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Sales and marketing agency, Canada

A Canadian Sales and Marketing Agency: Automating Prospecting Without Losing Lead Quality

A Canadian sales and marketing agency replaced manual Google Maps prospecting with a scheduled scraping and qualification pipeline. The build was projected to cut poor leads by 70 per cent.

Timeline: Six-week deployment roadmap

• 5+ hours Projected weekly time saved on research
• 70% Projected reduction in poor-quality leads
• 40% Projected increase in sales team focus time
• 10x Projected lead volume capacity

The problem

Manual lead research was slow and could not scale, which limited the sales pipeline and kept skilled staff on data entry instead of client engagement. Building prospect lists meant searching Google Maps and websites by hand. The lists mixed qualified and unqualified prospects, verified contact details were hard to find and often wrong, and leads were stored in scattered places with no central structure.

• Hours spent searching Google Maps and websites to build lists
• Lists mixed qualified and unqualified prospects
• Verified contact details were hard to find and often inaccurate
• Lead data scattered with no central structure

What we built

We built a scheduled pipeline that runs daily or weekly. The Apify Google Maps Scraper pulls business listings for chosen keywords and locations, and a filtering module discards any business without a website. For each qualified business, the Apify Contact Detail Scraper extracts and verifies email and phone details from its website. Business details, URLs and contacts are then saved as new rows in a Google Sheets lead database, ready for the sales and marketing team.

Tools: Apify Google Maps Scraper, Apify Contact Detail Scraper, Google Sheets API, Scheduled triggers

How we ran it

Every engagement follows The Claro Build Framework.

1. Assess: We looked at how prospect lists were built and stored, and identified manual research, unfiltered lists, unreliable contact data and scattered storage as the bottlenecks.
2. Design: We designed the keyword and location mapping, the qualification filter and the contact validation rules, with Google Sheets as the single lead database.
3. Build: We configured the Apify scrapers, built the filtering and contact extraction logic, then connected Google Sheets and tested before production launch.
4. Sustain: The agency was handed the system under The Adoption Standard: a structured 45-minute handover call and documentation written as the pipeline was built.

The result

The pipeline was projected to save more than five hours a week of manual research, cut poor-quality leads by 70 per cent, and let the team scale to ten times more leads across multiple keywords and locations without extra manual effort. Leads are now stored centrally in Google Sheets instead of scattered across files.

Client name withheld for confidentiality.