How Varos built an automated web research tool for company categorization using AI
Description
Varos provides Real-Time KPI Benchmarks for eCommerce and SaaS.
Industry
SaaS
Size
11-50
Customer since
Integrations
Stack AI is proud to collaborate with Varos to power their automated web research tool for company categorization. This time-consuming process requires looking at the companies online, scanning through their product information, and manually classify them. Using Stack AI to implement the AI system, Varos automated the lead qualification process with Generative AI, saving more than 800 manhours.
Opportunity: Qualifying leads manually is a time-consuming process
Varos faced the challenge of conducting large-scale web research. This involves an extensive process of examining online company profiles, analyzing product information, and classifying them accordingly.
- Time-Intensive Web Research: Extracting online company profiles and product information is a resource-intensive process when done manually.
- Classifying Companies: Analyzing vast amounts of companies' data is a tedious process. Moreover, it is difficult to ensure a systematic approach when classifying leads.
Solution: Implementing an Automated Web Research Tool for Company Categorization using Stack AI
Varos implemented an AI-powered internal tool enabling Varos' operations team to automatically perform web scrapping through a list of companies, extract relevant information (e.g., product, team, industry, etc.), and categorize each lead. They did it without expanding their technical team, and using Stack AI instead to build and customize the tool to their particular business process.
- Web Scraping: Automate the scanning and analysis of online data, enhancing the efficiency of market research.
- Lead Categorization: Leverage Large Language Models to accurately categorize companies, leading to a better qualification process.
Results: Faster Company Categorization
Varos improved its customer qualification process through two main value levers: time efficiency and quality. It also triggered the interest internally to explore further AI applications across their teams.
- Increased Efficiency: Reduction in the time and resources required for web research and qualification, leading to more efficient market analysis.
- Higher Quality: More systematic approach and greater volume of data used for the qualification, resulting in better marketing analytics.