Predictive AI model trained on 3 years of CRM data now scores and prioritizes leads automatically.
A regional real estate firm with thousands of inbound leads per month — and agents wasting most of their day calling prospects who were never going to buy.
Lead volume wasn't the problem — prioritization was. Agents worked leads first-come-first-served, spending equal time on tire-kickers and ready buyers. Follow-up on high-intent leads often came days late, and the firm had three years of rich CRM data sitting unused: property views, inquiry patterns, financing status, response behavior.
We built a predictive lead-scoring model trained on those three years of historical CRM outcomes. The model evaluates every new lead in real time across dozens of signals — browsing behavior, inquiry timing, budget indicators, engagement velocity — and pushes a 0-100 score straight into Salesforce with plain-language reasoning. Hot leads trigger instant agent alerts; automated nurture sequences warm up the rest until their score rises.
Agents now open their day with a ranked call list instead of a chronological queue. Close rates rose 180%, the average sales cycle shortened by 45%, and the model maintains 92% scoring accuracy against actual outcomes. The firm attributes $2.4M in added annual revenue directly to the system — from data they already owned.
The AI model paid for itself in the first quarter. Our agents finally spend their time where it matters, and the numbers prove it every month.
Complete digital overhaul including technical SEO, Google Shopping campaigns, and conversion-focused landing pages.
Custom GPT-4 powered chatbot that handles 80% of customer queries autonomously, improving satisfaction while slashing costs.
Full-stack web development, Google Business optimization, and hyper-local SEO strategy put this healthcare brand at #1.
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