Engineering Sub-Second B2B Lead Enrichment & 2-Opt Route Optimization Pipelines in Python

Описание и применение

Пайплайн на Python 3.11 очищает списки B2B‑контактов от фрагментов и недействительных доменов, а также оптимизирует маршруты с помощью 2‑Opt TSP, экономя время и обходя платные API.

Задача

Sales operations teams face dirty B2B lead lists with corporate chains and dead domains, and inefficient zig‑zag field routes that waste 25–35% of a rep’s shift.

Результат

The pipelines run in a 256 MB container, process 2,000 leads in sub‑second time, remove 53% of invalid contacts, and produce a route that cuts travel time by up to 35% without paying per‑element API fees.

Рабочий процесс

• Scrape 2,000 clinic URLs from a local map directory
• Extract raw email candidates using a regex
• Filter out asset filenames, Sentry DSNs, and junk domains
• Validate domains asynchronously with DNS MX and A record lookups using dnspython
• Cache results to avoid repeated lookups
• Return verified email list and classify corporate chains
• Generate geographic coordinates for each verified lead
• Apply nearest‑neighbor heuristic to order stops
• Run 2‑Opt TSP to refine route
• Output optimized route and distance metrics

Доказательства

Both problems can be solved in pure Python 3.11 inside a 256 MB container with zero headless browsers and zero paid third‑party API keys.

Источник

You scrape 2,000 healthcare, dental, audiology, or veterinary clinics from a local map directory—only to realize 35% of them are disguised franchises or private‑equity DSOs/MSOs, and another 18% of scraped emails are sentry.wixpress.com telemetry strings or dead domains.

Источник

Zig‑Zag Field Territory Routes: Once you have 15–25 verified local prospects in a metro area, ordering visits chronologically or alphabetically wastes 25–35% of a rep’s shift crisscrossing highways—while enterprise Route Matrix APIs charge steep per‑element fees (N × N matrix billing).

Источник