5-Steps to Impactful Conference Relationships & Networking 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.

The Road to Success: Integrating Data + Design Upon engaging with TechCrunch, we were able to utilize the registration data they had collected to perform 5 steps of analysis:

1. Individual’s Comprehensive Profiles
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
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
1. We profiled their importance or connectivity using social network analysis, social media analysis, and social network mapping...complete 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, become too distracted by their network and can’t focus on building a business. We use existing open source network science models plus our proprietary ‘Power Score™’ and different biodiversity measurements to achieve this.
4. Machine Learning/Ai
1. We used 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 our coverage 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 funders (depending on the conference). 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

Understanding Results So how did this innovative approach help TechCrunch achieve success? We utilized the outputs discussed to assign a value, sometimes referred to as the average Serendipity Value or Personal Power Score™, to each and every person and company. We then overlaid all the weightings, described above, to inform the top recommendations.

While there are companies who can provide many of the individual functions described here, it is a true innovation/industry first to combine these advanced collaboration functionalities. What I mean is, we are able to utilize all data inputs and in real time, select the desired weightings and determine and apply these to the selected number of people that need to be connected.

Creating a Connected, Impactful Future We are currently working with a number of innovation departments, incubators, co-working spaces, event and conference organizers, corporations, and Universities, to help better the way people connect and ‘team’, improving their day-to-day and impacting the greater global good. Incorporating the open source ‘People Science’ approach to our advanced collaboration practices, we’re eager to partner with pioneers focused on moving the whole system approach forward.

The original version of the 5 steps was published on the #1 Innovation Excellence Blog. 

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