When we started with MeetMatch, we wanted to make something different, something no-one had ever experienced. We wanted to build the perfect networking solution.

So why is networking with MeetMatch 40 times more effective/ efficient than with any other solution?

The simple & short explanation:

1. Random matching gives you a chance of at most 5% to meet somebody interesting, while the MeetMatch matchmaking algorithm makes this >50%.
2. MeetMatch follows up in real-time who is available to network. This makes that you can have 4 times more effective meetings.
3. Together 4 x 10= a factor 40 more efficiency.

The detailed explanation:

First, let’s take a look at the assumptions for this:

• You have at least 100 participants for your event
• We choose the option to go for business networking, in a context of employees from SME’s, who are experienced networkers
• You have 1 hour reserved for networking, meaning about 7.5 meetings per participant. Longer is possible with same results, if you have more participants.
• As formula for efficiency, we take efficiency= number of long term interesting contacts met/ time invested. The measuring unit for efficiency is E= IC/h, with IC being the number of Interesting Contacts you met during the event.

Then, we have 3 steps:

1. Who is interesting for you?
   We take the generally-accepted definition for this: a person is interesting for you if you decide afterwards to actively maintain your relation, e.g. by calling him every 2 months.
2. Artificial Intelligence for matching= efficiency x 10
   Here we have to look at the different alternative solutions to organize networking at an event:
   1. No tools
      When no specific tools/methods are used, you go to somebody random. Research shows that the chance to meet someone that is interesting for you (we call this the hit rate H= number of interesting people met/ number of people met) is less than 5%. We continuously monitor the hit rate of the MeetMatch algorithm, which is constantly above 50%, and increasing with further improvements of the algorithm.
   2. Human matchmakers
      Some event organizers assign “human” matchmakers to optimize matching. Research has shown that humans can never reach a hit rate higher than 30%, mainly because they never take into account all the info that a computer has. There are 2 possibilities:
      1. The organization asks participants to enter network questions. Typically, less than 30% fills this in. Still, the advantage is that this makes it a lot easier for the human matchmakers to process the information. Suppose you assign 2 matchmakers for 100 participants. For each participant with a network question, they can typically offer maximum 1.5 interesting people to talk to. 100 participants x 30% x 1.5 meeting partners= 45 interesting contacts. Above 100 participants, it becomes impossible for the human matchmakers to know everybody and match the right persons. 45 contacts out of a theoretical maximum of 750 means a 6% hit rate. But networking based on network questions is considerably less efficient than based on arguments for long term win-win collaborations, so the hit rate is far below 5%.
      2. The human matchmakers decide who is interesting for whom, without network questions. This is a lot harder, because you need to know a lot about persons before you can match them. In reality, this is only feasible (and done) with input from the participants and extensive human preparation. Let’s assume preparation of 20 minutes by the participant and 1 hour per participant by the human matchmakers. The time investment factor is useful networking time/invested time=60 min/(20min+60min+60min)= 1/ 2.3. But, it becomes very hard to remember all items with 100 participants, so efficiency is reduced by a factor 40%. Together this means a hit rate of 30% / 2.3 * 40%= 5 %.
   3. Offers/ requests
      Some events ask people to enter offers and requests. The most efficient way to do this is to use a mobile app. Because of the effort it takes, maximum about 50% of the participants fill in the necessary forms, and on average enter 1.5 request/offer maximum. In typical SME context, let’s assume 80% are “sales questions in disguise” (typically, the ratio is considerably higher). Experience shows that such sales talks end up being very annoying and event organizers therefore typically rather avoid them. So, they should be eliminated further. Typically, short term requests/offers have very little relation to how interesting somebody is long term. Let’s take the very optimistic assumption that, for each request, you find 0.5 long term contact, on average. This takes considerable preparation because you have to enter your profile/requests/offers (20 min) and you have to go through the requests/offers and select people and make a meeting appointment (25 min). So, the time investment factor is 60 min / (45 + 60) min= 1 / 1.75.For 100 participants, the result is therefore 50% participation x 1.5 requests x 20% non-sales questions x 0.5 long term contact/request x 1/1.75 time investment = 4.3 % equivalent hit rate, on average.
   4. Network questions
      Some events ask people to enter network questions. Optimistically, 50% of people fill in 1 question. On average:
      • 50% are “sales questions in disguise”
      • 40% are requests to solve a short term issue, with little relevancy to being an interesting long term contact
      • Optimistically, 10% are good-quality network questionsOptimistically, 1 network question results in 1 long term business relationship. The result is therefore 50% participation x 10% quality questions x 60min/(60min+30min) = 3.3%.
   5. Advanced matching
      Some apps do advanced matching. In reality, this means:
      • They match on similarity of some keywords they ask the participants to enter.
      • They match, based on the personal profile of the participants –e.g. hobbies like running and skiing- , and have no view on the companies behind them. This makes them rather irrelevant to matching for a long term relationship.The result is a matching hit rate close to 10%, improved by participants doing further manual selection of the proposed meeting partners. The time investment factor brings the equivalent hit rate to 10% / 2= 5%.
3. Real-time matching= efficiency x 4
   Knowing who you want to talk to is one thing. But how do you find this person? MeetMatch is the only solution in the world that follows up in real-time who is available to meet. The second-best alternative is that you have automatic planning of meetings, beforehand. This still brings several disadvantages:
   1. People don’t show up. There could be several reasons for this:
      • The other person simply did not come to the event. On average, events have about 20% no-shows.
      • The other person is in an interesting conversation and wants to continue.
      • Some presentation at your event is delayed.
      • …Typically, this means that 50% of meetings don’t occur.
   2. Since meetings are planned upfront, they have a fixed length, irrespective of whether they are deemed interesting for both parties or not. A typical length which is taken is 15 min. Since meetings with MeetMatch take 7 min on average, this is a factor 2.

So, taking everything together gives an efficiency increase of a factor 40.

Further background

The number of interesting contacts for a MeetMatch event scales very easily: e.g. for 60 participants: 60 participants x 6 meetings x 50% hit rate = 180. The efficiency for MeetMatch is constant at about 6 meetings / 45 min * 50% * 95% average participation = 3.8. Note that the theoretical maximum attainable efficiency is about 7 meetings/ h = 7, which is anyhow only attainable for a large number of participants.

# participants	Interesting contacts	Theoretical maximum# interesting contacts	Efficiency (IC/h)
60	180	~360	3.8
100	300	~600	3.8
150	450	~900	3.8

Note that the MeetMatch matchmaking hit rate increases with increasing number of participants. But, since we have no sufficient measurements on this correlation, we ignore this factor for now.