How customer matching works
A high-level explanation of how MCC matches Cuppa Lovers to your café and how to think about it as a café owner.
Most café-finding tools rank cafés the same way they rank hotels: an aggregate star rating with a paid-promotion overlay. MCC works differently. Here’s the high-level picture.
What we match on
When a Cuppa Lover takes the personality quiz, we generate a profile that captures how they actually drink coffee. The profile is a mix of preferences and tendencies: the kind of room they relax in, what they value in service, the kind of order they default to, the time of day they’re most often in a café, what they’re willing to walk for.
When you build your café profile, you’re describing the same dimensions from the café’s side. The tags you select, the photos you upload, the description you write, the price band you indicate, the loyalty programme you set up — all of it forms a profile.
Our matching system compares the two profiles and produces a percentage match for that Cuppa Lover and that café. A high score means strong overlap; a moderate score means some overlap; a low score means the café probably isn’t what they’re looking for today.
What this means for you
The takeaway: your match score with a given customer is shaped by how well your profile reflects what your café actually is. Two practical implications:
Be specific in your profile. A café that describes itself as “everything to everyone” matches everyone weakly. A café that describes itself precisely matches its true audience strongly. The latter is what you want.
Refresh your profile when your café changes. If you’ve shifted from third-wave specialty to a more relaxed neighbourhood spot, update the description, photos, and tags so the matching system catches up.
What we don’t share
We don’t share individual customer profiles with you. Cafés never see a specific Cuppa Lover’s quiz answers or full profile. The dashboard shows aggregated, anonymised personality breakdowns (“32% of your matched customers are Sanctuary Seekers”) rather than individual records.
What we don’t disclose publicly
The exact mechanics of the matching system are proprietary. We don’t share dimension names, weights, or the formulas that produce match scores. What we publish here describes the user-facing behaviour, which is the part that matters for how you build your profile.
See also
Last updated: 7 May 2026.
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