Case study Prospecting and CRM system design
Building a modern prospecting system when more activity wasn’t enough.
An independently initiated research and prospecting system that turned regulatory data, public account intelligence, stakeholder mapping, and contact verification into usable commercial infrastructure.
Revenue infrastructure · Independently developed at RAINThe challenge
What needed to be solved
The growth problem was not activity alone. An incomplete account universe, single-threaded contacts, inconsistent enrichment, and ambiguous institution matches limited the quality of every call and email that followed. I needed a system that could show who belonged in the market, who mattered inside each account, what the source record supported, and what was genuinely ready for CRM use.
The approach
What I personally owned
Initiated the system independently and defined the commercial standard: complete account coverage, ranked stakeholders, source evidence, and explicit no-match outcomes rather than optimistic guesses.
Combined financial-institution regulatory records with public company research, advertising signals, LinkedIn Sales Navigator, Apollo enrichment, and prior CRM evidence.
Built primary, secondary, and tertiary stakeholder coverage around marketing, growth, communications, member experience, and executive responsibility—not a single narrow title.
Designed quality controls for duplicate institutions, shared domains, rebrands, current employment, role eligibility, company-ID alignment, phone confidence, and import readiness.
Separated system output from business impact: the record proves research scale and deployment readiness, but does not yet prove meetings, pipeline, or revenue from the initiative.
Evidence
What the record shows
financial institutions researched and resolved
ranked stakeholder records in the audited system
credit-union accounts prepared in a CRM deployment file
unresolved records forced into a verified status
The figures come from audited workbooks. They demonstrate the scale and quality of the operating system—not downstream meetings, opportunities, pipeline, or revenue, which are not established in the reviewed record. Institution names, contact details, targeting logic, and internal CRM mechanics remain confidential.
Transferable value
What this demonstrates
Independent commercial problem-solving
Account-based prospecting design
Data-quality and CRM judgment
Practical AI applied to revenue work
Operating principles
What transfers to the next challenge
Better inputs improve every sales action downstream
A documented no-match is better than a false match
Rank coverage around the buying problem
Measure adoption and revenue separately from build quality