Insurance Agent Recruitment: 36 Qualified Agencies with AI
36 qualified target agencies handed over to sales in one week, with contact details and callback already scheduled. Want to understand how AI unlocks the first contact?
Anyone tasked with growing a network of agencies knows: the problem isn't finding numbers, it's reaching the right person. In insurance agent recruitment, the obstacle is the decision-maker, shielded by switchboards and secretaries. In this case study, we explain how an Italian insurance company entrusted its first contact to an AI voice agent built on DeepAgent. In one week: 685 attempts, 275 completed conversations, 36 target agencies passed to sales with confirmed day, time slot, and contact details. ## TL;DR - 685 attempts, 275 conversations, and 36 qualified agencies in just one week. - 13.09% conversion rate on completed conversations; 5.26% on total attempts. - 7-phase flow: essential qualification, value proposition, callback with contact details. - Nearly half of attempts are stopped by switchboards/IVRs: list quality and timing are crucial. - 32 out of 36 qualified contacts were made between 10 AM and 12 PM; 33 in a single day. ## The Client: An Insurance Company Seeking New Agencies in the Territory ### Who they are The client is an Italian insurance company that distributes its products through agencies and intermediaries. To grow its non-life insurance branches, it needs new partners in the territory: agents and sub-agents with an adequate portfolio, free from constraints that would prevent collaboration. ### The Starting Point Agency network development was done much like almost everywhere else: area sales managers would call, seek out the principal, ask initial questions, and often discover halfway through the call that the profile was incompatible. Hours of work were spent filtering, even before presenting the offer. ## The Challenge: Cold Contacts, Switchboards, and a Single Decision-Maker The goal wasn't to sell over the phone but to build a qualified initial discussion with the right decision-maker, to be passed to sales only when it was worthwhile. Three key obstacles: - Reaching the principal. Cold B2B lists: many calls end up at IVRs, on hold, with answering machines, or sub-agencies. - Qualifying without sounding like a call center. Few questions, credible tone, no wasted time with those not in target. - Filtering with clear rules. Exclude brokers, life-only agencies, non-life portfolios below threshold, exclusive mandates, and existing relationships with the client. This is the B2B version of B2B insurance lead generation: the lead isn't a private individual; it's an agency that can become a stable channel for years. ## How the Voice Agent Works in Insurance Agent Recruitment The flow was built in seven phases, to provide sales with a ready-to-work handover. 1) Profile and Exclusion Rules. Target: agencies with potential in non-life branches. The conditions that block follow-up are clearly stated: the agent does not interpret them. 2) Opening. Introduces itself as the commercial department, asks for the principal or manager, and requests permission for a few questions. 3) Progressive Qualification. Role (agent/sub-agent), location, companies handled, single or multi-mandate, prevalence of non-life/life, size of the non-life portfolio. 4) Value Proposition. For those in target, it proposes a no-obligation discussion with the Area Sales Manager about products, platform, and commission plan. 5) Callback and Contact Details. Agrees on day and time slot, collects email and direct number. 6) Summary. Recaps who will call back, when, and to which contact details. Sales receives a ready-to-work contact immediately. 7) Data Reading. Reachability, completed conversations, qualified profiles, and scheduled callbacks are measured separately: in B2B outbound, switchboards heavily impact raw data. An outbound B2B AI voice agent doesn't replace the salesperson: it removes the filtering burden and delivers only conversations worth their time. !Insurance Agent Recruitment: 36 Qualified Agencies with AI — figure 1 ## The Results: 36 Agencies Ready for Discussion in One Week | Indicator | Result | |---|---| | Period | One week | | Call Attempts | 685 | | Completed Calls | 275 (40.15% of attempts) | | Qualified Agencies with Scheduled Callback | **36** | | Conversion on Completed Calls | **13.09%** | | Conversion on Total Attempts | 5.26% | | Total Conversation Minutes | 488.5 | Translated: approximately one qualified agency for every eight completed conversations. By "qualified agency," we mean an in-target decision-maker who has accepted a callback from sales and confirmed contact details. This is not a signed agreement; it's an initial discussion with the right person. ## Where One in Two Calls Stops The most insightful data is not