A/B test
Thirteen campaigns,
run both ways.
FMCG, a Deluxe Company, is the leading provider of direct mail marketing for financial institutions, and already used a range of foundational SMB data sources. It ran an A/B test: campaigns targeted with Enigma's revenue and processing-volume data against campaigns targeted solely on traditional providers. Thirteen campaigns, five institutions, three products — Enigma won every one.
Enigma + FMCG Direct — 13 campaigns, 5 financial institutions, 3 products
- Customer
- FMCG, a Deluxe Company
- Industry
- Direct mail marketing for financial institutions
- Test design
- A/B — Enigma data vs. traditional providers
- Headline result
- Up to 380% conversion lift
13
marketing campaigns in the test
5
financial institutions on whose behalf they ran
3
financial products: merchant services, lending, banking
380%
highest conversion lift measured, in business lending
Source: Improving Audience Targeting with Enigma Data, enigma.com. Conversion measured as new accounts created.
01
The hypothesis
Segment on what a business actually processes.
FMCG hypothesized that it could boost campaign ROI by segmenting and targeting businesses more precisely on revenues and processing volumes. Doing that required a new data source — the foundational, well-known SMB datasets it already used didn't carry those signals.
The test compared targeting campaigns built on Enigma's revenue and processing-volume data against targeting based solely on data from traditional providers.
It spanned 13 different marketing campaigns on behalf of 5 financial institutions, across 3 financial products: merchant services, business lending and business banking. Conversion was measured as new accounts created.
02
The results
Every client, every product, a lift.
In merchant services, FMCG used Enigma Merchant Transactions Data to identify businesses already processing cards, understand the amount of transactions, and differentiate e-commerce from in-person volume.
In business lending, the same data assessed revenue stability, ability to pay, and existing debt load — the segment that produced the single largest lift in the test, at 380%.
For broad new-business acquisition, Enigma data validated business activity, cross-verified revenue assumptions and calibrated profitability potential. Lifts were smaller here but consistent across all four clients.
Campaign 1 — Merchant services: acquire new merchant services relationships
| Client | Conversion lift with Enigma data |
|---|---|
| Client 1 — super-regional bank | 160% |
| Client 2 — regional bank | 165% |
| Client 3 — super-regional bank | 295% |
| Client 4 — regional bank | 290% |
Campaign 2 — Business lending: acquire new business lending relationships
| Client | Conversion lift with Enigma data |
|---|---|
| Client 1 — super-regional bank | 380% |
| Client 2 — regional bank | 290% |
| Client 3 — super-regional bank | 85% |
| Client 4 — regional bank | 75% |
| Client 5 — super-regional bank | 90% |
Campaign 3 — New business relationships, broadly
| Client | Conversion lift with Enigma data |
|---|---|
| Client 1 — regional bank | 55% |
| Client 2 — super-regional bank | 45% |
| Client 3 — regional bank | 55% |
| Client 4 — super-regional bank | 45% |
Also in production
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