ReferralBug · Case study 07
Turning Local Jobs into AI-Driven Revenue Systems
Original case study: Turning Local Jobs into AI-Driven Revenue Systems
ReferralBug developed an AI-driven local growth system for service businesses seeking a more predictable alternative to broad paid acquisition. Each completed job became an expansion trigger: the system identified promising nearby households, organised neighbourhood outreach, activated referrals, and coordinated follow-up. AI-informed mapping, targeting, content production, and analysis connected marketing activity with operational considerations. The programme also tracked location- and campaign-level conversion patterns to prioritise further expansion and refine messages, timing, and outreach frequency.
The challenge
The work addressed high acquisition costs, scattered customers, missed local demand, inconsistent referrals, disconnected marketing, and limited use of AI for data-driven neighbourhood scaling.
What the work involved
- Used AI to identify and prioritise nearby households based on location, property type, service likelihood, and conversion potential.
- Targeted 25–50 homes after each completed job through AI-assisted mapping and fan-out neighbourhood coverage.
- Implemented automated post-job referral triggers, personalised outreach, and trackable reward mechanisms.
- Connected marketing, referrals, follow-ups, content reuse, and performance tracking; optimised timing, messages, and expansion zones from data.
Documented outcomes
- Customer acquisition cost fell by as much as 73%.
- Jobs per neighbourhood increased 5.2×.
- Reported customer retention reached 89%.
- The post reports improved route density and operational efficiency.
Source note
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