CUSTOMER STORY

Accelerating Commercial Conversion with Virtual Assistants: More Leads, Less Effort

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Region

Spain

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Sector

Food Industry

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Duration

2 months

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Technologies Used

Scrapers for extracting company profiles and information


AI-based matching model


Generative AI


Workflow management

What We Solved

In companies with large sales teams and long commercial cycles, manual lead management leads to wasted time, classification errors, and missed opportunities.

We identified that sales teams were investing too many resources in prospecting, initial contact management, and intent analysis, making it difficult to scale their sales processes and reduce their customer acquisition costs.

How We Solved It

We implemented a complete solution based on customizable virtual assistants that automate the entire lead management cycle:

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1.

1. Automated Lead Scraping and Identification

We used advanced scraping techniques to capture qualified leads.

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2.

2. Smart Classification and Enrichment

We applied AI models trained with CRM and ERP histories to automatically classify and enrich leads.

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3.

3. Automated Initial Contact and Nurturing

Virtual assistants handled the first contact with personalized messages and continued nurturing if no response was received.

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4.

4. Intent Detection and Efficient Closing

Automatic detection of lead intent to route it to a salesperson or continue with the virtual agent.

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5.

5. Seamless CRM and Channel Integration

All activity was logged and integrated into the client’s systems with no manual intervention.

What Results We Achieved

Scalable and Effective Prospecting Automation

75% reduction in time spent on initial lead searches by the sales team.

Improved Initial Conversion

40% increase in response rate for outbound campaigns thanks to personalization and automated follow-ups.

Optimización del embudo de ventas

30% reduction in cost per qualified lead (CPL) by eliminating repetitive manual tasks.

More Accuracy, Fewer Errors

90% of leads were correctly classified and enriched without human intervention, reducing errors and management time.

Greater Traceability and Continuous Learning

All interactions were logged and analyzed to continuously improve the recommendation engine.

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