How Vega Reactivated Inactive Users and Booked Meetings with an AI Voice Agent
Discover how DeepAgent reactivated inactive users with the AI voice agent Vega: 514 contacts worked and 266 conversations completed in just one week, with meetings booked and structured product insights collected.
Discover how DeepAgent reactivated inactive users with the AI voice agent Vega, booking meetings and gathering structured product insights. In this case study, we show how an AI voice agent for inactive user reactivation can contact thousands of profiles, automate callbacks, and transform every conversation into valuable data for onboarding and product development. ## TL;DR - 514 target contacts, 459 calls, and 266 completed conversations in 1 week of campaigning. - 29 meetings automatically booked (a **6.31%** conversion rate on completed calls). - Up to **7 callbacks** per contact, without manual team intervention. - Structured insights on activation blockers to improve onboarding, pricing, and messaging. - Replicable model for SaaS database reactivation with fixed operational costs.  ## The Context: An Untapped High-Potential Cluster DeepAgent had a database of registered Italian users who had never activated (no agent created, no purchases). A warm segment, already past the registration form, but stalled in onboarding. Key questions: what blocked them, and can they be recovered scalably without manual outreach? The answer came with Vega, an automated AI win-back campaign. ## The Challenge: Reactivating Hundreds of Leads with Limited Resources - Book 1:1 support appointments to unblock activation. - Gather qualitative feedback on perceived barriers. - Operate autonomously, avoiding man-hours on calls and reminders. For many companies, SaaS lead database reactivation is postponed due to high volumes, difficulty in consistent management, unpersonalized email sequences, and low response rates. Manual outreach simply doesn't scale. ## The Solution: Vega, an AI Voice Agent for Inactive Databases Vega is an outbound AI voice agent configured to call inactive users, understand their blockers, and propose a mini-demo. The flow involves 5 steps: 1) Identify the initial motivation for registration. 2) Explore the blocker that prevented initial activation. 3) Offer a 15-minute mini-demo. 4) Book independently on the team's calendar. 5) Perform up to **7 callback attempts** for those not reached. Every conversation produces transcripts and summaries with categorizable insights: not just appointment setting, but market listening at scale.  ## The Implementation: Three Phases in One Week ### Phase 1 — Configuration and Launch (March 23, 2026) Segmentation of registered but unactivated Italian users; definition of objectives (appointment + feedback), conversational script, callback rules, and calendar integration. ### Phase 2 — Outreach and Callbacks (March 23–27, 2026) Out of **514** contacts in the cluster, Vega made **459** calls, with **266** conversations completed. Automatic callbacks (up to 7) maximized coverage without supervision. ### Phase 3 — Feedback Collection and Conversion Two-fold output: booked appointments and a dataset of qualitative feedback on pricing, onboarding, channel compatibility, and compliance. Transcripts and summaries archived for analysis. ## Results: Numbers and Real Impact - Meetings booked: **29** - Conversion on completed calls: **6.31%** - Campaign duration: **1 week** (March 23–27, 2026) - Automatic callbacks per contact: up to **7** KPI Table | Metric | Value | |---|---| | Contacts in cluster | 514 | | Calls made | 459 | | Completed calls | 266 | | Meetings booked | 29 | | Meeting rate on completed calls | 6.31% | | Campaign duration | 1 week (March 23–27, 2026) | | Max automatic callbacks per contact | 7 | The same volume of outreach, handled manually, would have required weeks and a dedicated team. The real advantage is also qualitative. ## Qualitative Impact: What the Market Told Us The AI voice agent for user activation feedback generated structured insights, categorized into four main areas: ### Technical and Platform Blockers - Errors during configuration - Difficulty setting up the first agent - Perceived lack of Italian language support - Problems awaiting support ### Informational and Understanding Blockers - Unclear pricing - WhatsApp compatibility not immediate - Doubts about compliance (e.g., medical sector) - Forgot having registered ### Contextual and Timing Blockers - Lack of time - Projects on hold - Preference for human support in setup ### Disinterest or Solution Change - Chose a competitor or developed in-house - University/experimental project concluded - No current interest Quotes from users: > “I couldn't understand the cost and if it works with WhatsApp.” > “I can't create the agent and I've already sent an email.” > “The bot crashed a couple of times. I found another system.”  ## Why it Works: Replicable Success Factors - Dual objective: every call generates value (qualitative data) even without a commercial outcome. - Total autonomy: **459** calls and callbacks without repetitive man-hours. - Contextual personalization: questions about motivation and real blockers, less resistance than cold calls. - Speed: one week end-to-end. - Structured data: transcripts and summaries that guide product and onboarding. ## What the Market Learns from This Case Study An AI voice agent for inactive user reactivation doesn't just replace commercial phones: it's a market intelligence tool that operates in parallel, converts where genuine interest exists, and informs product, onboarding, and pricing decisions. For SaaS teams: segment the cluster, define clear objectives (appointment + feedback), launch in 1 week, analyze transcripts. The **6.31%** on completed calls is a concrete and replicable benchmark. ## Discover Our Fleet of Agents Vega demonstrated that reactivating inactive users can be fast, autonomous, and data-driven. Beyond meetings, the value lies in the friction map that guides roadmaps and communication. Want to test it on your database? Request a demo: one of our AI agents will call you back, and you can see in real-time how it works for your case.  ## Frequently Asked Questions ### How does an automated AI win-back campaign work compared to an email sequence? An automated AI win-back campaign makes proactive calls, manages up to 7 callbacks, qualifies interest in real-time, and directly books appointments on the calendar. Unlike email, it collects structured vocal insights (reasons for blocking, objections, timing), increasing response rates and reducing conversion time. It is ideal for warm but inactive databases. ### What integrations are needed to use an AI voice agent in my SaaS? Integrate CRM/marketing automation for targeting, calendar for booking, and, if necessary, a help desk for handover. For SaaS lead database reactivation, it's useful to add webhooks to update statuses (callback, booked, not interested) and archive transcripts and summaries for product analytics and compliance. ### What KPIs should I monitor, and what results can I expect? Monitor: reach rate, completed conversations, meeting conversion, no-shows, average call duration, callbacks per contact, categorized reasons for blocking. In this case: **6.31%** meeting conversion on completed calls, 459 calls in 1 week, and up to 7 callbacks. Results vary based on database quality, timing, and offering. ### Is the AI voice agent GDPR compliant and suitable for regulated sectors? Yes, if configured correctly: clear legal bases, privacy notice, consent/opt-out management, DPA with providers, encryption at rest and in transit, limited retention, and audit trails. For an AI voice agent for user activation feedback, it's possible to disable audio recording, retain only pseudonymized transcripts, and apply specific rules for regulated sectors. ### How long does it take to launch the first campaign? Generally 3–5 business days: cluster segmentation, objective definition (appointment + feedback), script and vocal persona setup, calendar/CRM integration, testing, and go-live. Callback rules, time slots, and metrics are configured in advance to scale the automated AI win-back campaign without operational burden on the team.