Building a Cleaner Prospecting Base for a B2B SaaS Team
The client needed more than a large technology list. We narrowed the market to companies that matched the targeting criteria, identified the right decision makers and delivered clean, verified prospect data ready for outreach.
Delivered for Avanoo
At a glance
- Industry
- SaaS & Technology
- Scope
- 67,000 qualified prospects
- Turnaround
- 9 working days
01The challenge
What the client was actually up against
The starting point
The client wanted to reach a clearly defined group of B2B SaaS companies.
The problem was not a shortage of companies or contacts. Broad technology databases already contained thousands of them. The real problem was relevance. Companies that appeared similar on the surface often served different markets, operated under different business models or fell outside the client’s target profile.
The contact side created another problem. A senior title did not automatically mean the person was responsible for the area the client wanted to discuss. The requirement was therefore not simply a large number of technology contacts. The client needed 67,000 qualified prospects that matched the requested company criteria and contact criteria, with usable business contact information already checked before delivery.
02The approach
The sequence we actually worked through
Project focus
Account qualification and decision maker research
Scope of work
- Account research
- Prospect qualification
- Decision maker research
- Contact enrichment
- Email verification
- Data cleaning
- Segmentation
How we solved it
10 stages, in order
- 01
We translated the targeting brief into clear research criteria.
- 02
Our team researched companies against the requested market, business type, geography and other account requirements.
- 03
Businesses that did not fit were excluded and replaced.
- 04
Once an account qualified, we researched the people responsible for the functions the client wanted to reach.
- 05
The contact data was enriched and passed through the verification workflow.
- 06
Duplicate records were removed.
- 07
Company names and important contact fields were standardised.
- 08
Standard valid emails went through final verification and QA.
- 09
Catch all contacts were reviewed separately through an additional process rather than automatically being treated as valid.
- 10
The work continued until the full requested set of 67,000 qualified prospects was ready.
03The result
What the client received
Scope delivered
67,000
qualified prospects
Turnaround
9
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 67,000 qualified prospects.
- The final dataset contained qualified companies, relevant decision makers, verified business contact information, standardised fields and practical segmentation.
- The structure made it possible to filter and work with the data without another large round of research or cleanup.
- Valid delivered emails followed Prospectal’s standard of less than 1 percent bounce rate under normal sending conditions.
- Not Found contacts were excluded from usable contact delivery.
What changed in the data
Before
- Broad technology data
- Mixed company relevance
- Senior contacts without clear role relevance
- Duplicate and inconsistent records
- Manual checking still required before outreach
After
- Qualified SaaS companies
- Relevant decision makers
- Verified contact information
- Standardised data
- Clear prospect segments
- A cleaner starting point for outreach
04The impact
What it changed day to day
Result
The client received the full requirement of 67,000 qualified prospects within 9 working days.
The project was handled across Prospectal’s research and verification team, with account qualification, contact research, enrichment, verification and final quality review managed as connected stages.
The size of the delivery did not change the underlying requirement. Each prospect still needed to match the targeting criteria before becoming part of the final dataset.
Instead of starting with a broad database and deciding what was useful afterwards, the client received a dataset already built around the audience it wanted to reach.
In the client’s words
It wasn’t the biggest list we’ve received, but it was the most accurate. We got exactly what we needed, and our team didn’t have to spend time checking the data again.
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