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 any other solution?
The simple & short explanation:
- Random matching gives you a chance of at most 5% to meet somebody interesting, while the MeetMatch matchmaking algorithm makes this >50%.
- MeetMatch follows up in real-time on who is available to network. This makes that you can have 4 times more effective meetings.
- 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 the context of employees from SMEs, who are experienced networkers
- You have 1 hour reserved for networking, meaning about 7.5 meetings per participant. Longer is possible with the same results, if you have more participants.
- As a formula for efficiency, we take the efficiency= number of interesting long-term 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:
- Who is interesting to you?
We take the generally-accepted definition for this: a person is interesting to you if you decide afterwards to actively maintain your relationship, e.g. by calling him every 2 months.
- Artificial Intelligence for matching= efficiency x 10
Here we have to look at the different alternative solutions to organize networking at an event:
- 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 to 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.
- 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:
- The organization asks participants to enter network questions. Typically, less than 30% fill this in. Still, the advantage is that this makes it much 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 a maximum of 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 human matchmakers to know everybody and match the right persons. 45 contacts out of a theoretical maximum of 750 mean 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%.
- The human matchmakers decide who is interesting for whom, without network questions. This is a lot harder because you need to know much about people 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 the preparation of 20 minutes by the participant and 1 hour per participant by the human matchmakers. The time investment factor is beneficial networking time/invested time=60 min/(20min+60min+60min)= 1/ 2.3. But, it becomes tough to remember all items with 100 participants, so efficiency is reduced by a factor of 40%. Together this means a hit rate of 30% / 2.3 * 40%= 5 %.
- 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, a maximum of about 50% of the participants fill in the necessary forms, and on average enter 1.5 requests/offers maximum. In a typical SME context, let’s assume 80% are “sales questions in disguise” (typically, the ratio is considerably higher). Experience shows that such sales talks become very annoying and event organizers typically avoid them. So, they should be eliminated further. Typically, short-term requests/offers have minimal 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 contacts, 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 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.
- 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 relevance to being an interesting long-term contact.
- Optimistically, 10% are good-quality network questionsOptimistically, and 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%.
- Advanced matching
Some apps do the advanced matching. In reality, this means:
- They match on a 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 somewhat irrelevant to matching for a long-term relationship. The result is a matching hit rate close to 10%, improved by participants manually selecting the proposed meeting partners. The time investment factor brings the equivalent hit rate to 10% / 2= 5%.
- No tools
- 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:
- 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.
- Since meetings are planned upfront, they have a fixed length, irrespective of whether they are deemed attractive for both parties or not. A typical size which is taken is 15 min. Since meetings with MeetMatch take 7 min on average, this is a factor 2.
- People don’t show up. There could be several reasons for this:
So, taking everything together gives an efficiency increase of a factor of 40.
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)|
Note that the MeetMatch matchmaking hit rate increases with an increasing number of participants. But, since we have no sufficient measurements on this correlation, we ignore this factor for now.