Building a Decision Maker Dataset for UK FinTech
A broad financial services database created too much noise. We identified the FinTech businesses that actually matched the market, researched the right senior contacts and delivered a focused dataset.
Delivered for CoreTek
At a glance
- Industry
- FinTech
- Scope
- 5,500 qualified prospects
- Turnaround
- Around 2 working days
01The challenge
What the client was actually up against
The starting point
The client wanted to reach a defined group of FinTech businesses in the UK.
A broad financial services database created too much noise. It could contain traditional financial companies, banks, payment businesses, lending platforms, financial software providers and other organisations that operated very differently. The requirement was 5,500 qualified prospects that matched the actual FinTech market and contact criteria.
The problem therefore had two parts. First, determine which businesses genuinely belonged in the client’s target market. Second, find the senior people inside those companies whose responsibilities matched the reason for outreach.
02The approach
The sequence we actually worked through
Project focus
Account qualification and senior contact research
Scope of work
- Market research
- Account qualification
- Business model review
- Senior decision maker research
- Contact enrichment
- Verification
- Segmentation
How we solved it
9 stages, in order
- 01
We translated the targeting brief into clear company qualification criteria.
- 02
Our research team reviewed potential FinTech accounts according to the required location, business model, market and other relevant attributes.
- 03
Companies that did not fit were excluded and replaced.
- 04
Only qualified accounts moved into contact research.
- 05
We then identified the senior people whose current responsibilities matched the client’s objective.
- 06
Available business contact information was enriched and verified.
- 07
Duplicate records were removed.
- 08
Company and contact fields were standardised.
- 09
Catch all contacts went through the additional review workflow before any were retained.
03The result
What the client received
Scope delivered
5,500
qualified prospects
Turnaround
Around2
working days
Delivery quality
Under 1%
Less than 1 percent standard bounce rate on valid delivered emails under normal sending conditions
The dataset
The client received 5,500 qualified FinTech prospects.
- The delivery included qualified UK FinTech companies, relevant senior decision makers, verified business contact information and structured segmentation fields.
- Standard valid emails followed Prospectal’s less than 1 percent standard bounce rate under normal sending conditions.
- Reviewed catch all contacts, when retained, were handled separately with an expected maximum bounce range of around 2 to 3 percent.
- Not Found records were excluded from usable contact delivery.
What changed in the data
Before
- Broad financial services data
- Mixed account relevance
- Industry labels without enough context
- Senior contacts with unclear responsibility
- Manual filtering still required
After
- Qualified UK FinTech accounts
- Relevant business models
- Appropriate senior decision makers
- Verified contact information
- Structured prospect segments
04The impact
What it changed day to day
Result
The client received a prospecting dataset built around the FinTech market it actually wanted to reach.
Instead of buying or filtering a broad financial services list, the team started with qualified companies and relevant senior people that had already been researched against the brief.
The full 5,500 qualified prospect requirement was completed in around 2 working days through Prospectal’s team based research, verification and QA workflow.
In the client’s words
Broad financial services data was mostly noise for us, with banks, lenders and payment companies all mixed together. What we got back was genuinely relevant to FinTech, with the right senior contact at each company, not just someone whose title happened to look right.
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