B2B prospect research · 46 contacts across six segments · Synthetic dataset

DemandMap

Customer personas + market signals

Personas shaped by Job Title Analysis ready

Review one example before researching

Open “View example customers” under any persona, then continue.

Customer CSV

Explore the sample CSV.

Upload customer file

Choose the demo CSV to explore.

Shape the groups

Persona count

Fewer = broader categories. More = narrower.

6

Cluster overlap

Broad = more shared customers.

Balanced

Traits

What should make customers similar?

Customer distribution

How each customer datapoint fits into the persona categories

Not matched

0

One persona

0

Two personas

0

Three or more

0

Persona candidates

PersonaCustomersResearch

Select a persona to research.

Current needs

View market-search details

Where personas overlap most

Data & model activity

Processing and model activity

CSV parsing is local. Selected nonblank values are sent to OpenAI for semantic analysis; market research uses NewsAPI.

Customer similarity map

Color shows the strongest persona match. A ring means the customer fits more than one persona at the current overlap setting.

All customer matches

Customer	Role	Company	Persona matches

Current CSV

About these personas

Generated from representative rows in the selected fields. Use them as research starting points, not verified facts about every customer.

Method

DemandMap creates semantic vectors for selected fields, combines them by customer, and applies Fuzzy C-Means. “Persona count” controls the prototype count (K). “Cluster overlap” adjusts the independent membership cutoff; the underlying scores are affinities, not probabilities.