funder was already funding a competitor we made sure that there were no matches that could offer conflicts of interest
    3. Geography: It doesn’t make sense to have an APAC startup matched with a Midwest only funder
    4. Industry expertise: We didn’t allow matches of a biotech company with a space exploration-only funder  

In Summation: Data Driven Connectivity

Although every innovation project is unique, utilizing these five steps will help shift the way that you and your team approach innovation and the profitability connected to ensuring the right people meet each other.

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Brandon Klein is the Co-founder of the software start-up, collaboration.ai and an active member of The Value Web, a non-profit committed to changing the way decisions are made to better impact our world.Brandon understands that better teams are fundamental to all of our success. As a global-thought leader, ushering in the ‘Future of Work’ revolution, he paves the way using data + design to accelerate innovation in the Collaboration Revolution.

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