the conversion rate, but where attempts end. Out of 685 calls, 214 received no answer (31.24%) and 121 ended up on answering machines or automated responders (17.66%): almost one in two did not reach a person. Another 45 failed for technical reasons, and 30 were cancelled. This is why the two conversion rates must be read together. On total attempts, 5.26% seems low; on actual conversations, it becomes 13.09%. The biggest lever is not the script, it's the list: separating switchboard numbers from direct contacts. !Insurance Agent Recruitment: 36 Qualified Agencies with AI — figure 2 ## The Conversation That Explains the Method The best call of the period lasted 3’17’’ and started poorly: the line was noisy, and the initial exchanges were repeated. The agent recovered, verified the principal, collected essential information: multi-mandate, non-life portfolio above threshold. The contact accepted a callback from the area manager for the morning, left a number and email. The agent reread the data and corrected the address when the contact specified it. Clean, operational closing. Conversations that work almost always follow this sequence: decision-maker, essential qualification, portfolio size, meeting value, callback slot, contact details, summary. ## What This Case Teaches (Including Areas for Improvement) An honest case also shows uncomfortable numbers. Within the week, results were not uniform: **33 out of 36** qualified contacts were made in a single day. The script was the same: the quality of the list, timing, and availability of the principals made the difference. This is the first point to optimize before the next launch. Priorities for those wishing to replicate the model: - Clean up lists. Separate switchboards/IVRs from direct numbers, in reports and before calling. - Call in the morning. Between 10 AM and 12 PM, **32 out of 36** qualified contacts were made (sample to be confirmed with new comparable launches). - Be transparent immediately. When asked, "Is this a recording?" the agent declares being a voice assistant and briefly explains the call's purpose. - Shorten qualification. Only essential questions; deeper dives are for the salesperson. Learn more: - AI Voice Agents in Banking and Insurance: Use Cases → article - Outbound Pre-qualification in Lending → Fincontinuo case study - AI Voice Agent for B2B Sales → guide !Insurance Agent Recruitment: 36 Qualified Agencies with AI — figure 3 ## Conclusion In insurance agent recruitment, the value of AI is not making more calls, but delivering only in-target principals to sales, with callbacks already scheduled. In this campaign: **36 qualified agencies** in one week and **13.09%** conversion on completed conversations. Want to see how it adapts to your territory? Request a demo: we'll test the list, timing, and filter together. ## Frequently Asked Questions ### How does an AI voice agent integrate with existing commercial processes? Integration is light: the qualification script aligns with your process, and the agent updates the CRM with outcomes, contact details, and callback slots. Exclusion rules ensure consistency with policies. Sales receives only actionable contacts, allowing agency network development to proceed without burdening operations. ### What data is needed to start, and how long does setup take? You need a B2B contact list (even raw), exclusion rules, a question framework, and minimum criteria (e.g., non-life focus, thresholds). Typical setup takes a few days: tone, fields to collect, and handover to the Area Manager are validated. The pilot launch measures reachability, quality, and conversion. ### How is ROI measured in a recruitment campaign with AI voice? ROI is viewed on three levels: reachability (switchboards, answering machines, no answer), conversation effectiveness (completed, duration, drop), and outcome (qualified, scheduled callbacks, initiated negotiations). In the medium term, conversion to mandates is tracked. This metric connects the outbound B2B AI voice agent to real value. ### Is the AI voice transparent? How to handle the objection "Are you a call center?" Yes. At the first objection, the agent clarifies that they are a voice assistant from the commercial department and briefly explains the call's purpose. A sober tone, few questions, and a final summary reduce friction. Transparency protects brand and compliance and improves B2B insurance lead generation. ### Is it better to call in the morning or afternoon? Data from this case indicates morning: between 10 AM and 12 PM, 32 out of 36 qualified callbacks were scheduled, with 33 successes concentrated in one day. This is not a universal rule: it should be tested across multiple comparable launches and, where possible, using lists cleaned of switchboards/IVRs and inactive numbers. Talk to sales → · Create your agent →