Key takeaways
- Manual matching depends on what one organiser remembers.
- Software matching needs good profiles to work from.
- Spreading requests fairly stops the same members being asked every time.
Manual and automatic member matching
| Manual | Automatic | |
|---|---|---|
| Based on | The organiser's memory and notes | Member profiles and rules |
| Scales with | The organiser's time | The quality of profiles |
| Explains its choice | If the organiser writes it down | If the software shows its reasons |
What member matching usually looks at
- Fit: does the member offer what is needed?
- Place: are they in, or do they serve, the right city?
- Willingness: have they said they want to be asked?
- Responsiveness and history: do they reply, and have past introductions gone well?
- Fairness: when were they last asked?
Fairness and consent in matching
Without limits, the most visible members get every request and burn out, while newer members never get a chance. Good matching caps how often one person is asked and doesn't penalise newcomers for having no history. It also asks before sharing anything: a match is a suggestion until both people agree.
Member matching in Milvao
Milvao ranks members who can be asked by service fit (25%), city fit (25%), fairness (15%), accepting requests (12%), replying quickly (8%), introductions completed (8%) and Did you connect? answers (7%), plus a small bonus for matching profile answers, with reasons shown for every suggestion. By default it asks the best fit, one member at a time, up to three per request. Owners can choose to approve each match instead. See automatic matching and matching modes.