Case Study
From Data to
Experience Design
We built brand personas from thousands of prospect profiles — then scored every creative decision against them.
The Problem
Most retail tech vendors walk into industry events with one pitch. They design the experience for an imagined "average buyer" — a person who doesn't exist. Wasted conversations follow. Messaging lands flat. The booth speaks to everyone and connects with nobody.
What if you could know your audience — who they are, what they're looking for, and how senior they are — before the doors even opened?
Enrichment
Thousands of prospect profiles sat in our market intelligence pipeline. Names, companies, job titles — raw but incomplete. We layered on publicly available data: company size, industry vertical, seniority level, department, geographic region.
What came back: rich, structured profiles covering company, country, role, department, industry, and seniority — across virtually every record.
Countries represented
Unique organizations
Profile completeness
| # | Name | Company | Industry | Country | Seniority | Department | Size |
|---|---|---|---|---|---|---|---|
| 1 | M. Karlsson | NordicRetail AB | Food Retail | SE | C-Level | Management | 2,400 |
| 2 | A. Fernández | Grupo Comercia | Fashion Retail | ES | Director | IT | 8,100 |
| 3 | J. Kowalski | Berger Innenausbau | Shop Fitting | DE | C-Level | Sales | 340 |
| 4 | R. van Dijk | FreshChain BV | Grocery | NL | Manager | Operations | 1,200 |
| 5 | P. Moretti | Lux Consulting | Consulting | IT | C-Level | Strategy | 85 |
| 6 | S. Novák | RetailTech CZ | IT & Security | CZ | Director | Engineering | 520 |
| 7 | L. Dubois | Carrefour Digital | Hypermarket | FR | Manager | Marketing | 32,000 |
| 8 | T. Yamamoto | AsiaStore Inc. | Department Store | JP | Director | IT | 4,700 |
| 9 | E. Müller | Bau+Plan GmbH | Architecture | DE | C-Level | Management | 60 |
| 10 | K. Andersen | ScandiMart AS | Convenience | NO | Manager | Procurement | 900 |
Sample data — names and companies are fictional. Structure reflects the actual enriched dataset.
Scoring
We didn't start with industry labels. Instead, we scored each profile against our actual product categories. What did they explicitly say they were interested in? That question eliminated circular reasoning — no pre-labeling industries as "relevant" and then counting matches.
Scoring Model
Category match
Seniority modifiers
What Emerged
Nearly 1 in 4 profiles showed measurable interest.
Persona Discovery
Scoring didn't just rank profiles. It revealed clusters. Four buyer personas emerged from the data — each with distinct motivations, seniority distributions, and industry footprints.
The Analytics Buyer
~8%- Seeking analytics, computer vision, customer frequency
- 50% C-level, 32% Director-level
- Departments: Management, IT, Sales
81% who want one capability want the full suite
The Digital Explorer
~12%- Adjacent tech: POS kiosks, digital signage, omnichannel
- Broader industry spread, geographically diverse
- 60% POS kiosk systems, 57% digital signage
Often paired with omnichannel and self-checkout interest
The Senior Decision-Maker
~4%- High seniority, indirect category match
- C-level from consulting, architecture, shop fitting
- Not seeking analytics — but hold purchasing authority
Decision-makers who didn't know they needed you
The Passive Attendee
~76%- No analytics category interest selected
- Important to identify — don't waste resources
- Qualify out, not in
Knowing who isn't your audience is equally valuable
Visual Validation
Personas gave us a lens for every creative decision. We scored visuals, messaging, and collateral against each one. The question shifted from "does this look good?" to "does this connect with The Analytics Buyer?"
Creatives designed for everyone connected with no one. Persona-aligned messaging scored 2–3x higher.
We sharpened messaging per persona and reworked the visual hierarchy for the senior audience. Every design choice now had a reason behind it — not gut feel, but data.
What We Learned
Five things the data taught us
Full-stack buyers are real
Prospects interested in one analytics capability almost always want the whole suite. Selling point solutions to platform buyers leaves money on the table.
Category interest beats industry labels
What prospects explicitly seek matters more than their industry tag. A shop-fitting CEO who selects "in-store analytics" outranks a retailer who selects nothing.
The audience is more senior than you think
Nearly half were C-level. Three in four held decision-making authority. Lead with business outcomes and ROI — not technical architecture.
Unexpected industries show real intent
Consulting, architecture, shop fitting — all showed measurable high-intent rates. They're likely integrators, specifiers, or channel partners worth pursuing.
Geography reveals hidden opportunity
Intent rates swing wildly by market. Some smaller regions hit 20%+ high-intent rates — easy to miss if you only look at absolute numbers.
Your data already tells this story
The profiles you need to build buyer personas probably already sit in your CRM. The methodology is replicable. What's missing isn't data — it's the decision to use it.